Conagra Data Scientist Brian Archey Explains Why Retail Data Should Be Bigger Than Retail Media
BRIAN ARCHEY: I’ll try not to get in trouble by giving away anything exclusive. But I will say that “advertising” and “supply chain” are those entities that have been thought of separately. If you look at the retail cloud data offerings in the market, for example, they’re for analytics and ad activation.
We see it as a circular philosophy.
The old model is a linear model. You look at sales, see a downward trajectory for a product, or sales among a certain segment. Therefore, you are running marketing as a lever to improve performance.
We can determine how consumers reacted to certain marketing content and the impact on sales. These are the typical inputs for the advertising return on investment. But these could be inputs for a consumer behavior that we want to influence, which could relate to the packaging or the supply chain. If we can influence consumer behavior in a way that we want to replicate, it comes down to advertising.
When marketing, supply chain and sales inform each other, it’s a system that works on all engines.
The idea of retail data collaborations isn’t new, but what is new in retail data sharing capabilities now?
Digitalization of the real world [online cash registers, for instance] necessary to reach a certain point to synthesize large chunks of data. It has been a game changer over the past five to ten years.
And, to be fair, there is the question of trust between the retailer and the manufacturer. It took a while for the industry to agree that we are in the same boat: that if we can allow some collaboration on data, we can get better information and better products.
Was the biggest obstacle trust or technological development?
It was technically impossible 10 years ago. But the real power lies in the trust in the partnership. Otherwise, with the data they have, individual companies can only go to a certain point. When you start to think about what information you can glean between a manufacturer and a retailer, this is really what it has provided us with.
However, there have been so many announcements of retail media and cloud data partnerships over the past couple of years.
With numerous retail and cloud media integrations, the brand creates audience segments to activate against, pushes their targeting metrics to the retailer’s machine learning, and uses a retailer-built cloud portal. Then the platform begins to target.
But our data partnership with 84.51 ° is much broader. We are able to access data for all kinds of hypothesis testing across the organization.
“Whole Organization Hypothesis Testing”?
An example: As Thanksgiving approached last year, we had questions about what the holidays would look like. Will people have more but smaller events? Will ordering groceries online have an impact on Thanksgiving eating habits? [Conagra sells meat products, not to mention its sides, sauces and desserts with a big stake in Thanksgiving dinner].
We were partners at the time with Kroger, so we started putting these exploratory models in place ahead of the Thanksgiving holiday to see if we could tell which direction some of these trends were going.
This meant that we could examine the size or pieces of turkeys sold. Different types of side products are purchased based on these combinations. We can see from the data that small group Thanksgiving dinner shoppers might opt for turkeys or smaller pieces, and maybe hot dogs. [Conagra also owns Hebrew National] or other non-traditional tariff.
And that means we can do the same type of analysis on the next vacation.
Now we are testing hypotheses based on individual households that have been anonymized and followed for a year or more: what spending habits from last year will stick around? Do we see the same patterns in online grocery shopping?
This helps in terms of scoring our digital marketing efforts. But also informs our supply chain activity.
Is e-commerce a priority for an in-store brand like Conagra?
The answer is yes.
There is an element of direct to consumer sales that we have explored and intend to develop. But think of all the digital shelves where our products exist. Consider Kroger’s online grocery store, Amazon, a service like Instacart.
Especially with the pandemic, there has been such rapid adoption. People who probably would never have done online grocery shopping in their lives have become followers of online ordering.