tl;dr
This report shows the relationship between your products based on your customers' purchasing behaviour
A Parent product is the primary product. Clicking the arrow next to it reveals a Relationship Table showing how all other products (Children) relate to it
Understanding these relationships helps you improve cross-selling, bundling, store merchandising, and AOV
New: Use the Bundled / Unbundled toggle to filter between bundled and standalone product data
How does it work?
This report lets you analyse how your products relate to each other based on customer purchasing behaviour. The layout becomes very intuitive once you understand the Parent/Child structure.
Choose your dimension
Use the dimension dropdown at the top left to choose how to group your products. Available options:
Product Title — groups by product name
Variant Title — groups by product variant
Product Title & Variant — combines product name and variant
Product Title (Checkout) — uses the product title at the time of checkout
Product Type — groups by product type
Product SKU — groups by SKU
Product Vendor — groups by vendor/brand
Product Title - Product Type — combines product name and type
The Parent / Child structure
You see an Overview Table listing all your products. Each row has an expand arrow ">" next to it.
Click the arrow to reveal a Relationship Table nested underneath — this shows how every other product (the Children) relates to the product you clicked on (the Parent).
Bundled vs. Unbundled
Use the Bundled / Unbundled toggle (top right of the table) to filter:
Bundled — shows data for products sold as part of a bundle
Unbundled — shows data for standalone product purchases
Date Range
For the Relationship Table (Children), Klar uses the full order history of those customers — before, during, and after the selected date range — to calculate relationship values. This gives you a more complete picture of how products are bought together over time.
What can I analyse?
Overview Table
This table shows how popular each product is with your customers:
Customers — how many customers have bought the product at least once
Orders — how many net orders contained this product
Total Units Sold — how many net units have been sold
Avg. Units per Customer = Total Units Sold / Customers
Repurchase Rate — % of customers who bought the product more than once
First Order Rate — % of new customer orders that contain this product
Repeat Order Rate — % of repeat customer orders that contain this product
Relationship Table
This is the core of the report. Click the ">" next to any Parent product to expand it and see how each Child product relates to it:
Bought by the same customer — % of customers who bought both the Child and Parent product (in the same or different orders)
Bought in the same order — % of customers who bought both in the same order
Bought together in first order — % of customers who bought both in their very first order
Bought directly after — % of orders containing the Child product placed by a customer directly after ordering the Parent
Bought after — % of all orders containing the Child product placed by a customer after ordering the Parent
What can I analyse:
Let's look at both tables in this report, the Overview Table and the Relationship table, and go over the metrics that they contain and what they mean.
Overview Table
This table gives an overview of how popular you different Products are with your customers and when they are mostly bought. Let's go over the metrics:
Customers - how many of your customers have bought the product at least once.
Orders - how many of your net orders contained this product at least once
Total Units Sold - how many net units of the product have you sold already.
Avg Units per Customer - Total Units Sold / Customer
Repurchase Rate - the percentage of the customers that bought the product, that bought it at least one more time.
First Order Rate - the percentage of orders from New Customers that contain this product
Repeat Order Rate - the percentage of orders from Repeat Customers that contain this product
What can I use this for?
Identify products to bundle
Products frequently bought together — or one directly after the other — are prime bundling candidates. A well-chosen bundle drives AOV and makes the purchase decision easier for the customer.
Refine your cross-selling and up-selling strategy
Use the relationship data to design targeted post-purchase communication. If you know that customers who buy Product A are likely to buy Product B next, you can automate that recommendation.
Increase the effectiveness of your merchandising
If your data shows that customers buy high heels and dresses together, create a collection featuring your newest items from both categories and promote it to that audience.
Analyse the impact of new product launches
A product designed to attract new customers should show a high First Order Rate and a low overlap with existing products. If it appears mostly in repeat customer orders alongside established products, it's not reaching the new audience you intended.
Find unusual relationships
Unexpected product pairings can surface genuine insights about your customers. Follow up on these through customer surveys or interviews — unusual behaviour often reveals things your data alone can't explain.