There are multiple ways to rank your search results. It can start from the basics like “show randomly ordered products having occurrences of the search phrase”. Later it can go through more advanced techniques, like TF-IDF which evaluates the importance of the search term for each matching product. But all of these techniques do not solve the business goal of the search – sell and maximize the store’s profit. Here more complex website search techniques are entering the stage. A self-learning search can analyze the store visitors’ actions, past search queries. Based on this, it can order items to be more attractive for a visitor.
What does a self-learning search do?
More advanced search engines, like Kea Labs Smart Search, go even further. They may combine multiple factors in order to predict the chance of buying a product and evaluates the potential profit of a sell.
The self-learning search is the one that constantly optimizes search results and ranks the 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.
What is being analyzed?
Based on this information, the search detects patterns of behavior and estimates what products shall be shown first. The self-learning search may also support external factors, like seasonality or new-in-store items.
All of this is happening without any manual work: you don’t need to monitor search statistics and tweak the results.
Manual adjustments are possible too if you want to go even further in customization – they may be very beneficial for your marketing campaigns. For example, would you like to promote a certain brand? Or do you need to get more space in your warehouse and sell the unpopular items?
In-store self-learning search is a very powerful tool that can automatically help you sell more. Schedule a demo to learn more and see it in action.