2/29/2016 update - We've had a number of requests to expand on this post and provide examples of behavior-based conversion incentives. We decided to write a 3-part series on this topic. You can read the first one here. Whenever I launch a new SaaS product I obsess about sales and onboarding details. Should I offer a free trial? How long?Or should I have a free version with no trial (freemium)?The blogs and books have opinions but most are based on limited data or anecdotes from one SaaS marketing team. Here at MadKudu we try to answer these questions based on data - how our customers are selling. This week Erik, Sam and I tried to learn how trial length affects SaaS conversions and revenue acceleration. Here’s what we learned.
We selected 9 companies with different models and relatively clean data. We then identified every trial user who converted to a paying customer and grouped them by of days it takes to convert. This is a small sample and quick analysis Our goal in writing this post was to share our preliminary results with you as soon as we found them. We will update you as we learn more. No doubt some conclusions will change. We removed “Day 0” conversions Our goal was to isolated situations where a customer signs up for a “trial” with the expectation of testing a product before purchasing. In some instances a SaaS company offers a trial but also closes deals through sales reps who charge before the customer signs up. We removed all instances where the customer converted the same day as signing up. Some customers also test a product with a personal email address and then use a company email address to purchase - we exclude these edge cases. These are high-volume B2B SaaS companies This probably doesn’t apply to photo-sharing app or $1,000,000 enterprise licenses. These 9 companies sell subscriptions for $5-$5K/month and get 10 or more free trials daily. Customers who didn’t convert are ignored This data only analyzes customers who ultimately converted from free trial to paying- it doesn’t tell us anything about improving overall conversion rates. Optimizing for sales acceleration can come at the expense of conversion rates. You can accelerate sales by giving 1-hour trials, sending threatening emails, and deleting all of their data if they don’t pay - but I don't recommend it :-)
Here is a simple example you can copy to illustrate the process.
2 days after customers sign up for this fictitious SaaS company 142 (87+55) converted, 142/305 = 47% of the total who will eventually convert.
After looking at the number of daily conversions we graphed their accumulation relative to starting a trial and came up with the following.
This graph shows the rate at which a SaaS company converts trial customers to sales. For example, here is how evaluate the results for Company C:
Company C offers a 30-day free trial - not surprisingly, most customers who decide to pay do so at the end of their free trial. But customers also convert before and after the 30th day of the trial. This graph illustrates the rate at which that happens. This graph tells us nothing about overall conversion rates since we excluded customers who tried the product and didn’t convert. For instance, saying “80% of Company C’s trial customers convert by day 40” would be an incorrect conclusion.
You accelerate sales (and get a high-five from Tomasz Tunguz) by pushing this curve up and to the left without sacrificing top-line revenue.
SaaS companies accelerate sales to hit profitability faster. VCs like Redpoint’s Tunguz look for companies who can get customers to start paying sooner.
We studied relationships between trial lengths, conversion rates and models - the results surprised us. In some cases a company tested different trial lengths over the period or otherwise had some noisy data. We didn’t attempt to run cohorts but isolated most of them by averaging with TRIMMEAN. Again, this is preliminary.
It takes most SaaS customers a little more than a month to test out and purchase a product. This general rule seems to hold true regardless of trial length or whether a product has a free version. This is ... well ... surprising.For instance, look what happens when we isolated a SaaS company Freemium (G), 14-day trial (E), and a 30-day trial (C).
Not surprisingly, freemium conversions are faster since customers can quickly choose to purchase the premium versions. And, of course, a 14-day trial accelerates faster than a 30-day trial. But these curves converge when 80% of customers convert, around 40 days after a trial starts.
A free trial creates artificial purchasing urgency. But there isn’t anything magical about the last day of a trial - some customers continue to convert at their own rate based on incentives or their perceptions of value. Every single company we studied had customers who converted more than 100 days after signing up - most had customers who converted 6 months after signing up.
Ok - this is cool. When we first noticed these results we assumed it was an error. It isn’t. Check out how similar the curves are between company B and C:
Identical curve ... different businesses. Why? Both companies have 30-day trial SaaS products and similar pricing. But that’s where the similarities end. They sell completely different solutions to different types of customers. One seems like it would have a much faster adoption rate than the other. After a little investigating we have a hypothesis: both companies rely primarily on the final day of the trial to drive conversions. In other words, both companies may be missing opportunities to accelerate sales by:
That’s why we call the “S” curve the $ curve - we see opportunities for using predictive analytics to grow revenue by developing unique conversion incentives for different customers.
See how silly that sounds? Obviously we suggest you do the very opposite. The trial period isn’t magic - whether it is 14 days, 30 days, 177.6 days, or π days. Your customers will convert at different rates based on who they are and what they do. Which … drum roll please … takes us to our conclusions ...
Running A/B tests looking for the 1-size-fits all trial period might be a waste of time - there probably isn’t a magic number for all of your customers. Instead, try to optimize your conversion rates based on qualification and behavior.
If you simply toss all post-trial customers into your “nurture” campaign you are almost definitely missing opportunities. Predict those most likely to buy based on qualification and behavior - target and pursue them aggressively for at least 90 days after your trial ends. Or ask us to do it for you.
The lesson from Observation 1 above is that your customers will convert at different rates. Identify behavior predictors and create incentives to convert them as fast as possible. This is especially true if your conversion curve fits the “S” pattern above.Yes, we can also do this for you.
The end of a free trial only serves one purpose - creating purchasing urgency. The best time to create this urgency is soon after the customer generates value from this service. This isn’t as hard as it sounds - and I can prove it to you: try MadKudu for free - we won’t ask you to pay until we start creating real value for you. Wasn't that simple?
You’re probably wondering … what’s up with Company A?
Why does it appear to do a lousy job at sales acceleration? Did Company A hire a bunch of monkeys to answer support emails?
Nope. No monkeys. Company A has an awesome product and knows their customers well. Their product is complex and has a slower adoption rate because a customer needs to integrate it into their infrastructure over time - that’s why they have a slow-and-steady adoption rate powered by converting and up-selling freemium users. What works for them probably won't work for you. Every SaaS business is different and special, especially yours. Predictive analytics isn't magic or a robotic solution - just the math we use to amplify what is already special about you.
We love doing posts like this but it also takes us a HUGE amount of time to look through and interpret the data. If you want us to write more like these please share it on Twitter and vote for it on Growth Hackers. photo credit: Thalo Porter
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