Mobile conversion rates vs desktop conversion rates

Lower mobile conversion rates vs desktop

Most of the time (not all of the time), you’ll see a gap between mobile and desktop conversion rates, with sessions from mobile devices typically converting at lower %s than traffic from desktop sessions.

This gap does not necessarily mean that mobile sessions are under-performing.

This performance gap between desktop and mobile is often quite natural, with mobile users having lower propensities to convert, and therefore having less-elastic conversion rates (i.e. less time or inclination to complete longer conversion processes)

What should this gap be then?

This ‘should’ comes from having knowledge, and access, to many similar sites in a particular industry.

For example, we’ve done a lot of conversion optimisation work in the residential property sector, across many different brands & sites.  And providing these sites and forms have relatively good UX across their journeys, and relatively typical levels/types of traffic, then mobile conversion rates should be around 50%  lower than desktop conversion rates.

These gaps can vary across industries though:

If you look hard enough, you can find more detailed studies on this device performance gap, with higher sample sizes.  Just like this one from Statista, showing a 170% transaction rate gap between mobile & desktop sessions on ecommerce sites:

Elasticity of mobile conversion rates

The below is an example of how we tend to assess, and then reduce those mobile v desktop gaps – to see how elastic those mobile conversion rates are.  How far we can stretch them, and how we know what to do to stretch them.

#1 Analyse journey differences by device

In the below example, we can see that across all stages of this journey, mobile sessions are less likely to flow to the next stage than desktop sessions.  Especially after conducting a search, where the gap between mobile and desktop flows is much greater:

#2 Investigate why this gap is greater

There could be further clues in analytics here – e.g. mobile sessions coming from social media ads are more likely to search for specific product categories which are lower in stock.

The ‘why’ though, typically comes from good user research.  This is why it’s so important for good analysis to feed usability testing plans and discussion guides.  So usability tests focus on the most pertinent tasks.

For example, user research could show that there is a requirement for users to be able to save searches on mobiles, so they can access them later on a larger device.

#3 Forecast & targets for changes

E.g. we believe that by introducing save functionality, we will increase our return rate for mobile users who view search results by 50%, and therefore generate an extra £15,000 a month

#4 Implement and measure change

Tag the changes, and have KPI-monitoring reports ready (Google Analytics, Google Sheets, Google Data Studio) so you can measure impact.

Try and look at performance metrics around the change too.  E.g. returning users after saving might have increased by 60%, surpassing our original target, however, we may see a declining flow through rate from users who don’t save, and fewer in-session transactions from mobile users flowing from the home page.  Analyse as much as you can!