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How Traditional eCommerce Product Filtering Can Lead To Shopstration

By Eli Cohen, Chief Product Officer

From behind their computers or handheld devices—and no matter where in the world they are—shoppers can select exactly what they want to buy navigating online through large inventories and using product filters to narrow down their search. 

However, this often leads to shopstration as the traditional filters solution are limited and create more barriers in finding what shoppers are looking for and completing the purchase, resulting in lower sales conversions and average order value (AOV). In this blog, we will explain the limitations of the classic filters mechanism that prevent shoppers from finding what they want and how Visual Search Navigation offers a more effective solution.

The Limitations Of Traditional Product Filters

Tradition product filters are there to help online shoppers navigate through many products, allowing them to narrow down the list of items to a manageable number that satisfies their specified search criteria. However, when using traditional search filters, shoppers commonly encounter the following scenarios that create barriers, leading to higher cart abandonment and lower sales conversions. 

  1. Traditional product filters are less user-friendly. In most cases, these filters appear on a separate screen or block what you’re looking at by taking over the screen entirely. Both scenarios disturb and holt the natural flow of shopping. And should the shopper be unhappy with the final filtered results, it is a difficult and tedious process to go back and redefine their filter criteria. 
  2. Traditional product filters are less mobile-friendly. Traditional filters were developed and first integrated on desktops, meaning that the mechanism was not planned to be mobile and thus can encounter several limitations when trying to be used in a mobile space. With Generation Z’s spending habits being mostly driven by mobile shopping and an estimated $143 billion in spending power, being mobile-friendly is not a point to overlook. 
  3. Shoppers only get basic sorting options. Online catalogs that use traditional product filters are usually not deep tagged by sophisticated AI-based technology. As a result, traditional product filters allow shoppers to sort by key filters like color, size, and price, but not by more detailed attributes like length, sleeve type, pattern, etc.  

    Search results for red party dress

Visual Search Navigation: A New Visual On-The-Fly- Filtering Built for Mobile

AI-based Visual Search Navigation is changing the way people discover products online. This innovative technology empowers shoppers with visual on-the-fly filtering that is natural to the purchase flow. It has never been easier for online shoppers to find exactly what they have in mind, or to refine their search if they are unsatisfied with the results they are seeing. 

Additionally, Visual Search Navigation, an intuitive visual experience, displays as a top floating vertical widget, offering a fun and friendly UX that appeals to your shoppers and to mobile devices. The deep tagging technology of Visual Search Navigation automatically labels the retailer's online catalog with a rich and accurate number of product attributes. In turn, these attributes are then presented to the online shoppers as visual cues (icons), which allow them to narrow down and filter their results by any and all product attribute—no matter how complex.  

Envision A Bright Sales Future—With Visual Search Navigation 

Online retailers worldwide are already enjoying higher sales conversions after implementing Visual Search Navigation technology. Just look at REVOLVE, for example. This popular millennial fashion retailer has seen a 16% increase in sales conversions in their dresses, tops and pants categories since partnering with Visual Search Navigation pioneer Donde Search.

REVOLVE search using Visual Search Navigation

But the advantages of Visual Search Navigation don’t stop with increased sales conversions, higher AOV, and less shopstration. By mapping and extracting the rich and accurate set of product attributes, retailers now have a deep understanding of their shoppers’ personal style DNA, allowing them to offer effective personalized recommendations of products their shoppers are most likely to buy.

Donde Search uses Computer Vision and AI to turn retail catalog images into a highly structured data-set that connects detailed style attributes about products to consumer shopping behaviors, thus improving merchandising, product discovery, and personalization across e-commerce platforms. You can follow Donde on Twitter, LinkedIn, Facebook, and Instagram.