> For the complete documentation index, see [llms.txt](https://docs.abtasty.com/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.abtasty.com/commerce/recommendations/analyse-your-recommendation-strategy-performance.md). # Analyse your recommendation strategy performance ## Overview By the end, you will be able to: * Identify how much revenue your strategies are directly contributing * Understand how visitors are moving through your recommendation funnel * Spot which products, categories and strategies are driving the most impact * Adjust the report to focus on the data that matters most to you {% hint style="info" %} The report is populated from real visitor and transaction data. If your strategies were recently activated, give them a few days of data before working through this tutorial. {% endhint %} ## Analyse your recommendation strategy performance {% stepper %} {% step %} #### Open the report and set the date range Navigate to **Reporting** from the main navigation.
At the top of the page, check the **Last refresh** timestamp to confirm how recent the data is. Next, look at the date range picker in the top-right corner. It defaults to a 30-day window. For your first read-through, keep the default so you have enough data to see trends clearly.
{% endstep %} {% step %} #### Checking the Direct revenue contribution The performance overview section sits near the top of the report. The **Direct contribution estimate** banner shows an estimate of how much revenue your recommendation strategies helped generate.
Look at the four figures in the banner: 1. **Revenue helped:** the estimated euro value of sales influenced by a recommendation interaction, within your configured attribution window. 2. **% of total revenue:** the portion of your overall revenue generated from those influenced sales. 3. **Transactions helped:** the number of orders that involved a recommendation interaction. 4. **% of total transactions:** the proportion of all orders that included a recommendation interaction. {% hint style="info" %} Each figure shows a trend indicator next to it. An upward arrow means performance improved compared to the previous period; a downward arrow means it declined. {% endhint %} **Part of the catalog** displayed to the right of the banner indicates the percentage of products from your catalog that appear in at least one recommendation.
{% endstep %} {% step %} #### Analyzing visitor engagement with recommendations Scroll down to the **behavioral analysis** section. Two charts are displayed side by side.
1. **Visitor exposition rate:** shows the percentage of all site visitors who were shown at least one recommendation. The average value appears above the chart, while daily bars and a trend line display how this metric evolves over time. 2. **Exposed visitors click-rate:** shows the percentage of those visitors who clicked on a recommended product after seeing a recommendation. Interpreting these charts: * A **high exposition rate** combined with a low click-rate indicates that recommendations are frequently displayed, but the products shown are not compelling enough to generate clicks. * A **low exposition rate** indicates that your strategies are not reaching enough visitors. Verify that your strategies are deployed on the appropriate pages across both client-side and server-side campaigns. {% endstep %} {% step %} #### Follow the conversion funnel
To the right of the charts, find the **Conversion funnel**. It shows three stages: 1. **Product click**: visitors who clicked a recommended product. 2. **Add to cart**: how many visitors (who clicked on a recommended product), added the product to their cart (shown as a percentage of clicks). 3. **Transaction:** of those who clicked, how many visitors completed a purchase. Below the funnel, the transaction rate shows the overall purchase rate for visitors exposed to recommendations. {% hint style="info" %} A large drop between ***Add to cart*** and ***Transaction*** may indicate friction in your checkout flow. \ \ A large drop between ***Product click*** and ***Add to cart*** may suggest the recommended products are interesting to browse but not compelling enough to purchase {% endhint %} {% endstep %} {% step %} #### Compare visitor segments in the key indicators table
The key indicators table compares three visitor segments: 1. **All visitors**: everyone on your site 2. **Exposed visitors**: those who loaded a page with an active strategy 3. **Engaged visitors**: those who clicked a recommended product Look at the **Transaction rate**, **Revenue per visitor**, and **Average order value** bar charts below the table. Engaged visitors should show noticeably higher numbers across all three metrics. This will help demonstrate the value of moving visitors from passive exposure to active engagement. {% hint style="info" %} If engaged visitors show a much higher revenue per visitor than all visitors, your recommendations are working well once visitors interact with them. If the gap is small, it may suggest that engaged visitors were already high-intent buyers regardless of recommendations. {% endhint %} {% endstep %} {% step %} #### [Reviewing merchandising insights](#user-content-fn-1)[^1] In the **Merchandising Insights** section, you will see five tabs for: **Top 25 products**, Pinned products, **Top 25 categories**, **Top 25 brands** and **Top 25 Strategies**.
1. Start with **Top Products**. The table shows each product's displays, click-through rate, add-to-cart rate and revenue contribution. 2. Then click **Top Strategies** to see which individual strategies are driving the most revenue across all your campaign types: Web, Personalization, and Feature Experimentation in a single ranked view. {% hint style="info" %} If a product has a high display count but a low click-through rate, it may be overexposed or placed in an irrelevant context. Click **Export data** to download any table for further analysis. {% endhint %} {% endstep %} {% step %} #### Filter the report to a specific strategy or device
Narrow the report to investigate a specific area. Click **Filter** in the report header. A panel will open with four options: * **Locations**: filter by where on your site the strategy is placed. * **Strategies**: select one or more strategies to isolate their performance * **Product categories**: focus on a specific product category * **Devices**: compare performance on desktop vs. mobile {% hint style="info" %} Try filtering by a single strategy that caught your attention in Step 6. The entire report, all charts and tables, will update to reflect only that strategy's data. To return to the full view, click **Reset filters** at the top of the filter panel. {% endhint %} {% endstep %} {% step %} #### Adjust your attributions settings (optional) If the Direct contribution estimate in Step 2 seemed unexpectedly low or high, review your attribution settings. Click **Accuracy settings.** * **Attribution method:** controls how long after a recommendation interaction a transaction is still counted as influenced. The default is 24 hours. If your products have a longer purchase cycle, switching to 7 or 30 days will give a more complete picture. * **Cookie consent rate:** controls how the platform extrapolates data from consented visitors across your full visitor base. If you know your site's actual cookie consent rate, enter it here for a more accurate estimate. * After making changes, click **Save changes**. {% endstep %} {% endstepper %} [^1]: review top products and strategies --- # Agent Instructions This documentation is published with GitBook. GitBook is the documentation platform designed so that both humans and AI agents can read, navigate, and reason over technical content effectively. Learn more at gitbook.com. ## Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question. Perform an HTTP GET request on the current page URL with the `ask` query parameter, and the optional `goal` query parameter: ``` GET https://docs.abtasty.com/commerce/recommendations/analyse-your-recommendation-strategy-performance.md?ask=&goal= ``` `ask` is the immediate question: it should be specific, self-contained, and written in natural language. `goal` is optional and describes the broader end goal you are ultimately trying to accomplish on behalf of the user. GitBook uses it to tailor the answer towards what is most useful for that goal. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.