On the surface, it sounds like such a simple equation: more insights, better results. After all, the more you know about customers and their shopping habits, the easier it should be to anticipate their needs.
In practice, though, that’s not always the case. Recently, I attended the Category Management Association (CMA)’s annual conference in Dallas. There, I had the pleasure of talking to many retailers and consumer packaged goods (CPG) brands. One of the recurring themes during those conversations was the difficulty that those organisations have when trying to turn insights into actions.
That challenge tends to be particularly acute in the area of assortment. Retailers and brands have a wealth of data to draw from when making their ranging decisions, but often struggle to use that information effectively. As a result, while they might aspire to create a range that resonates and differentiates, the reality is often a little different.
The discussions I had at the CMA event got me thinking, and I believe that there are five main reasons why more insight doesn’t always lead to greater success in-store.
Let’s take a look at each—and what can be done about them.
Today, the vast majority of assortment tools rely solely on performance data. Our own platform — dunnhumby Assortment — is one of the few to incorporate customer loyalty data. As a result, it’s also one of the only tools that can help you accurately respond to changing customer needs.
Naturally, that leads to a lot of wasted time and effort—which is why dunnhumby Assortment specifically addresses that disconnect. Assortment provides a collaborative space in which sales, CatMan, and planogram teams can work together.
Moreover, dunnhumby Assortment uses industry-leading artificial intelligence (AI) to create “space aware” recommendations. Machine learning methodologies help to ensure that—as well as being customer-centric and commercially viable—assortments work within the physical constraints of a specific store as well. That leads to greater efficiency and better results.
But what if you knew that—while your category was shrinking—your brand was growing? Or, as a retailer, what if you found that one of your categories was growing when it was shrinking at other banners? That is insight, because it gives you a position from which you can act.
Unanchored from anything else, raw data tends to be unhelpful at best—and actively misleading at worst. So, while more insight can drive greater success in store, more data is very unlikely to.
At dunnhumby, we focus on finding the balance between what’s best and what’s achievable.
Would you like to know how dunnhumby Assortment can help you turn data into actionable insights? For more information visit dunnhumby Assortment.
Create customer-centric ranges using AI-powered science
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