How effective data foundations and consumer insights drive campaign performance in DTC healthcare and e-commerce

In this Article

A competitive, complex consumer landscape

Competition has never been more intense in the dynamic and growing consumer health and wellbeing sector. 2025 has seen new market entrants like hair loss treatment company Hair + Me, any number of weight loss services like Juniper and SheMed high on social media feeds and supermarket Morrisons in partnership with Phlo moving into the on-demand online healthcare space alongside existing high street giants Boots, Superdrug and Asda.

This new and intense competition also comes with a new reality: increasingly fragmented consumer behaviour that upends traditional marketing assumptions.

Younger age cohorts drive healthcare growth

Our Voice of the Nation (VOTN) survey examining consumer sentiment finds Gen Z and Millennials in the driving seat of the elective healthcare market. Weight-loss treatments like Mounjaro and Ozempic are expected to surge by 40% in 2025 due to these younger age cohorts.

Notably, Gen Z shows equal interest across genders, unlike older age groups where women dominate. Cosmetic treatments are also gaining traction, with well over 10% of Gen Z and female Millennials planning to pay for them, compared to less than 3% among Gen X and Baby Boomers.

While aesthetics is clearly playing a role, other deeper consumer motivations are also emerging.  Notably, survey respondents who consider health a top national issue are significantly more likely to self-fund treatments. Among Gen Z males in this group, 16.2% plan to pay for weight-loss treatments in 2025 — well above the average of 4.9%. And just as importantly, the VOTN data somewhat counterintuitively shows that demand for elective healthcare products and services in general spans both affluent and less affluent groups.

Age-related wellness and health products drive innovation

In short, our VOTN data reveals a complex blend of beauty, wellness, and proactive health management, with younger generations investing in elective healthcare to enhance both how they feel and how they look.

This trend is reflected in the innovation and increasingly digital activation seen in the fertility and female health space relevant to these age cohorts. Period care pioneer Daye is launching a new at-home hormone testing service for a host of biomarkers like reproductive hormones, thyroid function and Vitamin D. Male fertility company, testhim, which provides consultations, testicular scans, sperm DNA and other diagnostic testing, is also launching specialist fertility supplement testhim M+and a groundbreaking online monthly support group.

Complex, demanding consumers require sophisticated, multi-layered segmentation

So, with Gen Z and Millennials increasingly self-funding weight loss, cosmetic treatments and holistic wellness products and services of all kinds, DTC and e-commerce healthcare brands must truly rethink how they engage with this increasingly data-savvy, image-conscious audience. Informing integrated campaigns that blend social commerce, influencer marketing, paid advertising, organic and direct marketing content. Our VOTN survey also found that nearly two-thirds of Gen Z consumers (63%) have purchased goods and services via a social media platform like TikTok Shop and Instagram, making this a crucial channel for healthcare businesses to understand and potentially utilise.

But to do that effectively in practice, DTC and e-commerce healthcare brands need more than just surface-level insights. They need robust, layered data foundations that help them target the right consumer with the right kind of message at the right time in the right place. Even with first-party consumer data, it’s a significant challenge. Without it, reaching existing or identifying potential customers is almost impossible for brands.

You can see an example of this in our VOTN survey, which showed that for weight loss treatments, there appears to be greater levels of demand both at the more affluent end of our Acorn segmentation spectrum *and* at the least affluent end, potentially for differing reasons.

This requires integrating geodemographic, behavioural, lifestyle, and attitudinal data to move beyond ‘off-the-shelf’ consumer segments and into understanding consumers in a deep way that understands the likelihood of them engaging with specific healthcare products and services and why – enabling brands to drive efficient spend on the right customers – and remove disinterested or low-value ones – in a market with such broad appeal

It’s also only by taking this multi-layered data approach healthcare brands can build strategic data-driven campaigns that resonate on a genuinely personal level in the manner desired by younger generations. Critically, delivering on the perennial, somewhat paradoxical Gen Z demands for high levels of privacy, but also similarly high levels of personalised products and brand messaging.

Turn insights into activation for D2C and e-commerce health campaign success

But as we know, data, in isolation, holds limited value. Its real power is unleashed through activation – the transformation of insight into strategy. And in a world where consumer expectations are rising and attention spans are shrinking, the ability to deliver timely, relevant, and meaningful engagement is an outright competitive advantage. And it can only be achieved through a deep, data-driven understanding of people.

For D2C and e-commerce health brands, this understanding and successful activation requires them to:

  • Identify high-value customer segments for targeted acquisition and retention
  • Predict churn and retention patterns within subscription-based models
  • Inform campaign messaging with real-world consumer behaviours and motivations
  • Develop nuanced personas reflecting not just demographics, but attitudes, values, and lifestyle choices
  • Personalise content across relevant digital channels, from email to in-app experiences
  • Build lookalike audiences for acquisition campaigns on platforms like Meta and Google
  • Optimise digital spend by measuring performance and refining segmentation over time

This is where the transformation power of comprehensive datasets, such as CACI’s Ocean database, which offers over 700 variables at an individual and household level, comes in. Ocean includes everything from financial situation, media consumption and digital behaviours to lifestyle preferences like veganism and exercise to whether consumers have a smart watch or fitness band.

When combined with geodemographic tools like Acorn – segmenting over 1.6 million UK postcodes using more than 800 variables – and supported by bespoke data analysis, brands can unlock a truly multidimensional view of their audiences wherever they are.

This approach allows brands to move beyond generic targeting and into a space where campaigns are not only more relevant but also more respectful of consumer expectations – a win-win for younger cohorts who dislike intrusive and irrelevant brand messaging but demand personalisation nonetheless!

Data insight for a dynamic healthcare future

As healthcare consumers’ expectations evolve and the consumer health and wellbeing market with it, so must the strategies brands use to engage them. Success for D2C and e-commerce healthcare brands doesn’t just hinge on understanding who consumers are today — it’s about being able to anticipate who they’re becoming even as new healthcare technologies, products and devices become available. By being able to able to identify and engage high-lifetime value customers as early as possible, brands also have a greater chance to capture markets as they evolve.

The effectiveness of multi-layered segmentation in improving marketing precision now – and as AI becomes more integrated – is well established. CACI’s ability to deliver on this today with our consumer data and bespoke strategic segmentation capabilities ensures brands are future-ready

Data isn’t just a tool – it’s a strategic asset. Brands that invest in sophisticated segmentation and activation today will be best placed to drive sustainable growth tomorrow.

Speak to our healthcare consumer segmentation specialists today.

Case study

How CACI updated the Ocean consumer lifestyle database using AI techniques

Summary

CACI’s Ocean database contains variables relating to consumer attitudes and behaviours of the UK population at individual and household level.

Whilst already providing a market leading solution, a major update gave CACI the opportunity to rebuild many of the associated predictive models using AI techniques to even further improve the modelling, and to make predictions more balanced and “fair” across demographic subgroups such as sex and age groups. 

Industry

Technology

Products used

Challenge

Traditional classification techniques optimise “mathematical accuracy,” which measures the number of predicted labels that match the true labels; however, optimising solely for this measure can result in an imbalance in prediction quality across Yes and No labels (as to whether particular behaviours, interests or attitudes are exhibited), and unfairness across demographic subgroups such as sex and age, especially when there is a natural imbalance in the true Yes/No label proportions, i.e. where behaviours have a strong skew towards a particular sex or age group.

Addressing these deficiencies is an area of ongoing research within the AI community. 

Icon - Outline of a person with three ticks next to them

Ocean enhances clients understanding of their customers by indicating their likely attitudes and behaviours 

Icon - Outlines of three people

Traditional modelling methods can be biased in terms of prediction quality for different sexes and/or age groups 

Icon - Magnifying glass with the outline of three people

The challenge was to remove this bias, achieved by developing new AI based techniques that can optimise across both sex and age groups 

Solution

Advances in machine learning science and computational power allow Ocean to use a targeted technique for each variable rather than a one-size-fits-all approach. ​ 

CACI has developed new in-house classification techniques that significantly improve standard methods to ensure balanced prediction quality across both Yes and No predictions and demographic subgroups.  

For fairness, various measures can be used. CACI specifically optimises its predictions as measured by the Equalised Odds Difference, across sex (Male/Female/Unknown) by default or across age bands or both. 

Results

Fairness has been implemented across age and sex to ensure we are more accurately predicting attributes and behaviours whilst eliminating bias. 

In addition, a set of insightful driver variables has been added, enabling the modelling to achieve a better understanding of the real world, and over 100 new variables have been introduced for the latest version of Ocean. 

Ocean Consumer Lifestyle Database - Three women shopping together in front of a clothing store

What is Marketing Mix Modelling (MMM)?

In this Article

Benefits of marketing mix modelling (MMM)

For any marketing activities to be successful, understanding consumers’ behaviours and whether a channel is oversaturated is essential. While data and analysis play undeniably important roles in this, marketing mix modelling (MMM) plays an even greater one, representing the merging point of data and analysis with the psychology of consumer understanding.  

Marketing mix modelling (MMM) is a statistical tool that enables an understanding of how each part of an organisation’s marketing activity impacts consumers’ behaviours, sales, return on investment (ROI) and more. Through MMM, an organisation’s performance can be broken down by channel and various types of data can be incorporated to evaluate the effectiveness of marketing activities and determine which are making the most substantial differences to the organisation’s overall performance. 

  • Enables organisations to quantify and measure marketing channels effectively to assess which drive the most sales and return on investment 
  • Equips organisations with long-term insights that will bolster planning through effective forecasting and marketing campaign generation based on previous performance  
  • Helps organisations allocate budgets according to the best performing channels due to measuring growth based on investments
  • Instils confidence due to its statistical reliability and being privacy-safe, both of which are particularly important in a post-cookie world
  • Offers organisations a holistic view of the impacts that various factors will have on achieving specific KPIs, ensuring marketers can make more informed decisions based on how and when marketing activities will impact KPIs. 

How do marketing mix modelling (MMM) & commercial mix modelling (CMM) work?

Marketing mix modelling (MMM)

Marketing mix modelling (MMM) is used by organisations aiming to understand how marketing activities impact KPIs being measured. Its ability to measure the impact that certain pricing choices, promotional offers, product launches or advertising campaigns may have on sales makes it a game-changer for organisations. 

In MMM, the dependent variable used to assess the relationship between sales and marketing activities is usually:  

  • Sales volume: to assess the impact of different marketing activities on sales 
  • Revenue: to track the amount of money generated by sales 
  • Competitor analysis: to understand how your organisation’s marketing activities are affecting your position in the market. 

In contrast, the independent variables in MMM are the marketing activities or factors that might drive those results, such as: 

  • Advertising spend: the amount invested in promotion across various channels. 
  • Price: to explore the impact of price adjustments on sales 
  • Promotions: discounts, coupons, or offers that could increase sales 
  • Distribution: the potential impact of product availability across various locations on sales. 

Commercial mix modelling (CMM)

Commercial mix modelling (CMM) is an analytical approach that examines a variety of commercial factors that drive an organisation’s performance. It begins with collecting data from across the organisation on pricing, promotions, distribution channels, products and more, combining the resulting data into a cohesive dataset.

The insights presented within the dataset help organisations gauge which factors contribute most to performance and where investments result in the highest returns. It also enables organisations to test various scenarios— price changes, promotional adjustments, changes within distribution channels— to assess the potential impact on performance. Through this, organisations can optimise their overall commercial mix to grow and become more profitable.  

How does commercial mix modelling (CMM) differ from marketing mix modelling (MMM)?

While both commercial mix modelling (CMM) and marketing mix modelling (MMM) are granular approaches that help organisations analyse the impact of marketing activities, their scope, methodology and applications differ.  

Scope

CMM offers a broader approach when it comes to evaluating the marketing activities that would impact an organisation’s performance, integrating various functions to optimise revenue and profitability. It encompasses external, non-marketing data sources such as weather, seasonality, competitor pricing, interest rates, etc.  

MMM, on the other hand, is more partial, purely marketing data that offers a more detailed and expansive result. As a statistical analysis method, it quantifies the impact that marketing activities— campaigns, paid advertisements, promotions, etc.— have on specific KPIs. Focusing more on media and investments rather than a wider marketing strategy, its granularity is what marks its stark contrast to CMM.  

Despite the broad scope of CMM, it is just as granular and technical as MMM. 

Methodology

CMM blends analytics, business intelligence and strategic insights, considering both internal and external factors that can affect an organisation’s growth. The approach entails: 

  • Scoping & data auditing:
    • Understanding the KPIs and defining whether the model should target revenue, acquisitions, renewals or some combination form the scoping basis. Data auditing includes tech and journey mapping to determine the stages comprising the funnel for lead gen and closing, as well as the tools and tech used at each stage. 
  • Data collation & cleaning:
    • This includes a data request to outline the full scope of what can be used in the model, with cleansing, organising and playback taken into consideration to check for completeness and broad accuracy. During this stage, data is also combined and reaggregated for ingestion into the model. 
  • Exploratory analysis & feature configuration:
    • Plotting all the raw data to understand distribution and periodicity and exploring this raw data to identify gaps and anomalies is conducted during this stage. Correlation analysis helps find feature relationships and possible collinearity, feature types are configured for use in the model and decay is applied (AdStock) to channel features to simulate the memory effect of advertising.
    • Diminishing returns to channel features simulate channel saturation and other transformations such as smoothing or feature combination.  
  • Pre-processing & feature engineering:
    • Calendar and dummy variables can be included to represent milestones and seasonality, with each variable transforming across a range of parameters to find the most realistic behaviour. 
  • Commercial mix modelling (an iterative process with pre-processing & feature engineering):
    • Once the model for the approach is scoped (e.g. logistic vs. linear, pooled, nested, hierarchical) and fit for processed features to optimise accuracy and generalising power, it is then checked against existing commercial knowledge and external priors and returned to feature processing to refine variables and tune parameters accordingly.
    • All candidate variables are imported and tested from the pre-processing stage. Finally, the model is refined continually by adjusting variables to optimise statistical measures of accuracy. 
  • Optimisation & simulations:
    • The present channel saturation is analysed, the optimal channel mix is delegated for specific budgets and results are presented from scenario simulations to understand which channels have headroom and which are oversaturated.
    • A budget guide is provided for optimising revenue and the ability to plan for different scenarios: mitigating headwinds, capitalising on opportunity and planning contingencies. 
  • Next steps & recommendations:
    • Recommendations are given based on budget optimisations and added value. 

MMM, in comparison, focuses on econometric modelling and regression analysis to determine the contributions made by various marketing channels on an organisation’s outcomes. Econometric modelling is a statistical, mathematical approach that quantifies the relationship between marketing activities and business outcomes, built with historical data. Regression analysis is a technique used within econometric modelling to measure the impact of independent variables (marketing activities) on dependent variables (sales or revenue). 

Application

Senior executives and C-suite employees may use CMM for longer-term strategic planning and decision-making, whereas MMM would be used by marketing teams to optimise spending and budget allocation towards campaigns or advertisements.  

The broader scope of CMM enables senior executives and C-suite employees to gain a complete picture of the various commercial drivers and their impact on marketing rather than isolated results. On the other hand, the granularity of MMM ensures marketing teams strategically plan and forecast how changes in spending across channels might impact sales and plan scenarios accordingly. 

How to build a marketing mix model

The first step in building a marketing mix model will be to collate and prepare your data. This will involve collecting historical data on sales and marketing spend across different channels and should go back far enough in time to effectively capture market conditions and seasonality fluctuations. 

Next, selecting the appropriate model to facilitate this will be crucial. Selecting the model can come from its robustness or flexibility, catering to your organisation’s unique needs. 

Building the model will come after this. This will include defining the relationship between marketing spend and sales or other KPIs and considering carryover effects, saturation or external factors. 

Furthermore, fitting the model will use your historical data to estimate the parameters of the MMM. Once the model is fit, the results can be analysed to precisely determine their contributions towards each marketing channel. 

Finally, the insights gleaned from these results can help you adjust marketing strategies accordingly, increase budgets within the highest-performing channels and reduce it in those underperforming. 

Examples of marketing mix modelling (MMM)

Organisations across a variety of industries can apply marketing mix modelling (MMM) to lead to improved outcomes. A few of such examples include:  

  • Consumer Packaged Goods (CPG): Gathering data on sales, advertisements, campaigns and pricing can help CPG organisations understand which channels—digital advertising, TV campaigns, etc.— drive the most overall return on investment. 
  • Retailers: From seasonal promotions to discounts and the influence of both in-store and online presence, retailers can leverage MMM to understand peak performance periods, digital sales and foot traffic to allocate budgets accordingly or reassess promotional calendars.  
  • Financial Services: Financial institutions can use MMM to evaluate their multi-channel advertising efforts and ensure they are reaching the appropriate audiences, encouraging sign-ups.  

Why businesses should choose CACI to carry out CMM 

CACI supports businesses in their delivery of optimised marketing efficiency by: 

  • Determining the value and performance of activity through evolved multi-touch and econometric modelling 
  • Producing results to sustain and increase growth through targeted investment and improved marketing performance 
  • Delivering improved accuracy, consistency and availability of marketing performance insights 
  • Enhancing capability by evolving data, technology and process 
  • Supporting the provision of ongoing strategic and delivery resource 
  • Helping businesses dig into bespoke segments and utilise in-house data products to unlock insights 
  • Offer businesses location-based insights into the effects that marketing has at various levels, from stores to regions.  

Find out more about the impact that marketing mix modelling can have on your business by contacting us today

Click here to read our short infographic to learn how CACI’s Commercial Mix Modelling can transform your business strategy.

Sources:

Case study

How The Midcounties Co-operative use data-led decision-making for their location planning strategy

Midcounties Co-operative logo

Summary

The Midcounties Co-operative is a large consumer co-operative fully owned by its members, which operates the Your Co-op family of businesses. Founded in the mid-19th century to share goods and services at a responsible price in the community, the Midcounties Co-op presently operates from more than 230 food retail stores in the UK, largely across the West Midlands, Oxfordshire, Gloucestershire and Wiltshire. The organisation also trades nationally through the Co-op Pharmacy, Co-op Travel, Co-op Childcare, Co-op Energy and Phone Co-op businesses, as well as operating a funeral care business and Post Offices. Every Co-op business is built on robust ethical values designed to foster a strong business and community.

Company size

5,000+

Industry

Retail

Products used

Challenge

When Ross Lacey joined Midcounties in 2017, he stepped into the newly created role of Location Planning Manager. His task was to help the business grow through a greater focus on location analytics and data-led decision-making.

The team built some strong working relationships with developers and agents, but in order to continue to grow the new site pipeline in line with the ambitions of the business, they needed to adopt a more targeted approach.

This meant developing accurate and reliable spatial and geo-demographic modelling to understand catchments in the context of business objectives and performance.

Solution

CACI’s InSite tools and data provided the comprehensive information Ross needed to analyse the core trading area. He analysed mapping data and catchments in every village and town in the Co-op’s trading area, looking at existing stores, competition and demographics.

The model has been continuously updated since it was created, feeding in new data from CACI that reflects changes in catchments, communities and demographics. Ross and his team have also adopted new HTML mapping tools which make it easier to share links with colleagues around the business who request site and catchment information.

Working closely with CACI, the team has recently developed a suite of dashboards that present key information about store performance within a catchment in a visual format. These are automatically updated, so the most useful and comparative data is continuously available without the need to design individual reports. Ross is also impressed with the aesthetics of the dashboard output: “It’s important to me that data we share with colleagues is easy to understand and well-presented visually: the reports have been really well received and had an impact around the business because of this.”

Results

The InSite tools, dashboard and data have given Midcounties reliable evidence for new site investment prioritisation. According to Ross:

“The rigorous approach has built strong confidence in our pipeline of planned sites. As well, greater confidence in our sales forecasting has enabled us to be more aggressive in our rental offers as we compete with other multiples for the best sites. Since introducing the model into our new site appraisal process, we’ve seen strong and consistent performance from new sites.”

With the automated and visual reporting from the dashboard and well-defined catchment analysis processes, Ross and his team can work more efficiently and free up time to champion data-led decision-making in other areas of the Midcounties.

Case study

How Virgin Media successfully met the End of Contract Notification Regulation

Virgin Media logo

Summary

The Ofcom regulation launched 15 February 2020 outlining that customers must be sent an End of Contract Notification (EoCN) 10-40 days before their contract ends. These should include details of the account such as current contract deal and associated offers. The regulation is designed to raise awareness to the customer that they’re out of contract and their price may change. Virgin Media, a major provider of broadband, TV, phone and mobile services in the UK, had never previously sent out such a notification, nor were their systems ready to do so.

Company size

10,000

Industry

Retail

Services used

Products used

Challenge

Virgin Media requested support from a dedicated CACI Adobe Campaign consultant to assist with the creation and the facilitation of the end-to-end solution. CACI’s Senior Consultant, Fraser Rallison, joined the team to support Virgin Media with its EoCN campaign.

Implementation

Virgin Media faced significant challenges in implementing EoCN. Its existing systems were not equipped to produce these notifications, necessitating the creation of an end-to-end solution from scratch. Not only was this a complex task, but it had to be managed within a very tight timeline, with substantial financial implications if the deadline was missed.

Communication

Additionally, Virgin Media needed to ensure the accuracy and clarity of the notifications to avoid customer misunderstanding. In addition, they needed to identify the most appropriate and accessible way to contact customers, whether via email or special formats like audio or braille.

Solution

The EoCN approach consisted of passing data through several different data systems. The process began by selecting all eligible customers using Virgin Media’s source billing system. This data is then released to be transformed into a more customer friendly format. Once complete, the customer specific offers are appended and the data is delivered into Adobe Campaign.

Within Adobe Campaign, six individual workflows were created to release over 20 different data files. These workflows ensured that the data coming through was correct and accurate with no missing values which could cause confusion to the customer. The data is also checked to ensure all offers are correct and make sense to the consumer.

Once these steps are complete the data is reviewed to identify the most appropriate way to contact customers. This is identified by reviewing their previous email engagement, quality of email address and whether they require a notification in a special format (such as audio or braille).

Once these checks and classifications are complete a bespoke report is built from Adobe and shared with project stakeholders with a request to approve the accounts should they match the project plan. Once sign off is agreed the data passes a final two-stage quality assurance check before then being released to separate email and direct mail agencies.

Results

The level of granularity within the workflows allowed Virgin Media to better understand the offering provided to customers. The flexible and dynamic approach has also lead to a significant amount of customers communicated to since 15 February 2020 when the regulation came into force. With the support of CACI, Virgin Media has kept within the EoCN regulations and avoided substantial fines.

Case study

How CACI supported Tesco to quickly join the dots and suggest seamless approaches to problem solving

Tesco logo

Summary

Tesco approached CACI to get support from our data specialists on a new project to connect the dots using CACI data.

Company size

10,000+

Industry

Retail

Products used

Challenge

For some years Tesco analysts have used map data from CACI to help define store delivery catchment areas. They have also used data from CACI to help them understand where the uptake of the company’s home delivery service was likely to be highest. 

Digital mapping

Latterly Tesco.com, Britain’s biggest grocery home shopping retail business, has introduced a new, more advanced routing and scheduling system to plan home deliveries by its fleet of over 2,000 vans; and in the light of its established relationship with CACI, the retailer again turned to the company to supply appropriate digital map data for both the UK and Ireland. 

Solution

To work on this software, CACI has supplied Tesco with premium vector street-level map data, which includes essential routing information such as one-way streets, banned turns and address ranges. The premium mapping data was also used to provide a visually pleasing map background for display and presentational purposes. 

Tesco.com generally delivers to homes from 8am right through to 11pm from Monday to Friday, as well as up to 10pm at weekends, so it is vital for the company to be able to route its vehicles to take account of changing traffic speeds and flows at different times of day and at weekends. 

CACI has therefore also supplied Tesco.com with Traffic Patterns, a data set that contains average traffic speed on individual road segments, calculated from past traffic flow measurements and differentiated by time of day and day of the week. 

Results

Digital map data assembled, prepared and formatted by CACI is playing a key role in the continuing expansion of Tesco.com. 

According to Ben Dito Smith, the Location Strategy and Analysis Manager for Tesco.com : “Efficient, timely delivery is a fundamental feature of our home shopping proposition, so it is essential for us to use the most appropriate software and data available for our delivery planning system.” 

Tesco.com delivers to consumers’ homes from larger retail stores and from a small number of specially designed dotcom stores. The home shopping business on its own now turns over more than £2 billion. 

Crate full of apples with a food truck in the background with more crates being emptied

Case study

How River Island uses ResolvID to effectively perform identity resolution on customer data

River Island logo

Summary

Founded in the 1940s, River Island is now one of the UK and Ireland’s largest fashion retailers. A British high street icon specialising in trend-led, affordable fashion, it was one of the first high street retailers to launch online in the 1990s. The company operates through more than 300 stores globally, as well as e-commerce platforms, with 40% of revenue made from online sales. As an innovative retailer always at the front of the market, the business knew providing seamless, personalised, omnichannel customer experience was vital, but needed to improve its customer data to deliver on a Single Customer View (SCV).   

Company size

5,000 – 10,000

Industry

Retail

Challenge

When River Island began building a marketing and analytics data technology environment with an SCV in-house – a single record that merges all customer data in one place – it recognised that its current customer data set was not deduplicated. It needed real-time identity resolution that could return a single unique customer identifier to River Island.

Data management

Bringing the entire SCV in-house posed a significant operational challenge to River Island. There were many different data feeds that needed to be terminated and also incoming and outbound data that lacked clarity and needed re-evaluation.

Problems of the past

This challenge was compounded by the fact that the original data feeds were also set up by employees who had since left the business, resulting in a trial by fire with their SCV.

Solution

CACI configured ResolvID, a cloud native solution hosted on Amazon Web Services (AWS) Cloud infrastructure, to supply River Island with data cleansing, standardisation, identity resolution and deduplication. Developed with a microservices architecture, the bespoke platform offers significant advantages through its scaling, resilience and flexibility when rapid changes and improvements are required.

ResolvID comprises horizontally and vertically scalable microservices that perform different functions with a seamless interface to enhance River Island’s accessibility. The solution leverages advanced deterministic name and address matching techniques in conjunction with digital and non-digital identifiers specific to River Island customers and their data. As part of this initiative, CACI took a three-step approach to effectively perform identity resolution on River Island’s customer data.

Results

Leveraging ResolvID has resulted in many tangible benefits for River Island, including the creation of various customer dashboards to monitor more targeted figures and generate better, more timely data that bolsters targeted customer campaigns. There have also been noticeable improvements in workload efficiencies, such as cutting down the time required to action workloads to increase the team’s focus on refining their future strategy of doing more with their data to retain oversight on customer performance.

This real-time capability now enables the confident and immediate actioning of data and customer signups to produce effective campaigns based on genuine buying behaviours and generate accurate results.

Women shopping for clothes in a shop, looking at items on a clothes rack

Case study

Supercharging Hotter Shoes’ customer experience strategy with Fresco segmentation

Hotter

Summary

Hotter Shoes is the UK’s biggest footwear manufacturer. It’s a digitally led, omnichannel specialist footwear brand with a clearly defined, large and growing target audience.

Company size

5,000

Industry

Retail

Products used

Challenge

Hotter already had a strong heritage in direct-to-consumer marketing when Stephen Shawcross, Senior Global CRM Manager, joined the company four years ago.

Stephen explains: “Like many retailers, we had an abundance of data but it was fragmented. Our first challenge was to bring all the data we had together. We created a true omnichannel single customer view (SCV) that included online, store and contact centre order data, footprint 3D scanning and augmented reality fitting data, web browsing data and email engagement data.

Bringing all data into one location

Creating a single customer view

Solution

CACI’s Fresco data stood out from the competition to offer the level of dynamic detail that Hotter needed. The CRM team was able to match 98% of consumers that order from Hotter to a CACI segment, at an individual customer level.

CACI’s consultant provided “amazing” support for Stephen and the team, with initial training and advice about data mapping and regular check-ins to make sure they have everything they need.

Stephen says, “The big appeal of Fresco was being able to map to an individual customer. A lot of profiling customer systems offer flat pen portraits but aren’t necessarily actionable. CACI matches a customer to a segment and means you can do something with it in real time. We immediately stepped up the level of personalisation beyond buying and browsing behaviour to supercharge our Customer Experience Strategy.”

Results

The combination of buying behaviour, digital engagement, foot-scan data and CACI demographics means Hotter Shoes’ marketing is hyper-relevant and offers true personalisation at scale.

Stephen explains:

“At the highest level, we personalise based on CACI segment, recency, frequency, monetary value (RFM) commercial segmentation and channel preference across all customer touchpoints.”

Hotter is able to create relevant, personalised website homepage images, messages and email content as well as Google pay per click ads, social media posts and direct mail. The profiling is specific and sophisticated – there are currently 27 different direct mail variants. We can prospect with social media marketing, finding and targeting lookalike audiences.

Hotter is also exploiting the Fresco data to support acquisition among new customer groups. Beyond their traditional market of customers aged 55+, the firm is looking to attract the next generation. Fresco segmentation is helping the team identify the most likely personas and to design messages, campaigns and products that will appeal to them.