The Problem with Average Conversion Rate Benchmarks

I am often asked what an ‘average’ conversion rate is, especially for e-commerce websites, which people find useful when they want to compare their own sites against others, or as a way to set themselves targets for increasing their own conversion rates. The truth of the matter is that this approach is extremely problematic on a lot of levels. This post explains those problems and offers an alternative:

Average website conversion rates: the issues

Most anecdotal sources claim average global conversion rates to be somewhere between 3-5% but there are actually very few genuine sources of data beyond what people ‘say’, also people have been ‘saying’ the same anecdotal percentages for at least about 10 years that I can remember. Sure there are various bench-marking studies out there, but this is usually either questioned research or data based on opted-in users of a specific optimisation or digital analytics vendor, so likely skewed.

Furthermore, regardless of where the data comes from it is very important to understand the problems with trying to benchmark yourselves against the market when it comes to metrics like conversion rate:

1) People calculate conversion rate in different ways. The most common method is unique buyers / unique visitors. However, Google Analytics (for example) uses the % of visits/sessions which result in a transaction. Other tools may use slightly different variations on both of these. In order to illustrate this the following shows how real conversion rates can differ using two different methods (data from a real e-commerce retailer):

GA conversion rate Unique conversion rate
May 0.79% 1.38%
June 0.88% 1.18%
July 0.88% 1.35%
August 0.80% 1.53%
September 0.73% 1.09%
October 0.71% 1.16%

2) Now, this is doubly complicated because ‘unique visitors’ and ‘unique buyers’ are very problematic metrics which different tools report in different ways. For example, GA reports ‘absolute’ unique visitors, which is the total unique cookies over the selected time frame, however Adobe (for example) is able to calculate monthly unique visitors and can also supply daily unique visitors, which when added up have massive duplication. To illustrate this look at how different conversion rate can be made to look with a twist in the unique visitors metric (again, real data):

Average monthly conversion rate over 6 months (unique conversion method as above) = 1.28%

Total conversion rate over 6 months (with 6 month absolute uniques) = 2.62%

Therefore, as you can see,  if we were submitting a conversion rate to be included in some ‘average benchmarks’ we could pick either 0.71% (GA October conversion), 1.53% (Unique August conversion) or 2.62% (Total 6 month conversion), all of which are true in their own way.

3) Now, add to this the fact that every single e-commerce website is in some way unique. Even within the same supposed ‘category’ of apparel you could have a site selling socks and a site selling $500 mountaineering coats. How could those sites possibly have the same conversion rate, or more to the point why should they have the same conversion rate? On top of that, each site has a different purchase process, different objectives, different steps.

If the benchmark is submitted through pooled users of a tool, how do you know what they were measuring? Maybe only certain products, or only for certain types of customers.

Who cares about averages anyway?

The conclusion of the above is that it is not a good idea to try to set yourselves targets (or create business cases) based on industry benchmarks. However, you nevertheless do need to understand what kind of improvement you need to aim for, to use in business case analyses or as a target. A better way to do this is to target yourselves on an improvement to your own existing conversion rate, and NOT to focus on what the actual rate itself should be. This means you need to work out what kind of improvement you might reasonably be able to accomplish, and the method can be integrated into the business in the following way:

  1. Identify all the conversion-improving activities that you know you will be able to achieve over a given period of time. For example: a change in the dynamic of different traffic sources; an improvement in check out drop off; improved ‘how to buy’ information etc
  2. Initially, some considered estimates can be made as to what improvements in these drivers you might be able to achieve. The aggregate of these changes can therefore provide a total improvement in conversion. However, it is important to understand that at this stage this is largely guess-work.
  3. Design some tests which will determine more accurate figures for the above. For example, a persuasion for people to bookmark the site could provide data on how incremental direct traffic could impact conversion rates. Run these tests over time to refine the process of forecasting and business case analysis
  4. This therefore becomes a part of the ongoing process of optimisation where you are systematically learning what can be achieved through different types of initiatives and prioritizing accordingly.

Further reading:

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