Data Coopetition: The Delicate Relationship Between CPGs, Private Labels and Humans

In our latest Viewpoints guest blog, Rachael Hadaway of Symphony RetailAI discusses how retailers and suppliers can better streamline data to better improve operations, including when it comes to private brand selections.

It used to be that CPGs and retailers were both at the mercy of market data companies to get access to shopper, market and competitor information to make decisions. However, technology advancements such as affordable cloud, big data and related data-as-a-service providers upended many of those dependencies.

Companies with bigger budgets (including CPG conglomerates) now have in-house data science teams, and many retailers have adopted leading data science practices and predictive analytics solutions. This gives both retailers and CPGs access to myriad insights from various parts of their businesses to improve their relationships beyond the typical areas of merchandising and supply chain.

It seems like it would be a no-brainer for the two sides to collaborate, but with so much of that relationship hinging on the information the other one owns, the two occasionally find themselves on a collision course. However, companies can take steps to minimize these conflicts and maximize mutual success.

Data Savviness Breeds Data-Driven Decisions
One way to avoid those tensions is for both sides to lean on predictive and prescriptive data generated by artificial intelligence and machine learning (AI/ML) to bring objective insights to the conversation. This is now easier: employees across all teams at CPG and retailer companies are now more knowledgeable and data-savvy than ever before.

These “citizen scientists” are knowledgeable enough about an area (i.e.: data analysis) outside of their core responsibility to help them be more strategic with the domain focus of their job.

grocery data

The rise of these citizen data scientists can be attributed to two factors. First, advances in analytic solutions have made diverse and large datasets more accessible, bypassing the need for advanced technical degrees or manual coding to interpret the information.

And secondly, omnichannel shopping inherently enables retailers and CPGs to capture more data, generating more detail, and thus more insights than ever before. The volume of data and speed with which it flows adds a new dimension to the parameters considered in this conversation.

Don’t Divide the Pie. Grow It Instead.
What all this means is that there needs to be more collaboration instead of the quid pro quo prevalent today. By collaborating and sharing intelligence based on AI, both suppliers and CPGs can create synergies to help generate incremental sales and higher profits for both the retailers and manufacturers.

It also facilitates running simultaneous offers across multiple channels that can measure true incremental sales and return on investment. This allows the retailer to see if the collaboration was beneficial and how it can best leverage the information for conversations about their national brands and private labels.

Don’t Let Data Stagnate in a Data Lake
Without AI, data can be a liability. Any retailer has more than enough of it, but if they can’t use it to get to know customers better, it’s all for naught.

There will always be competitive facets to the CPG-retailer relationship, but with the right amount of collaboration, human intelligence and AI, the number of times the two end up on that collision course can be greatly reduced. And when there is the occasional collision, it’ll be no more than a fender-bender.


Rachael Hadaway Symphony RetailAI

Rachael Hadaway serves as SVP of product management for Symphony RetailAI, and has worked in the retail and CPG insights industry for 20 years. Before joining Symphony RetailAI, Rachael held executive positions with 84.51°, a data and analytics company owned by Kroger, where she was most recently SVP of product and design. Rachael has worked with global clients such as Kroger, Macy’s, Pepsi, P&G, ConAgra Foods and more.

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