Personalisation and NPD – how technology is shaping the retailer/brand relationship

When we think about collaboration between retailers and CPGs, we typically do so in terms of partnership working arrangements and the sharing of data and insights. Arguably less common – for now, at least – is the shared use of evolving technologies. While retailers and suppliers may employ those kinds of tools in support of their own objectives, they’re not always found at the heart of today’s collaborative partnerships.

That’s unlikely to remain the case for much longer, however. In the results of a study conducted by dunnhumby in 2021, a significant number of retail and CPG respondents said that they saw plenty of ways in which technology could aid in their future collaborative endeavours – from “improving the accuracy of their forecasts” through to the creation of “efficiencies within their organisation”.

“Technology” is an expansive term, of course, and something that means many different things to many different people. To our respondents, though, technology tends to mean one thing in particular – the tools and software they need in order to apply advanced data science to their challenges.

It’s no surprise, then, that our survey participants were also quick to point out some of the more specific technological use cases that they saw. Alongside issues like assortment optimisation and the ability to engage audiences in real-time, the most popular responses surrounded personalisation, forecasting, and measurement.

Even focusing solely on that top three, there are a vast number of ways in which technology could be applied to help drive better standards of collaboration between retailers and CPGs. With that in mind, I’d like to look at some of those potential applications – and how they might benefit retailers, brands, and customers alike.

Using advanced data science to personalise shopper communications

Retail media has become an integral part of the modern grocery experience, particularly in the wake of a dramatic rise in the number of people shopping online during the pandemic. For CPGs, retail media presents a unique opportunity – the chance to communicate with customers at key moments across the shopping journey, both instore and online. For retailers, monetising those channels can make a major contribution to the bottom line.

To be truly sustainable, though, retail media programmes also need to be run with the customer’s best interests in mind; repetitive, untargeted, or obtrusive advertising is only likely to reduce the quality of their shopping experience. Relevance is essential, helping to maximise the return on advertising spend for CPGs, and giving retailers the reassurance that retail media delivers an additive experience to customers.

Relevance is also something that can only be achieved by using shopper data in a cohesive and intelligent way, and this is an area in which advanced data science plays a major role. The smarter that predictive algorithms become, the better the recommendations that result – giving retailers and brands the opportunity to deliver nuanced, highly personalised experiences across everything from banner ads to search.

Maximising the effectiveness of trade promotions

Trade promotions form a perennial part of the average CPG’s strategy, accounting for billions in marketing spend on an annual basis. At the same time, research suggests that the vast majority of brands struggle to manage their trade promotion budgets effectively[1], and are unsure about the best way to maximise their return on investment from those initiatives.

As a result, it’s little wonder that so many CPGs are keen to invest in technologies that can help them analyse and optimise their activities here. Increasingly, this includes forecasting technologies that have evolved to bring new and sophisticated modelling processes into the equation. These build on traditional statistical modelling techniques, allowing brands to understand a wider set of variables such as demographics, brand preference, loyalty, and price sensitivity.

While there hasn’t always been the greatest amount of transparency between retailers and CPGs about the performance of trade promotions, that is now beginning to change. Retailers are increasingly aware of the critical role that brands play in helping them serve relevant offers to customers, and shared decision making – based on the collaborative analysis of data as outlined above – is starting to become the standard.

When it does, the rewards are usually clear to see. In the UK, for instance, joint planning on trade promotions between a major retailer and one tea manufacturer drove more than £1m in incremental category sales, as well as helping the brand to outperform the market for the first time in two years.

Aiding product development by peering into the future

As explored in a previous post on retailer/CPG collaboration, product innovation can be an excellent way to drive differentiation in a homogenous market. The key to successful innovation, of course, is knowing what shoppers might want next before they even do themselves.

Traditionally, that has meant that the ideation aspect of new product development has been based largely on a combination of real-world purchasing data with anecdotal evidence from surveys and focus groups. That approach helps to bridge the gap between what customers expect today, and what they’re likely to need tomorrow.

Today, with social listening platforms growing ever more sophisticated, many brands have taken the opportunity to let machines do much of the heavy lifting. By scraping everything from social media sites and forums through to retailer and brand websites, those tools are providing CPGs with the aggregated insights they need to forecast future needs and make smarter decisions about their R&D investments.

As useful as that is from an efficiency perspective, it’s equally valuable through the lens of collaboration. Not only does forecasting of this kind help brands map their future innovations specifically to customer needs, when combined with shopper-level data it can help them build a compelling case as to why a retailer might want to carry that new product as an exclusive.

With all that said, one thing remains clear: no matter how effective it may, technology must always be an enabler of collaboration, rather than the cause. Bright new ideas and tools may help to take us further, but trust, transparency, and a genuine desire to put the shopper first will always take priority.


[1] Rethinking your trade spend to maximize ROI – Strategy&

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