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The Customer Data Grid: a sales-oriented segmentation

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Several weeks ago, we discussed the four dimensions of customer value as a way to track and measure opportunities among your customers. Using a number of dimensions for value helps you understand why customers are valuable, and as a result, which ones you should prioritize. As a brief recap, customers who bring in revenue are wonderful but not the only ones your sales team should be reflecting on. Loyal customers imply regular revenue, while customers not spending much money can still engage with your brand (think about the cross-sell and up-sell opportunities) or tell their friends (if they have positive, happy experiences).

With this in mind, we introduce the next iteration of our research and analysis: the Customer Value Segmentation. This segmentation is driven by the four dimensions of customer value, and pave the way for simple, business-oriented rules to help with your marketing campaigns and sales planning. The grid is illustrated below:

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As with the four dimensions of value, customers are labelled as positive or negative in each of the four value categories. Using these values, we answer the following four questions about every customer:

  • Valuable or Wistful? Customers can be seen as valuable (i.e., spending large amount of money with your company) or wistful (i.e., never spending any and as such, and thus left wanting).
  • Loyal or unloyal? Customers who purchase regularly are seen as loyal customers, while unloyal ones have irregular or rare purchasing patterns. Note that valuable customers can be loyal or unloyal, depending on how they purchase and depending on your specific products or industry.
  • Engaged or disengaged? Engaged customers reach out on a regular basis by calling you for help or customer support. Alternatively, they are active in public fora, discussing your brand and products. Disengaged customers, on the other hand, are ones you rarely hear from.
  • Positive or negative? Finally, we analyze the actual sentiment of each customer to build a picture of whether or not they are happy with their experience, as it relates to your company.

Given the above, you can label each customer as one of two categories (a good or a negative one) for each type of customer value measure. With this approach, you can group your entire customer base across the four metrics, and as a result, get a segmentation based on the different ways that your customers generate value for your company. Below is an example for a fictional company.

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In this specific case, we see that 29% of the company’s customers are in the dark green square on the top left. These customers are engaged, valuable, loyal, and have positive sentiment towards the brand — in other words, they are the happiest and best customers one can hope for. Alternatively, review the orange 1% in the bottom row — these customers are unloyal, not valuable, and disengaged with the brand; their one saving grace is they have positive sentiment to the company. It is clear that something is holding the back from making a purchase, even though they seem to have  positive views about the company.

The power behind this framework is its ability to be industry agnostic — notice that in the above example, we never had to worry about the industry we are working in. Furthermore, it provides us with an ability to actually investigate what to do next. In the case of the “Orange 1%” above, we know we can build on the positive sentiment the group has towards our brand and eventually actually make a sale. Other segments have  different types of challenges and ways to capture value.

It is with this approach that our platform helps generate specific business rules and recommendations for our customers and clients, and helps drive improvements in customer value and wellbeing. Do you use a similar approach? Let us know!

2 Responses to “The Customer Data Grid: a sales-oriented segmentation”

  • Paul Hermann

    - Reply

    The dark green square is in the top left my dear Wojciech… I hope you wrote better slides ;)

    • Wojciech Gryc

      - Reply

      Thank you kindly, Paul. Edited and fixed!

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