Case study: DATA & ANALYTICS

Suppliers: Learn How Big Data Analytics Justified Custom Retail Product Displays by Aligning with Demographics.

Case Study at a Glance


A Fortune 100 Consumer Packaged Goods (CPG) company


Our client wanted to determine how custom product displays would influence retail buyer decisions. Would they be able to convert more browsers into buyers by using demographic data that aligned closely with stores and regions to influence display configurations? Our client was considering producing custom displays but needed to justify the additional cost.


Vertex Data Scientists segmented buyers into groups and noted their purchasing preferences based on age, ethnicity, region, income, and other factors. By analyzing the store’s location, geography, and population demographics, Vertex built AI models that definitively showed how custom displays would positively influence buyers’ first moments of truth and impact buying decisions.


Our client began producing custom displays that matched regional and store demographics. Shoppers in a particular store are now able to find their preferred brands and assortments with greater ease. As retailers learned more about their buyers' preferences, they were able to adjust shelf space and displays to accommodate trends and increase sales across the product category.

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Do Custom Retail Displays Help Cut Through the Clutter of Product Choices? The Data Says You Can Bet On It.

Problem & Background

A lot of thinking goes into planning retail spaces that showcase the products competing for consumer attention. The first moment of truth occurs when a shopper pauses – or doesn’t – to notice your product. Shelf displays present suppliers and retailers a critical aspect of that first moment of truth and  influence buyer decisions as they browse aisles.

Our client, a global manufacturer of household products, had been using a one-size-fits-all display for its products in all stores in all markets. They suspected they could improve how they positioned their brands and variations considering demographics like age, ethnicity, and income as they pertained to brand assortments, sizes, and styles. Once these were determined, different displays could be planned to align with the findings.

Of course, creating display variations costs more than using one universal design. At what point does the cost of producing tailored displays generate profits beyond what the generic design would? And, how many different displays should be considered, realizing that each one adds to the cost.

Our customer approached us with the challenge of proving how product assortment and display affect sales, considering demographic trends and target groups. This job would require advanced data analytics and the ability to share visual representations of complex scenarios.

Challenge accepted.

The Vertex Solution

Vertex set out to discover that if store traits were considered and special planograms were designed to match regional demographics, the sales lift would justify the cost of multiple display designs. Working with existing retail POS data from our customer, demographics data from other market research conducted by companies like Nielsen, and the decision tree created by our customer’s shopper-based design teams set us on our course.

Vertex segmented shoppers into groups based on their demographics data including:

  • Income
  • Education
  • Life stage (young couples, early parents, empty nesters…)
  • Age
  • Ethnicity
  • Region
  • Presence of children in household

Target Demographics Data

But that wasn’t enough. Certain retail store traits also bore consideration like the store size and its footprint. Vertex analyzed point-of-sale data at each location, noting patterns and shopping preferences.

One thing stood out: Shoppers at certain stores demonstrated predictable buying patterns that were different than at other stores. By analyzing the store’s location, geography, and population demographics, Vertex data scientists built AI models that clustered stores into groups based on specific characteristics. A busy mother shopping at Walmart has different expectations than her counterpart in Kroger. What works well in Target may be lackluster at Meijer. What appeals to middle-aged shoppers in rural Kentucky doesn’t have the same attraction to millennial shoppers in urban Los Angeles.

Knowing a few things about consumer background in different stores helped suppliers and retailers design planograms to break through the clutter of choices confronting shoppers. Because our client’s portfolio offered numerous brands and products, Vertex plotted the strongest, most value-based assortment data on different dashboards to demonstrate the power of demographic profiles to derive business decisions. We shared this multi-array analysis with executive-level decision-makers. By using a single application interface, analysts were able to evaluate by retailer past performance and projected trends and impacts.

In one example, we demonstrated that a specific scent was more favorable in a region of the country for our client’s air fresheners. By being aware of this, our client could plan to send higher quantities and reserve more shelf space – as well as specify on the planogram where and how this item should be positioned.

By looking at a region, we could select the retailer and their regions’ attributes we wanted to explore. For instance, how many young parents vs. empty-nesters were there? What was the prominent ethnicity? Presence of children? We worked through a progression of all the variables and could draw accurate conclusions about buyer personas. We were able to cluster stores based upon buying preferences and show how one brand may have a broader appeal than another. A store in a high-income area would benefit by stocking premium brands while a low-income area should focus on value brands.

Target Demographic Map

This powerful knowledge helps determine the levels at which different brands are stocked on store shelves, where they are placed, and how much space they get. By increasing the “shoppability” of items at different stores, buyers can easily find the products they’re looking for. And that increases sales.


Over time, our predictions were able to show that our client could expect a sales lift when products and brands that appealed to a store’s demographics were positioned most favorably. The sales percentage could be forecasted. Our client could design displays that attracted their buyers quickly, minimizing moments of indecision when they could select a competitor’s brand.

Our supplier and its retailers have made better use of shelf space, allotting more room for the products and assortments that are in demand in those areas. Sales were projected to increase in the range of 105-120 IYA and predicted results were achieved in few categories it was rolled out.

Key Benefits

  • Brand and assortment preferences are better represented in stores and regions
  • Sales have increased in all markets
  • Custom product displays cut through competitor clutter
  • Shelf displays positively influence a buyer’s first moment of truth
  • Sales increase in the range of 5-20% in certain categories year over years

About Us

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