By Ellie-Rose Davies, Content Manager at IMRG Retailers have more data than ever before, yet many still struggle to understand it, or turn it into meaningful growth. In this blog we look at the data opportunity, exploring how retailers are effectively using their data to offer excellent customer experiences, and the ways it can improve performance. Sections of this blog include:

• Getting the data foundations right
• Using product data to drive performance
• Using behavioural data to capture intent
• Turning payment data into conversion insight
• Understanding the why behind behaviour
• Using fulfilment data to deliver on expectations

Getting the data foundations right

Before retailers can turn data into action, it first needs to be accurate and usable. Getting the basics right at the point of capture plays a key role in everything that follows, from personalisation to fulfilment. Barley Laing, UK Managing Director at Melissa, highlights the importance of high-quality customer data. One of the most important things retailers can do he says, is to make sure they have access to accurate customer address data from the start. Such data plays a valuable role in providing customer insight, supports the creation of a single customer view (SCV) and drives customer satisfaction, loyalty and spend. Barley says, Having clean customer data is even more important in the age of AI. That is because poor quality data can lead to biased and flawed results. Just as importantly, having access to accurate customer address data helps to ensure a fast and efficient delivery process supports a standout customer experience, and avoids costly returns. To obtain accurate customer address data use address lookup or autocomplete tools. These can improve the whole experience, making it much more likely that a purchase will be completed.

Using product data to drive performance

A practical next step is product. Sitting at the centre of search, merchandising and the on-site experience, product data can play a key role in whether shoppers find what theyre looking for, get the information they need, and feel confident enough to buy. Productcaster Senior Client Success Manager, Samantha Bluckert at Summit Media reveals the power of product data. She says, Optimising product feeds through reviewing data quality scores, feed health and product attributes, is a frequently ignored, yet powerful lever for retail growth. By taking the learnings from analysing top-performing products and enhancing feed richness, retailers can significantly improve ad relevancy. We see that this strategy often leads to increased impressions and CTR, alongside lower CPCs. Samantha notes how It is essential to ensure high-priority fields, such as titles and descriptions, align with customer search queries and answer common questions. By combining internal data-driven insights with Googles best practices, businesses can achieve sustainable growth and effectively prepare for the shift toward agentic search, ensuring data readiness for future trends. Further reflecting on product data and agentic AI is Justin Thomas, VP Sales EMEA North at Akeneo. Justin tells us that Retailers have more than enough data, what they lack is usable product intelligence. Most are now using Product Information Management (PIM) and Product Experience PX to unify behavioural, transactional and product data to turn insight into action. Some have moved further to use Agentic AI to operationalise this at scale so their data is always structured, connected and activated based on every customer scenario, says Justin. At Noli, LOréals AI beauty platform, enriched product data enables hyper-personalised BeautyDNA recommendations across 200,000+ combinations, to ensure every product attribute, description and specification is accurate and correctly structured to facilitate the AI-powered mapping and serving of personalised product recommendations. Alexander Otto, Head of Corporate Relations at Tradebyte reinforces the value of utilising product data for growth. He describes how Retailers dont lack data, they the ability to structure and analyse it effectively. Some of the most effective brands are combining Product Information Management (PIM) with ongoing data analysis to turn fragmented product and stock data into a single, actionable source of truth. This is where real impact happens, says Alexander. What this means is that By analysing enriched product data, retailers can improve discoverability, optimise assortment by channel, increase conversion, and reduce returns by aligning expectations with reality. Yet this remains one of the most underutilised levers for growth. Alexander argues that Retailers that treat product data as a commercial asset, not just an operational task, are better positioned to scale across channels, respond faster to demand and deliver more consistent customer experiences. Skye Wilson, Performance Manager, at Shoptimised provides an excellent case study for using product data in practice. She says, One of my clothing clients had a strong CTR, but a low conversion rate. One product in particular was getting a lot of clicks, but size options were limited. So I looked into the data and found that parent SKUs with a full size range had a much higher conversion rate than those with broken stock. This is a simple piece of data that most online retailers have access to, but is often overlooked. Skye reveals her strategy: Based on that, I created a Full Stock custom label and built an additional Google Ads campaign to push these products. Focusing on full stock items has improved the conversion rate, increased ROAS, and given customers a smoother experience from click to purchase.

Using behavioural data to capture intent

After product, the next step might be understanding what customers are actually doing in the moment. While much of retail data looks backwards, behavioural signals offer a more immediate view, helping retailers respond to intent as it happens, rather than after the fact. Dan Bond, VP of Marketing at RevLifter, highlights how much of this data is already available, but often overlooked. He says, Most retailers ignore behavioural intent data: visitors who view products multiple times across sessions but never convert, shoppers who build baskets but abandon them at the same price threshold, customers who only buy when a competitor runs a sale. This isn't demographic data or purchase history. It's live signals about what would actually change behaviour right now. Dan reveals how this can be used to make offers smarter: The retailers doing this well run control groups to test whether an offer genuinely drives incremental revenue or just discounts sales that would have happened anyway. The data's already there. The question is whether you're testing what it tells you, or just reacting to last month's conversion rate, shares Dan. Building on this, Lewis Husbands, Client Success Specialist at ShoppingIQ, explains how retailers can rethink how they interpret that behaviour. We try to view data as more than a ledger of past clicks. Ultimately, it's a map of the customers literal journey but also thought process. Retailers often struggle with tracking and attribution during the incubation period (when a shopper steps away to think about a purchase). Lewis says, During this gap, cognitive load becomes the enemy. If the path back to the checkout is high-friction, the customer is likely to simply abandon. He adds that acting on this insight means reducing that friction in real time. Turning insight into action means shifting from tracking to hosting. The art is continually using data to proactively reduce that mental load. Lewis agrees with Dans point on how there is data opportunity to make offers smarter. He says, Retailers are succeeding with Meta's Highlight your promotions feature, through promotional overlays on Google Shopping creatives to offer immediate value, and also via integrated CRM speaking to their website. The key is for tech to feel like a shop floor representative that guides users through sizes, features and availability.Importantly, In an era where we are all more privacy-conscious, the mantra has to be helpful, not intrusive. By connecting qualitative signals with smart tech, you aren't just following a user, you are respecting their time and solving their problem, which Lewis says is integral for LTV.