Product search is failing consumers – as well as platforms

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E-commerce platforms and marketplaces have become the go-to mechanism for consumers to search for many of their regular and irregular purchases, with at least 63% of all shopping journeys starting online .

But as users widen the scope of the products they search for online – ranging from groceries to appliances to cars – it’s clear that they’re running up against a very impersonal and frustrating product selection experience. The way product search works on websites often presents us with either far too much choice, or too little. And the mechanism used to indicate our needs and wants is inflexible and often very limited in scope, adding no real intelligence into the search process. These frustrations and others are reported time and time again during our research into user experience of existing platforms, and are widely reported by others (e.g. see Baymard, NN Group).

This frustration causes a lack of trust and confidence in the user that they are making the right choice, and drives them into a range of behaviours to try to compensate. Some may set very few criteria and then end up endlessly scanning the large result set, trying to avoid missing products that may be suitable. Others explore the search criteria in a semi-scientific ‘what-if’ manner to try to understand how these settings impact the results returned. Another approach we often see is where users open vast numbers of results in new browser tabs so they can eliminate them one by one to avoid losing sight of any results when the search is (accidentally) refreshed.

It’s clear that current search mechanisms aren’t allowing us to look for products in a human way – which has a detrimental impact on sales, customer loyalty, and brand perception. It also leads to unnecessarily high rates of product return. And as we will discuss in more detail later, constant tweaking of the search mechanism isn’t getting to the root of the problem.

So why is product search currently failing to deliver the right consumer experience? If we could use just one word, it would be: Filters.
filter_for_laptopFrom a UX perspective, filter bars offer an impossible trade-off between usability (a few clear and simple filtering options) and usefulness (a highly complex list of requirements). On the other hand, searching using full-text in a search bar requires the use of powerful Boolean search requirements – the backbone of library search for generations – but few people know how to use Boolean search, so it’s often not even included.

Regardless of whether a filter bar or search bar is used, the core problem is that e-commerce platforms and marketplaces are essentially still applying the decades-old methods behind old SQL-style search queries. This means tagging each product with a set of attributes, and ‘filtering’ out from the results the products that do not meet the user’s requirements as specified in the filter bar or search bar. That type of binary 0 or 1 logic is not how humans think!

And having such a rigid structure in place means that in order for traditional filter-based search to work at all, there needs to be highly structured and templated product data. This is generally not the case, and minor data issues can cause significant system problems and user confusion.

Although the traditional filtering method might sound like a perfectly good approach to a database developer, it is not surprising that consumers report significant frustration. They don’t trust the results, and this hurts the trust they place in the brand. In general, the classic filter system is not suited to intelligently guide users along their search journey.

So how is the e-commerce and marketplace industry trying to counteract a growing dissatisfaction with the way that product search works for consumers, which is negatively impacting propensity to buy and damaging customer loyalty? As we said earlier, it’s often by layering other functionality over the top of the existing filtering system. Consumers are bombarded by ‘personal messages’ – offered choices based on ‘top ten picks’, constantly reminded that ‘other people are buying’, and told what is ‘trending’ – in an attempt to create a sense of urgency or fear of missing out. Many sites also utilise quick-links to allow consumers to skip the search process altogether, as well as videos or filter wizards. But adding more content here just results in a crowded and confusing user interface and makes it even more complicated for consumers to focus on what they are looking for. And for the platform operator, there are even more data points to maintain, and layers of additional technology to control. None of this is solving the core problem; the use of a static filter/search bar that utilises an in-out binary selection approach.

So what is the answer? How can search be improved beyond the outdated and ineffective tools we see now?

  • What if users could indicate their needs and wants at any point in the search journey, and be able to refine and select options that they’d previously discounted, without needing to return to a filter or search bar?
  • What if instead of making on/off choices for a limited set of product features, the consumer could instead express a wide range of preferences? And if they could indicate an importance ranking to those preferences?
  • What if product search was re-imagined as a dialogue consisting of multiple steps, rather than the current one-shot approach?

This is the future of product search that we’ve been exploring, and as part of this series of articles we’ll be demonstrating solutions that solve the problems of product search for consumers and platforms.

 

Next: Enhancing filter based product search, step by step

 

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