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Do you have the right data to drive your collaboration strategy?

In today’s hyper-competitive grocery environment, collaboration between retailers and CPGs is more than just a “nice to have”; it’s a genuine strategic enabler, something that can help tackle a diverse set of challenges that range from profitability through to the quality of the customer experience.

As impactful as it can be though, collaboration – and specifically shopper first collaboration – doesn’t just happen. To ensure that their efforts are successful, and conducted with their customers’ best interests in mind, retailers and brands need access to the granular, individual shopper-level data that can help them hone their strategies based on real insight rather than gut feel.

The unique value of retailer-owned data is clearly recognised within the industry itself. Asked to specify which data sources they believed would prove most useful to their future collaboration efforts, more than three-quarters (77%) of respondents to a recent dunnhumby study put shopper-level data in their top three. While online browsing data and syndicated EPOS data placed second and third, they enjoyed significantly less support in comparison (53% and 48% respectively).

As heartening as this is for a business that works regularly with that kind of data and can testify to its transformative impact, we do see some cause for alarm here too. Clear though the importance of retailer-owned data may be, there’s a difference between appreciating its value and actually being able to capture or scrutinise it.

To turn insights into action, and to put their data to work in service of the customer, retailers need to work collaboratively and transparently with their suppliers – making decisions based on what’s right for shoppers rather than just margin or sales data.

As much as they might like to, though, many retailers still can’t do that, lacking  the systems they need to collect shopper-level data and “democratise” it in a way that makes it usable by their CPG partners. That has significant implications from a collaboration perspective, and heightens the importance of implementing a fit-for-purpose data strategy that can support a constructive dialogue between the retailer and its partners.

Creating a collaboration-ready environment

The importance of understanding customers in terms of their individual purchasing behaviours can’t be understated when it comes to collaborative planning. By blending their own data with the expertise and brand equity of their supplier partners, retailers can drive a fundamental shift in their ability to communicate with shoppers and execute effectively against their needs.

Achieving that kind of harmonisation, however, means having the right processes and structures in place when it comes to data. Namely:

  • the technology and infrastructure needed to collect and store high volumes of data, linked directly to a retailer’s loyalty or CRM programmes and EPOS systems.
  • a storage model that enables that information to be processed and analysed, such as a data lake or central depository.
  • a democratised structure that allows key stakeholders from across – and outside – the business to access and analyse that data as part of their decision making.

The last of these points is particularly important. So long as they have access to it, CPGs can complement shopper-level data with their own insights and learnings. That includes sources such as market-wide sales data, household panel data, and other quantitative and qualitative research, all of which can help to create more sophisticated, shopper-relevant brand and category strategies.

Moreover, with the right arrangements in place, CPGs can effectively serve as an extension of a retailer’s category teams – helping them generate contextual, relevant insights that can be delivered at the shelf. Let’s look at a few examples of how that could work in practice.

1.Combining multiple sources of online data, and working together to understand them

Online purchasing data can be an excellent way to understand how customers shop different categories and brands, particularly when complemented with offline data to create an omnichannel view. That’s not the only kind of online data that retailers can share with their CPG partners, though.

Browsing data, which includes information on how customers navigate a site, what they search for, how they add to their baskets, and more, can be an effective planning tool as well. Used well, it can help CPGs understand how to adapt their range to online only and multichannel shoppers, run media placements in the most effective areas of a site, and simplify product discovery.

2. Tackling the problem of out-of-stocks with a collaborative approach to forecasting

While the extreme disruptions in global supply that resulted from the pandemic may now be behind us, inventory continues to be a complex issue. Getting it right means providing CPGs with the data they need to make better projections about supply, including the ability to highlight any shortages or pressure points in advance.

Done right, that offers benefits for both parties. By minimising the need for substitutions, retailers have a better chance to grow basket spend amongst online shoppers, as well as maximising the efficiency of their online picking operations. In turn, CPGs can improve their profitability by reducing the risk of late shipment penalties.

3. Meeting a breadth of shopper needs with a more targeted range

The danger of adding products to a category based purely on sales performance or margin contribution is that retailers can end up with duplicate-heavy range that over-indexes on the same set of shopper needs. By providing CPGs with data about customer behaviours, retailers can spark more informed and productive discussions about their assortment.

That presents multiple advantages. Firstly, it can help to prioritise and deprioritise items based on their relative importance to, and performance with, customers. Secondly, it can help to prevent items that are important to certain shopper groups from being removed if they begin to underperform from a commercial standpoint. Finally, it can provide the insights required to help retailers arrange their displays in harmony with shopper needs – ensuring that customers can find their preferred products quickly and easily.

As compelling as these examples may be, they’re really just a small snapshot of a much bigger opportunity. And while it might require work – and in some cases, investment – a collaboration-ready approach to data can deliver value all the way from.

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