Search has evolved over the years, first starting with traditional keyword search, then moving on to Vector (AI) databases. But nowadays, a new type of search is being popularized : personalized search. As it’s name implies, this technique consists of personalizing the search (keyword or vector) using stored events (or interactions) for each user. It is closely related to the other popular personalization system called recommendations.
What is personalization ?
Personalization consists of customizing the search results based on the user’s preferences. This is done by collecting user events, that are triggered whenever the user interacts with or does an action on the website, and using this to tailor the search results to the user’s preferences and needs. The personalization model (collaborative filtering) trained on your user events allows it to learn all about your users as a group and automatically customize the search results, so no further manual input or fine-tuning necessary.
All of this elements contribute to establishing your website as an intelligent and time-saving platform for your users, enhancing its overall image.
Why use personalized search ?
Personalized search enhances the user experience by presenting relevant content or products that align with the user’s interests, making it easier for them to find what they’re looking for. After all, all types of personalization consist of helping the users discover content they otherwise never would have found. By understanding each user’s unique needs and preferences, personalized search improves engagement, satisfaction, and ultimately conversion rates. It also fosters customer loyalty and retention by delivering a more personalized and enjoyable browsing or shopping experience. According to Accenture : 91 percent of consumers are more likely to shop with brands that recognize, remember, and provide them with relevant offers and recommendations.
You can also further improve you personalized search by vectorizing it. While traditional personalized search will return results based on the query and then reorder them based on how much they are of interest to the user, vector search (or AI search) will understand context and meanings of the words in the query and instead of matching keywords will return the ones with the most meaning.
You can enhance your personalized search even further by implementing vectorization. Unlike traditional personalized search, which returns results based on the query and then reorders them based on user interest, vector search (or AI search) understands the context and meaning of the words in the query. Instead of simply matching keywords, it prioritizes results that align more closely with the intended meaning, thus delivering more relevant content. These more relevant can then be personalized for each user. But beware, this type of search (AI personalized search) is a far more costly solution (Algolia, Recombee, Google Retail) than both AI search and personalized search individually.
How to use personalized search in WordPress
To make use of personalized search in your WordPress installation, you could check out WPSolr. It can integrate Algolia, Google Retail and Recombee into your WordPress.
WPSolr adds search (keyword search, AI/vector/semantic search, personalized search) & recommendations that are compatible with a host of engines (Apache Solr, Elasticsearch, Opensearch, Algolia, Weaviate, etc…) so you can fulfill all your needs and greatly increase your conversions and CTR with one single plugin.
