RetailX Report · In association with Epsilon
The new rules around finding customers and building loyalty.
The modern marketing landscape is flooded with data, yet connecting these fragmented signals into a single view of the customer is harder than ever. As privacy regulations have tightened and acquisition costs soared, what’s the best way forward for marketers?
This report provides an essential framework for breaking down data silos, understanding the trust paradox, and navigating an AI-mediated shopping world.
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In a cookies-enabled world, marketers could easily analyse individual consumer behaviour. Today, despite marketers having huge amounts of data, that’s no longer an option — but new techniques are emerging for reaching customers.
Reliable Forecasting From Mixed Signals, backed up by RetailX survey results, lays out techniques and strategies for dealing with this new commercial landscape. It covers state-of-the-art techniques in customer acquisition, defining identity, finding the right customers and building loyalty, and how AI is changing the game.
Executive insights & takeaways
Customer data is plentiful, but it’s spread across many environments — from first-party data to social platforms, search engines and insights generated by real-world interactions. That makes a coherent view of customer behaviour difficult.
Customers expect personalised service, but won’t hand over personal information until trust is established. The signals that sit at the point of transaction are becoming more important — hence the rise of retail media.
Marketers need to understand whether it’s price, brand familiarity, or another factor — good reviews, or the reassurance of a high-street presence — that best conveys value to customers.
The challenge is no longer simply generating demand, but identifying which customers stay valuable over time — the right customers. Loyalty is no longer just a retention strategy; it has become an acquisition tool.
Despite some misgivings, customers are using AI. Marketers must optimise for both human and AI-assisted shoppers — giving a strategic advantage to brands with strong customer relationships and rich first-party data.
What’s inside · Data Workbench insights
Shopper data is growing and the channels to reach shoppers are proliferating — so how do retailers and brands find customers in this fragmented market?
As consumers move across an increasing number of channels and platforms, the next question follows: how do marketers actually know who those customers are?
Having grappled with the complexity and cost of finding customers, the next challenge is even more demanding: deciding which customers are actually worth finding in the first place.
AI is changing consumer habits in as-yet unpredictable ways. So what impact is consumer AI going to have on finding and retaining the right customers?
Real-world case studies included
Combined first-party data with Connected TV (CTV) to engage “tech hunters”, boosting in-store sales by 20%.
Proved that upper-funnel video branding directly drives a 6% revenue lift when paired with lower-funnel retargeting.
Turned brand campaigns into profit-focused channels, achieving a 4.1× higher purchase rate via multichannel user journeys.
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Produced in association with Epsilon.