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 they’re 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 Google’s 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, L’Oréal’s 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 don’t 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 customer’s 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 Dan’s 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.
Turning payment data into conversion insight
Product and behavioural data shape how customers shop, and payment data shows whether that journey converts. Sitting at the point where intent turns into transaction, it offers a clear view of what’s working, what isn’t, and where revenue is being lost.
Kevin Griffin, VP Growth at PXP, explains why this is such an underused opportunity.
He says, ‘Payment data is one of retail’s most underleveraged sources of commercial intelligence.’
‘Transaction-level insight tells you something immediate: what’s working right now, and what isn’t.’
Kevin exclaims, ‘Refusal patterns, payment method performance, and conversion rates by device or customer segment aren’t just operational metrics.
‘- They’re signals that inform real trade decisions: which markets are ready to scale, which checkout journeys are quietly losing customers, and which payment methods your highest-value shoppers actually want to use.’
‘Retailers who connect this layer of insight to their broader strategy tend to make faster, more confident decisions.’
Nikhita Hyett, General Manager EMEA at Signifyd, points to the missed opportunity in how retailers handle failed transactions.
“Retailers are rich in data but often overlook its most actionable forms – behavioural signals, failed transactions, and declined orders.”
She explains how this data can be used to improve both conversion and experience.
‘Leading brands use this data to reduce friction and personalise journeys, turning insights into growth. For example, analysing checkout abandonment and failed payments helps identify hidden barriers, while smarter segmentation, both by risk profile or purchase intent, ensures genuine customers aren’t wrongly declined.’
‘This is critical, as many rejected orders are legitimate, directly impacting revenue and loyalty,’ shares Nikhita.
‘Retailers who activate these insights see stronger conversion and retention, proving that success lies not in collecting data, but in using it to balance customer experience and internal risk.’
Understanding the ‘why’ behind behaviour
While payment data and behavioural signals can highlight where customers drop off, they don’t always explain why. Tools like heatmaps and journey tracking can point to where friction exists, but understanding the reason behind it often requires direct input from customers.
Garret Cunningham, VP of Customer Experience & Optimisation at Columbus, emphasises the importance of combining quantitative and qualitative insight.
“We always emphasise the importance of qualitative data to all our retail customers. Quantitative data is powerful in telling us what is happening within the business and on their website, however it just tells us the ‘What’. Qualitative data allows us to hear more directly from the users… this is how we get to understand ‘Why’.”
This can uncover barriers that wouldn’t otherwise be visible in standard reporting.
“Understanding that drop of rates in the basket for a Jewellery company who specialise in engagement rings was being driven by concerns that their partner may see the delivery packaging and ruin the surprise.”
“Or finding out that add to basket rates on the clothing products of a cycling retailers’ websites where underperforming compared to other product categories because the product images only should the product on a flat surface, so the users couldn’t get a good enough understanding of the fit.”
To gather these insights, Garret highlights the value of direct customer feedback.
“Open ended survey questions are a superb way to gather insightful quantitative data from customers in a passive way… post purchase to ask the users about their experience, what almost stopped them completing… what was their motivation…”
Using fulfilment data to deliver on expectations
Fulfilment is a critical part of the customer experience. From delivery speed to stock availability, it’s where expectations are put into practice — and where data can help retailers deliver more consistently.
Fulfilmentcrowd highlights how this relies on having connected, usable data across the business.
‘The biggest opportunity here for retailers lies in being able to act on data across an increasingly complex, omnichannel environment. They say ‘it’s easy for data to become fragmented across systems, making it harder to understand performance and respond effectively across the board.’
‘The most successful omnichannel retailers benefit from a single, unified view of their operations, enabling them to monitor demand by regions, and make confident decisions around stock placement and fulfilment, boosting overall global performance.’
Building on this, Andrew Scanlon, Head of Sales and Marketing at Paxon, explains how fulfilment data can be used to improve both efficiency and the customer experience.
‘We work with many retailers and brands to utilise sales data to make fulfilment a crucial part of shopper satisfaction and brand building.’
‘Sales data is really valuable for optimising stock inventory management’ as well as ‘boosting shopper satisfaction and avoiding out-of-stock scenarios. Retailers can ‘strengthen brand loyalty by cutting lead times.’
‘Advanced warehouse management systems’ can ‘utilise purchasing data to rotate and position stock, speeding-up fulfilment and dispatch, and helps meet shopper demand for faster deliveries.’
He also highlights how fulfilment can become a channel for personalisation.
‘By looking at factors like what shoppers bought we can tailor in-pack and on-pack offers and messaging, turning an eCommerce order into a compelling point of sale.’
Gavin Murphy, Chief Marketing Officer at Scurri, points to the opportunity beyond delivery itself.
‘Retailers are not short of data, but they often underuse the signals that sit beyond the point of purchase… Post-purchase data is a rich but overlooked source of insight.’
‘The most effective retailers are using this to shape smarter segmentation, personalise communications and optimise delivery choices in real time. For example, understanding when customers engage with tracking updates can inform marketing timing and channel strategy.’
Gavin exclaims, ‘When operational data is treated as a customer experience asset, it becomes a powerful driver of conversion, retention and long-term value.’
Summary
Retailers may not be short of data, but those seeing real impact are the ones using it with purpose. From building strong data foundations to optimising product, understanding behaviour, improving conversion and delivering on fulfilment, each stage of the customer journey offers an opportunity to act.
The common thread is clear: growth doesn’t come from collecting more data, but from using the right data at the right moment to meet customer needs and remove friction.
Published 08/05/26
