Cohort analysis is a powerful technique for understanding how groups of users behave over time. How does the behavior of users who registered this month differ from those who registered last month? Are open rates for email marketing campaigns impacted by when a person signed up? And so on.
Generally cohort analysis requires pivot tables and hours of Googling how to build cohorts in Excel. The fact is, cohort analysis is important, and it really shouldn’t be that hard. DataHero allows you to simply drag on two different dates and immediately see how cohorts behave over time. Answer questions like:
How frequently are contacts returning to my site?
Take a look at when contacts visited your site last, based on when they were initially created.
For example, in the chart above we see that for contacts who were created in July, we saw a large spike in visits for the week of Sunday September 13th and the week of Sunday, July 19th. If contacts who were created the week of July 19th have their last visit as July 19th, this may be bad news for my company, as these contacts are not very engaged and may need some enhanced nurture campaigns to educate them on your product. However, also note the spike in traffic the week of Sunday September 13th across all cohorts. This may be a result of a specific email I sent out, or a particular promotion.
How long does it take subscribers to become customers?
Chart contact creation date against customer creation date to see how long it takes someone to upgrade to customer once you’ve started nurturing them via various email campaigns.
For example, in the chart above we can see that there was a large spike in customer creations in the week of September 13th, largely from contacts who were created in the week of July 19th. Perhaps there was a specific email that was sent to them during this time, or a particular promotion was about to expire. Either way, we now know what works and how to replicate it in the future.
When is the best time to send email to get the highest open rates?
We can apply a similar idea to contact creation date and email open dates. This will allow us to see when the optimal time to send nurture emails or newsletters.
In the example chart above we can see that generally users have their first email open date the same week that they were created, meaning they are engaging with content immediately. If we start to see dropping email open rates within that first week of contact engagement, this may be cause for concern.
These are just a few examples of the insights you can get through cohort analysis from your marketing data. DataHero’s drag-and-drop cohort analysis doesn’t just work with dates – you can also use categories, countries, and other attributes to segment and analyze your data. Ready to give it a try?
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