In freemium marketing, product analytics are the difference between conversion and confusion

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Jeremy Levy Contributor

Jeremy Levy is CEO and co-founder of Indicative, a product analytics platform for product managers, marketers and data analysts. A serial entrepreneur, Jeremy co-founded Xtify, acquired by IBM in 2013, and MeetMoi, a location-based dating service sold to Match.com in 2014.

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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.

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.

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.

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