One of the keys to getting customers hooked on your SaaS product is offering a free trial. Letting someone experience your product for free for seven to thirty days is a great way to establish trust with the potential customer, let them experience the product, and also gain insight into how they will use the product (customer segmentation).
On a recent project, I was reviewing data for a client and noticed a very interesting pattern in the login histories (not really, but we will call it logins since the real data can’t be shared) for trial users. This particular client offered a 7-day credit card trial with auto convert to a selected plan (i.e. monthly or annual). What I expected was a nice curve from day 1, declining each day, relatively smoothly and then an increase in logins after conversion.
However, after summarizing the login data for the first ten days of service (including 3 days for the auto convert), I found a sharp decrease in logins from day 1 and day 2, as well as a blip on day 6. See the chart below.
What was even more fascinating is how the other analysts and “experts” at the company interpreted this data. Some of the comments are below:
- “Wow, people pay us and use less?” – referring to the drop is usage on day 8 after becoming a paying customer
- “Those auto convert reminder emails are working, driving usage!” – referring to the increase in logins one day prior to trial end on day 6
- “Looks like we need pay per login” – referring to the sharp decline in logins from day 1 to day 2
- “If we can get the customer to use beyond day 4, we have them!” – not sure how this really fits in as we haven’t correlated logins with LTV, yet
- “People are cheap” – referring to the people logging in on day 6 to use the product prior to cancellation
- “If you are going to login to cancel your auto convert on day 6, wouldn’t you try the product one last time?” – again, referring to day 6
The chart is quite simple, a single line with 10 data points. What isn’t simple is really what this data means. In fact, I don’t think we can make a decision directly from this data. Rather we need to further understand what the trial users are actually doing on day 6 and how users with logins on day 7 compare to the users on day 1 (is this a bad a marketing channel). It would also be great to dive into patterns of logins just prior to churn or trial cancellation.
What fascinates me the most, is not only the different perspectives on the data, but the deeper questions that come out of the data. Data and customer insights are evolutionary. The more you know, the more you ask questions and the more the decisions and knowledge evolve.