Before we start looking at how Klevu ranks products as part of our native algorithm, let’s look at the data we index by default.
What do we index?
At the time of indexing product data, Klevu collects product information such as the product’s name, URL, price, categories, stock status, and a set of attributes associated with the product, etc.
How is relevancy calculated?
Each of the product fields referenced above carries a certain weight from a ranking perspective. For example, in our algorithm, the name field is given the top priority and carries more weight, followed by the other fields such as categories and the long description. There are default weights assigned, however, over time, our search algorithm learns and adapts these weights based on user behavior.
Depending on where the keyword appears in the product fields, Klevu calculates an overall relevancy score for each product.
What are the different boosting scores and what is their impact on the relevancy score?
As the name suggests, boosting scores are used for boosting relevancy scores - they are multiplicative parameters. In other words, whatever the relevancy score of a product, it is multiplied with the relevancy score to obtain a new, final, relevancy score.
There are different ways a product can be boosted. Admin users can use bulk product promotions functionality from KMC to boost multiple products by creating rules. In this case, if multiple rules apply to the same product, the one with the highest boosting score is used. The other method for boosting products comes from either passing a boosting score in the feed or by using the individual product promotions utility in KMC to assign a score to each product. In this case, the score manually assigned to a product overrides any score assigned to the same product by any bulk boosting rule.
Klevu's analytics engine also calculates boosting scores (a.k.a. self-learning scores). These scores are calculated based on the analytics data (e.g. clicks, checkouts, etc.) and define how popular different products are overall on the website and for individual search terms. Further, the Self-learning score will also consider the rating data if you have chosen to enable it. These self-learning scores are added to the manual boosts to calculate a final boost score for each product. Finally, when calculating the final relevancy score, these final boosting scores are multiplied with the relevancy score to give them a relative (to search relevancy) boost.
How do keyword-based product promotions and exclusions work?
Using the keyword-based product promotions utility in KMC, users can specify products they want to show first when a certain keyword is searched. In this case, the promoted products are shown at the designated positions in the search results. Similarly, using keyword-based product exclusions, users can remove certain products from the search results for specific keywords.
What is the default product sorting/ordering algorithm?
First of all, Klevu separates products by their stock status. The products that are available and in stock are placed at the top, followed by the ones that are out of stock. Within each of these sections, if there are products boosted at a keyword level, they are always boosted to the top positions. The remaining places in each set of search results are then sorted by their final relevancy score (i.e. after the boosting scores are multiplied with the relevancy score).
When using smart category navigation, what impact do self-learning and manually set promotions have on product ranking?
The primary reason why the add-on smart category navigation is called smart is that it uses self-learning scores to organize products on the category pages.
If you have used category-specific promotions (category-specific bulk boosts and manually positioning products on top of the category pages), these promotions will be applicable only on the respective category pages. Such rules do not have any impact on the search results.
Where products are manually positioned on top of a category page, their positions are respected and the remaining places are filled up as per their promotions and self-learning scores as described above.