Whether you’re talking Rollercoaster Tycoon or Dropbox, freemium offerings continue to be a common way to attract new users to consumer and enterprise software products alike. Once onboarded to the free platform, some users will eventually convert to paid plans, while many more will stay in the free tier, content with whichever features they can access. Research on the topics of freemium conversion and customer retention is plentiful, and companies are continually challenged to make even incremental improvements in freemium conversion. Those that can stand to reap significant rewards. Better use of product analytics will help them get there.
Feature Usage Tells The Tale
The volume of data coming in from software users is staggering. Every feature used during every session tells us something, and the sum of those learnings helps product teams understand each customer’s journey, by leveraging product analytics connected to the cloud data warehouse. Actually, the volume of data has never really been the issue. Giving product teams access to the data and enabling them to ask questions and glean actionable insights—that’s another story.
While marketers are using established campaign analytics platforms and traditional BI is available for looking at a handful of historic metrics, product teams often can’t readily mine the data to ask (and answer) the customer journey questions they want to pursue. What features are most used? When does feature usage tend to decline before disengagement? How do users react to changes in the selection of features in the free vs. paid tiers? With product analytics, teams can ask better questions, build better hypotheses, test for results and quickly implement product and roadmap changes.
This makes for a much more sophisticated understanding of the user base, allowing product teams to look at segments by feature usage, how long users have had the software or how often they use it, feature popularity and more. For example, you might find that usage of a particular feature is over-indexing among users in the free tier. So move the feature to a paid tier and measure the effect on both upgrades to the paid tier and the free churn rate. A traditional BI tool alone would come up short for rapid analysis of such a change
A Case Of The Free-Tier Blues
The goal of the free tier is to drive trials that lead to an eventual upgrade. Users that don’t upgrade to a paid plan remain a cost center or simply disengage. Neither generates subscription revenue. Product analytics can have a positive impact on both these outcomes. For users who disengage, for example, product teams can evaluate how products were used (down to the feature level) differently between users who disengaged quickly vs. those who engaged in some activity over a period of time.
To keep from dropping out fast, users need to see immediate value from the product, even in the free tier. If features aren’t being used, it may be an indication that the learning curve on the tools is too high for some users, decreasing the chances that they’ll ever convert to a paid tier. Product analytics can help teams evaluate feature usage and create better product experiences that are more likely to lead to conversion.
Without product analytics, it would be difficult (if not impossible) for product teams to understand why users are dropping off. Traditional BI wouldn’t tell them much more than how many users disengaged, and it certainly wouldn’t explain the how and the why of what’s happening behind the scenes.
Users who stay in the free tier and continue to use limited features present a different challenge. It’s clear that users experience value from the product. The question is how to leverage their existing affinity and move them into a paid tier. Within this group, product analytics can help identify distinct segments, ranging from infrequent users (not a high priority) to users who are pushing the limits of their free access (a good segment to focus on first). A product team might test how these users react to further limits on their free access, or the team might try a different communication strategy to highlight the benefits of the paid tier. With either approach, product analytics enables teams to follow the customer journey and replicate what’s working across a broader set of users.
Bringing Value Throughout The Entire Customer Journey
As the product becomes better for users, ideal segments and personas become more apparent, providing insight for campaigns that can attract lookalike customers. As customers use software over time, product analysts can continue to glean knowledge from user data, mapping the customer journey through to disengagement. Understanding what precipitates customers churning—what features they did and didn’t use, how usage changed over time—is valuable information.
As at-risk personas are identified, test to see how different engagement opportunities are successful in keeping users on board and bringing them into paid plans. In this way, analytics is right at the heart of product success, prompting feature improvements that lead to more customers, helping to keep existing customers for longer and building a better product roadmap for all users, current and future. With product analytics linked to the cloud data warehouse, product teams possess the tools to take maximum advantage of the data to ask any question, form a hypothesis and test how users respond.
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