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The Essential Core of Faceted Search

A few weeks ago, we published an introductory post about faceted search. The value is clear to see for both retailers and consumers. Retailers get higher conversion rates and consumers get a more streamlined and rich user experience. Sounds like a win-win. Then why don’t we see it implemented across the board on every ecommerce website we visit? A survey conducted in 2014 said that only 40% of ecommerce websites implemented faceted search.

Faceted navigation is hard to do. It’s not just about optimizing the ecommerce platform, but there is the core need for a comprehensive and enriched product catalog that has a deep level of product metadata. This metadata can then be fed into platforms that enable this kind of navigation. Extracting this metadata requires product data science and domain expertise. There are certain high level filters that a lot websites have like say category, sub-category, brand, price range, etc. These are generic and easier to implement. It’s the next level of faceted fields that are harder to extract. Facets need to be extracted from product descriptions, manufacturer specifications, etc. and then applied to different fields.

This next level is product category-specific and is contextual to different categories. For example, if someone is searching for multivitamins, some of the facet types would include form (tablet, capsule, liquid, soft-gel, chew), specialty (gluten-free, sugar-free, organic, vegan), type (one a day, two a day), pack size etc. These are specific facets that are only applicable to this particular type of product category.

Faceted navigation helps consumers who type in a search query as well as those who are just browsing. One of the more important reasons for retailers to implement this is to ensure that consumers are not in a situation where their search returns zero results. Facets can be used to ensure that there is a certain count for each type and only those results are displayed. One retailer website that serves a rich faceted search experience is REI. Say I’m looking for a jacket. First, I see all the 472 matches for that top-level category.

coreoffacetedsearch (3)

screenshot from rei.com

 

Then the interface lets me drill down in different ways. In addition to a sub-category, brand, size, color, and price range, I can drill down further based on…

Feature

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screenshot from rei.com

Best Use

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screenshot from rei.com

Back Length

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screenshot from rei.com

Insulation Type

coreoffacetedsearch (2)

screenshot from rei.com

The other challenge is to ensure that the right facet is applied to the right field so that that even when I drill down on say Women’s Rain Jackets, the rest of the filters are re-calibrated for an accurate product count.

This is a hard problem to solve but one that ecommerce companies need to address, especially as consumers are accessing retailer website on multiple screens. As more and more users are shopping on mobile, retailers need to think about the experience that they are providing. For a woman shopping on the bus on her way back from work, it’s invaluable that a retailer website provide her the best experience possible given her intention and the size of the screen that she is using and the relative ease of just having to click on filters to get to the right product.

Product catalogs have to be optimized for providing this rich experience. Due to the number of choices available, finding the right product online is like trying to find a needle in a haystack. Let’s get to the needle!

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