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Skubl

Platform

The predictive layer behind every shopper’s experience

Skubl starts with your store data, predicts what each shopper is likely to do and be worth, and uses those predictions to choose the pages, products and offers they see. Every change is measured against a holdout.

This page explains the parts underneath Skubl’s ecommerce personalization.

Illustrative example · sample store

Inside the dashboard

Your store, measured honestly

Revenue, orders, AOV, repeat rate and returning revenue from your synced orders, each with its definition. Anything Skubl can’t measure yet says so instead of guessing.

Skubl overview dashboard with revenue, orders, average order value, repeat rate and a performance trend chart
The Skubl dashboard, shown with a sample store’s data.

The predictive models

Every shopper profile carries these scores, built from your store’s own customer and order data.

Likelihood to buy
How likely a shopper is to purchase in this session or soon after, used to decide when an incentive is worth offering.
Churn risk
How likely a customer is to stop buying, so you can act before they lapse.
Predicted lifetime value
How much a customer is likely to spend over time, so high-value shoppers get premium treatment from their first visit.
Audience scores
Scores that place each shopper in the audiences that matter to your business.

Platform FAQ

What data does Skubl use?

Products, customers and orders from your store through the native Shopify connector, plus on-site behavior recorded by the Skubl pixel (pageviews, clicks, scroll depth and conversions). See ecommerce CDP for how profiles are built.

How are predictions used?

Predicted likelihood to buy, churn risk and lifetime value decide which content, products and offers each shopper sees, within the rules you set.

See the predictions on your own store

Connect your store to see likelihood to buy, churn risk and predicted lifetime value for your customers.

Prefer a walkthrough? Book a demo