AI and Predictive Search

Predictive (or autocomplete) search first surfaced in 2004 at Google. Since then, it is common practice in all sophisticated search systems. Autocomplete can help bridge the gap between technology and human input, helping to answer search queries quickly and efficiently. Over the years AI has continued to take site search to a whole new level, we’ll take a look at some of the ways how.

Why Use Predictive Search?

When it comes to search, the more advanced the better. Whether your shoppers are high or low intent, the search has the capacity to turn their searches into conversions. When search is focused on products and user intent, it can lead to higher revenue and ROI. 

Shoppers expect a predictive search, it completes their user experience. It also solves many problems that otherwise might arise such as:

  • Misspelling
  • Not knowing correct terminology (especially for technical products such as toners or car parts)
  • Having vague ideas of what they are looking for
  • Needing to find something fast

Personalized Search

Predictive search can also be combined with personalization techniques, which allows for a tailored search experience. If you know that a shopper has previously searched for certain items, then you can predict they might be searching for something similar this time round.

Their search history and user behavior can help improve their search experience, but also search experience overall. If you know trending topics that your customers are searching for, this can help you suggest this to more shoppers. This will improve your predictive search capabilities and improve the user experience. 

How Does Predictive Search Work?

Natural Language Processing and Machine Learning is what facilitates predictive or autocomplete search to work. The combination of the two of them means that contextual meaning can be inferred from the search query, rather than just the basic keywords.

The Use Of AI In Search

AI processes and indexes data in ways that help to turn it into useful outputs. Your AI needs to be fed with data from your catalog, such as product descriptions, brand and category information, customer reviews, and anything else relevant.

Then the machine learning algorithms analyze the information, improving the accuracy and becoming better acquainted with customer behavior. This can then be used to better predict customer intent, and continue to learn with the new data input. 

Vector Search

AI powered search solutions also have the ability to search based on context rather than just on keywords. Vector search allows search to be conducted in a more human way than ever before. This is through using machine learning to find related objects with similar characteristics. AI helps to translate similarity of text, images or audie to create a more scalable search.

Vector search means that the customers can be met at a more human level than ever before. It means that the search actually understands user intent, drawing sophisticated parallels between what they type and what they are searching for.

AI and Predictive Search

Using search without AI will simply not provide the results that your company needs. A search that does not help predict what the users are typing, will result in less sales. Shoppers want to have their needs met quickly, and to feel as though the business has their best interests at heart. Predictive search makes a positive experience for the shoppers, and sees increased sales for merchants. It’s a worthwhile investment for any eCommerce business.