Creating an Optimizer
The main page displays a grid of all your existing optimizers. To add a new one, click the Add New Optimizer button.
You will then be directed to the optimizer creation form, which contains the following general configuration fields:
| Parameter | Default value | Description |
|---|---|---|
| Enable optimizer | Yes | Toggles whether the optimizer is active and applied to the storefront. |
| Store View | (Empty) | Defines the scope of the optimizer. An optimizer can only be assigned to a single store view. |
| Model | Constant | Determines the scoring mechanism used by the optimizer: • Constant: Applies a fixed score multiplier. • Based on attribute value: Applies a dynamic boost proportional to a specific product attribute’s value. • Based on behavioral data: Applies a dynamic boost proportional to a selected behavioral metric (e.g., views, sales). |
| Optimizer Name | (Empty) | The internal name of the optimizer for administration purposes. |
| Active From | (Empty) | The start date for the optimizer. Useful for scheduling temporary merchandising events or sales campaigns. |
| Active To | (Empty) | The end date for the optimizer. |
| Request Type | (Empty) | Defines the specific storefront context where this optimizer will be applied: • Catalog Product Search: Standard catalog search results page. • Catalog Product Autocomplete: Product suggestions in the autocomplete dropdown. • Category Product View: Standard catalog category navigation pages. • Quick Order Suggest Search: Product results in the quick order suggestion search. • Related Products: Products displayed in the “Related Products” blocks. • Upsell Products: Products displayed in the “Upsell Products” blocks. • Cross-sell Products: Products displayed in the “Cross-sell Products” blocks. • Visitor Products: Products displayed in visitor-specific recommendation blocks. |
Rule Configuration
This section features a flexible rule editor allowing you to define the exact conditions products must meet to be affected by the optimizer.
Examples of rules you can create:
- “Color is Blue”: Apply a boost exclusively to products where the “Color” attribute is “Blue”.
- “Only discounted products”: Apply a boost to products currently featuring a special price.
- “Only in stock products”: Push immediately salable products to the top of your product lists.
Optimizer based on metric (behavioral data)
How this works This model allows you to apply a dynamic relevance boost that scales proportionally based on real user interactions (behavioral metrics collected by the tracker).
| Parameter | Default value | Description |
|---|---|---|
| Metric | (Empty) | Select the specific behavioral metric the boost will be based on. Options include views (daily/weekly/total), sales (daily/weekly/total), conversion rates, and revenue (daily/weekly/total). |
| Boost impact | (Empty) | Defines the mathematical function used to scale the boost. Options are Low (logarithmic), Medium (square root), or High (linear). |
| Metric value pre-multiplier | (Empty) | (Formerly named “Boost value”). A constant multiplier applied to the metric’s value before the Boost Impact function calculates the final score multiplier. |
| Allow negative boost | No | Controls whether products with very low metric values are actively penalized. • No (Recommended): Products with low metric values (which mathematically result in a score multiplier < 1) are safely excluded from the optimizer, preventing them from being artificially pushed down the results page. • Yes: Reverts to legacy behavior where a low metric value can mathematically reduce a product’s overall relevance score. |
Calculation Examples Assuming you set a Metric value pre-multiplier of 5 (formerly named “Boost value”), here is how the final product score multiplier is calculated based on a product’s behavioral metric (e.g., total views, sales, etc.):
- Low Impact
log(metric value * pre-multiplier):- Metric = 100 ➔ score multiplied by
log(100 * 5)= 2.70 - Metric = 5,000 ➔ score multiplied by
log(5000 * 5)= 4.40 - Metric = 8,000 ➔ score multiplied by
log(8000 * 5)= 4.60
- Metric = 100 ➔ score multiplied by
- Medium Impact
√(metric value * pre-multiplier):- Metric = 100 ➔ score multiplied by
√(100 * 5)= 22.36 - Metric = 5,000 ➔ score multiplied by
√(5000 * 5)= 158.11 - Metric = 8,000 ➔ score multiplied by
√(8000 * 5)= 200
- Metric = 100 ➔ score multiplied by
- High Impact
metric value * pre-multiplier:- Metric = 100 ➔ score multiplied by
100 * 5= 500 - Metric = 5,000 ➔ score multiplied by
5000 * 5= 25,000 - Metric = 8,000 ➔ score multiplied by
8000 * 5= 40,000
- Metric = 100 ➔ score multiplied by