Jeremy Levy
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The freemium marketing approach has become commonplace among B2C and B2B software providers alike. Considering that most see fewer than 5% of free users move to paid plans, even a slight improvement in conversion can translate to significant revenue gains. The (multi) million-dollar question is, how do they do it?
The answer lies in product analytics, which offer teams the ability to ask and answer any number of questions about the customer journey on an ad-hoc basis. Combined with a commitment to testing, measurement and iteration, this puts data in the driver’s seat and helps teams make better decisions about what’s in the free tier and what’s behind the paywall. Successful enterprises make this evaluation an ongoing exercise.
Sweat the small stuff
A freemium business model is simply a set of interconnected funnels. From leads all the way through to engagement, conversion and retention, understanding each step and making even small optimizations at any stage will have down-funnel implications. Start by using product analytics to understand the nuances of what’s working and what isn’t, and then double down on the former.
For example, identify specific personas that perform well and perform poorly. While your overall conversion average may be 5%, there can be segments converting at 10% or 1%. Understanding the difference can shine a light on where to focus. That’s where the right analytics can lead to significant results. But if you don’t understand what, why and how to improve, you’re left with guesswork. And that’s not a modern way of operating.
There’s a misconception that volume of data equals value of data. Let’s say you want to jump-start your funnel by buying pay-per-click traffic. You see a high volume of activity, with numbers going up at the beginning of your funnel and a sales team busy with calls. However, you come to learn the increased traffic, which looked so promising at the outset, results in very few users converting to paid plans.
Now, this is a story as old as PPC, but in the small percentage that do convert, there’s a lot to learn about where to focus your efforts — which product features keep users hooked and which ones go unused. Often, the truth of product analytics is that actionable insights come from just a fraction of the data and it can take time to understand what’s happening. Getting users on board the free plan is just the first step in conversion. The testing and iteration continue from there.
The dropped and the languished
Within the free tier, users may languish — satisfied with whatever features they can access. If your funnel is full of languishing users, you’ve at least solved the adoption problem, so why are they stuck? Without a testing and tracking approach, you’ll struggle to understand your users and how they respond, by segment, to changes.
A standard business intelligence tool will tell you if 1,000 users come in and 50 convert to paid, but it won’t tell you much about the 950 who didn’t. You may know the what, but do you know the how and the why? To go deeper, you need product analytics, not dashboards. Your product team can’t depend on just one metric — the overall conversion rate, for example. That’s what traditional BI is for, reporting on a particular outcome.
Product managers are empowered when they have the analytics to show how users engage with features and what happened before and after. To understand how the customer journey affects both conversion and retention, product teams should ask questions about which features customer segments engage with, what behaviors users exhibit, what touchpoints they make, where friction exists, how paths from free to paid differ and more.
In the free tier, you might discover an unused feature or one with such high usage it prevents users from migrating to paid plans. In that case, you may want to remove it from the free tier or put limits on it. At one time, our company had a free plan with a billion events per month. The goal was to differentiate ourselves with such a high-volume offering, but as we looked into the data, we found customers who should have been upgrading and weren’t. A clear line delineated the “should be free” and the “should upgrade.” We found we could still offer a certain volume of usage in the free tier to set us apart, but not so much that it disincentivized highly engaged free-tier users from moving into paid plans. We changed the offering and shepherded those users into revenue-generating tiers.
Our experience is typical of a product-based analytics cycle built on forming a hypothesis, implementing changes and evaluating the results. There are unlimited facets of the customer journey, and many of them merit further investigation. The more well-reasoned your hypotheses, the more useful your tests will be. This approach makes the best use of the finite resources you can dedicate to analysis. As a product manager, you deserve to ask the right questions from your own desk.
You may choose to focus, for example, on the first time to value: the moment in the initial engagement when the user experiences the benefit of engaging with your offering. Product teams know customers need to see value in the first few days. If customers don’t, they’ll disengage. So, before worrying about converting customers from free to paid, the company has to demonstrate value, even in the free tier. A quick, early touch — access to training or videos — supports usage and significantly improves retention.
Social media companies know this better than anyone. When users don’t engage early on — by earning likes and followers, let’s say — they don’t come back. These companies know exactly how to keep users engaged, and whatever you think about their results, we can learn a lot from them about the value of funnel and product analytics.
What are companies missing?
The entire ecosystem in terms of how companies think about using data has changed. Product management used to be about intuition, gut notions and whatever innate talent the team had. Now, product teams are information workers — data consumers. While there’s no lack of data, there is a gap in product teams’ access to analytics that make sense of the data. Product managers already believe that having the right tools to understand customer behavior is key to improving results. That belief is not necessarily shared as they look up the executive chain, where the inevitable question is, “Why do we need another BI tool?” Teams need a different script on why product analytics are a necessity.
BI tools rely on SQL, which has limited ability to inform the complex analytics of customer journeys. Remember, BI is about having a single metric. It doesn’t empower product teams to self-serve on hundreds of questions. Product teams need this kind of agility because they carry the burden of optimizing tiers and maximizing user value. So, the notion that companies need something more than a traditional BI tool still requires education today. The companies that get it are the ones who recognize customer behavior is the chief concern of what’s driving the business, not a tangential concern.
My No. 1 piece of advice for a company whose conversion is underperforming is to start by mapping the customer journey. You have to understand which features are used and which aren’t. How are customers engaging in the free tier? What levers are driving them to convert or not? For example, in a mobile app scenario, the company should look at how long users use the app before upgrading and how frequently they use each feature. Having the journey(s) defined and measured is the starting point to understanding what’s working and what isn’t. In the absence of measurement, there’s no hypothesis, no iteration and no way to test and learn.
Mapping product usage through the hourglass
We all understand a typical acquisition funnel as a triangle pointing downward. But a customer once told me there’s another side to the funnel, which makes it like an hourglass — one funnel coming off the first. This picture in my mind helped solidify the notion of how conversion doesn’t stop once the customer is acquired. How do you measure engagement after onboarding?
Product analytics span the entirety of the user lifecycle, all the way through to when customers disengage. Product teams need to continue to follow customers and feature usage, test new hypotheses, and iterate months and even years after acquisition. For example, they should look at how new features are used, if new and old customers use features differently, and more. The work doesn’t stop simply because a customer moves from free to paid. What about a la carte upsells? Or moving from one paid tier to the next? Or retention? Product teams need to be able to answer these questions.
A company may, for example, release a new feature and make it available to all tiers for a short time, then limit it to a paid tier. Such a change is likely to affect conversion and maybe retention, too. Users might get irritated and drop off. But how will you know? If you’re going to make product changes (and you should), you should do so within an infrastructure that enables you to report on the customer journey. Otherwise, you’re flying blind and missing key details.
If used correctly, product analytics provide for an ongoing experience — test, measure, iterate, repeat. To become an even better product team, you need fast answers to your questions about all the different twists and turns of the customer journey. Your next big insight could come from something that’s happening among only a fraction of your users. Customers languishing in your free tier represent an opportunity.
Form a well-reasoned hypothesis as to why they’re not converting. Maybe you’re giving away too much of what makes your product special. Consider what changes can incentivize those users to move along, then watch their journeys closely so that whatever happens, you know why.
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