Let’s say you’re segmenting traffic to your website.
You’re segmenting traffic because, in a digital world brimming over with messages, you want to make sure that the messaging on your site is Relevant to the right user at the right time.
You believe that a more customized user experience will lead to more orders, demo requests, phone calls etc. So, you have structures in place to deliver appropriate messages to your different audiences, each with distinct needs and expectations.
But I must ask, how are you segmenting your visitors?
You might be grouping them by device, by traffic source, by demographic data.
And these buckets are all viable:
- Your desktop visitors may behave differently than your mobile visitors
- Visitors coming from a Facebook ad may respond better to social proof triggers than those coming from organic search
- Older visitors may browse your products differently than younger visitors
But the ultimate goal of segmentation, like conversion optimization, is to increase conversions. With that in mind, this post is all about that one segment you probably aren’t looking at: converters versus non-converters.
To clarify, your converter segment is not necessarily the same thing as your repeat-customer or Loyalty segment. Your converter segment includes anyone who converts, whether or not they’ve converted before.
Rather than focusing on different general visitor segments, you should turn your attention to the behaviors that differentiate visitors who convert from visitors who don’t.
When you focus on general visitor segments, you’re working from the top of the funnel to the bottom. Why not work from the bottom of the funnel, up? After all, that’s where the money is!
Correlation vs. Causation
First things first: when you’re looking at differences between converters and non-converters on your site, you must be wary of correlation versus causation.
It’s almost impossible to know whether converters are behaving in a distinct way because they’re already motivated to buy (correlation) or because the elements on the page have enabled those distinct behaviors (causation).
For example, does a converter browse more products than a non-converter because they’re already motivated to buy before arriving on-site? Or does an on-site UI that emphasizes browsability encourage converters to browse (and therefore convert)?
It’s similar to the search bar quandary: typically, visitors who search convert at a higher rate. But do they convert because they search (causation) or do the search because they’re already more motivated to buy (correlation)?
It’s a bit of a “the chicken or the egg” situation.
Fortunately, at WiderFunnel, we’re able to test on many retailers’ websites and take note of certain patterns. On multiple instances with different clients, we have observed clear and drastic differences in key user behavior metrics between visitors who convert and visitors who don’t convert.
These differences paint a picture of how your visitors shop. You can use this information to improve your UX and add features that’ll help your general visitors behave more like converters than non-converters. The hope is that encouraging non-converters to mimic the behavior of converters will lead to them actually becoming converters.
Moral of the story: If you observe impactful differences between converters and non-converters on your site, you should create a hypothesis that targets these differences.
WiderFunnel Optimization Strategist, Nick So, recently ran a test that did just that.
Let’s buy some shoes
One of our biggest clients is a global shoe retailer. Over the past 6 months, Nick noticed some patterns in their analytics:
- A high percentage of visitors that convert (like 60%) are returning visitors
- Converters visited 186% more pages per session on average and spent more time on page per session than non-converters
Meaning, the majority of converters on this site have already been to the site at least once before and they seem to spend much more time browsing than their non-converting counterparts.
It’s common sense that visitors who convert behave differently than those who don’t. But it wasn’t until we pulled the report and saw how big the difference was in their shopping behavior that we really thought to go down this path.– Nick So, Optimization Strategist, WiderFunnel
In previous testing, Nick had also observed that visitors to this site are responsive to features that increase the browsability of multiple products. He’d noticed the same sensitivity with some of our other retailer clients, where features that made it easier to compare products helped conversions.
We decided to run with this data. Our hypothesis was based on the idea that visitors who convert are most likely returning visitors, therefore, pointing them toward products they’ve already viewed will guide them back into the funnel.
The hypothesis: Increasing the browsability of the site by displaying recently viewed products to increase relevance for the visitor will encourage higher engagement and increased return visits, which will increase conversions.
Nick and the team tested a single variation against the Control homepage. The Control featured a “Recommended Products” section just below the hero section, displaying four of the client’s most popular product categories.
In our variation, we replaced this with a “Your Recently Viewed Products” section. We wanted to target those visitors who were returning to the site, presumably to continue in the purchasing process. The products displayed in this section were unique to each returning visitor.
Our variation won, consistently outperforming the Control during this test. This client saw a 6.9% increase in order completions.
Bottom to top
When you’re segmenting your audience, don’t forget about the segment that floats at the bottom of the funnel. Instead of identifying the differences that characterize visitors coming to your site, why not work backwards?
Look at the behavioral differences that distinguish converters from non-converters and test ways to help non-converters mimic the behaviors of converters.
Have you noticed drastic behavioral differences between your visitors who convert and those who don’t convert? Do you tap into this particular segment when you plan tests? Tell us all about it in the comments!
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