There are multiple ways to order search results. It varies from the basics like - show randomly ordered products having occurrences of the search phrase. Through more advanced techniques, like TF-IDF which evaluates the importance of the search term for each matching product. But all of these techniques doesn’t solve the business goal of the search - sell and maximize store’s profit.
Here more complex search techniques are entering to the stage. Search may analyze actions of store visitors, past search queries and order items to be more attractive for a visitor.
More advanced search engines, like Kea Labs search goes even further. They may combine multiple
factors in order to predict the chance of buying product and evaluates the potential profit of a
For simplicity, let’s omit this profit-related aspects of search, we’ll cover them separately here.
Self-learning search is the one which constantly optimizes search results and orders products to be
more attractive for the visitors. It analyzes how other visitors interact with your store, what
they’re looking for, what they view and put to their carts. Based on this information search detects
patterns of behavior and estimates what products shall be shown the first.
Self-learning search may also support external factors, like season or presentation of a new device.
All of this is happening without any manual work, you don’t need to monitor search statistics and tweek the results.
However, manual adjustments are possible as well. And they may be very beneficial for your marketing campaigns. For example, would you like to promote products of a certain brand? Or do you need to release space in your warehouse and sell non-popular items?
As you may see, in-store Search is a very powerful tool which can automatically sell more