Category pages are one of the most important drivers of discovery in an online store. They store all of the relevant products and expose them on a dedicated page, giving the shopper the opportunity to narrow down to a particular subcategory to find exactly what they are looking for. However, in many cases, categories can be extremely deep, and sifting through thousands of products spread across dozens of subpages is tedious and time-consuming.
Over 50,000 results.
That’s how many products there are in the top category of “Men’s Clothing” when visiting Amazon. Navigating down the 20 or so subcategories, I find the one i was looking for called “Shirts”. Still over 20,000 results. Refining it further into “T-Shirts” there are still over 9000 results to sift through if I want to find something that I want to actually buy.
And yes, I’m being overly dramatic for the sake of telling a story. The truth is that there are multiple options on the left-hand side where I can narrow down by Size, Color, Brand, Price range and customer ratings to find what I might be interested in. Amazon has done a pretty good job of translating over 50,000 results into a shortlist of six Carhartt T-Shirts available in 2XL.
But to quote Peter Griffin from Family Guy: do you know what really grinds my gears?
I shouldn’t need to do all of this. Amazon has my full browsing and purchase history for the last eight years or so. I have viewed shirts and t-shirts in the past, I have bought Carhartt products and shown a clear interest in that particular brand. So why do I need to traverse multiple category pages to find something that I might be interested in?
Removing Friction in the Discovery Phase
Optimizing the category experience to take into account previous behavior and interests is a cornerstone in Nosto’s Onsite Product Recommendations. Building on this example, I’ll show you three ways of alleviating discovery friction.
One retailer who leverages a combination of Browsing History Related and Best Sellers coupled with a “Current category” filter is Utsav Fashion.
If I visit the Lehenga Choli category, which is a form of full ankle-length skirts worn by women from India (thanks Wikipedia, I owe you one), there is a plethora of items to choose from. 2193 items to be exact. However, by leveraging a Best Seller recommendation with “Current Category” filter, we are able to narrow this down to the six most popular items.
Let’s see what happens when I go into a completely different top-level category for Saris and browse through a couple of them tagged as “Wedding Wear” and then come back to the Lehenga Choli category. This should now utilize the recommendation type “Browsing History Related”:
It’s elegant, it’s spot-on and most importantly – it’s relevant for the shopper. The underlying idea being that if I’m looking for that perfect wedding dress (which men in their 30’s usually are), I can look for related products across different categories. Essentially, this gives the retailer the power to store affinities towards certain styles, looks or colors and showcase the most relevant items whenever that shoppers lands on a new category page.
Let’s look at another tangible example, this time coming from New Zealand fashion house, Augustine. They have a top-level category for bottoms, which includes skirts, shorts, pants etc, and a similar setup of Browsing History related with a Fallback to Best Sellers.
Dark pants are clearly selling right now, and Augustine is further provoking primal instincts of social proof by clearly stating that these items are the de facto Best Sellers. Let’s go ahead and look at pair of shorts, and then come back to this view:
Now, the clear behavior cue towards shorts, or cooler garments in general, comes into play and the entire row – except one pair of persistent pants – stays behind. Again, a pitch perfect example of how to tailor to the needs of the shopper first throughout every stage of their journey.
It is important to note that behavioral affinities and previous purchase history persists after the session, so when I come back next week to look for the perfect shorts, the view will already be pre-populated for me. These scores suffer from diminishing returns and current trends, meaning that if I show a clear interest toward skirts in the heat of summer, and return to buy christmas presents in 6 months or so – the view will have automatically taken the change in trends into account. This behaves differently from industry to industry because the need for weather appropriate garments is very much influenced by trends. However, buying batteries from a electronics merchant much less so.
How to Start Optimizing Your Category Pages with Nosto (in 2 Simple Examples)
In these two examples, we are using a combination of two different recommendation types, coupled with an “include filter”. To achieve a similar kind of setup, you need to do the following in your Nosto admin account:
Create a new Recommendation Campaign targeting the Category page, with Browsing History Related selected as the primary recommendation type.
Add the “Currently viewed category” filter (you can also combine with price, margin or brand filters if you want to try something different).
Then create a fallback recommendation using the Recommendation Type called Best Sellers, and add the same “Currently viewed category” filter to stay within context on the category page.
When configured correctly, this is how the campaign should look. The treatment here is very similar to the examples from both Utsav Fashion and Augustine, and usually yields better revenue metrics than leveraging Best Sellers alone.
Eliminate Manual Processes with Category Merchandising
A well-optimized Category page does more than just offer your shoppers a more personalized experience. With an automated merchandising strategy, you can offer your customers a completely unique Category page experiences that are not only easy to maintain with limited team resources, but also allows you to continously test growth tactics that would otherwise require manual guesswork.
Skinnydip London is an example of this. During the pandemic, they lost multiple sales channels, were limited in team resources and worked with a manual merchandising process that made it harder to keep up with growing digital demand.
The brand saw an opportunity to turn their manual merchandising process into an automated strategy that yielded 8x ROI, saved them time and effort without expanding their resources, and paid for itself in just one month.
Curious to learn how? Check out Skinnydip’s success story for an inside look at their merchandising strategy (and how you can do the same).