The 3 Types of Customer Segmentation
It’s clear there has been a shift towards more “customer centric” business models. While this seems like an obvious strategy for any brand, execution frequently proves to be a challenge. It’s great to want to cater to your customers’ wants and needs, but when it boils down to it, it’s difficult to truly understand a single customer among the masses.
One technique that has proved effective for marketers to develop deeper insights about their customers is segmentation — i.e., breaking down a massive customer base into smaller subgroups based on a variety of factors. Using segmentation, you can better predict what sorts of products and promotions will resonate best with which groups of people.
There are several popular ways to segment customers: life stage, lifestyle, and RFM (recency, frequency, monetary). Each is useful, and combined they give a comprehensive view of the customer as an individual.
- Life stage segmentation deals with where a person is in the course of their life. Someone who is married with five children is likely to purchase different products than someone who is just entering college. While you can’t learn everything you need to know about someone based on where they are in life, it's a good base for profiling.
- The next type of segmentation is lifestyle. While the mother of five and the college student mentioned above may be wildly different in their day-to-day habits, they could both be incredibly environmentally conscious — only purchasing from companies that are eco-friendly. Understanding the way that customers live their lives is very important when it comes to supplying them with offers and promotions that they will find relevant. Sending the mother or the student a promotion for eco-friendly laundry detergent would probably lead to a purchase, whereas a coupon for cheaper — but more toxic — detergent would not.
- The final type of segmentation is RFM: recency (how recent was a customer's last purchase), frequency (how often do they purchase), and monetary value (how much they spend). This data helps determine the value a customer brings to an organization. In order to create these segments, one must first turn their customer data into scores and then those scores into segments. These segments can be used to identify your most valuable customers, and to tell you who is at risk of churn. They can also be used to glean insights about the demographic similarities of high-value customers which, in turn, can predict who is likely to generate the most revenue — before they make purchases.
Understanding your customers’ needs based on preference, life stage and purchase history is critical to the success of any marketing campaign. Customer segmentation can help businesses target their messaging to generate the highest return on investment.
Abhi Yadav is the co-founder and CEO of Zylotech, an artificial intelligence platform for customer marketing.
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