If you were playing retail buzzword bingo, the list would almost certainly include at least one of these words: “Retail Segmentation,” “Localization,” “Personalization,” or even “Store of the Community.” While these words all have a slightly different bend to the definition, they all essentially mean the same thing: actioning groups of stores or people uniquely based upon their individual preferences and needs. Most brands sold in retail–and every retailer–have at least one version of these words on their initiative list. Why? Because we all intuitively know that customers, consumers, and shoppers are all unique, can’t be bucketed simply by the retailer they shop, and need to be treated differently.
But, knowing intuitively that we should action uniquely against individual need and preference is different than knowing how to start the process, make it manageable, and prove effectiveness. To help with this, here are four critical lessons:
Lesson 1: Stereotyping is NOT a segmentation strategy
Every retailer has its shopper stereotype. Home Depot is personified by the suburban dad in his cargo shorts and DIY hubris. Best Buy is the youthful, hardware-savvy gamer. For America’s largest retailer, all it took was a viral blog for people to generalize the people of Walmart.
As marketers, you’d think we’d see through stereotypes, right?
Not always. Despite the extreme diversity among customers of America’s major retailers, we still see some brands treating “Walmart shoppers” like a strategic marketing segment.
The demographic range of major brick-and-mortar retail customers may surprise even the savviest media planners. Looking at Walmart, specifically, there are over 5,000 locations in the US, which often have dramatically different demographic skews based on location. So, even though Walmart positions itself as a value retailer, it would be a mistake to make all strategic decisions based on a retailer’s marketing proposition or assumed customer profile.
Both retailers, and the brands who sell through them, face this challenge. So, how can marketers properly account for the unique fabric of each store’s customers?
Lesson 2: Each neighborhood has a unique brand + audience chemistry
Without human context, sales data doesn’t tell us why customers buy. So, when each retail location performs differently, there’s a human answer that lies at the store level. That answer is rarely evident in brand sales reports.
To overcome this challenge, Callahan works with Esri to analyze every neighborhood in the US to understand the dominant segment, or tapestry, within granular area clusters like census tracts, block groups, zip codes and county subdivisions. In other words – it’s the human data.
So, rather than allowing ourselves to treat all Walmarts and their shoppers the same, we can now create a rich understanding of the people in each store-level neighborhood. Not just age and income, but cultural interests, family values and personal hobbies.
Lesson 3: Unify sales data and human insights to paint a complete picture
At some point in our career, we’ve all found ourselves staring at an Excel spreadsheet with market-by-market sales data and tried to find deep, profound meaning. You probably never found it. But forgive yourself; without human context, the story in a spreadsheet can only go so deep.
Our clients come to us quite a bit with similar situations. With one CPG partner recently, we discovered its DMA-level store groupings to be overly broad and misleading–and regrettably costly to the media budget.
When we applied neighborhood-level analysis, we learned that store performance within each DMA varied wildly. Large metro stores had previously appeared to be high performers, but the opposite was true. It was the suburban locations with lower population density that were driving the sales and erroneously lending attribution to their urban counterparts. And unfortunately, with DMA-driven media, the bulk of impressions were naturally skewing toward the city populations–far away from the high-performing suburbs.
To remedy this approach, we took a store-level lens to media targeting and messaging. It wasn’t regional geography that held the customer insight. It was household composition, family values, and neighborhood culture that were the true indicators of store-level sales performance.
In our Walmart example, we selected four of the top-performing segments that represented the values of the brand and were reflected positively in sales. From those, we built out a new media targeting strategy.
Lesson 4: When measuring results, isolate variables (test and control)
In our CPG brand example, we learned enough to improve several elements of our approach, including both targeting and messaging. But to measure properly, we changed one variable first: targeting. Rather than geo-target all stores, as we did with our previous campaign, we targeted just those whose neighborhoods matched our new demographic and psychographic sweet spot.
With that change alone, media impact on sales nearly tripled.
In prior campaigns, media had lifted sales by about 33% compared to control. But now, with neighborhood-specific targeting, sales lift jumped to 91%. This was a direct result of reaching people who were more likely to buy the product.
Looking ahead, we not only have the opportunity to be hyperlocal in our targeting. We can now apply neighborhood-level cultural context to targeting and be hyperhuman, too.
While we have shared our four critical lessons, the key to making retail segmentation effective is having the right mix of data to really know the human behind the number pattern. Without the human insight to support unique preferences and needs, segmentation almost always falls short on delivering results or is deemed too complicated to action, leaving segmentation only a buzzword. But, consumers now expect relevance from brands they love. With the disruption to consumer loyalty and trial at an all-time high, consumers expect brands to know them, serve them relevant content, and market to them as individuals. So, are you ready to meet their expectations?
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