What Is Personalization?
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Personalization strengthens interactive search: it adds a personal layer to the overall relevance experience. Adding personalized preferences into the search experience makes results more relevant for individual users.
Advantages of Personalization
Users greatly benefit from seeing individualized results. Queries mean different things for different people. For example, a user who searches for “harry”, and has a preference for children’s literature, will likely want to see “Harry Potter” results appear on the first page. On the other hand, politically-minded users may be more interested in seeing “Harry Truman” in their results.
Personalization also benefits your business by encouraging people to stay on your website for longer. This leads to more product discovery, and generates more interest and sales.**
How does Personalization fit into Algolia’s relevance strategy?
Effective relevance has two main goals:
- Enabling your end users to see products and services that match their expectations.
- Providing results that align with your business needs.
These twin goals are addressed by fine-tuning the first three layers of Algolia’s relevance strategy. These three layers are:
- Textual relevance - an intelligent and robust textual matching that includes typo tolerance, synonyms, filtering, and a host of other settings.
- Business relevance - a business-centric relevance that ranks results according to business metrics.
- Merchandising - promoting specific items, and displaying banners and top results. All of these contribute, in different ways, to producing relevant results that apply equally to all of your users.
Personalization introduces a fourth, user-based layer, on top of Algolia’s relevance strategy. Personalization - and more broadly Algolia’s Insights & Analytics - injects user preferences into the relevance formula.
Creating reliable preferences
Before jumping into implementation, it’s necessary to understand the key to making successful personalized experiences. Personalization relies almost exclusively on facets. User preferences come from the categories (facets) of items that a user frequently interact with. For example, a user can have a preference for a specific author or genre when shopping for books, sport items while shopping for clothing, or gaming software while looking for media.
Algolia derives most facet-value preferences from the actions users take on individual products, not on the facets they click on. If you want to find out which authors a user likes to buy, you want Algolia to know every book the user buys. The associated authors are then derived from individual transactions and saved as Personalization preferences.
You can also tell Algolia whenever a user clicks on a facet. Facet-clicking can lead to great insights on user behavior; however, for most implementations, the best data will come from the buying habits of individual products.