Using new customer acquisitions to find hidden busy seasons

Finding out which months are best for acquiring new customers can help plan your marketing resources.

Say for example, you tally up your orders from new customers this year and see this sort of distribution:

• January 8.8%
• February 7.9%
• March 9.2%
• April 9.5%
• May 8.8%
• June 7.3%
• July 7.3%
• August 7.1%
• September 6.4%
• October 6.9%
• November 11.7%
• December 8.6%

You'll notice there are a lot of customers acquired in November which is expected, but March and April were also significant. More so than even December.

There's a solid busy season right when spring starts that is almost as good as the winter holidays, at least for new customers. It's probably also lot easier to get people's attention in March and April.

You might have known or guessed at that cycle already, but oftentimes cycles can be hidden by other activity. For example, if those months were slow for repeat customers then your overall order level might have been flat, even though they are busy acquisition months.

This sort of analysis is tricky to run in Shopify without crunching a lot of data by had. This is due to how they backdate repeat customers. Using their reports, you'd end up overstating the number of repeat customers the further back you look. This would understate customer acquisitions which can cause you to miss these hidden busy seasons.

The Cohort report in Repeat Customer Insights is a better option, as every customer is assigned to a cohort month with their first order. That keeps the data clean and means you can compare new customer acquisitions from month to month just by looking at the cohort sizes.

Eric Davis