Identifying contacts who are likely to take a specific action in the future or what factors motivates contacts to make a purchase is an important addition to your segmentation and filtering strategy.
Review the types of predictive filters you can use to enhance your campaign in the Predictive Models in Your Filtering and Segmentation article.
These filters can be used in many places across the Listrak Platform, such as:
Message-level filtering
Dynamic Content
Split Testing
Contact Filtering
Automated Campaigns
Below are a few suggested strategies you could use when adding predictive analytics to your segmentation and filtering strategy.
Message-Level Filtering
Identifying customers who have the largest predicted spend value within the next 12 months (Predicted Lifetime Value), allows you to message these potentially valuable customers in a unique way. Sending this group of customers messages thanking them for their purchase or providing a special incentive nurtures these relationships.
Learn more about adding predictive filters to your messages here.
Recurring Automated Campaigns
Messaging those most likely to make a purchase allows you to maximize your return from an email. For example, create a Recurring Automated Campaign for this group and leverage recommendations to include products from their preferred brand, category, or subcategory to create targeted messaging.
Dynamic Content
Dynamic content allows you to send personalized content to groups of contacts without building an entire email for each group.
Easily target contacts based on their stage in the customer lifecycle using the likelihood to churn predictive model. Increased urgency in your messaging, coupon codes, and other offers may entice at-risk or lapsed customers to return and make another purchase.
Click here to learn more about Dynamic Content in Listrak Composer.
Now that you've learned a few ways you can add predictive filtering to your segmentation and filtering strategy, you're ready to start building filters to enhance your marketing campaigns.