Sorting Rules within Category Merchandising uses a combination of Ranking Score and Weighting Factors which relies on separating products by splitting them into percentiles. In this article we will discover these concepts and explain how items are Sequenced into a certain order by using Product Attributes or Performance Metrics.
Every product within a catalogue is ranked in a specific order for every single Product Attribute and Performance Metric indexed by Nosto. This means that one product might rank 1st on price, whereas it can rank 17th on conversion rate.
When creating Dynamic Sorting rules you will notice that every product shown in the preview have an associated percentage score attached to it. Ranking Score indicates how good of a match this particular item is based on the Product Attributes or Performance Metrics that you are using for the rule.
For example, using high price as a factor, the most expensive product within the product catalogue would have a Ranking Score of 100% attached to it, whereas a product somewhere in the middle could have a Ranking Score of 57%.
When adding Product Attributes and Performance Metrics into a Sorting rule, you will notice an individual slider for every row added. This defines the weight and allows you to prioritize flexibly based on what you want to achieve.
If an item were to rank 1st on price, whereas it ranked only 17th on conversion rate, we could use the weights to try and normalize the scale to favour conversion rate when calculating the final results.
Example: The product ranks 100% on price, and let's assume 17th means that the product ranks 87% on the scale considering conversion rate. This is relative to the conversion rate of other items so the real percentage can be 87% or 15% in this example. If the weighting for price is set at 30%, and the weight for conversion rate is set at 70%, we can easily do the math to figure out the Ranking Score for this product.
This is done by calculating the percentages with the weighting factors, and adding up the total sum.
(1.0 * 0.3) + (0.87 * 0.7) = 0.909 = 91%
This item has a relatively high score when considering in what order to Sequence the products. This weighting allows for items with a poorer rank on price, but a higher rank on conversion rate to surpass the score of 91%, which is the expected behavior when using a weighting of 70/30 as we have done here in our example.
Category Merchandising applies a percentile rank for every Product Attribute or Performance Metric to split products into percentiles. Using this method it does not matter how wide or narrow the scale, how many or few items observed, or how clustered items are within the center of the scale.
By using traditional value-based ranking, most items would be clustered in the middle, with some outliers on the high-end and the low-end of the spectrum. By using percentile rank as the underlying methodology there is no bell-shaped curve when looking at a collection of items. Instead products are evaluated on a scale of percentiles for every Product Attribute or Performance Metric.