Boosting
Last updated
Last updated
In addition to precisely pinning items to a specific position, you can also change the order in which non-pinned items appear. In particular, for large results sets, automated boosting is a great way to fine-tune your Search or Category pages and increase click-through rates, conversions, margins, or other business metrics.
Filter boosts are a great way to promote and personalize results.
A filter boost has two fields, a Filter expressions and a boost score. The boost score has a value between 0 and 1, with 1 being the maximum available boost.
Results that match the filter expression receive a boost according to the given score.
Example
Assuming you want to promote Nike shoes on your "shoes" category page. The following Filter boost will boost Nike shoes by a score of 0.8.
But filter boosts aren't limited to pre-defined values like "nike" in the example above. Instead, the filter expression can reference dynamic parameters passed into the search.
To personalize results, you can pass information about the user, such as brand affinity, into the search query and improve the search results based on that data.
In the above query example, we pass a variable called "brandAffinity" to the search query. We can reference the variable in the filter boost like so:
Range Boosts can promote results that fall within a specific numeric range. Boosting is applied linearly between the start and the endpoint. For example, use Range Boosts to boost highly rated products, popular products, or inventory to be sold.
Example
Assume that you have a rating field that ranges between 0-5. You want to boost values between 2-4 linearly, where 2 receives the smallest boost and 4 (and above) the maximum boost. Thus, ratings between 0 and 2 will receive no boost.
The configuration for this would look as follows: the score boost of 0.4 is the maximum boost applied to values of 4 and above.
Filter | Score |
---|---|
Filter | Score |
---|---|
Field | Start | End | Score |
---|---|---|---|
brand = "nike"
0.8
brand = brandAffinity
0.7
rating
2
4
0.4