Personalization

Using our Analytics API, customer profiles are built by associating a unique customer identifier with various events. These events include page views, product clicks, and purchases, which are captured and sent through our analytics API.

The information gathered from these events is used to create a profile for each customer, enabling us to display personalized product recommendations.

Relevancy

Relevancy is a key factor in tailoring the customer experience. For each event, we assess its impact by assigning a weight to relevant measures and dimensions. These measures and dimensions are configured in the app to segment customers based on specific attributes. The personalization matrix in our demoshop visually represents these segments.

Half life

The weight of each measure or dimension is influenced by the frequency of events associated with them. To ensure that our recommendations remain relevant, we apply the concept of half-life to these weights. Over time, the relevancy of a measure or dimension decreases according to its half-life.

If the relevancy of a measure or dimension falls below a threshold of 0.0001, it is considered no longer significant. At this point, the measure or dimension is removed from the personalization matrix.

This approach ensures that only the most relevant attributes are used in creating personalized experiences for our customers.

Profile retention

Profiles are automatically deleted after a certain time of inactivity. The retention of profiles is determined by the maximum number of profiles allowed in your account. For instance, if your account allows up to 100,000 profiles, the addition of a new profile will trigger the deletion of the longest inactive profile once this limit is reached.