Increasing Free Trial to Customer Conversion to 70 Percent (w/ CallRail)

The metric: Trial to Customer Conversion %

In this episode, we’re covering Free Trial to Customer Conversion %: the percentage of people who start using your product or service for free, and end up becoming paid customers.

John Bonini chats with Jason Rozenblat, to learn howCallRailincreased conversion % as high as 10 points for some customer groups.

You’ll learn:

• The metrics their company obsesses over, and who owns them

• How they’re able to reduce friction between sales and marketing by transparently sharing responsibility for the numbers

• And loads more…

Why Trial to Customer Conversion %?

CallRail is an incredibly data-driven company. They transparently share performance metrics across the entire team, to foster accountability and ownership.

定期通过观察指标,他们从来没有surprised to see a low or high number at the end of the month. And they work together to try and identify negative (or positive) trends when they see them happening in real-time.

In this episode, Jason Rozenblat (VP of Strategic Accounts) shares how CallRail grew Free Trial to Customer Conversion %: the percentage of total users who begin a free trial and end up becoming paid customers.

While they have other metrics they obsess over (namely MRR, ARR, and ARPU), Jason and his team are especially focused on “Free Trial to Customer Conversion %” because it directly impacts deal size and close rate.

It’s also important, in that it’s a shared metric across the team. Jason works with the Demand Gen team to grow it, and it’s shared by any teams that touch the website because those teams also care about driving traffic and improving website conversion rate. The two go hand in hand.

This includes engineering, product marketing, and customer marketing. So they obsess over this metric as an entire organization.

How They Improved It

Jason and his team found a number of things that have contributed to growing this metric.

They changed from monthly to weekly cohorts.

By measuring cohorts weekly rather than monthly, they were able to get much more granular and ask, “what happened this week, that was different than other weeks?”. This helped them spot causes of growth or decline in real time, as opposed to waiting until the end of the month and looking at a post-mortem.

They also started tracking cohorts in 5-week intervals (from 2-week intervals).

Since they have a 2-week free trial, they used to analyze cohorts in 2-week intervals. But they started to realize that there was always a long tail of trials that would close way past the 2-week mark.

Users would extend their free trials, or come back after a trial had expired in order to enter payment information.

So by extending the sales cycle, and measuring cohorts in 5-week intervals, they got a clearer picture of what was going on and had better data to make decisions from.

They changed the way they handled lead assignments.

Before, if sales reps were going to be using PTO the following week, they’d be pulled out of rotation to ensure they weren’t assigned any new free trials to manage. But what they didn’t account for was all the free trials that were set to expire on while that rep would be out of office.

So they changed the way they handled lead assignment, to make sure that whenever a free trial expired, it was passed off to a rep who would be in office during that time. They also started to view expired trials as viable leads, and focused more on converting them.

They focused on sales training and education.

On top of all of this, they continued training sales reps on how to better handle objects, close deals, etc.

Results

他们看到的结果是惊人的. Some months, they increased their Free Trial to Customer Conversion rate as high as 10 points.

With the traffic and free trial volume they had at the time, that change alone could add an additional 100-150 customers, or $100k of ARR.

And in certain cohorts, they saw trial to conversion rates of 70-80%.