SaaS Best Practices: Measuring Trial Conversion Rates – Part 1

Defining an active user and setting a baseline

Over the next few weeks, Totango will be posting a blog series on best practices for measuring conversion rates of trial usage for Software-as-a-Service (SaaS). Trial conversion is arguably the single most important business metric for SaaS companies since the model is based on two key parameters: customer acquisition cost and customer lifetime value. The trial conversion moves customers from the acquisition phase to the lifetime value phase and as more potential customers become paying customers, the customer acquisition cost goes down and the customer lifetime value goes up. Simply put, the ratio between customer lifetime value and customer acquisition cost is the entire profit of a SaaS company.

It is important to make sure that the measurement of trial conversion addresses three basic concepts:

1. Simple to measure;
2. Simple to understand;
3. Be actionable.

Unfortunately, trial conversion is not that simple to measure correctly (most organizations do it, but haphazardly) because there is no “single source of truth” per se. That is, trial conversion comes from multiple business processes (marketing and lead generation, in-house sales, and the product itself), which muddies the ability to measure it definitively. As a result, to get an accurate trial conversion number, organizations need to make sure that all the data collected is aligned among the business processes mentioned above.

The second challenge is “noise,” or trials that are “dead on arrival.” These users may have signed up for a trial, but have no intention of buying. They are just playing with the software because they can; it could be for educational reasons, it could be for other reasons. Taking these “dead on arrival” trails into account creates a very blurry picture, which is difficult to take action on.

Considering the challenges of measuring trail conversions (and the need for simplicity), the first step is to define the active, or effective trials (trials who came with the intention to buy and now are evaluating the service) and weed out the “dead on arrival” trials. There are different ways to do this of course, but one example could be measuring active trials based on a second day of usage or perhaps based on what the user is actually doing. Once the SaaS organization defines an active user, a baseline can be established. A baseline is taking the current number of trial conversions (and perhaps taking into account historical information as well, if available), and setting metrics around that.

With a baseline set that weeds out “dead on arrival” trials, organizations can tweak the service they sell or the various parts of their sales and marketing processes to improve trial conversions. Perhaps the organization needs to focus on marketing to get better leads because the current leads aren’t good enough. It could be that the sales process is not effective and it needs to be improved. Or it could be that the service itself needs improvement. Ultimately, the SaaS organization needs to measure continuously in order to put a finger on the right problem.

Imagine an organization that had, for the duration of July, 1,000 new signups for trial. Out of those accounts, 10 ended up “converting”. On the face of it, the conversion rate is 1%.

However, dig a bit deeper and in many cases, you see that many, if not most, of those 1,000 trials never had a “buying potential at all”, evident by the fact that they never did a serious evaluation of the service

(note: it would be nice if numbers in real life would be so round and simple to calculate in ones head!)

Why is this important? First off, because it gives a more real indication to what is going on within the sales team’s pipeline (they are succeeding in selling to 1 out of every 10 prospects not out of every 100), and it is easier to motivate people to improve a metric they intuitively feel is true.

But that is not it, in our next post, we’ll explore what the trial conversion metrics mean and how SaaS companies can best act on the data that is collected to increase conversion rates.