News &amp; Insights

New Real Estate Agent Partners for Q3 2024

Outra welcomes new real estate agent partners this quarter, joining us to harness the power of our Pre Mover product and reach home movers nationwide

Powering Targeted Lead Generation with Pre Mover

We're happy to announce our new real estate agent partnerships this quarter as more top real estate agents across the country join forces with us to leverage our Pre Mover product with the following joining our community;

Newton Huxley
Nest Seekers International
Andrea & Co
Property Foundations Group
Clive Gregory at EXP UK
Xchange Properties
Home4 Estate Agents
Wayne & Silver
Ellis Winters

Pre Mover is helping agents stay ahead in a competitive market by giving them access to the latest data science backed leads and insights, enabling them to connect with home movers at the perfect moment in their moving journey.

Why Agents Are Choosing Pre Mover

The real estate landscape is rapidly evolving, and agents need more than just traditional marketing strategies to stay competitive. Our Pre Mover product has been specially designed to harness the latest in predictive analytics and home mover data, giving real estate agents an advantage by identifying leads at the earliest stages of the home moving journey.

Here’s why our new signings are excited to integrate Pre Mover into their lead generation strategy.

Dynamic Market Insights - Agents gain real-time insights into home movers in their area, backed by our robust data science capabilities. This means they’re not just reaching out to every house, they’re targeting households with unmatched accuracy.
Customisable Targeting - Pre Mover allows agents to focus on areas and households that align with specific property types, whether it’s attracting first-time buyers, down sizers, or families upgrading to larger homes. With customisable targeting, agents can spend more time on quality leads.
Enhanced Customer Connections - Understanding a potential client’s stage in the home mover journey is invaluable for creating meaningful connections. Agents can craft timely, relevant messaging that resonates with home movers when they’re most likely to take action.
Fulfilment Services - We take care of everything from design, print and distribution so you can spend more time converting targeted home mover leads.

Building on Success With Real World Results

Our Pre Mover product has already empowered numerous agents to transform their lead generation results. Through this innovative approach, they’ve been able to grow their client base by focusing their efforts precisely where they’re needed.

Delivering the best in targeted real estate lead generation

As we continue to grow, our commitment to innovation and supporting real estate agents remains strong.

We’re constantly enhancing our Pre Mover product to include more granular home mover insights, AI powered predictions, and seamless integrations with agents’ existing workflows to keep them at the forefront of lead generation technology.

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News

Case study

How HUX’s ROAS reached new heights with Outra’s lookalike targeting

HUX Health, an e-commerce supplements brand, partnered with Outra to enhance its marketing efficiency using precision lookalike targeting. In just three months, HUX saw a 146% increase in ROAS), a 60% decrease in CPA, and a 32% increase in purchase volume. Outra’s unique data-driven approach enabled HUX to target the right customers more effectively, significantly improving their marketing performance despite geo-targeting restrictions on Meta and Google.

Launched in 2022, HUX creates the best tasting health supplements using expertly blended and scientifically proven ingredients. HUX is an aspirational brand marketing predominantly on Meta and Google.

The challenge:

In the first eighteen months HUX was averaging a return on ad spend (ROAS) of below 1 and a cost per acquisition (CPA) of £55. To really thrive the way HUX users thrive, the founders had to improve these metrics. But at the same time Meta and Google were introducing more restrictions on geo targeting making it harder to manage ad spend. As experienced entrepreneurs, the HUX team knew they had to be innovative and take a different approach to get ahead of the competition.

Outra has a unique data set that allows for precision targeting of customers. It feels like an unfair advantage
_{- Damien Byrne, EVP, HUX}

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The solution:

Outra approach targeting from a unique angle. Our machine learning goes far beyond how Google Analytics uses data like age and location. Rather than analyse individual personal data, our algorithms analyse a host of household data like financial status and lifestyle preferences. Outra AI dives deep into the interactions of 2600 data points for every household in the UK which enables us to predict their behaviour and generate a product specific score for over 30 million UK residential addresses. The house someone lives in tells us a great deal about their lifestyle aspirations.

In this way we create lookalike profiles that are GDPR compliant. They also circumvent the Meta and Google restrictions so we can create target postcode districts and long-lat disks with a high density of prospects.

Creating a lookalike profile for HUX

The first stage in creating a lookalike profile for HUX was to ingest all the household data about their existing clients. Then we trained our machine learning model to give each household a HUX score and create the lookalike profile of the top scoring households.

Before working with Outra, the HUX team thought their target market was mainly hipsters in their twenties. But when we crunched the data it turned out most of their customers were slightly older, more affluent and better educated. Their customers tended to be a lot more interested in the ingredients and the science of the products, rather than just the look and feel of the brand.

Giving HUX cost effective UK wide reach

Using Outra’s data, the HUX team were able to target lookalike households all over the UK and spot patterns in their locations. For instance the Outra model even revealed that many of the customers lived near parks, perhaps because more affluent people live near open spaces and also have the opportunity to exercise more often.

‍The results:

After three months using Outra data, HUX achieved:

32% increase in Purchase volume
47% reduction in spend
60% decrease in Cost Per Acquisition
146% increase in Return On Ad Spend

The Benefits:

One of the big benefits of the Outra data was that HUX could use it across all of its marketing channels. There is no division between what works best for PPC and what works best for paid social.

Outra unified the channel audiences and ensured they were consistent
_{- Damien Byrne, EVP, HUX}

We also worked closely with the HUX activation team enabling competitor analysis and AB testing on messaging. This resulted in HUX narrowing down activity to five high performing ads. They were also able to refine customer flows through the funnel and steadily upsell to increase the average order value.

Outra works with many different types of clients and our data scientists create a lookalike profile, or customer centric propensity model for each one. For startups the aim is to enable the acquisition of customers with speed and efficiency often against very tight metrics. For larger established companies, the aim may be to identify and buy media with higher attribution rates. We are comfortable working with all the marketing stakeholders, including our clients’ tech and data departments, people and existing activation agencies.

Often a client will ask their agency to use our data. The agency can be reluctant, but once they see the results, they want to white label it so they can offer it to their other customers.
_{- Lucinda Edwards, Head of Growth, Outra}

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Case study

Heylo slashes cost of leads for shared ownership with Outra's Propensity to Buy audience targeting

Heylo, a leading shared ownership provider, partnered with Outra to implement their Propensity to Buy audience targeting. This integration drastically reduced Heylo's cost per lead from £15 to £3, increased applications by 129%, and boosted high-quality leads by 43%. Outra's data-driven approach allowed for precise targeting on Meta and Google, overcoming recent geo-targeting restrictions and optimising marketing efficiency.

Established in 2014, heylo's Shared Ownership solutions bring owning a home within reach for millions of previously excluded buyers. heylo has become one of the UK’s leading shared ownership providers, and works in partnership with national, regional and local housebuilders, and wider stakeholders to deliver affordable homes. heylo operates under two brand names:

With interest rates currently so high, there is a growing demand for affordable homes. But as heylo's Marketing Director Dan Jones points out, in property the margins are always tight:

We have a small marketing budget, so we have to make it work effectively
_{- Dan Jones, Marketing Director, heylo}

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The Challenge: targeting effectively on Meta and Google

heylo predominantly markets on Meta (Facebook and Instagram) and Google (PPC). Ideally they want to target people who live close to the specific property and would be interested in shared ownership. But this has become a lot harder due to recent restrictions on geo targeting.

The Meta targeting restrictions have made the cost per lead rise significantly

heylo's ethos is to make owning a home affordable to previously excluded buyers, and would never discriminate. But as Dan Jones says “pounds aren’t being spent where they should be spent” and digital marketing agencies don’t have solutions to overcome these restrictions.

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The solution: Outra’s Propensity to Buy, fine tuned for shared ownership.

Outra are the only people in the UK that have this property data, and are able to activate it so effectively

Last year heylo started using Outra’s Propensity to Buy solution and the results were startling. For instance in the four months previous to using Outra, the average cost per application to Your Home was £15. Less than two months after integrating Outra data into their digital acquisition marketing, that average was down to £4.

That is a 74% drop in the cost per application. Overall Your Home saw a 129% increase in applications and a 43% increase in high quality leads. Outra were able to do this by positioning Meta’s 17km disks precisely over high intent areas for shared ownership.

Since we started using the Outra UK wide informed radii placements, we have seen a record level of applications at an all-time low cost.

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Outra’s Propensity to Buy models

Outra’s Propensity to Buy solution analyses and scores every household within a set radius of the property being marketed. This means Heylo can create GDPR compliant custom audiences that remove the restrictions of the Meta disk altogether.

There are three core components to the score:

Product fit - a set of machine learning models that predict how likely each household is to be interested in a specific bedroom count, property type and property style.
Readiness to move - a model that considers how long people have lived in their current house and life events that would trigger a house move.
Affordability - This model takes account of the household income and equity to calculate if they can afford the property.

The scoring models are built with "Privacy by Design", and are based on household-level data rather than personal data. In other words Outra target households rather than individuals and is therefore GDPR compliant.

As Carolin Sievers, Lead Data Scientist at Outra says, there's a load of data going into the models. “We use almost 200 features from our datasets to inform the model. For instance the current attributes of the property they live in, how long they've lived there for, their demographic data and lifestyle choices.”

‍Outra’s ability to focus on people interested in shared ownership

The key differentiator for Heylo is that Outra has fine-tuned their model specifically to identify people that fit the profile of someone that would be interested in shared ownership.

The benefits: Scaleable and focused programmatic marketing

A big part of Dan Jones’ job is getting the right recipe for Heylo's digital acquisition marketing. Being able to live-ramp Outra data into their Meta and Google channels with custom segments that can go down to postcode level, enables them to target clusters of high intent customers. Dan can also use broader keywords cost effectively because he has been able to zone into locations using 1km disks rather than Meta’s 17km disks.

The fact that Outra’s data models are proprietary means they can be optimised for each client’s needs. They score each household so even specific letterboxes can be targeted for direct mail campaigns.

I love working with a partner who really is on the same page and knows what my priorities are

Case study

How Pre Mover Transformed UPSTIX iBuyer Acquisition Strategy

Upstix Case Study for Pre Mover‍: “Moving house is such a fundamental trigger point for so many suppliers. Pre Mover gives us an edge that others don’t have”

“Moving house is such a fundamental trigger point for so many suppliers. Pre Mover gives us an edge that others don’t have”

Upstix, a leading house iBuyer in the UK, provides people with a new way to sell their home. But as a disruptive start-up, the biggest challenge for COO Frederick Jones is identifying and marketing to people who are actively planning to sell their house.

Using the Pre Mover AI tool from Outra enables Upstix to not only target who is about to put their house on the market, but also to focus on the segment of those most likely to use an iBuyer.

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The challenge: Identifying and engaging people actively planning to sell their houses

The most valuable consumer group in the UK are home-movers. They spend more before, during and after their move than any other consumer segment.

For Upstix to survive in a very competitive market, Frederick has to be able to maximise the return on his marketing investment by successfully targeting people who are actively planning to sell their house. “My challenge is to originate actionable leads in a very cost-effective way. We just can’t afford to spray and pray.”

On average homeowners spend £13,000 within a year of moving on expenses like renovations, furniture and electronics.

The solution: Pre Mover has a proven 1 in 4 success rate of identifying people selling their house in the next 6 months.

Precise Segmentation

Outra have trained Pre Mover to not only identify the people in the UK planning to sell their house, but also to focus on the people most likely to use an iBuyer. Pre Mover uses 2,300 data points and 130 signals to identify people who are looking for a quick sale, potentially due to probate or financial distress, and that have the type of property Upstix can sell on quickly. “Pre Mover gives us the base level of home-movers, and then we can apply various other layers on top of that around individuals in the household and the type of property, so we're able to precisely segment the type of person and specific location of houses on streets.”

No matter what segment of the home-movers your company targets, Pre Mover can be configured to find them.

Continual optimisation

A key part of the partnership between Outra and Upstix is the continual optimisation of the tool. “We do a 360 degree refresh every three months and we evaluate the success of every campaign to improve the machine learning. This segmentation analysis feeds into where we target our social media and direct mail spend, and the look-alike audience for email campaigns. It is an iterative process so we can constantly improve and retarget our marketing with Pre Mover.”

Machine learning within Pre Mover means that the longer it focuses on a segment, the more precise it gets, and so results continually improve.

Sales and marketing integration

Frederick also sees the benefits along the whole sales funnel. “It’s not just about targeting people initially, but also about how they go through sales and actually become clients. The whole sales funnel is continually fine tuned from top to bottom.”

90% of Upstix’s leads come from targeted advertising. This means that Frederick’s team isn't waiting for people to find them by chance as they search online. Upstix are proactively approaching the right people at the right time. What is more, the successful conversion rate on these leads is 12% which is four times the industry average.

“When leads come in, our sales team is spending time speaking to people who understand our service, and are in a position to sell. So we are having much more impactful conversations”.

The benefits: Evidence that your marketing is delivering return on investment

“A lot of companies promise better data analytics and more leads, but most don’t deliver”

Real estate DNA

A big factor that gives Frederick confidence in Pre Mover is that real estate is in Outra’s DNA. Outra’s founders have decades of real estate experience and a deep understanding of what data is meaningful and informative.

Fail-safe accuracy

The other big reason why Frederick believes in Pre Mover, is that there is a fail-safe way to check its accuracy. “If Pre Mover predicts a house move, say 98 Elscott Road, we can check on The Land Registry six months later, and see if that property actually has changed ownership”. This gives us a definitive score-card for Pre Mover, and a sure-fire way of optimising checking its accuracy.”

“To see the success of the model makes me feel really confident. It gives us an edge, and makes me believe the business will be a success”

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Case study

The Art of Targeted Advertising with Home Mover AI

The Art of Targeted Advertising with Home Mover AI

Maximise ROI with Home Mover AI's Precision Timing in Advertising

Tait Lawson

Marketing Director

In the intricate dance of advertising, timing is not just a factor—it's the central stage. Home Mover AI has revolutionised this aspect, bringing an unprecedented level of precision to targeted advertising. By tapping into the predictive power of Home Mover data, advertisers can now reach potential customers exactly when they are most likely to make key decisions; significantly boosting the effectiveness of marketing campaigns.

Section 1: Applying Home Mover Insights Across Industries to Reinvent Marketing ROI

Elevating Marketing Strategies with Predictive Accuracy

The Home Mover product from Outra, with its advanced AI and predictive analytics, offers an innovative approach to enhancing marketing ROI. By accurately predicting when individuals are planning to move homes, this tool enables advertisers across various industries to time their campaigns to this critical decision-making phase. This precise targeting ensures that marketing messages are not just relevant, but also delivered at the most opportune moment.

‍Real-World Impact Across Diverse Sectors

For instance, a furniture retailer can utilise Home Mover data to identify prospective customers who are in the process of moving, offering them timely deals on home furnishings. Similarly, a telecom provider can target households moving to new areas with special broadband and TV package offers. These targeted approaches ensure higher engagement rates, thereby leading to improved conversion rates and maximised sales efficiency.

‍Section 2: Innovative Use Cases Across Industries that Benefit from Home Mover Insights

‍Versatility of Home Mover Data in Targeted Advertising‍

The application of Home Mover data is not limited to a single industry; its benefits span across numerous sectors. This predictive tool can be used by companies in fields ranging from home entertainment to personal finance, each leveraging data to meet their specific advertising needs.

‍Precision in Campaign Execution‍

The strength of Home Mover lies in its ability to offer high-intent consumer targeting. For instance, an insurance company can utilise this data to identify households that are relocating to new home, and might be in need of home insurance products. This level of precise targeting enables businesses to not only reach the right audience but also to do so at a time when their products are most needed, thereby significantly enhancing campaign success and ROI.

‍Section 3: How Home Mover Data Has Become King of Targeted Success with Predictive Marketing in the Digital Age

‍The Evolution of Digital Advertising‍

Amidst the rapidly evolving digital advertising landscape, predictive marketing has taken centre stage. The ability to forecast consumer behaviour and preferences has become increasingly vital for marketing success. Home Mover data, with its rich predictive insights, emerges as a critical asset in this new era, enabling advertisers to craft campaigns that are not just reactive, but proactively aligned with consumer needs.

‍Home Mover as a Cornerstone in Predictive Marketing‍

Home Mover's data stands out as a key player within this transformative period. It provides advertisers with a powerful lens in which to view potential customer transitions, particularly in the context of moving homes. This insight proves crucial, as a home move often triggers a series of purchasing decisions. By tapping into this data, marketers can anticipate needs and offer solutions before the consumer actively seeks them out, thereby positioning their brands strategically in the customer's journey.

‍Shaping the Future of Marketing Strategies‍

The predictive power of Home Mover data is reshaping how marketing strategies are formulated. Instead of a scattergun approach, advertisers can now employ precise, data-driven strategies to target consumers. This shift not only improves the relevance and effectiveness of advertising campaigns but also enhances the overall consumer experience, as messages and offers are more likely to align with the consumer’s current life stage and needs.

‍Section 4: Anticipating Consumer Behaviour By Leveraging Data for Effective Campaigns

‍Harnessing Predictive Insights for Timely Engagement‍

The core strength of Home Mover data resides in its ability to anticipate consumer behaviour with remarkable accuracy. This predictive insight is invaluable for advertisers aiming to engage with their audience at the most opportune moments. Understanding when consumers are likely to make significant life changes, such as moving homes, allows for the crafting of campaigns that are not only relevant but also exceptionally timely.

‍Strategic Campaign Planning with Home Mover Data‍

By leveraging Home Mover insights, advertisers can strategically plan their campaigns to coincide with these key decision-making periods in consumers’ lives. This could involve targeting individuals with offers for home-related services and products, or providing timely information that assists them during the moving process. Such targeted and well-timed advertising not only improves campaign performance, but also enhances customer experience, leading to stronger brand loyalty and long-term engagement.

‍Section 5: Future-Proofing Marketing Strategies with Home Mover AI

‍Adapting to a Changing Marketing Landscape‍

As the marketing world continues to evolve, staying ahead of the curve becomes increasingly important. Home Mover AI is an essential tool in this respect, offering a forward-looking approach to predictive marketing. By understanding and anticipating the shifts in consumer lifestyle and behaviour, advertisers can future-proof their strategies, ensuring relevance and impact in a consistently evolving market.

‍The Continuous Evolution of Targeted Advertising‍

The integration of AI and data analytics in marketing is not a fleeting trend but a fundamental shift. Home Mover data is at the forefront of this shift, continually refining the way advertisers approach campaign planning and execution. Its role in the future of marketing is not merely about targeting the right audience but also about fostering a more intuitive and responsive advertising ecosystem.

Conclusion

The art of targeted advertising in the digital age has been significantly enhanced by the advent of predictive tools like Home Mover AI. This innovative approach has revolutionised the way advertisers plan and execute their campaigns, ensuring that they reach consumers precisely when they are most receptive. The predictive power of Home Mover data has not only redefined marketing ROI but also established a new standard for customer engagement and satisfaction. As the marketing landscape continues to evolve,the strategic use of such AI-driven insights will undoubtedly play a pivotal role in shaping the future of targeted advertising, making timing not just a factor, but the essence of successful marketing strategies.

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Insights

Predictive Home Mover Marketing How Any Industry Can Benefit

Discover the power of predictive Home Mover marketing to boost ROI across diverse sectors

Tait Lawson

Marketing Director

In the fast-evolving world of marketing, the ability to anticipate consumer needs and behaviours is a coveted advantage. Enter the realm of predictive marketing, a technique that leverages data to foresee consumer actions. At the forefront of this revolution is Home Mover data, a tool that, while closely associated with real estate, holds untapped potential for a myriad of industries. This article delves into the versatility of Home Mover data, exploring how it can serve as a linchpin in understanding consumer intentions and reshaping marketing strategies across various sectors.

‍Section 1: Anticipating Consumer Behaviour by Leveraging Data for Effective Campaigns

‍The Importance of Consumer Insights‍

In today’s data-driven marketing landscape, understanding consumer behaviour is not just beneficial; it's essential. Home Mover data provides a window into the minds of consumers before they make significant life changes. This home mover information is invaluable, as it gives marketers in any industry a head start in tailoring their strategies to meet the evolving needs of their audience.

‍Shaping Advertising Strategies‍

The ability to anticipate when a consumer is planning to move can revolutionise advertising campaigns. For instance, retailers can target pre-movers with promotions on home furnishings, while automotive companies might highlight family-friendly vehicles. This foresight allows for a more strategic, targeted approach, ensuring that marketing efforts are not only seen, but are also relevant and compelling.

‍Section 2: Personalised Marketing for Any Business by Crafting Tailored Ads with Home Mover Data

‍The Era of Personalisation in Advertising‍

Personalised marketing has become the cornerstone of successful advertising campaigns. In an age where consumers are bombarded with generic advertisements, the ability to stand out with tailored messaging is invaluable. Home Mover presents a unique opportunity to personalise advertising efforts based on imminent life changes.

‍Creating Customised Ad Experiences‍

By leveraging Home Mover's predictive data, businesses across various industries can craft ads that directly address the needs and preferences of home movers. For example, a telecommunications company can offer special broadband packages to individuals moving to a new area, or a pet supply company can target pet owners moving to a property with more outdoor space. Such customisation not only enhances the relevance of the ads but also significantly boosts consumer engagement.

‍Section 3: Applying Home Mover Insights Across Industries To Reinvent Marketing ROI

‍Transformative Impact on Marketing Returns‍

The utilisation of Home Mover data has been shown to revolutionise the return on investment (ROI) in marketing campaigns across diverse sectors. By targeting consumers at a time when they are most receptive to certain products and services, businesses can achieve higher conversion rates and better returns on their marketing spend.

‍Case Studies and Examples‍

Consider the case of a home improvement retailer that utilised Home Mover data to target individuals planning to move. By offering tailored promotions and services, the retailer witnessed a significant uptick in sales and customer engagement. Similarly, a financial services company leveraged this data to offer home insurance and mortgage products to potential movers, resulting in a higher uptick of their services and an improved ROI.

Conclusion

Home Mover data transcends its traditional real estate confines, proving to be a versatile and powerful tool in the realm of predictive marketing. Its ability to provide insights into consumer behaviour and intentions before a major life event like moving house, is invaluable for marketers across various industries. By enabling personalised advertising and enhancing marketing ROI, this data not only meets the current demands of a data-driven advertising world but also sets the stage for future innovation. As businesses continue to seek more targeted and effective marketing strategies, the role of predictive analytics, especially in the form of tools like Home Mover, is set to become increasingly pivotal.

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Insights

Insurance Home Mover Shield To Safeguard Revenue

The Home Mover Shield To Safeguard Revenue in Insurance

Home Mover lifecycle insights and AI is crucial to the insurtech stack to safeguard revenue and customer value

In the fiercely competitive landscape of the insurance industry, the ability to protect and grow revenue streams is paramount. Senior decision-makers and C-suite executives are constantly on the lookout for innovative strategies that align with this objective. Understanding the Home Mover lifecycle through the power of data and AI offer a unique advantage by predicting customer household moves, thereby enabling insurers to make timely strategic adjustments. This foresight not only retains revenue but also minimises potential disruptions in the customer journey.

The Home Mover Strategy for Insurers To Maximise Customer Lifetime Value

The Essence of Customer Lifetime Value
In the realm of insurance, understanding and maximising customer lifetime value (CLV) is crucial for sustained success. CLV represents the total worth of a customer to a business over the entirety of their relationship. For insurers, this means not just selling a policy, but nurturing a long-term relationship that evolves with the customer’s changing needs.

Leveraging Home Mover Insights
Gaining deep insights through data and technology into when and why customers are likely to move homes, insurers can tailor their services to accompany customers through different life stages. Whether it’s adjusting coverage when a customer moves to a bigger home or offering additional policies relevant to new life circumstances, these targeted services significantly enhance CLV.

Unleashing Home Mover Data for Diverse Insurance Product Cross-Selling Power

The Strategic Advantage of Cross-Selling
In insurance, cross-selling is not merely a sales tactic; it's a strategic approach to deepen customer relationships and enhance revenue. By offering customers additional, relevant products, insurers can address a broader range of customer needs, reinforcing their role as comprehensive service providers.

Utilising Home Mover Insights for Effective Cross-Selling
Predicting when a customer is about to move through home mover lifecycle data and AI plays a crucial role here. When a customer is in the process of moving homes, their insurance needs invariably change. This transitional phase presents an opportune moment for insurers to cross-sell various insurance products - from property insurance to contents insurance. By aligning these offerings with the specific circumstances of the home move, insurers ensure relevance, which is key to successful cross-selling.

Tailoring Upsell Strategies Using Home Mover Predictive Insights

Upselling as a Revenue Growth Lever
Upselling, the practice of encouraging customers to purchase more coverage adding products, upgrades, or other add-ons, is a powerful tool for revenue growth in insurance. It goes beyond mere transactional sales; it's about providing customers with value-added services that genuinely enhance their coverage.

The Role of Predictive Insights in Upselling
Home Mover lifecycle data insights and the power of AI is becoming invaluable within the insurtech stack. These insights enable insurers to identify moments when customers might be most receptive to upselling. For instance, if the data indicates a customer is moving to a high-value property, it’s an opportune time to suggest premium insurance packages or additional coverage options. Tailoring upselling strategies in this manner ensures that offerings are not only timely but also highly relevant to the customer's current needs and lifestyle changes.

Retention Reinvented With Home Mover Insights To Secure Insurance Customer Loyalty

Enhancing Retention through Personalised Interactions
In insurance, retaining a customer is often more cost-effective than acquiring a new one. Home Mover insights offer a robust solution to the retention challenge by enabling highly personalised customer interactions. By understanding when customers are likely to move, insurers can proactively offer relevant services and support, demonstrating attentiveness to the customer's life changes.

Building Loyalty with Continuous Value Delivery
Loyalty in the insurance sector is fostered not just through competitive pricing but, more importantly, through continuous value delivery. Home Mover insights enable insurers to stay one step ahead, anticipating customer needs and offering solutions before the customer even recognises the need themselves. This proactive approach builds a strong sense of trust and reliability, essential components of customer loyalty.

Future-Proofing Insurance Revenue with AI

Anticipating Customer Behaviour Shifts with AI
The future of insurance revenue lies in the ability to anticipate and adapt to customer behaviour shifts. AI and predictive analytics, as exemplified by Home Mover, are key to this adaptability. By harnessing these insights, insurers can not only protect existing revenue streams but also identify new growth opportunities.

Adapting Strategies for Sustained Growth
The dynamic nature of AI-driven insights allows insurance companies to continuously refine their strategies in line with evolving customer preferences and market conditions. This agility is crucial for not just surviving but thriving in the rapidly changing insurance landscape. Home Mover lifecycle insights combined with AI and technology represents a significant step in this direction, offering a tangible way for insurers to future-proof their revenue streams.

Empowering Modern Insurers to Safeguard Revenue and Customer Value

Outra's Home Mover emerges as a pivotal tool in the strategic arsenal of modern insurance companies, particularly in the quest to safeguard and grow revenue. By maximising customer lifetime value, enabling effective cross-selling and upselling, and enhancing customer retention, this tool represents a comprehensive solution to several key challenges facing insurers today. Moreover, the integration of AI and predictive analytics signals a broader shift in the industry towards more adaptive, customer-centric models. As senior decision-makers and C-suite executives navigate this evolving landscape, tools like Home Mover are not just advantageous; they are essential for future-proofing revenue in a world where understanding and anticipating customer needs is paramount.

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Insights

Elevating Customer Experience for Insurance in 2024

Elevating Customer Experience for Insurance in 2024

The journey towards an AI-driven insurance future, led by innovations like the Home Mover, is not just imminent; it is already underway.

In an era where customer experience (CX) dictates the rise or fall of companies, the insurance sector is not immune to these winds of change. Traditionally viewed as a necessity rather than a choice, insurance services are now undergoing a radical transformation, primarily driven by personalisation and predictive analytics to redefine customer engagement through cutting-edge AI capabilities.

Section 1: Customer-Centric Insurance With Home Mover Personalisation

The Importance of Personalisation

In today's digital age, personalisation is not just a buzzword but a business imperative. Customers expect services that are not only efficient but also tailored to their unique needs and life situations. By leveraging deep insights into household moves, innovative technology and AI enables insurance companies to craft highly personalised experiences that resonate with each individual customer.

Facilitating Personalisation with Insights

Harnessing the power of big data and AI to predict when individuals are likely to move home allows elevated customer experiences within the insurance sector. This predictive capability allows insurance providers to offer tailored insurance solutions at just the right moment. Whether it’s updating a homeowner's policy or suggesting new coverage options, these timely interventions foster a sense of understanding and care, crucial for building long-term customer relationships.

Section 2: Insurance Evolution: Home Mover Insights and the Life Insurance Revolution

Transforming Traditional Life Insurance Offerings

Life insurance, a cornerstone of long-term financial planning, has traditionally been a static offering, often disconnected from the dynamic nature of customers' lives. By integrating predictive insights into customer movements, life insurance providers can now adapt their offerings to align seamlessly with the evolving life stages and needs of their clientele. A seismic shift to the insurtech landscape to better understand consumer needs with a more dynamic product offering.

The Power of Predictive Personalisation

‍Imagine a scenario where an insurance provider can anticipate a customer's move to a larger home, possibly indicating a growing family. Armed with this insight, the provider can proactively offer enhanced life insurance coverage or family plans, resonating perfectly with the customer’s current life situation. This level of personalisation, powered by predictive analytics, not only delights customers but also positions the insurance provider as a proactive, caring partner in the customer's life journey.

Benefits of Timely and Tailored Solutions

The benefits of this approach are manifold. Customers receive life insurance solutions that feel personalised, timely, and relevant, significantly enhancing their satisfaction and trust in the provider. For insurance companies, this translates into deeper customer engagement, increased policy uptake, and a competitive edge in a market that is increasingly customer centric.

Section 3: Retention Reinvented: How Home Mover Insights Secure Insurance Customer Loyalty

Addressing Customer Retention Challenges

In the competitive landscape of the insurance industry, retaining customers is as crucial as acquiring new ones. Traditional retention strategies often fall short in understanding and addressing the evolving needs of customers. This is where Home Mover insights become a game-changer.

Personalised Interactions and Continuous Value Delivery

By leveraging data on imminent household moves, insurance providers can tailor their communications and offerings to meet the changing needs of their customers. Such personalised interactions are not just about selling more; they're about delivering continuous value, showing customers that their insurance provider understands and adapts to their life changes. This approach cultivates a sense of loyalty and trust, making customers more likely to stay with their provider for the long haul.

Impact on Customer Loyalty

The impact of using Home Mover insights can be significant. Customers who experience this level of personalisation and attentiveness are more likely to view their insurance provider as a trusted advisor, not just a service provider. This shift in perception is critical for long-term loyalty, as satisfied customers are not only more likely to renew their policies but also to recommend the provider to others, thus driving both retention and new customer acquisition.

Section 4: Redefining Insurance Revenue With Home Mover AI

Revolutionising Traditional Revenue Models

The introduction of predictive analytics through tools like Outra's Home Mover is not just transforming customer experience; it's also redefining how revenue is generated and protected in the insurance sector. Traditional models often rely on static customer profiles, leading to missed opportunities and a reactive approach to customer life changes. With predictive insights, insurance companies can shift to a more dynamic, proactive model.

Optimising Offerings for Revenue Protection and Growth

The predictive capabilities of the Home Mover product allow insurance companies to anticipate and respond to key life events of customers, such as moving to a new home. This foresight enables the optimisation of insurance offerings to suit these new circumstances, thereby not only retaining existing customers but also increasing the potential for upselling and cross-selling relevant insurance products. Such tailored solutions are more likely to be embraced by customers, leading to enhanced satisfaction and, consequently, revenue growth.

The Future of Insurance Revenue with AI

As the insurance industry continues to evolve, the integration of AI and predictive analytics heralds a new era of revenue models. These models are customer-centric, responsive, and agile, aligning closely with customers' real-time needs and preferences. The Home Mover product exemplifies this shift, presenting a promising future where insurance revenue is not just about policy sales, but about building and maintaining enduring, value-driven customer relationships.

Conclusion - Home Mover Lifecycle AI is a beacon of innovation for insurance

Understanding the predictive Home Mover lifecycle with AI is a beacon of innovation in the insurance sector, significantly elevating the customer experience. From personalising insurance offerings to revolutionising life insurance and reinventing customer retention strategies, the predictive insights provided by this tool are reshaping the industry. Moreover, the impact of these insights extends beyond customer satisfaction, playing a pivotal role in transforming revenue models and securing long-term financial success for insurance companies.

As we look to the future, the integration of AI and predictive analytics in insurance promises not just enhanced business outcomes but also a more intuitive, responsive, and customer-centric industry. The question now is not whether insurance companies will adopt such technologies, but how quickly they can adapt to leverage these advancements to their fullest potential. The journey towards an AI-driven insurance future, led by innovations like the Home Mover, is not just imminent; it is already underway.

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Insights

2024 Real Estate Goals - Proptech and Data at Your Service

Keeping ahead of proptech advances is essential for any early-career exec in establishing themself as a contemporary property professional

Residential property moves into 2024 with determination, yet still not without some measure of nerves. The uncertainties the market has shown through 2023 are certainly not yet resolved, and it’s against this backdrop that professionals across the sector must shape their strategies for achieving meaningful goals in 2024.

Today, proptech is the most ubiquitous of industry terms, raising its head in almost every conversation. As the power of proptech tools offers all property professionals so much potential for achieving their targets and objectives, it’s worth taking a moment to recount where, precisely, we are on the proptech journey.

First appearing well over thirty years ago, proptech – property technology – refers to the application of technology within the real estate business. In its first iteration, up until around 2000, this involved little more than the use of spreadsheets for managing listings and client and financial data.

The second stage of the proptech revolution started in the dotcom boom of the late nineties, and transformed the sector by providing customers, by then all with high speed internet connections, with tools for searching listings online.

The third stage, in which we now find ourselves, is the most exciting of all. With the aid of innovative tools, software and platforms it is revolutionising traditional agency methodologies and practices at both a strategic and executive level, through the analysis of high volumes of consumer and market data, and the implementation of strategies built around insights obtained from that data. This isn’t merely a further incremental addition to the business of estate agency. It's a fundamental shift that redefines the way professionals engage with the sector.

So what does this mean for the rising stars and junior professionals now navigating the complexities of the industry? The answer lies in its transformative potential. Proptech offers today’s generation of property professionals tools and insights which are essential for elevating their performance by providing a lens through which to analyse market trends, consumer behaviours and property data with unprecedented clarity.

This, in turn, enables them to make informed decisions, forecast market movements, and steal a march on competitors (as well as on colleagues) by drawing inferences others may not have made, and responding swiftly to evolving demands. For anyone in the early years of their career, this kind of technology, which younger professionals reared on the use of data in all kinds of contexts may find far easier to integrate into their work than their seniors, offers a launchpad from which to succeed and thrive amidst the changing tide. Using theses kinds of advanced analytics, automation tools and collaborative platforms, younger professionals should be able to leapfrog traditional career barriers, and supercharge their trajectory within the industry.

The power of leveraging data in real estate

It would be a grave misjudgement at this point to view property insights driven by analytics as a ‘coming’ wave. While the applications and impacts seem certain to proliferate in the next few years, practical use of the tools is very much ‘here and now’.

Fred Jones, formerly Managing Director of Uber, is COO at ‘instant’ home offer platform, Upstix. The company uses proptech data analytics innovator Outra’s Pre-Mover platform to identify the specific properties and buyers it wishes to target.

“We get an amazing amount of data through Pre-Mover”, Jones explains. “This includes demographic type, propensity to sell and property type, all in different regions within the UK. It's just brilliant. In the first three months of using this tooling, we increased our conversion by 3.5x.”

In an era in which so many sectors are already ruled by data-driven decision-making, this is no mere trend. It’s fast becoming the linchpin for real-estate success, having transformed from an advantage to a necessity for professionals aiming to achieve challenging industry goals.

So why does this hold so much significance? The answer lies in the strategic advantages it makes available. Until relatively recently real estate, as a business, depended on instinct and experience. Today, it is increasingly driven by empirical insights and predictive analytics. The capacity to leverage comprehensive data repositories means professionals can navigate the market with precision, foresight, and unparalleled efficiency.

The value of data extends far beyond mere statistics. It embodies a treasure trove of patterns, trends, and invaluable insights which, when deciphered, provide a blueprint for success. It puts the pulse of consumer behaviour, the whole picture on market fluctuations, and the ebb and flow of property dynamics all at the fingertips of individual agents and strategic managers able to harness them wisely.

Like Fred Jones, Robin Patterson, the former owner of Sotheby's Realty and now Founder of Upstix, is evangelical in his belief in the data analytics and insights provided to his firm by Outra’s product, Pre-Mover.

“Any real estate business looking for data on either their buyer or seller… Outra is able to provide it, which allows them to be very focused on their core markets,” says Patterson. “It allowed us to improve our buy boxes, lead generation and pre-seller records, enabling our pre-move model to be focused and target those people looking to move.”

The secret, of course, lies not only in the quality of the data (Outra’s Pre-Mover monitors 30.8m UK households, tracking 2,300 attributes per record via more than 60 quality data sources), but also in the tech used to leverage it. From advanced analytics and machine learning algorithms to intuitive platforms and automation tools, this is what amplifies the capability to extract the actionable insights that enable property professionals to anticipate market movements, identify untapped opportunities and tailor strategies with pinpoint accuracy.

Whether seasoned veterans or emerging talents, it’s this marriage of data with exceptional technology that transforms all of those records and attributes into strategic intelligence, and empowers property pros to stay ahead in a landscape defined by its dynamism and competitive edge.

Trends and Innovations in Proptech

The kind of predictive analytics employed by Upstix through Outra’s Pre-Mover product is one of the key aspects of modern proptech, enabling executives and managers working in UK residential real estate to forecast future trends, behaviours and outcomes, and so make better decisions, optimise performance and gain valuable competitive advantage.

Embracing these tools, and taking ownership of them within the organisation, offers more recent recruits into the real estate business a clear roadmap not only to success against 2024 business goals, but to accelerated career profile and industry success. So, moving into 2024, what are the most important trends and innovations in this kind of proptech?

In the area of demand and supply forecasting, predictive analytics can help real estate professionals to understand the current and future demand and supply of residential properties in different locations, segments, and price ranges. This can help them to identify opportunities, adjust pricing, and allocate resources accordingly.

In customer segmentation and personalisation, products like Outra’s Pre-Mover can help to segment and target customers based on their preferences, behaviours, and needs. This can help agents to tailor marketing, communication, and service strategies to each customer segment and deliver a personalised and engaging customer experience.

Predictive analytics can also help real estate professionals with valuation and appraisal, making it possible to estimate the value of residential properties based on factors such as location, size, condition, amenities, and market trends. This can help with appraising properties accurately, negotiating deals effectively, and avoiding overpaying or underpricing.

Data analytics can also be used to optimise an agency’s portfolio of residential properties by identifying the best mix of assets, markets, and strategies. This can help it to maximise its returns, diversify its risks and align its portfolio with its strategic goals and objectives.

Pre-Mover identify the earlier discovery of hyper-targeted leads; identification of high-intent audiences; optimisation of marketing spend; increased ROI through reduced cost per lead; competitive advantage through being able to approach high-intent customers sooner; increased revenue from increased access to highly motivated vendors; increased market share; and accelerated growth of market share for new branches as key benefits of their product. Addressing, as this does, almost every essential metric of residential agency performance, it’s impossible to take issue with the company’s founder, Giles Mackay, when he argues that, “In the dynamic landscape of real estate, harnessing the power of data is not just a strategy; it's a necessity. Early identification of motivated sellers is a prime example of how data can be the catalyst for exceeding sales targets." 

Hitting performance and career goals for 2024

For any junior professional working in residential real estate and determined to surpass annual goals, position themselves ahead of peers and add to their achievements in 2024, the signposts could scarcely be clearer.

First, proptech is now the key transformative actor in the reinvention of residential sales as an industry, and by actively participating in its adoption and application in the organisation, an executive can position her/himself both at the head of annual performance tables, and in pole position for career fast-tracking.

Next, sector data, data analytics and the insights that these can provide can boost both your personal and company performance in all areas of the business.

Lastly, advances in proptech are continuous. Keeping ahead of these is essential for any early-career exec in establishing themself as a contemporary property professional whose success is attributable primarily to their facility with data and data tools. The websites and product downloads of proptech leaders (such as Pre-Mover creator Outra) provide a valuable starting point for expanding knowledge of the landscape and laying the foundations for making 2024 a true breakthrough year.

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Insights

Unlocking Real Estate Success With Data-driven Insights To Propel You Past Your 2024 Targets

Data-driven insights can no longer be viewed as a ‘nice to have’ competitive advantage.

Bullishness and determination count for a great deal in sales, and nowhere more so than in property sales. They cannot, however, do much to counter the uncertainty the market that will carry over from 2023. Sellers will enter 2024 continuing to weigh lower valuations than they’d like against the prospect of increasingly lower purchase prices and mortgage rates that may begin to edge down again soon – unless they don’t!

In a market with this level of instability, beating sales goals calls for a far more dependable, which means data driven, strategy.

"In the dynamic landscape of real estate”, argues Giles Mackay, founder of real estate data analytics leaders, Outra, “harnessing the power of data is not just a strategy; it's a necessity. Early identification of motivated sellers is a prime example of how data can be the catalyst for exceeding sales targets." 

How can data help real estate professionals meet and exceed sales targets?

Mr Mackay’s assertion is not unjustified. Data can empower anyone involved in residential sales, by providing actionable insights that help optimise effort, streamline processes and which will, ultimately, lead to beating of sales targets.

With effective predictive analytics, anyone in residential sales has comprehensive insights into market trends, buyer behaviour, and seller patterns at their disposal. By understanding these trends, they can anticipate market movements, adjust strategies accordingly, and target their efforts more effectively.

Leveraging data then allows for precise targeting of potential clients. By analysing demographics, preferences, and past behaviours, it becomes possible to identify and focus on the most promising leads, optimising use of both time and effort. By using predictive analytics to forecast when properties are likely to be listed for sale, sales teams can engage proactively with sellers, gaining competitive edge and increasing the likelihood of closing listings.

Data-driven insights also offer a solid foundation for making informed decisions around sales strategy. Whether on pricing or geographic targeting, for example, comprehensive data combined with robust analytics enables calls to be made with vastly improved confidence. In addition, at individual listing level, data can help build stronger client relationships by providing insights into client needs and preferences. This allows agency sales teams to deliver more personalised advice, guidance and service, fostering trust and increasing loyalty.

“The real Focus for everybody in the real estate space, “ says Fred Jones , COO of ‘quick sale’ cash buyer, Upstix, “is to get sellers before anybody else gets there. If we can do that then we've revolutionized the way anybody runs the real estate space.”

How identifying motivated sellers before they list improved performance.

Being able to identify motivated sellers before they list their properties delivers substantial advantages in achieving sales targets. Connecting with sellers identified as highly likely to list before their property hits the market lets you establish rapport and build relationships. This increases the likelihood of securing the listing, giving a head start in the sales process. It also, of course, reduces competition from other agents. By being the first point of contact, you have a better chance to negotiate terms, potentially agreeing more favourable conditions for both you and the seller.

Data analytics also offers the potential to offer upcoming sellers the option of an off-market deal. This means you may be able to offer a solution to specific needs or wishes, such as privacy concerns or the desire for a quick completion, and potentially expedite their sale.

With the insights provided by high grade analytics making it possible to understand a seller's motivation early on, sales teams also have an improved basis on which to tailor sales strategies. Whether this is by highlighting certain property features, offering more flexible terms, or addressing a specific concern a seller may have, the data insights make it possible to craft personalised approaches which maximise the chances of landing the instruction and achieving a successful sale.

Building a relationship with a seller before they list their property may also lead to a higher closing rate, through being able to better understand seller expectations, manage negotiations, and guide the sales process more effectively.

With HMRC National Statistics recording a provisional, non-seasonally adjusted, estimate of the number of UK residential transactions in September 2023 down 19% on September 2022 (and down 2% on August 2023), advantages of the kind gained through identifying motivated sellers before they list could step any sales team closer to not only achieving, but impressively outperforming, their 2024 targets.

The best data and analytics tools to help you pass 2024 targets.

Proptech as a sector is booming, but while platforms aimed directly at buyers and sellers proliferate, the market for serious data and analytics designed specifically to help agents identify sellers ahead of listing is dominated by Outra’s ground-breaking product, Pre-Mover.

“In all my years involved in residential development and estate agency”, says Dominic Grace, former Head of London residential Development at Savills, “I've never seen anything as phenomenal, in terms of its power, as the Outra platform. It gives you lots of information at a really granular level… so it means you can run your whole business much more efficiently and effectively.”

Pre-Mover tracks more than 2,300 attributes each on 30.8m UK households, amounting to 75 billion data points at property level. According to Outra’s Chief Data and Technology Officer, Peter Jackson, “We make 900,000 predictions every month; they've not even listed their house yet and we're predicting when they're going to list. That's quite remarkable performance.”

Upstix’s Fred Jones agrees. “The UK housing market is hugely volatile and has changed massively over the last 12 months alone. The Outra data helps us be agile and adapt our marketing strategies to respond to that. It's allowed us to be on the front foot and capture new leads rather than being reactive.”

No longer ‘Nice to have’. ‘Must have’ in 2024.

It would be foolish to imagine 2024 won’t harbour significant challenges for the residential sector, compounded by those lingering market uncertainties. Against this unpredictability, however, data stands out as the key driver for success. Leveraging data-driven insights has become not only ‘advantageous’, but essential for exceeding sales targets.

By understanding market trends and buyer behaviours, and anticipating seller patterns, directors, area leaders and managers in firms of any size can gain a comprehensive understanding of the landscape and use this to recalibrate strategies, precisely target potential clients, and engage proactively with prospective sellers before their properties hit the market.

It is this last benefit, the ability to identify motivated sellers before their official listing, that is the real game-changer. Establishing early relationships opens doors for negotiations, reduces competition, and allows for tailored sales approaches that really impress and win over sellers.

As 2024 approaches, the use of data-driven insights can no longer be viewed as a ‘nice to have’ competitive advantage. It’s now the linchpin for success; the single most decisive factor in empowering you to not just meet, but to significantly exceed, 2024 targets.

Insights

A Data Advantage To Navigate Real Estate Volatility

Data analytics promises bold and ambitious real estate management teams a level of disruptive and transformative power

Leadership brings challenges. For anyone guiding the fortunes of a business of any scale, these challenges go with the territory. Indeed, many would say these are the territory.

While every commercial sector has its own issues to wrestle with, for anyone leading a UK residential real estate business at this time, the hurdles to be overcome (and, on the flipside, opportunities to be embraced) arise largely from the uncertainty against which the sector finds itself operating.

In the words of former Foxton’s CEO, Nic Budden, "The UK residential estate agency market is one of the most competitive and dynamic in the world, but it is also one of the most challenging and complex. We have to deal with the fluctuations and uncertainties of the housing market, the evolving preferences and behaviours of our customers, and the increasing competition and regulation in the industry. We have to be agile and innovative to stay ahead of the curve and differentiate ourselves from the crowd.” The accuracy of tis view has only been compounded by further economic turbulence.

Staying ahead of the crowd, as Mr Budden observed, really is the key issue. In a heavily populated market, how can real estate businesses develop strategies capable of securing real market advantage, when borrowing rates are high, buyers are nervous and hesitant, sellers are uncertain, the economy is sluggish and policy from the Bank and Government must be viewed as ‘continuously subject to change’?

Data analytics, real estate and a glimpse into the future

In the age of sophisticated data science and Artificial Intelligence tools, the answer seems certain to lie in innovative and timely use of analytics.

Think of the advantage to be gained at every level of the business, from operational and budgetary consideration at local office level, to organisation-wide forecasting and planning, were residential agency businesses to exploit relevant consumer and market data on a continuous basis.

In what ways could AI-driven data analytics of this kind give agents ready to embrace their forensic power and invaluable insights a competitive edge?

Fed with extensive and expertly defined data points and signals, data analytics could provide a tactical commercial weapon of a kind never before seen in real estate. Able to analyse market trends, consumer behaviour and economic indicators in myriad ways, agency leaders would be able to identify emerging market patterns, prioritise areas in which listings were about to blossom or to slow or, at a granular level, predict accurately, months in advance, when particular properties were likely to list.

At an organisational level, consistent use of data analytics in managing the business could provide estate agents with the ‘crystal ball’ of their dreams, enabling informed decisions, insight-driven business planning and early moves to obtain instructions, and providing the keen competitive edge needed to cut through the uncertainties of the market and outpace competitors.

The advantages for agents of identifying listings ahead of time

Accurate and reliable foresight into future listings could provide strategic advantage for real estate businesses in numerous ways. Early access to information about properties coming onto the market would enable agents to engage proactively with potential sellers, reaching out to offer their expertise and services before the property lists. An approach of this kind not only demonstrates a proactive operation, but positions the agent as a dependable professional partner.

Agencies able to identify soon-to-list properties early would also lay claim to the opportunity to secure the listing exclusively, gaining greater control over the marketing process and allowing for a more tailored and focused strategy to maximise a property's visibility. Similarly, with data-driven ability to analyse comparable properties and market trends, agencies could take a more authoritative position in helping sellers set competitive and realistic prices. As well-informed pricing enhances the chances of a successful transaction, this stands to minimise the time properties spend on the market.

There would be further advantages to be gained, too. In property marketing terms, amed with early visibility of upcoming listings across an area, agents would enhance their ability to optimise sale price for their clients, advising on when precisely to release a property onto the market to maximise its value. Similarly, by identifying properties before they enter the market and engaging early with vendors, agents would be able to streamline the transaction processes, preparing documentation, conducting pre-listing inspections, and addressing potential issues ahead of actual listing. This would again reduce the time the property spends on the market, creating a smoother sale process for all parties.

With the ability to predict upcoming property listings, agencies could become more proactive market leaders, able to control deals and run even the largest chain with greater precision and, as a result, enhanced success.

How early, data-driven insights show the way through uncertain economic times

By now, most real estate agency management teams have come to terms with the lingering uncertainty in the landscape. It’s ‘the way it is’. But while it’s a far from ideal environment in which to try to solidify success, managements equipped with powerful, AI-driven data analytics tools could gain high value wins.

Underpinned by the right dataset, a robust real estate tool could facilitate accurate forecasting of economic shifts by analysing predictive indicators such as employment rates, inflation and interest rates. Armed with these insights, managements would be able to anticipate how changes in the broader economy might impact the housing market, allowing them to adjust strategies on pricing, marketing or investment recommendations, accordingly.

Reaching beyond raw economic data, an effective tool might tap into sentiment analysis of market participants, letting agency teams observe shifts in consumer confidence and adjust approaches accordingly. For example, with indications of positive sentiment returning, an agency team might consider it a good time to turn up marketing efforts, while negative sentiment might advise greater caution.

Inevitably, economic uncertainty gives rise to trends and shifts in consumer behaviour. Able to analyse a myriad of data points, however, real estate businesses could observe emerging trends such as shifts in housing preferences, demand for specific amenities, or changes in preferred locations, all before these became widely apparent. Businesses with access to these insights would then be able to position themselves ahead of the curve, aligning their offerings with evolving consumer preferences.

For every business in the sector, there is also the clear need to assess and mitigate the new and increased risks inherent to transactions in times of uncertainty. Through analysis of historical data and market trends, powerful real estate tools will help agency managements identify the potential for, say, market downturns or fluctuations in values. This knowledge would make it possible for leaders to develop contingency plans and advise clients accordingly, greatly enhancing resilience.

In a market in which economic conditions can vary significantly at regional and micro-market levels, granular analysis also has the potential to help teams leading chains operating across larger areas to understand the nuances within different locales. Localised insight of this kind could prove invaluable in tailoring strategies to specific regions, optimising marketing efforts, or capitalising on opportunities that may be unique to certain areas.

Outra’s Pre Mover analytics management leads the way for real estate businesses

The application of powerful data analytics to residential real estate is a paradigm shift that can redefine success in the industry for everyone from single-office agencies through to the largest, nationwide chains.

Innovation in the space is led by Outra’s disruptive ‘Pre Mover’ product. Observing over 30 million UK households with more than 2,300 data points and 130 signals on each, Pre Mover generates highly accurate predictions about properties which are soon to be listed. It integrates analysis of real world events, such as residential sales and rental data, with a broad array of household insights, demographic data and other influencing factors to accurately predict households planning to list as far as six months in advance.

Decision time for agency leadership.

Data analytics promises bold and ambitious real estate management teams a level of disruptive and transformative power similar to those obtained in comparable sectors by organisations willing to embrace early and remodel operations and strategies around its impactful insights.

While the market maintains its current unpredictability, the consideration for all management teams should be the risk of being left for dead by competitors taking this opportunity to re-engineer their strategies and approaches around potent, AI-driven data science.

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Insights

Work From Home Trends and the Race for Space

Impact of remote work on housing

In early 2020, overnight working from home (WFH) became widespread across the globe. As lockdowns have ended in the UK, we have embraced ‘the new normal’ with hybrid working models more common than ever. However, for some, the weekly commute has returned. This change in working behaviour across the UK has, over the past 2 years, dramatically impacted house prices.

With such disparity between working behaviours, Outra’s data science team has sort to identify who are these hybrid or remote workers, and how this is influencing the UK’s housing market. By analysing commuting data, we have looked to identify where the resulting ‘race for space’ is occurring. To start to understand Outra used data from a variety of sources, modelled to provide insight and identify trends, including;

passenger data for the 40 UK stations with themost marked difference in passenger numbers - the 20 highest and the 20 lowest.
we identified suburban stations with the highest and lowest change in entry and exit volumes as a proxy for WFH.
the numbers of people who commute to work using the Office of Road and Rail information for the 12 months ending in March 2021.

Pre and post lockdown trends

In locations where there were minimal increases in house prices, compared to the wider UK trend, we identified 3 key factors. Where these were present, homes near these back-to-work stations, prices only rose by 8.7%.

Train stations largely located in and around London, where properties are higher in value. Occupants are more likely to work full time, travelling to and from the office.
Higher number of flats and apartments. Fewer bedrooms compared to more central properties, meaning occupants are less likely to have access to adequate WFH space.
Property occupants near train and bus stations are more likely to be in managerial roles. Increased seniority requires a more regular presence in the office — resulting in more frequent commuting.

Where we are seeing the ‘race for space’, is where people in the inner cities sought to transition to WFH locations, which in turn has fuelled the UK’s property price growth. Property values near WFH stations have increased by 13.5%, outpacing average country-wide growth since 2020.

Local properties are more often owner-occupied and bigger in size, giving less incentive to commute.
Property occupants living far away from their offices and nearest stations are more likely to work from home.

Working from home trends today

Now the UK has fully opened up, a majority of employers have embraced new ways of working. As workers demand a better work life balance, Covid WFH practices also demonstrated that businesses could still be just as productive. As we saw with key worker lockdown policies, working from home is more feasible for some types of workers than others. High earners are the ones most likely to be benefiting from a hybrid work environment, with 38% of workers earning £40,000 or more currently working both in the office and at home.

Meanwhile, lower earners are less likely to WFH at all. Lower earners who reported hybrid working between 27 April and 8 May 2022 included:

8% of those earning up to £15,000

24% of those earning between £15,000 and £20,000

21% of those earning between £20,000 and £30,000

32% of those earning between £30,000 and £40,000

There also seems to be an age element to those either able to or wanting to WFH, as those aged 30 to 49 most likely to do so.

These changes in working patterns have had a material impact on the housing market. Any further changes or an impending recession will either reverse or accelerate those shifts. Only by understanding the unique data points that create each individual householder can businesses adjust to new trends.

View our Interactive map

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Insights

Who can afford a home today?

Millennials' homeownership decline

Weathering the Storm of the Cost of Living Crisis

Business survival in economic crisis

The retail, utilities and credit sectors are facing one of the greatest challenges of the past 40 years: As the economic situation worsens, a recession becomes more and more likely. Households feel the pressure and businesses need to adapt to survive. So, what can companies do to weather the storm and thrive during this new challenge?

The perfect storm

In a post-Covid world there were glimmers of growth, but as the world has opened back up, we’ve faced a new wave of challenges. As a result, the UK is seeing a dramatic reduction in spending. On June 24th, the Office of National Statistics (ONS) announced that the volume of goods sold in-store and online in May 2022 fell by 0.5%.

With inflation at its highest level since the 1980s, we’re experiencing all-time lows in consumer confidence. As food and energy prices continue to rise, this trend is likely to continue throughout the summer.

Some of the industries most affected by the crisis are retail, credit, and utility. Each industry faces unique challenges and in turn, will need to deliver a unique strategy in response.

Retail businesses will need to be smarter at customer targeting and retention to ensure a healthy flow of new shoppers while preventing the loss of existing loyal customers.

Credit providers must address the increased need for credit along with an increased risk of defaults or fraud.

Utility companies must improve their processes for supporting vulnerable customers to adhere to regulations, along with a moral duty to support these households.

Data insights and the 2022 consumer

Consumers are adopting more defensive spending behaviours to ensure they have enough money to pay for the essentials. These tactics include self-imposed checkout limits at supermarkets, reducing expenditure on luxuries, or borrowing to fund everything from clothes to food.

The change in consumer behaviour creates a reduction in disposable income for businesses in the affected sectors. With a reduction in income comes a hit to companies’ bottom lines — threatening the survival of many of them. Small businesses are particularly at risk as they lack the cash flow necessary to recover from low-income periods.

Each UK household is unique. For consumer-focused businesses, understanding each of these, whether they are an existing customer or not, will be key to success. By using data that provides detailed insight at an individual post box level, businesses can gain a true understanding of the UK consumer.

Leveraging customer data science and data modelling provides the ability to turn data into wisdom — which in turn, forms the bedrock for any business or marketing strategy through an understanding of the unique nature of each customer, and the unique issues they are facing.

Adapt. Overcome

In the current environment, identifying which UK households are most likely to be affected by the cost of living crisis helps businesses adapt to the needs of their customers. By knowing the financial status of customers at a household level, businesses get a live snapshot of current behaviours. And through modelling that live snapshot, we can then predict customers' future behaviours.

All consumer-facing businesses can use this insight to understand:

Who are my customers, and how are they dealing with increased living costs?
What is important to my customers?
How do I ensure my marketing messaging is effective?
Who should I target, and what’s the best channel to use to do it?

But this data can also help overcome unique challenges within each sector.

For Retail, this insight can identify which physical store locations might face the most amount of pressure from reduced takings. Those located in the most hard-hit locations are likely to see a reduction in foot traffic and profits. Using data science, retailers can implement strategies ahead of time to address the issue, such as stocking cheaper products, creating targeted promotions or advertisements, or even reducing the number of physical stores.

Credit agencies and lenders can use the data to pinpoint households that are more likely to default. This goes beyond the process of deciding on whether to extend credit or not at the point of sale. It can identify households that already have credit, but now have a propensity to default. To better support those that face financial hardship as well as to manage credit risk.

The same is also the case for Utility providers. The current energy crisis has put an increased regulatory burden on providers. Our analysis has identified 5,055,359 vulnerable households out of the circa 30 million in the UK by modelling features beyond simple financial flags. Each one of those is considered a financially vulnerable household that they are obliged to actively provide additional support.

Data science at a household level can help consumer-facing businesses adapt and overcome the challenges that lay ahead. Through understanding where consumers are at this moment in time, and where they might be in six months, we can identify behaviours and keep your business ahead of the curve.

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Energy

The Hidden Reality of UK Energy Costs

Understanding UK's energy crisis

As winter begins to set in, the reality of the cost of living crisis is beginning to take hold. While inflation has been affecting the economy as a whole, the increase in UK energy costs has been especially steep. Even with government intervention, energy price hikes will place huge pressure on UK households. In particular, the most vulnerable, those with larger homes, and homes with a lower EPC rating.

The crisis isn’t affecting customers equally. Our data has identified regional variations in household finances and building quality which will mean some homes will face higher costs than others. Consumer facing businesses and regulators need to understand these regional differences at a household level, to not only maintain the bottom line, but to ensure the most vulnerable customers are being supported.

UK faces average energy bills of over £4,000

UK energy costs have increased due to the rising price of wholesale gas across 2021 and 2022. In April, household energy bills increased by 54% and consumers faced a further 80% rise in October.

In response, the UK Government established the Energy Price Guarantee. The programme caps the average unit price for dual fuel customers paying via direct debit from 1^st October; 34.0p/kWh for electricity and 10.3p/kWh for gas. This is on top of the £400 Energy Bill Support Scheme.

What is important to note is that this means bills are not capped – they are still based on usage. So, those with larger homes, homes with a lower EPC rating, or those with additional heating needs (such as remote workers) will face bills far in excess of the governments proposed nationwide average.

The government has stated that the average household energy bill will be £2,500 per year. But our data has uncovered that due to these factors, the reality is that average UK energy costs will be more than £4,000 - a 150% increase from 2021.

“Across the UK, the minimum year-on-year increase according to our calculations will be 169%, which will result in more consumers having to tighten their belts and reduce their spending elsewhere to afford to pay their bills.”
-

Peter Jackson, Chief Data and Product Officer at Outra

What is the regional reality?

The UK has vast geographical differences, so a single average figure for bills can be extremely misleading. Financial disparity across the UK is nothing unexpected, but the scale at which the cost of living energy crisis will affect different regions could have long-term effects on households. By diving into Outra’s data, we have identified at a household and regional level where the rise will have the greatest impact.

Regional breakdown of UK energy bills from 1 October 2021

The chart demonstrates that while bills will rise to roughly £4,000 on average, some regions will face costs of nearer £5,000. On the other end of the spectrum, while London and the North East have the lowest annual bills, they will endure the largest relative price increases of around 200% each.

The reality of the cost of living crisis will be further exacerbated by the north/south income disparity. Colder areas like Scotland and the North, and more deprived regions in major cities will bear a larger cost burden.

Household data, means regional solutions

In the face of the cost of living crisis, understanding each individual household will be essential for any consumer facing business. It’s clear that national averages do not accurately represent the reality of the UK consumer. By being able to anticipate the economic forces impacting UK customers at a household level presents immense opportunities.

Whether you are looking to support vulnerable customers, retain existing customers when belts are being tighten, or looking to expand into a new customer base, household level data can provide the insights you need to be successful.

Every household is made of up its own unique data points. Each one builds upon the other to create detailed personas of who your existing customers are, and helps to identify new lookalike audiences.

Disposable income is being squeeze, and the reality of the cost of living is yet to set in. Only data driven marketing solutions can help your brand forecast risk, predict churn and deliver personalised messaging. Afterall, we’ve already identified around 5 million vulnerable UK households – do you know where they are?

Find out more about our Vulnerability Index.

Insights

Vulnerability Summit 2023 Round Up

Outra co-sponsors Vulnerability Summit

On the 20th of July, Outra proudly co-sponsored the Collaboration Network’s Vulnerability Summit in conjunction with BSI. The Vulnerability Summit contained a wide range of expert speakers and panellists featuring representatives from the Vulnerability Registration Service, Citizens Advice, Samaritans and many more. Below are some of our teams’ key takeaways from the event.

Single Source of Truth
Many organisations and charities typically rely on their own customer parameters as a source for vulnerability which is not a full-picture approach. An accurate representation of vulnerability uses enhanced and predictive data to deliver unparalleled insights into who a business’s vulnerable consumers are or might be. The latter, ‘might be’ is crucially important here. Layering data and modelling it to identify those ‘at risk’ of vulnerability is the way in which businesses can stay ahead of the curve and predict, pre-empt, and prepare so they can best cater to the needs of those at high risk. Of course, this is especially relevant with the Financial Conduct Authority’s ‘Consumer Duty’ coming into effect this year.

Financial Services
The content of the summit included discussions around the financial services sector and how businesses in this space can best engage with their vulnerable customers. Sentiment showed the goal for the financial services sector is to reach a point where customers feel able and well-informed enough to notify organisations on their need for help and for there to be sufficient infrastructure to deliver this help, almost to a self-regulated degree.

Predictive data
Building on this need for a regulated delivery of help and support, it was felt that organisations should diversify their approach to vulnerability. Using Outra’s predictive Vulnerability Index, organisations can identify vulnerable households and implement the appropriate measures well in advance of when households may decide to ask for help. Predictive data can help identify vulnerable individuals who might be financially excluded due to factors such as low income, lack of credit history, medical reasons, or other barriers. With this knowledge, businesses can operate more inclusively to a broader segment of the population, reducing the risk of financial exclusion and promoting economic well-being.

Leveraging data responsibly creates a more equitable and supportive environment for all consumers, regardless of their vulnerabilities or challenges.

News

The Power of Change in Segmentation

Utilising change for predictive segmentation

It is often said that the only constant is change. Why, therefore is change rarely used as a variable in segmentation? The purpose of segmentation is to create powerful models that make predictions based upon the data that feed them. For example, the surprise Brexit vote could easily have been predicted if changes in immigration patterns had been analysed before the referendum. The areas in the UK that had experienced a significant net increase in immigration since 2011 were more likely to vote Remain, whilst locations which over the course of six years remained stable in terms of their multi-culturalism voted Leave. In this case change was the predictive variable.

So how can this be applied to marketing?

Very easily. For instance, in the case of consumer credit. Typically, credit providers use scorecards based on huge volumes of demographic and financial behaviour data to determine which customers should be offered extended levels of credit.

But these models, in our experience, are seldom as predictive as they should be. And the reason for this is that they do not use longitudinal transactional data to track changes in the financial situation of customers.

The scorecards tend to feed a single segmentation which is credit worthiness rather than a two-dimensional segmentation which should also contain financial transaction data over periods of time. If change was made the predictive variable more appropriate limits could be offered to customers making the credit industry more responsible.

Over the past 30 years, the trend in the data industry has been for increasingly granular data to be used as the foundations for classification systems, not least because of improved computing power.

As this has happened, the misconception that more detailed data is better than less granular data has manifested itself. However, often this is not the case.

Sometimes the most powerful data is tracking change rather than volume or depth of the data.

So, the question to ask yourself is what changes can you track and would they form the basis of a powerful prediction for your business?

Insights

Culture

Reflections on my first 100 days at Outra part 3

Outra's rapid agile innovation

Reflections on my first 100 days at Outra part 2

Data strategy insights in 25 days

Peter Jackson

CDTO

Some more reflections after my first 25 days. Yes, you’d expect me to report after my first 100 days but things here at Outra seem to move 4x faster than anywhere else.

I still read many articles and posts, and hear the data community discuss the problems encountered and created by data being a subset of the technology domain within organisations. Essentially the CDO reporting up into the CIO or CTO.

Caroline Carruthers and I have called out this issue for a long time, indeed writing about it in our first book, ‘The Chief Data Officer’s Playbook’. At fear of covering some of this ground again it is worth repeating a few points.

If technology and the CTO were going to ‘crack the data problem’ then surely after decades of the CTO / CIO role being established in organisations this would have been achieved by now?

Second, ‘Data’ is a different discipline to ‘technology’.

Finally, and quite compellingly, there was some discussion on LinkedIn recently about the relationship / hierarchy between data strategy and technology strategy. In my view an organisation has to set its ‘business strategy’ first, then other strategies like HR strategy, Data Strategy, etc. are written to support and deliver the business strategy, and finally the technology strategy is written to support and deliver these other strategies.

How odd it would be if the tech strategy was written first and this dictated the data limits, boundaries and ambitions of the data strategy and ultimately the business strategy? Just think though how often we come across this.

Is the master carpenter given a shovel and told to get on with his job?

I am in the most amazing position at Outra of working alongside a Chief Data Scientist, who understands commercial priorities and business outcomes, and a CTO (what a powerful triumvirate) who truly understands and prioritises data catalogues, data lineage, access to data and agility. Along with high levels of data literacy throughout the organisation.

That is how great data products and services, transformational things, get created and operationalised. Perhaps a model for all organisations who wish to leverage the value and opportunities in their data and become data enabled.

Reflections on my first 100 days at Outra

Reflections on fast-paced data strategy

Peter Jackson

CDTO

In the Chief Data Officer’s Playbook, Caroline Carruthers and I wrote a chapter about the first 100 days as a CDO. You’re correct, I haven’t been at Outra for 100 days yet, but things here seem to move 4x faster than anywhere else.

So, some reflections after my first 25 days. These reflections will come in a series of posts. I did heed our own advice to listen and learn in the first 100 days (lots of Cake and Coffee, Caroline would be proud of me!)

Many people have heard me say this before!

‘The world’s most ambitious organisations, regardless of sector or vertical, need to leverage data analytics and insight to become data driven. These organisations want to be data-driven to excel at operational efficiency, create value, delight customers and citizens, engage employees, gain competitive advantage, meet regulatory demands, and join the pack of leaders rather than fall behind into the ranks of the laggards. Business is becoming increasingly complex, the pace of change is accelerating, and markets are evolving rapidly.’

Well, to be honest, organisations that have true ambitions to leverage data science face 3 big hurdles:

Investment in technology to ingest data, bring it together, engineer it and then do the data science;
Investment in people in a very hot data skills market to do the clever things required.
A long time to value realisation.

What I’ve discovered in my first 25 days at Outra is that we’ve invested in the technology, recruited the people and are ready to deliver value for our clients. I’ve also discovered that we provide (Data Science as a Service (DSaaS), accelerating our clients’ journey so they can meet those ambitions.

Bringing together multiple data sets and applying market-leading data science, transforming Data into Wisdom; the Outra products enable organisations to become data driven and accelerate into the pack of leaders. Outra moves an organisation rapidly up the DIKW pyramid from Data to Wisdom.

Author: Peter Jackson, Chief Data and Product Officer

Insights

Energy

New EPC Regulations Putting Pressure on Landlords

UK rental homes' energy efficiency

Did you know that 59% of tenants live in rented homes that aren’t energy efficient?

Millions of homes in the UK Private Rental Sector (PRS) don’t even meet basic standards of decency. In fact, 2 million homes (1 out of 10 in England) contain a Category 1 Hazard, meaning they are in such poor condition that they put tenants' health or safety at risk.

To help restore the imbalance in the UK PRS, the Government is rolling out the new Minimum Energy Performance of Buildings (No. 2) Bill to increase the minimum energy efficiency levels for all rental properties.

What do the new EPC regulations mean for landlords and tenants?

What is the Current State of EPC Ratings for Rental Properties?

At Outra, we combine data and innovation to get an unparalleled, unified perspective on the UK's EPC situation. We compiled the publicly available EPC data and matched it at the individual property level. Where property data is missing, we used data science to model what the EPC of that property will be.

According to our recent analysis, there are 5.6 million rental properties in the UK (excluding Scotland), and 59% of those homes have an EPC rating of D or below. More specifically, 49% are D rated, 9% are E rated, and 1% are F or G rated.

That’s 3 million homes that cost more to run than they need to!

Our research also shows that London and West Midlands are the worst-performing locations, with 62% and 60% of homes rated D or lower in each area, respectively.

Breaking it down even further, the worst-performing local authorities are:

Maldon (75%)
Harrow, Craven (74%)
Southend of Sea, Waltham Forest (73%)

The best performing local authorities are:

Tower Hamlets (31%)
Basingstoke and Deane (36%)
Welwyn Hatfield (38%)

It’s clear that the UK lacks symmetry in terms of living standards, but the government is trying to improve the situation.

What Do the New EPC Regulations Mean for Landlords and Tenants?

The new bill mandates changes to the EPC regulations, requiring new tenancies to have an EPC rating of at least Band C from 31 December 2025. For existing tenancies, the same rules will apply from 31 December 2028.

This means the average tenant will eventually pay lower energy bills than the average homeowner. With the dramatic increase in UK energy costs this year, this new bill could be a game-changer for low-income households.

Although, it’s not all sunshine and rainbows. The new EPC regulations also risk removing some rental homes from the market and putting additional pressure on the UK’s Private Rental Sector.

The estimated cost of upgrading a privately rented property to a C rating is £7,646, meaning it could cost up to £25.7 billion to upgrade the 3 million rental properties rated D or less. That’s a lot of money that some landlords either can’t or don’t want to pay.

According to a recent survey, more than half of landlords with properties rated D or lower were considering offloading their properties to avoid paying for upgrading them. When landlords choose not to extend or renew rental agreements, tenants will be forced to find new places to live.

How can the UK embrace this change?

The new EPC regulations will benefit the UK’s PRS on every level. Landlords will have more sustainable properties that are easier to sell or rent out, and tenants will get a decent standard of living through better housing quality and lower energy costs.

But, the only way to prepare for the changes the new EPC regulations bring is to understand where potential issues might be and mitigate any risks. With the housing market already in crisis, we need reliable data and insights to properly plan for and manage changes without causing further detrimental effects.

News

Outra analysis shows financial vulnerability will play a crucial role in deciding the next general election

Financial distress in key constituencies

Outra Joins VRS as FCA’s Consumer Duty crackdown approaches, highlighting 2.5M UK households at ‘High Risk'

UK household financial vulnerability

Outra analysis unveils the struggle of homeownership in the face of rising mortgage rates.

Alarming decline in homeowners' affordability

Is your organisation ready for the FCA Duty of Care?

FCA's Consumer Duty Act: Impact & Compliance.

Learn how the Outra Vulnerability Index can help your organisation

On July 31st 2023, the FCA Consumer Duty of Care Act – a new principle raising the standard of behaviour expected from firms – will come into practice. It seeks to ensure that all consumers, regardless of their situation, receive good outcomes and will have a significant impact on organisations, which will need to put a host of strategies in place to ensure that they are delivering the best possible duty of care to their customers.

Executing best practice for consumer protection standards

The new consumer protection act means that firms need to prove that they are working to create good outcomes for customers, in the context of their products and services; price and value; consumer understanding; and consumer support. In order to comply, an organisation’s purpose and culture will need to align with their obligations under the Duty, including all relevant discussions about strategy, remuneration and risk. They need to use data insights and technology to the best of their abilities to improve their services and outcomes for their customers – particularly those that are financially vulnerable. And the ever changing consumer landscape means that this will not be a one-off process – businesses will need continually to adapt to meet evolving challenges and expectations.

Organisations therefore need to deeply understand their customers’ requirements, characteristics, objectives and behaviours, at every stage of the customer journey. This will require firms to identify their target audiences at a granular level, while considering the characteristics, risk profile, complexity and nature of their products or services. Organisations need to prove – on an ongoing basis – that their actions are compatible with delivering good outcomes for customers. This means testing, monitoring and adapting communications for customers – taking into account any characteristics of vulnerability, the complexity of products, the communication channel used, and the role of the firm – to support good outcomes.

It also means properly considering outcomes for different groups of consumers instead of relying on broad averages which may not highlight where certain types of customers – such as those on low incomes or in vulnerable circumstances – are not receiving fair value. This is where data insights from broad demographics and geography to granular, individual information about your customers’ situations and behaviours comes into play.

Supporting Vulnerable Consumers

For firms to deliver their duty of care and support their customers’ good outcomes it is important that they can correctly identify and support their potentially financially vulnerable customers – defined as those that may not have the ability to recover from sudden financial shocks, such as an unexpected loss of income or an unanticipated increase in expenditure. It is important to note that financial vulnerability is not necessarily permanent. It can be short term or situational, and the current economic climate means that more and more customers are at risk of becoming financially vulnerable at some time.

If an organisation identifies customers as having the characteristics of vulnerability, it will need to implement a flexible and empathetic consumer support approach that takes account of their needs. Where they identify that good outcomes are not being achieved by their customers, they need to address this by introducing processes to recognise and tackle the factors that are leading to poor outcomes.

Firms are expected to have sufficient understanding of their customers’ behaviours and how their products and services function to demonstrate that good outcomes are being achieved by those financially vulnerable customers. In addition, they will be expected to be able to identify when particular groups of customers receive systematically poorer outcomes as this could indicate that the firm is not providing a duty of care for those groups or is in breach of its legal responsibilities. This is where data can really help. Access to the right level and depth of data gives organisations the ability to see – in real time – which of their customers are at risk of becoming financially vulnerable so that they can offer the right levels and channels of support at the right time.

What do organisations need to do?

Key to the Duty of Care Act is the concept of fair value. Organisations need to understand and assess fair value relating to the quality and benefits of their products or services. With the new principles, a single generic template for assessing fair value may not be sufficient for products with different characteristics or target markets. This could be as broad as understanding your customer groups well enough that, for example, those groups with characteristics of persistent debt should not be charged high fees or interest rates. Or it could be more specific, such as knowing your customers sufficiently well that, when interacting directly, communications are tailored to meet their individual needs, and they can be supported before they need to ask for help.

Firms are required to monitor and review the outcomes that their customers receive, which means they will need to leverage the appropriate data insights. They need to have a robust plan in place that includes the data they want to use and how they will address data gaps to demonstrate that their products and services offer fair value for different groups of consumers, as well as monitoring and regularly reviewing their customers’ actual outcomes to address any risks to good customer outcomes.

Insufficient or inadequate data will hamper the ability to undertake robust assessments. This means that firms need to very carefully consider their current data capabilities and where technology can be leveraged to improve consumer insight, as well as where longer term solutions could be developed to enhance data and advance functionality in monitoring and evidencing consumer outcomes.

Knowing your customers

Having the data insights to know your customers is a key aspect of the FCA duty of care. In applying the principles of the Duty of Care act, firms can learn to harness the power of data and technology, using it to target the correct audiences for their products and services, and identify, understand and support their vulnerable customers on an individual basis. With this comes a benefit to organisations as well as consumers – if they can get the implementation right, firms can deliver better outcomes for their customers, as well as deepen their trust.

What are the risks of non-compliance?

The FCA will take a robust approach to how firms are implementing the new rules. It is clear that firms will need to be ready to meet the consumer duty rules by the deadline at the end of July, so businesses need to take action now. Potential breaches need to be tackled at an early stage, meaning that it is crucial for firms to understand and evidence the outcomes their customers are receiving so they can monitor their compliance on a continuing basis. Penalties for non-compliance include fines or sanctions up to and including removal of approvals.

Furthermore, the threat of negative publicity and/or customers choosing to use firms which are focused on good outcomes means that being prepared for the Duty of Care Act should be top of the agenda for all forward-looking businesses.

Insights

How will financially vulnerable customers impact your business?

Rising bill struggle risks utilities

How To Build Your Ideal Customer Profile

Crafting ideal customer profiles

How confident are you about your customer profile? Understanding who you’re talking to is vital as marketers need ways to cut through the noise of other messages and strike a chord with consumers.

Unfortunately, speaking to customers seems to be getting harder. The latest online marketing trends show that customer acquisition is becoming more expensive as costs have increased by 60% over the last six years.

Similarly, targeting customers will soon become more challenging as third-party cookies are removed. With recession rumours circulating across major economies like the US and UK, marketing budgets may soon be under more scrutiny as senior leaders look to maximise ROI.

CMOs and other marketing professionals must find ways to ensure the success of their campaign efforts. In this article, we explore how to re-focus and re-energise your marketing activity by building an ideal customer profile using internal and third-party data.

Current online marketing trends

Lacklustre ROI from multi-channel campaigns
While businesses still aim to deliver seamless digital experiences to customers, multi-channel marketing hasn’t lived up to the hype for the most part.

Managing complex marketing campaigns across disparate mediums created a significant amount of work for a limited pay-off. What’s more, rising apps like TikTok have sparked renewed interest in vertical video formats

As firms look to plan their forthcoming marketing strategies, the stalled growth from multi-channel marketing has provided a strong lesson in using what works.

Changes to third-party cookies
Ironically, as marketers reexamine their advertising options, new barriers have arrived in the form of changes to third-party cookies.

These data sources, once used to measure consumer habits and advert performance, are being scrapped by Google from 2023. Other firms like Apple are also changing their operating systems as part of a wider shift towards consumer privacy.

As a result, recent surveys have shown 41% of marketers believe their biggest challenge will be their inability to track the right data.

Navigating the uncertainty of what will work
With the landscape of online marketing changing, there’s considerable uncertainty about the future.

Senior marketing leaders are understandably keen to identify the next growth engine for their organisation to drive both awareness and sales. Yet, budget limitations and tough economic predictions make it feel like marketing professionals are fighting with one hand tied behind their back.

Fortunately, there’s a way to overcome the combination of issues outlined above; that is, stunted growth, growing pains and ambiguity about strategic next steps. By building a more refined customer profile using third-party data, marketing professionals can focus their efforts on a more reliable type of high-intent consumer.

How to build your ideal customer profile

Your ‘ideal customer profile’ (sometimes referred to as ‘IPC’) defines the traits of your most valuable customers.

While your ‘target customer’ simply defines the generic segment of the total addressable market you wish to serve, your ideal customer profile goes even further. By focussing on purchasing intent and behavioural data, you can paint a more detailed picture using commonalities between your customers. As a result, you can create more reliable, data-driven insights to base your future campaign strategies on.

According to Gartner, the ideal customer profile is becoming a vital element for high-growth businesses that can help unify departments’ strategic decisions. Follow the steps below to build a comprehensive customer profile of your own and enhance your customer data analytics for future marketing campaigns:

1. Look at your current customer base
Your ideal customer profile largely comes from within your existing patrons, so it’s important to expand on your historical customer data analytics.

Some customers are open to taking part in surveys, so if you find that your data is somewhat limited, you can conduct new research to garner more insights.

2. Identify your top-paying tier
Once you have a grasp of your current customer base, begin by segmenting them using key demographic details. Some early categories you can focus on include things like where your customers live or work, their age, how long they’ve been in their current position and their income.

As you create a rough customer profile using these surface-level insights, you should also determine how much each customer spends on your products or services. This additional level of analysis will allow you to separate your customers from your ideal customers.

We’ll touch on additional categories in later steps that will help you build a more in-depth profile.

3. Use qualitative and quantitative data
While you’re segmenting your customers, make sure to use both qualitative and quantitative data.

The former should accurately describe your customer and their habits. However, the latter will allow you to make predictive analyses and take advantage of today’s online marketing trends more effectively.

4. Understand their goals and pain points
Once you’ve established a surface-level picture of who you’re looking to market to, it’s time to go deeper and understand what they want. Obviously, your product or service should feature as a part of the solution. However, try to understand the motivations of why they need it in the first place.

In particular, focus on what’s holding them back from making purchasing decisions in the form of paint points. Marrying these two facets can help build a more effective sales funnel, as you’ll be able to tackle key concerns along the sales process and guide customers into consideration more easily.

5. Identify their habits and preferences
Another way to build out your ideal customer profile is to account for behavioural and environmental habits. For example, consumers have set preferences on how they like to spend their time online and where they find information from.

Accounting for these habits can allow you to refine the types of resources you offer to your customers. For example, the majority of podcast listeners are aged between 26-38, while Facebook is the most popular social media platform for those aged 56+.

6. Utilise other third-party data sources
Unfortunately, your existing data can only go so far. By combining high-quality third-party data with your own internal records, you can identify a larger segment of high-intent customers.

External analytics solutions like Outra360 help you build an ideal customer profile easily and achieve an ultra-personalised marketing strategy equipped for today’s online marketing trends and beyond.

By applying data science to public UK household data, you can understand your audience’s attitudes, aspirations, and motivations. What’s more, you can identify high-intent customers and carbon copy lookalikes, and expand your ideal customer pool for maximal revenue. Altogether, our platform provides never-seen-before insights into your target audience.

Crucially, our data isn’t affected by the removal of online cookies, so it’s a reliable and future-proof source of customer insights.

Let third-party data provide new customer insights for your marketing

Third-party data is the solution to build your ideal customer profile and enhance your future marketing strategy.

By enhancing the insights into your current customers and expanding your view of potential prospects, third-party data can paint a more thorough picture of your highest-value customers.

As a result, you can establish a detailed and precise ideal customer profile and focus your marketing efforts on your highest value and more reliable customer prospects.

Insights

How Rising Living Costs Impact Vulnerable Pensioners

Supporting vulnerable pensioners.

400,000 Financially Vulnerable Pensioners Facing Bleak Winter as Gas Bills Soar

People with additional heating needs and poorly insulated homes face a particularly bleak winter this year, even with existing government intervention measures. That’s why, in this latest article on the cost of living crisis, we’re focussing on financially vulnerable pensioners.

The crisis is creating a mounting problem for businesses with a duty of care, like utility companies and lenders, whose leaders could fail to support vulnerable customers without insight into their struggles.

Learn more about the pressures facing vulnerable pensioners across the UK in our latest analysis and discover how to deliver targeted customer support using data science solutions from Outra.

The Impact of the Cost of Living Crisis on Financially Vulnerable Pensioners

At Outra, we have identified around 5 million financially vulnerable UK households. However, follow-up analysis has shown that around 400,000 of these are occupied by financially vulnerable pensioners.

Within this cohort, our data suggests that:

They are typically aged 65 and above.
Four in ten earn less than £20,000 a year.
One in five are classed as ‘just making ends meet’.
Nine in ten do not have a [private] pension to fall back on.
Many live in EPC D-rated properties with poor insulation and dated gas boilers.
Many require additional heating for health reasons

“Some of our most vulnerable households this winter will be the most exposed to hiked gas bills with very little real support coming from the government. Energy suppliers should be aware of these issues and offer some sort of relief to these customers who may otherwise face the unthinkable choice between heating or eating.”
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Peter Jackson, Chief Data and Product Officer at Outra

Previous analysis from Outra found that the UK faces average energy bills of over £4,000 a year under the new Energy Price Guarantee. What’s more, in addition to the UK-wide support scheme, UK pensioners are also entitled to the Warm Home Discount and £400 rebate. Unfortunately, the heating needs of vulnerable pensioners will dwarf both their incomes and the support measures provided.

How Do Vulnerable Pensioners Compare Regionally?

Energy prices are set to rise at different rates across the country, affecting some vulnerable pensioners more than others.

Some regions like the South West will face a 169% rise in energy costs, reaching nearly £5,000. On the other end of the spectrum, while London and the North East will have the lowest annual bills at around £4,000, they will endure the largest relative price increases of around 200% each. And so, pensioners living in these regions as well as colder ones in the north will face extra pressure due to the steep rise in UK energy costs.

How Can Businesses Support the Financially Vulnerable?

Businesses like energy and lending firms need to be mindful of their duty of care towards financially vulnerable customers. For example, customers may be unwilling or embarrassed to come forward to ask for help, meaning they could experience additional stress unnecessarily.

By utilising data science solutions, lenders and energy providers can establish accessible payment plans and tailored support. Not only will customers’ bills become more manageable, but revenues can still be collected (albeit over a longer period).

The same data set on UK households is also relevant to retail businesses looking to minimise losses as consumers adjust their spending. By using sophisticated customer targeting, retailers can adjust their revenue expectations or invest in new strategies to make up losses and weather the cost of living crisis more effectively.

At Outra, we have one of the widest, deepest and most insightful databases on the UK population including economic and behavioural data, as well as housing quality and property finances. We can help businesses navigate their options and understand customers’ evolving needs.

News

Energy

How can data science sell more Electric Vehicles?

EV industry challenges & data solutions

Petrol prices are soaring, igniting interest in EV cars across the UK as people look for new, more affordable ways to drive. And who can blame them, when fuel costs are making it too expensive for owners of older cars even to commute to work?

But here’s the rub: the UK doesn’t have enough EV charging points to meet new demand. In the meantime, the number of EVs in the UK is expected to reach 7m by 2030. Worse still, councils and companies don’t generally know where to install charging stations or which prospective customers can afford to own an EV, causing further practical and commercial complexity for a growth industry.

The only way to overcome the challenges in the EV industry is to provide councils, car retailers, and other stakeholders with adequate insights to help them target the right areas and households.

What are the Main Challenges in the EV Industry?

While more people in the UK want to switch to EV cars, many of them may not be able to buy one due to the lack of supporting technology, planning and infrastructure. Here are a few examples:

1. Charging Time

It takes 30 minutes up to four hours to recharge an EV vehicle fully, and most cars on the market can only manage up to 250 miles on one charge. Waiting hours for their car to charge is less than ideal for drivers used to pulling into a petrol station, filling up their tank, paying, and leaving in under five minutes.

Long charging times also make it inconvenient to drive EV cars on long journeys, since extensive pre-planning is required to avoid getting stranded without power.

Tesla is the only company that allows its customers to charge their EVs quickly. Their Supercharges can add up to 172 miles of range in just 15 minutes! Sadly, it’s the only solution on the market of its kind.

2. Geographical Bias and Funding

Did you know that 33% of public EV charging stations are located in Greater London? Only 6.2% are in the North West of England and roughly 2% in Northern Ireland. Clearly, some local councils enjoy considerably higher financial investments to install EV charging stations.

But what about the less affluent parts of the country? These councils will struggle to install enough stations. As a result, local residents must continue to use petrol cars –– even as fuel costs hit their incomes hard.

3. At-Home Charging

It’s currently illegal in the UK to run EV changing cables across pedestrianised areas, such as pavements. Meaning households that want to purchase an EV need to either have off-road parking or be reliant on the public charging network. Both EV manufactures and councils need to know where focus their time, effort, and money.

4. Targeting Households

The challenges in the EV industry also include companies that sell EV cars. Demand for their vehicles might be on the rise, but not everyone can afford a new car or have a propensity to buy one. So how do manufacturers and retailers know who to try and sell to?

How Data & Innovation Combined Can Revolutionise the EV Industry

Imagine if councils and EV manufacturers knew exactly which areas and households to target? They can!

Our Parking Model applies machine learning to official data, to intelligently predict which UK households have off road parking, with 89% accuracy.

By using this model, we have identified which regions of the country will be more reliant upon a public charging network, due to a lack of access to an off-street charging point. Allowing councils to plan for infrastructure, and the government to allocate funding.

Furthermore, EV car retailers can determine which households to target with their marketing to minimise cold outreach and reduce customer acquisition costs. Allowing the likes of BMW or Tesla to not only ensure they are talking directly to households that can and want to purchase their products, but also make sure they are using the right messaging.

The UK is amidst an accelerating climate change and transport revolution. By using data science, governments and retails can deliver transformative innovation to households across the UK.

Insights

Building a Greener Future: The Sustainable Housing Challenge for Landlords in Doncaster and Bradford

Landlords face sustainability pressure

The pressure is on landlords to make housing more sustainable, especially in regions like Doncaster and Bradford where energy efficiency standards are below standard. With 71% of private rentals in those regions holding an energy performance certification (EPC) of D and below, according to Outra data, the North of England’s landlords are set to face a hefty bill to upgrade their properties to meet new legislation.Properties across the board with low energy performance levels are no longer just frowned upon by prospective tenants who do not want the burden of hefty bills, but face becoming legally untenable as the UK government plans for a minimum EPC C rating for all privately rented properties by 2028.Our data was used by the BBC to highlight this growing trend.The market is already observing an exodus of landlords by reason of what is being dubbed a continued ‘war on landlords’ by commentators. Traditional ‘mom and pop’ homeowners are selling up and cashing out of the buy-to-let market, beset with hefty cheques to upgrade existing housing stock.So, what does this mean? Landlords in Doncaster, Bradford, and other regions with inadequate energy efficiency standards, must acknowledge this imminent deadline and take immediate steps to upgrade their properties. And if they cannot, then they will need to sell to someone who can or face owning a vacant, unprofitable asset.Worth a note too is that the transition to sustainable housing offers numerous benefits for both landlords and tenants. For landlords, investing in energy-efficient upgrades not only improves their property's market value but also reduces long-term maintenance and operating costs. On the other side of the coin, eco-friendly properties are increasingly in demand among environmentally conscious tenants.By providing sustainable housing, landlords can attract responsible and discerning tenants, resulting in reduced vacancy rates, and increased rental income. The question is not ‘will Landlords act,’ because they must. It is whether they act quickly or efficiently enough to come through to the other side, with a positive in the bank.To view the BBC News coverage - click here

Insights

Culture

Extraordinary Agility: Getting new products to market faster

Agile innovation in action

Peter Jackson

CDTO

Written by Peter Jackson, Chief Data & Product Officer.

I have heard so many companies talk about agile delivery processes and innovation streams to get new products to market quickly, but I have rarely seen it happen; perhaps the definition of ‘quickly to market’ is the variant. The large corporates are terrified of the ‘start ups’ who are seemingly able to develop and deploy innovative products at speed in response to changing markets, and so they should be - their lunch is disappearing.

An abundance of papers and reports have been written examining how the ‘start ups’ / ‘fintech’ / ‘insurtech’ ‘xxxtech’ manage to do this and frameworks have been developed that larger corporations try to implement to imitate the process. This is not another paper or framework, this is experiential.

I am in a very fortunate position at Outra to observe product development taking place at amazing speed and those products then becoming disruptors in the market. In the past three months in response to the Cost of Living Crisis, inflation and rising interest rates Outra has developed three new products and taken them to market. That is three months from ideation to market; with data engineering, data science, product design and go-to-market strategy as part of that process. THREE MONTHS! How is that possible? Without giving too much away here are the tips:

A totally committed team who believe in the aims of the business
A team who are both business literate and data literate and understand the data value chain from ingestion to product sales
Collaborative working across all the teams with people going the extra mile for each other
Highly creative people who are encouraged to think and explore
Great leadership who are involved at every level of the business and product development
Huge amounts of domain expertise
Early market testing
Focus on the MVP and product roadmap
Product development is not linear: for example we are working on the go to market at the same time as data engineering. We advance on all fronts at the same time
We don’t focus on hot-desking or hybrid working (we have no two team members who have the same work patterns) we focus on outcomes and delivery
Extreme sprints enabled through ‘development sessions’ where product development takes place in live time driven by product owners and domain experts working with data scientists, data engineers and product developers.

The secret sauce?

This way of working is unrelenting and is in the DNA of the business and structured through the Operating Model creating transparency and accountability. On top of this we are constantly looking at ways to improve our ways of working and thus improve our products.

At the same time the teams were supporting existing customers, onboarding new customers and improving our existing products, and building our longer term foundations for future innovation. It really is a multi-dimensional data strategy at work.

So what were those products?

The first is the Outra Vulnerability Index, an interactive online tool to identify the financially vulnerable households in the UK to help our clients engage, understand, manage and support their customers through the current Cost of Living Crisis.

The second is an Interactive tool that can analyse the whole of the UK Residential Housing Market down to the household level. The tool is split into four sections, History, Analysis, Data and Forecasting. We're currently witnessing the first major event in over 10 years where investors are having to consider a significant short-term change in house prices. The Risk tool will allow clients to make data-driven decisions on investments and mitigate as much risk as possible

The third product is a High Net Worth Index, again an online tool which enables brands to seek out a shrinking addressable market and understand their behaviours and requirements.

Insights

How Can Data Innovation Revamp Targeted Advertising?

Enhancing targeted advertising innovations

In December 2021, Facebook (now Meta) updated its Special Ad Category, which encompasses any ads relating to employment, credit, housing, and politics, to prevent discrimination.

The move is seen as a reaction to an event that unfolded in 2019. Then, Facebook restricted targeted ads in the US to prevent real estate and housing brands from deliberately excluding minority groups from campaigns.

The change involved removing specific targeting options that brands could use in a discriminatory fashion, such as age, gender, and postcodes or small radius geo-targets. As a result, advertisers became more reliant on Facebook’s algorithms to find the right audience for their campaigns.

Less targeted advertising leads to higher customer acquisition costs for your brand. However, by blending first-party data and innovative solutions with Meta’s lookalike algorithms, you can restore –– and improve –– your advertising performance.

Keep reading to learn more about what the changes to Meta’s detailed targeting options mean for your business. In this article we’ll cover how data innovation can convert challenges into opportunities.

Why Are Targeted Advertising Campaigns More Effective?

You’ve probably heard the expression: “If you try to reach everyone, you’ll reach no one”.

Over 50% of prospects turn out to be bad fits for businesses. However, targeted ads enable you to find and interact with customers who are more interested in your product, increasing your chances of making a sale.

The more details you have about your audience, the easier it is to win their business and generate revenue for your brand. When used correctly, targeted advertising helps you cut through the white noise and attract loyal customers who keep coming back.

Changes to Facebook Detailed Targeting Options: How Does it Affect Brands?

The changes to Facebook’s targeting mean companies in industries like real estate and credit lose access to important demographics and interest groups used to refine audiences. Here are a few examples:

Geotargeting
Advertisers often use postcodes or small-radius discs to geographically segment audiences. This segmentation uses1km ranges spread across maps to target the customers they’re most likely to convert. Facebook’s detailed targeting changes prevent brands wanting to run housing, employment or credit ads from targeting customers by postcode.

Because of the new restrictions, advertisers have had to increase their minimum disc size to a 15-mile radius. In a densely populated city like London, the targeting pool could increase from 49,200-57,900 to 5.7-6.7M customers.

The larger radius makes it much more difficult to target customers effectively, forcing brands to spend more on customer acquisition to achieve the same conversion rate.

Demographic targeting
Facebook has also restricted brands’ options for demographic targeting in their Special Ads, meaning you can no longer make changes to age and gender.

Audiences must include all genders and ages 18 through 65+. In addition, Facebook has removed some additional demographic, behaviour and interest options, and has excluded detailed targeting selections.

Instead, Facebook encourages brands to broaden –– not restrict –– their audiences and use the audience selection tools in exclusive, not discriminatory ways.

How Can Innovative Advertising Solutions Improve Targeted Ad Results?

The best approach to boosting your targeted advertising results will depend on your particular business model and goals, but here are three effective methods any brand can use:

1. Lookalike audiences
Getting the audience right is crucial to creating effective ads for your brand. By building lookalike audiences for your Facebook ads, you can target new people who will likely be interested in your business because they are similar to your existing customers.

Once you've created Lookalike Audiences for each audience segment, use these tricks to get the most out of your ads:

Target the same ads to each Lookalike Audience with initial bids.
Monitor how well the ads perform based on revenue per conversion or the lifetime value of the people in each audience.
Modify your bids for each audience based on your findings, bidding more for more valuable audiences and less for less valuable audiences.

When using lookalike audiences, the most important thing is to test and monitor how different settings perform. You need to continuously refine your audiences and look for new activities to measure to ensure your ads achieve maximum results.

2. Never stop innovating
Lookalike audiences are only as good as the seed audiences used to feed the algorithm. Existing customer lists may not be large enough for the lookalike algorithm to perform well, or may not be appropriate when looking for growth into new segments. At Outra, we have blended our own data with the client’s data to supercharge the algorithm and brought CPL performance in real estate up by 36% - back to its level before the targeting limitations were introduced.

Advertising technology is constantly evolving, unlocking new opportunities to create hyper-targeted ads. At Outra, we’re all about finding new ways to help brands leverage targeting data to plan, budget and execute marketing campaigns more effectively.

3. Content is king
Every ad needs high-quality, relevant content that entices your audience to click on the ad and find out more about your offer. If you’re short on ideas, use your already high-performing content (blog, YouTube videos, etc.) to create new Facebook ad campaigns.

Try repurposing content into:

Infographics
A video sales letter
A free video course
An e-book
Different angles for your ad

Creating multiple lookalike audiences requires numerous ad ideas to test. Repurposing high-performing content is an excellent way to fill that gap quickly with material your audience already loves.

With the Right Data & Innovation, Anything is Possible

Changes to targeted advertising don’t have to be an obstacle, but an opportunity! The increased focus on privacy and brands’ social responsibility is actually a positive development that companies can use to improve their marketing and general operations.

By keeping an open mind and embracing innovation, your brand can increase effectiveness throughout the marketing lifecycle: from media planning to content personalisation, precision targeting and campaign optimisation.

Innovation is the key to bringing advertising into the future. At Outra, we live for creating powerful solutions that transform basic ad targeting into multi-channel marketing campaigns that target your customers at the right time,
anywhere.

By using our data and innovative marketing solutions, we’ve gotten our clients back on track to achieve the same results as they did before the change. Our unique approach helped one brand achieve a 400% increase in weekly leads in February alone and increase conversion rate by 586%, resulting in a 64% decrease in Cost Per Lead.

What does this prove? That with the right data and innovation, anything is possible!

Insights

Are you more than just your spending habits?

Data-driven marketing insights

Data. Data. Data. It’s everywhere and nowhere. The internet has stormed into the show and taken it over, providing marketing professionals with a pile of information on customers. It’s changed the way we work, we communicate, we fall in love, and - especially in the last 2 years - helped us stay home, still get Nandos, and binge watch our favourite shows.

With all this data, it’s lucky that we have the computer power and ever-improving technology to support us analysing it. When once it was a poor marketing manager slumped over some numbers to figure out how next run a campaign, we now have teams of data scientists focused on creating insights.

Segmentation customers - and future customers - has become increasingly complex, built out with more, and more, (and more) data sets, especially with the recent dramatic increase in e-commerce. Instead of just knowing Mrs Smith bought a candle (from the White Company no doubt!), but we also know she got distracted for 30mins looking at new bed linen, she bought towels last month, and spend £150 on baby clothes in the past year. All this information provides a brand with enough data to segment Mrs Smith and provide her with a far more customised experience.

This all sounds great, right? We’ll what if you have TOO much data? Not just on one customer, but on all your customers. A problem that many of us who started in marketing 10+ years ago, never thought we would have.

As we get overloaded with data, then the segmentation becomes far too mechanical. The customers last action tending to become the focus. And with just transactional data, segmentation ends up being one dimensional. That’s no way to run your UX.

So, what’s the solution? To build a 360-customer view, that’s more than just customers spending habits, with complementary data, which gives you the granular segmentation and insights that you need.

This is what gets us excited. By using AI (smart computers, trained by smart people), combined with brands data, and our exclusive data, we help brands not just know their customers – but understand their customers. We all love it when brands provide us with an amazing customer experience, which is why we want to help every brand deliver this. Because not only does it improve the world we live (and shop) in, but with customer-centric businesses being 60% more profitable we get to see the businesses we work with thrive.

Insights

Are we about to see a landlord exodus?

EPC changes impacting landlords