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Framework #10

Ad spend drives sales of products that come back

Connect store returns data with your product feed and a custom label. Separate products with high return volumes to control their Google Ads budget and use the segmentation in your Meta Ads catalog.

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#10

High-return product control

Feed label
cl_returns
Required data
IdoSell
Type
Products
Framework #10

Why does it matter?

  • A purchase is not the end of a sale

    Returns reduce revenue and complicate profitability analysis. High sales do not necessarily mean a good business outcome.

  • Returns create additional costs

    Handling and storing returned products add costs for the store. Scaling advertising can make the problem larger.

  • The cause may be in the offer

    Defective products, inaccurate descriptions or sizing information need correction. Changing bids alone does not remove the cause of returns.

cl_returns

How to diagnose the problem?

Connect your store platform through an API and open Views → [store platform] → Returns.

  1. 1

    Choose a date range covering at least the last 90 days.

  2. 2

    Sort by returned product quantity to identify products with the most returns.

  3. 3

    Compare changes against the previous period and set a threshold requiring control.

cl_returns

Frameworks #6: How to take control of products with a high return rate?

Framework #10

Action strategy

  • ASave the segment in the feed

    In Models → Products, configure an available custom label for products exceeding your return threshold. Leave other values empty, run mapping and refresh the feed.

  • BLimit budget exposure

    Separate labeled products into a low-budget PMAX or PLA campaign and exclude them from other campaigns. If they generate losses, pause promotion until the cause of returns has been investigated and resolved.

KPI

What to measure?

  • Products in the label

    Check how many products exceed the chosen return threshold.

  • Returned units

    Monitor return volumes at product level.

  • Change against the previous period

    Assess whether the problem is growing, declining or remaining stable.

What if results don't improve?

Evaluate after at least 4–6 weeks, allowing for the return window and advertising changes to take effect. If the problem persists, inspect the product, description and sizing before increasing promotion again.

Returns segmentation helps limit spending on problematic products, but it should accompany profitability analysis and action on the business cause of returns.

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Frequently asked questions

Does a high number of returns mean a high return rate?

No. A bestseller may have many returns simply because it sells at scale. In Data Octopus, analyze volume in the context of sales rather than treating it automatically as a percentage rate.

Is there one correct return threshold for every store?

No. The threshold should reflect your assortment and sales scale. In Data Octopus, set it in the segmentation rule rather than treating an example value as universal.

Does the returns label need a specific name?

No. The source does not prescribe a fixed label value. In Data Octopus, choose a clear name and use the same value in campaign filters.

Does a smaller labeled segment prove the problem is fixed?

Not always. Reduced advertising may simply lower sales and subsequent returns. In Data Octopus, compare segment changes with sales volume and investigate the causes of returns.

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