Personalisation
The personalisation is an add-on feature. To enable the option, please submit a support ticket
When a customer enters a physical shop, they may express their preferences to an in-store assistant by highlighting the colours they like, the brands they prefer and what they have purchased before.
The in-store assistant would then use this information to show the customer products they are most likely interested in first, before showing them any others that still may be suitable.
Klevu A.I. is your online assistant.
This personalisation can be provided in two ways:
- including some information about the customer's browsing history with each request
- defining your own boosting rules based on the information you already know about the customer
You can read more about how this works in our Personalisation Guide.
If you provide the IDs of products which the customer has recently interacted with, the Klevu A.I. will figure out the common aspects of those products and influence the search results.
The easiest way to highlight this is with an example:
- In the JSON below, the four record IDs provided within context represent the products that a customer has clicked on before making a search. They all have the brand 'MNH'.
- There may be other attributes those products have in common, such as price, color, material, etc., however, we have specified in the query that we are only interested in personalisation based on 'brand'.
- When you execute the query, you will see some brand 'MNH' results are coming first.
Note that the impact of personalisation is not always apparent. Klevu is clever. If the customer has recently been looking at 'shoes' but now they are searching for 'washing machines', Klevu will know to disregard any interactions that are not relevant to the current search query.
If you have already built up a profile of your customer and would like to use what you know about them to promote certain results, you can use the boost object within each record query.
There are three ways the records can be boosted:
- filter conditions
- keywords or phrases
- IDs of specific records
For example, let's say you have an online store with an area where customers can log in.
- From your store's purchase history, you know that one customer is particularly interested in the brand 'KKE'.
- From your analytics data, you also know the same customer also looked at the product detail page of the product with ID: '31366487375934' many times.
- Finally, you have an area where customers can specify keywords of their interests, and this customer wrote 'comfortable'.
As a merchant with all of this information available, you can build up a profile about this customer. The sample to the right shows how you would convey this information to Klevu during a search.
To find out more about how boosting works with your existing merchandising rules, please read this article on How Personalisation Works.