As the Groove team sat around my kitchen table, the feeling I was experiencing most was worry.
It was January of 2013, and we were meeting to discuss our alarmingly high churn rate, which was hovering around 4.5%.
We were getting new customers at a steady, predictable rate, but users were leaving much too fast for sustainable growth.
As we pitched solutions back and forth, one thing became painfully clear: we had no definitive answer for why customers quit.
Sure, we had ideas. The app had bugs. We had too many features. Our onboarding process was complex.
And we were working hard to fix those flaws, but we still couldn’t point to any direct connections between a customer’s behavior within our app and their likelihood of leaving.
We had committed ourselves to a data-driven approach to user acquisition – readers of Groove’s blog have read about dozens of things we’ve learned from that undertaking – but we hadn’t, as one of our developers bluntly pointed out, applied the same principles to our retention efforts.
And so it was decided that, if we were to systematically reduce churn, we’d need to systematically study it and find its source.
After the meeting ended, that’s what I immediately set out to do.
What I found was illuminating.
There were very strong differences in the behavior of users who had abandoned Groove and the behavior of those who stayed.
Below, you’ll find some of the most valuable “Red Flag” Metrics (RFMs) we’ve found to identify churn before it happens and how we’ve used them to reduce churn to 1.6% over the past several months.
Which Engagement Metrics Were the Biggest Red Flags?
Disclaimer: Every business is different, and your customers will behave differently from our customers. My hope is not that you apply these exact metrics, but that you apply these principles and find your own RFMs. It takes work, but trust me (and see our results for yourself), it’s worth it.
When we looked at the numbers, there were two metrics that seemed to be the most significant in the first 30 days after a user signs up – length of first session and frequency of logins.
We found a large difference between the behavior of our churning users and the behavior of those who continued to use Groove.
There was a difference in total number of logins as well (and I’d recommend tracking it), but it wasn’t as disparate as the other two, so we focused on those.
Looking at one cohort (thanks to KISSmetrics for making this kind of data analysis simple), the average user who did not quit after 30 days spent three minutes and 18 seconds using Groove in their first session, and logged in an average of 4.4 times a day. The average user who quit spent 35 seconds using Groove in their first session, and logged in an average of 0.3 times per day.
What Abandonment Looks Like
That’s a massive chasm.
We began to send targeted emails to users who spent less than 2 minutes on their first session, as well as to those who (regardless of first session time) logged in fewer than 2 times a day in their first 10 days.
The first group was offered help with setting up their mailbox:
That email got a 26% response rate; and more than 40% of the users who walked through their signup with us were still Groove customers after 30 days.
The second group – many of whom had already set up a mailbox but had simply tapered off usage afterward – received a slightly different email, this time offering a personalized strategy session:
The response rate on this email was just over 15%; and nearly 50% of those users remained after 30 days.
In fact, the success of both of these campaigns has led us to implement similar tactics for onboarding all new users.
Takeaway: Look at your metrics and find the disparities between your most engaged users and the ones who’ve quit. Then, use what you learn to identify at-risk users and get involved right away.
Another (Less Obvious) Red Flag Metric
According to the White House Office of Consumer Affairs, 96% of customers who have a bad experience will not tell the business.
Instead, they’ll simply take their business elsewhere.
As a support company, we’ve used that insight to educate our users to keep open communication with their customers, so that issues get flagged long before they become big enough to drive people away.
But while developing our RFMs, we found a way to determine, with surprising accuracy, when customers were having issues they weren’t telling us about.
We know how long certain actions within Groove are supposed to take.
Creating a rule generally takes between 10 and 30 seconds.
Integrating a Twitter account takes around 20 seconds.
Customizing a support widget is 2 to 3 minutes.
We looked at a number of in-app tasks and how long users were spending on those pages.
Sure enough, some of the users who had quit had spent significantly longer than average on (usually) a single task.
When a user got stuck on a task – for example, creating a rule – they wouldn’t always tell us. In some cases, that session would be their last.
So we began to reach out to users who we thought might be stuck on a particular task. When a user spent far longer than average on a particular page in more than one session, we reached out and asked if they needed help.
The response rate to this email, while much lower than the emails above, was still around 10%; and around 30% of the users who responded were still customers after 30 days. That number may seem low, but the data suggests that there’s a very high chance they would have abandoned Groove otherwise.
Takeaway: Your customers probably won’t tell you when they hit a snag. Dig into your data and look for creative ways to find those customers having trouble, and help them.
Bonus: Using RFMs to Find Your HAPPIEST Customers
While RFMs can help you spot your “flight risks,” you easily can use the same data to leverage your most engaged customers.
Simply look at the outliers on the opposite end.
We created a segment of customers who, based on frequency of logins, were Groove power users. Then, we sent an email asking those customers for a referral.
We also sent the same email to a random group of customers without any pre-qualification and without the reference to usage.
Our qualified users sent us nearly 400% more referrals than the second group.
Takeaway: RFMs can help you find your best customers, too. Use that data to get referrals and grow your business.
The Importance of RFMs and Other Analytics
Now, more than ever, we (SaaS businesses) have access to amazing data.
And we’re always finding new and amazing ways to leverage that data to grow.
RFMs (which you may already use by a different name) provide an opportunity to keep our most at-risk customers on board, and to make our happiest customers even happier.
If you don’t use them, I hope you’ll start. It’s had a massive impact on Groove, and I’m confident it’ll have a massive impact for you.
Finally, a Request
Speaking of data, most startups and small businesses keep it private.
And while there are good reasons for doing so, it means that there’s precious little real-world data out there to benchmark ourselves against.
That’s why we’ve launched the 2013 SaaS Small Business Conversion Survey.
We’re asking SaaS startups and small businesses to (anonymously) share data on conversion rates, user acquisition strategies, and other facets of their businesses, to make us all more educated, more savvy, and more successful.
Plus, we’re offering THOUSANDS of dollars in prizes from partners like KISSmetrics, Unbounce, Mixergy, Buffer, Clarity.fm, and more.
If you gained value from this article, I’d really appreciate it if you would take five minutes to complete the survey here.
Or, to read more and learn about the prizes, check out our announcement here.