Things you would never want said about your website shopping experience:
These are direct quotes taken from real shoppers included in a recent study from Christian Holst, co-founder of the Baymard Institute, on the state of online product filtering and discovery.
The study reveals that only 16% of major, multi-million dollar ecommerce sites offer a reasonably good filtering experience. In an era where the shopper gets what the shopper wants, this should be a wake-up call to retailers that this vital part of the consumer shopping experience is not where it needs to be.
The unfortunate situation is that many of these great retailers have all the right products, but under-optimized ways for shoppers to navigate their inventory in an easy and inspiring way.
We were excited to have the opportunity to ask Christian for some additional insights on his ecommerce filtering research, and he’s got some valuable nuggets of advice that could help transform the online shopping experience retailers present to their shoppers!
Edgecase: In your opinion, why has ecommerce filtering been so slow to evolve to meet shopper demands? And how do you think those demands are evolving? (i.e. do they expect to be able to shop the way they do in-store?)
Holst: In our almost year-long research study on ecommerce filtering, we’ve found that getting the users’ filtering experience right requires a unity of having the needed filters available (product data structure), the right filtering interface, and the correct filtering logic (ecommerce platform). If just one is lacking, the users’ filtering experience will be shattered. Some of the major retailers we talk to say they have tried investing heavily in proper data structures and tagging without getting any results. Upon closer investigation, it turned out this was because of filtering specific issues with the User Interface (e.g. using a poor truncation design) or due to a limitation in an ecommerce platform’s filtering logic (e.g. not allowing multiple filter values of the same type to be combined). My point being, optimizing filtering requires a holistic approach, and will often need to involve simultaneous changes within both product data management, interface design, and platform logic to pay off.
Edgecase: Do you feel there has been a shift in shoppers’ impatience with under-optimized parts of the shopping experience?
Holst: We’ve been running large-scale usability studies on ecommerce sites for the past six years and there’s a clear increase in user impatience towards poor site flows, poor designs, and even temporary quirks. Users are simply spoiled by multi-million dollar investments in UX by Facebook, Amazon and Google, and have the same set of expectations when at other large- and mid-sized ecommerce sites. (Very small niche sites and individual artist sites being an exception, where users often have noticeably lower expectations.)
Edgecase: What is the number one offense a retailer makes with its ecommerce site filtering capabilities, and how can they improve?
Holst: Unfortunately, there’s not just one. Our most recent benchmark reveals that when measured across 1,750 manually reviewed filtering UX parameters, 84% of the top 50 U.S. ecommerce sites have a mediocre or poor filtering experience. [Tweet This] The most common culprits being a lack of key filtering options, poor filtering designs, and filtering logic that doesn’t align with users’ expectations. Within the lack of key filtering options, category-specific filters are among the worst offenses – see #1 in my blog on Macy’s Filtering Experience. For filter logic, another offense is not allowing users to apply multiple filters values of the same type – 32% of retailers don’t allow this! (See #3 in that same blog.) With regards to filtering design, yet another offense is an unclear “Applied” filter state. I encourage you take a look at my blog on How to Design Applied Filters for more information. But the list of offenses goes on…
Edgecase: What are the biggest obstacles to implementation when retailers begin to rollout or optimize category-specific filters?
Holst: Category-specific filters require ongoing evaluation. As a product category is added or the product type evolves over the year, category-specific filters will evolve as well. Take televisions as an example – before flat-panel TV started to become really thin, a specification like “display depth” existed but wasn’t something the average TV shopper would consider. As the product type evolved, display depths have become a major purchase parameter for some TV buyers and will need to be a category-specific filter type in a retailer’s TV category moving forward.
The curation of what product specs need to be offered as category-specific filters will evolve over time. While obviously applying for consumer electronics, this also occurs in other domains as well such as apparel and home furnishings as styles and trends are constantly changing.