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30-day trial? 14-day? Freemium? Here’s why it probably doesn’t matter

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.

Data from 9 representative SaaS companies

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.1

Example

Here is a simple example you can copy to illustrate the process.

Screen Shot 2016-02-12 at 4.36.16 PM

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.

Results

After looking at the number of daily conversions we graphed their accumulation relative to starting a trial and came up with the following.

Screen Shot 2016-02-12 at 4.47.23 PM

How to read this graph

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:

Screen Shot 2016-02-12 at 4.57.56 PM

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.2

Sales acceleration

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.

Screen Shot 2016-02-12 at 5.00.04 PM

SaaS companies accelerate sales to hit profitability faster. VCs like Redpoint’s Tunguz look for companies who can get customers to start paying sooner.

Observations

We studied relationships between trial lengths, conversion rates and models – the results surprised us.3

Screen Shot 2016-02-12 at 5.19.57 PM

Observation 1: It takes about 40 days to get 80% of SaaS conversions

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).

Screen Shot 2016-02-12 at 5.29.57 PM

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.

Observation 2: Half of SaaS conversions happen AFTER the trial ends.

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.

Observation 3: The “S” curve is the $ curve

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:

Screen Shot 2016-02-12 at 5.48.12 PM

 

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:

  • creating additional incentives to convert sooner, and
  • continuing to drive conversion after the trial ends.

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.

All of your customers are exactly the same – so use the same conversion incentive for everyone

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 …

My advice for accelerating your SaaS sales

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.

Pursue post-trial sales

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.

Create during-trial conversion incentives

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.

Optimize conversions based on value creation – not time

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?

Epilogue: YOUR business is special

You’re probably wondering … what’s up with Company A?

Screen Shot 2016-02-12 at 5.57.44 PM

Why does it appear to do a lousy job at sales acceleration? Did Company A hire a bunch of monkeys to answer support emails?

monkey (Mixed-Breed between Chimpanzee and Bonobo) playing with a laptop (20 years old) in front of a white background

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.

Want us to write more posts like this?

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

 

  1. 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 :-)

  2. 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.
  3. 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
  • http://fangrow.com FanGrow.com

    Thanks for sharing your knowledge!

  • Shreedhar Vepencheri

    This is so awesome!

    • Kevin Dewalt

      Thanks Shreedhar! Are you rethinking your sales strategy as a result of this data?

  • Frank van Polanen

    What about lifetime? Do you think there’s a difference in the lifetime of a user – based on the kind of trial?

    • http://blog.allaboard.io kevindewalt

      We haven’t actually studied that, so I don’t have any data. However, my guess is that other factors besides trial length will impact lifetime/churn. But perhaps longer trial lengths mean that customers make a more informed decision and last longer.

  • Jason Oakley

    Awesome article! A great follow up would be some specific examples of effective during-trial and post-trial incentives.

  • Jaeheung Yoo

    Very interesting analysis.. Thanks