Once you have captured customer analytics, the next step is to define groups of customers based on the data you captured. Customer segmentation refers to the process of placing your customer population into cohorts based on their attributes, interests, and behaviors.

Demographic segmentation

Demographic data includes basic attributes of each customer, including age, gender, language, geographical region. If you're collecting data from a mobile app, you might also capture information about each user's mobile device, including the device manufacturer, platform, and OS version.

You can define customer segments based on one or several of these attributes. For example, you could define segments like the following:

  • Males between the age of 20 and 35
  • Customers who speak English
  • Customers whose mobile devices run iOS 10 or later
  • Tablet users

Interest-based segmentation

If your application or web site collects information about customers' interests, you can use that data to create powerful segments. In our example application, All Things Sports, we might create the following types of segments, based on the information we collected during the sign-up process:

  • Football fans
  • Customers who are interested in live score updates
  • Customers who want to be notified of special offers for event tickets

You can create even more specialized segments by using both interest data and demographic data to create your segment, as in the following examples:

  • Male Seattle Seahawks fans between the ages of 20 and 35
  • Spanish-speaking soccer fans in the United States
  • iPhone users who are interested in receiving live score updates

Engagement-based segmentation

Customers have a wide array of options in today's marketplace, and users are likely to abandon your product unless you take proactive steps to understand their needs. At the same time, your continued success depends on loyal, revenue-generating customers. Both of these groups can be tracked using engagement metrics.

Your segments become especially powerful when they incorporate engagement metrics. Segments that use engagement metrics typically focus on a customer's lifetime in your app, their retention in the app, and their purchasing behavior. In our All Things Sports app, we might create the following engagement-based segments:

  • Customers who signed up in the past week
  • Customers with multiple sessions per day
  • Customers who have logged in and made a purchase within the past month

In order to be effective, these segments must be dynamic. In other words, if you look at any of the segments listed above one week, and then look at the same segment the following week, the list of customers who belong to those segments should be different. You can calculate these segments yourself, but it is far more effective to use a third-party tool to automatically refresh the list of customers based on the definition of the segment.

Customer segmentation with Amazon Pinpoint

Amazon Pinpoint helps you understand your customers' actions through advanced analytics, and helps you create segments based on demographics, behaviors, or other key performance indicators (KPIs) that are important for your application.

Amazon Pinpoint enables this segmentation through a set of filters driven by user engagement data, data imported from external sources, and data imported from other AWS services (such as Amazon S3 and Amazon Redshift). Amazon Pinpoint enables real-time, dynamic segmentation, which results in less work segmenting and more time building better products.

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