Using Klaviyo's AI-powered Predictive Analytics, it is possible to build segments to target customers by:
- Historic CLV: the total value of all a customer's past orders.
- Predicted CLV: a customer's predicted spend over the next year.
- Total CLV: the sum of a customer's historic CLV and predicted CLV.
- Predicted number of orders: the predicted number of orders a customer will place over the next year.
- Average number of days between orders: the average number of days between past orders for customers who have placed at least two orders. This average doesn't include the time from the last purchase to the current day.
This allows you to bucket your customers based on CLV and use this information to send campaigns and trigger segment-based flows.
For example, you may want to use historic CLV to build a VIP welcome flow or use predicted CLV to send targeted campaigns to customers who are predicted to spend a certain amount over the course of a year.
To learn more about how these predictions are calculated, check out the blog announcement from our Data Science team.
Please note that you will only be able to segment based on CLV if:
- At least 500 customers have placed an order. This does not refer to Active Profiles, but rather the number of people who have actually made a purchase with your business. If this section is on a profile but is blank, this means we don't have enough data on that individual to make a prediction.
- You have an ecommerce integration (e.g. Shopify, BigCommerce, Magento) or use our API to send placed orders.
- You have at least 180 days of order history and have orders within the last 90 days.
- You have at least some customers who have placed 3 or more orders.
Create a CLV Segment
To create a segment based on any of the above CLV properties, use the Predictive analytics about someone condition. Then, you can select your desired metric and value.
For example, let's say your average order value for customers is around $15. You may want to target customers who are unlikely to reach this average order value with discounts to try to push them towards their next purchase. To accomplish this, you can create a segment of customers who are predicted to spend no more than $5 in the next year and target them with a discount campaign or flow -- similar to a winback or re-engagement campaign.
Export CLV Segments
Exporting CLV data can allow you to further analyze and predict the behavior of different groups of customers. In addition to the CLV values listed above, you'll also be able to export Churn Risk Prediction. Churn risk will be exported into your CSV as a number between 0 and 1. For example, 0.45 would correspond to a 45% churn risk.
If you have a large number of one-time purchasers, you may have a high average churn risk. To lower your average churn risk, you may want to focus your marketing efforts on retaining customers after their first purchase. You can use Klaviyo's Predicted CLV metric to identify people who are not likely to purchase again as outlined in the example above. For more information on how churn risk is calculated, check out our blog post.
Once you export this data as a CSV, you'll be able to run your own analyses. Some calculations you may be interested in include:
- Average CLV: You can calculate the average customer value of a segment by averaging Historic CLV and Total CLV.
- Predict future spending of a segment: Sum the Predicted CLV of all the members of a segment and you will get the expected revenue from customers in this segment for the next year.
- Estimate the number of returning customers: First, average the values for Churn Risk Prediction. Then, subtract this average from 1. Multiply the result by the number of people in the segment. This will yield the number of customers who are predicted to return.