Customer Segmentation Definition
Customer segmentation is a process of dividing a customer base into smaller groups (segments) with shared characteristics. Depending on the amount of customer data you possess, you may utilize demographic data, customer preferences and past behaviors, or even zero-party data (e.g., submitted via a quiz) to perform the segmentation.
Customer segmentation is a good starting point for building a marketing persona (a personificiation of a targeted customer group), which will allow a brand to adjust its messaging, positioning, and offering on the basis of the discovered target audience traits and preferences.
In Voucherify, customer segments can be:
- Static – always contains the same number of customers. Customers are added to the list once and remain in the segment until they are manually removed.
- Auto-updated – customers dynamically join or leave if they match a given filter. If a customer's property is updated and matches the filter, the customer will automatically join the segment.
How to build a customer segment?
To find a proper segmentation strategy, you need to take a deep dive into the CRM and other data sources to choose segmentation criteria and filters. Note that advanced customer segmentation typically requires a combination of many filters. Some segmenting examples include:
- Demographic segmentation – based on customer age, gender, family status, or occupation. Note that this type of targeting may be considered intrusive if customers are not aware of the source of your data.
- Geographic segmentation – based on a postal code, city, state, or exact geolocation (if customers agreed to geo-tracking).
- CLV segmentation – customer lifetime value (CLV) is a good basis for targeting high-value customers. You can base this kind of segmentation on the estimated revenue per month or year.
- Activity segmentation – you can base the segments on your sales data. For example, the number of orders or the amount already spent.
- Preferences segmentation – again, you may utilize order data to find patterns in customer purchases and find products they buy most often.