As all product managers and designers know, even modest e-commerce sites are incredibly complex, and improving them is a constant challenge. But for most modern e-commerce sites the greatest issues revolve around their inventory’s discoverability – that is, the ability of users to find things. Discoverability is critical, not only to the current sale but to the likelihood that the user will come back in the future. But generally speaking, discoverability failure points involve the product result list or the product detail (either as a page or part of a list).
The Most Common Causes of Poor Discoverability
Poor discoverability is caused in many ways, but there are three problems that are the most common:
- Ten-Stop Shopping – Lack of user-journey item relationships.
- Zombie Lists – long, undifferentiated result sets.
- Black Box – insufficient information scent to make selections (or rejections).
What all discoverability problems have in common is that they are addressed by improved use of good information architecture – literally organizing your design around the essential “aboutness” of something. In the case of discoverability, the qualities of good IA involve the management of Categories, filters, and on-page product descriptions. (If you’re unfamiliar with these concepts, check out our recent discussion).
Tackling Ten-Stop Shopping
For far, far too many e-commerce sites, online shopping feels more like walking through a warehouse than a store, requiring the user to bounce across categories organized around logistics rather than user needs.
Key problem – Lack of user-journey item relationships.
Why it matters – The essential truth of e-commerce is that there may not be one platonic category structure that best serves all users. It is clear, when examining the best e-commerce sites on the web, that they don’t expect users to reach a product in the same way, but instead provide a number of different pathways while maintaining management integrity of the products in some core structure.
What it looks like – Basically if you see a product in multiple categories on a site, you are getting an example of polyhierarchy structure or multiple product placement. Essentially the system needs to support the presence of products in multiple nodes of a structure.
What’s hard about it – The two biggest issues have to do with pathfinding design and UX management. The former is seriously challenging. If a product is in two different parts of a structure, is the structure core, or is the alternative placement specialized or unique – that is, not part of the core navigation?
Amazon relies on a mix of both, with some products placed in multiple core structures and other product placements seemingly made only at the level of the department. An example of multiple placements in core structures would be paper towels being found in both the “home” and “business cleaning” sections.
But when we look at wooly category structures we discover that there is so much overlap that very creative UX solutions have to be set up to make it usable. Consider, for example, what Amazon did in the hunting/shooting/fishing space. Outdoor activities exist in a larger lifestyle space that would include people using a lot of different kinds of equipment in overlapping ways.
As a result, products might be useful for a lot of different named outdoor activities. Amazon uses standard categories on the main page, but created a polyhierarchy on the left hand side navigation to support this effort, as well as allowing users to move into spaces based on a given outdoor activity that still contained the same products.
Tackling Zombie Lists
The archetype of ecommerce isn’t a product page or a shopping cart, but a giant list of similar items. The core failures of almost every system with a large inventory can be seen in these long, bewildering lists.
Key problem – long, undifferentiated result sets.
Solution: Category and Filter enhancement
Why it matters – Of the three most common places where people abandon an item search, the largest by far is on a search result page. Sometimes this is just an issue of fit: your site may well not have what they are looking for. But as we increasingly compete with other sites for similar products, often the bigger issue is that the product was difficult to find. Organizations that create effective, discoverable product lists can see abandonment rates drop by as much as 30-50%.
What it looks like like – Category and Filter enhancement basically come down to the navigation elements that are used to guide a search or refinement. There are two main components:
- Category Navigation – This comes in many shapes and sizes, but basically emphasizes a sense of PLACE. Amazon’s best practice is to put categories at the top of a page, in order to allow the user to establish their sense of space directly. Very often they will take some key subcategory and put it at the top of the product listings as well.
Filter Sets – If categories give you a sense of place, filters, give you some way to zero in on some sense of ABOUTNESS OF A PRODUCT. Filters are tricky, in that they must operate effectively at the proper level of context for a category, but generally speaking they should be usable for the entire set of items in a product result set.
Properly done, this work can be instrumental in quality autocomplete support for your search. Well structured search autocomplete often looks like a compressed version of Category Navigation and Filter sets in a list of suggested search results. For many users, search autocomplete will allow them to skip the product listing page entirely.
What’s hard about it – Tagging, tagging, tagging. Assuming you are using a product – driven taxonomy (and you really, really should), the attributes that become categories and filters can live almost entirely in product attributes. But the richness of the filters and categories available to the products can only be as good as the work done to make the tags consistent, clear, and rich. For many vendors this is the greatest hidden cost of running an ecommerce site, but the value it provides is well worth it.
Tackling the Black Box
A close cousin to the zombie list, the black box anti-pattern occurs when a list of items in a list is insufficiently detailed to confidently make a selection or a rejection. If the only way to actually learn more about an item from a list is to click into it and peruse the item in more detail, the user may lose the thread and be unable to make a good selection between multiple items.
Key problem – Insufficient information scent to make selections (or rejections).
Solution: Product Detail Transparency
Why it matters – The second-most common abandonment moment in an e-commerce visit is on the product page. Again, sometimes this is fit; users might not like the price or the product itself. Product listing pages and detail pages that do not have sufficient information do not allow the user to comfortably make a decision about a product, either forcing the user to drill deeper into the interface before they’re ready, or to make assumptions about what the product can and can’t do.
What it looks like like – Assuming the existence of good-quality tagging, the biggest issues for reducing black box problems have to do with rich, readable attributes that are directly relevant to a user selecting a product. This exists at the page product listing page level and the product detail page level. At the product listing page level the considerations fall into overall page density (how many products does a user see on a screen) as well as individual product listings. The product detail page is about the prioritization and selection of details on the page.
What’s hard about it – Really the biggest challenge here is that there are some basic design rules, but each product type is going to require a different set of product details in a particular order, so the only way to really determine what works best comes from relentless A/B testing of layouts and templates at the level of the product category. This is time consuming, but if your overall layout plan is disciplined, it can be a standard part of your optimization effort.
Making it Happen – Product Managers and UX Teams
The people best suited to identifying these problems and suggesting solutions are product managers and UX teams. Yet for a variety of reasons – technology, business, and operations – product managers and designers are often not invited to understand or think discoverability in terms of information architecture.
This is unfortunate. It is critical that UX and product managers have a seat at the table when talking about the management of information, for no other reason than the fact that poor information quality limits UX quality.
Being facile with Information Architecture can also be a powerful way to improve design, because, after all, Information is part of the experience, and UX teams have opportunities to really make good data sing.
We hope these metaphors and examples give you the tools you need to start a discussion with your entire team about information architecture and how it can make your e-commerce site rock!