Overview

Follow this guide to learn how Elevar generates and delivers Comeback Score as a Boosted Event. This article explains the data used to train the predictive model, how customers receive a score, and how the signal can be activated in Meta and Klaviyo.

Required Inputs for Comeback Score:

Comeback Score is generated using historical Shopify data, including enriched customer- and order-level information. Elevar securely uses this data to train a model that identifies purchasing patterns unique to your brand.

How Comeback Score Works in Elevar

How Elevar Generates Comeback Score:

Elevar uses a predictive classification model to estimate whether a first-time buyer is likely to make another purchase within 90 days. The model produces a return probability, which is then converted into a binary score:

1- The customer is likely to return within 90 days.
0- The customer is unlikely to return within 90 days.

How Comeback Score Is Delivered as a Boosted Event:

After the score is generated, Elevar delivers it as a custom Boosted Event to connected platforms such as Meta and Klaviyo. Because the score is delivered through Elevar’s existing infrastructure, marketers can use it to build audiences, trigger flows, apply exclusions, or suppress campaigns without developing their own predictive model.

How Platforms Use Comeback Score:

Comeback Score allows platforms like Meta, GA4, and Klaviyo to act on a customer’s predicted likelihood of returning.

In Meta, marketers can suppress Score 0 customers from retargeting audiences to reduce spend on buyers who are unlikely to return. Score 1 customers can also be used as a seed audience for prospecting lookalikes based on customers who demonstrate a higher likelihood of repeat purchasing.

In Klaviyo, Score 1 customers can enter dedicated post-purchase flows designed to encourage a second purchase through loyalty incentives, product recommendations, or early access offers. Marketers can also reduce unnecessary discounts for customers who are already likely to return and reserve stronger win-back offers for Score 0 customers.

Use Cases:

Performance Marketing

Stop spending on buyers who are unlikely to return: Suppress Score 0 customers from retargeting audiences to avoid spending budget on one-time buyers with a low likelihood of returning. This allows marketers to redirect spend toward higher-potential customers.
Seed lookalike audiences with your best customers: Use Score 1 customers, your highest-likelihood returners, as the seed audience for prospecting lookalikes. These customers already demonstrate behavioral patterns associated with long-term value.

Lifecycle Marketing

Nurture potential buyers toward a second purchase: Trigger a dedicated post-purchase flow for Score 1 customers that includes upsells, loyalty incentives, or early access to new products. These customers are statistically more likely to purchase again.
Protect margins by reducing unnecessary discounts: Score 1 customers are already likely to return, so offering a large discount immediately after purchase may reduce margin unnecessarily. Suppress deep discounts for high-likelihood returners and reserve stronger offers for Score 0 win-back campaigns.