Designing the Perfect Feature Comparison Table
When Is A Feature Comparison Useful?
In observing customers in a few e-commerce projects, I found it quite revealing to notice how seemingly irrelevant a comparison feature appears to be to many customers. Quite often users will say that it clutters the interface, and that they never use the feature. The reason for it is simple: While we tend to purchase small low-priced items quite often, we tend to buy large high-priced items not so frequently. In fact, there are just not that many situations where we actually need a feature comparison.
The Building Blocks Of Feature Comparison
If we wanted to compare two or more items against each other to find the better fit, what would be the most obvious way to do that? With clothes, we would try them on and pick the one that feels right. But what if trying things on isn’t an option? When purchasing products online, we can rely on our past experiences, ratings, expert reviews, customer reviews and trustworthy recommendations to reduce the scope of options to just a few candidates.
Still, at some point, you might be left with a few too similar items — maybe one a bit too expensive, the other missing an important quality, and the third a recommendation from a friend’s friend. So, what do you do? You list all options, examine their attributes side by side, and eliminate options until you have a winner. (Well, at least most people do that.)
Translated to common interface patterns, this naturally calls for a structured layout that aids in the quick scanning of options — probably a good ol’ comparison table, with columns for products, and rows for their attributes. Once the user has selected products and prompted the comparison view, we can just extract all attributes from all selected products and list them as rows in the table. Should be easy enough, right? Yes, but that’s not necessarily the best approach for meaningful comparison.
Not All Attributes Are Created Equal
Ideally, we’d love to display only meaningful, comparable attributes that the customer cares about. Rather than extracting and lining up all product specs, we could determine and highlight all relevant product attributes, while keeping all other attributes accessible. This requires us to (1) find out what the user is interested in and (2) have consistent, well-structured data about our products.