Using the Freemium Model? Here’s How to Use Data & Convert More
Whether it’s Spotify or Zapier, freemium subscriptions are still a popular way to draw new users to consumer and enterprise applications. Some users will upgrade to paid plans after being onboarded to the free platform, while others will remain on the free tier, happy with whatever functionality they can get. There is a lot of research on the topics of freemium conversion and customer retention, and businesses are constantly pushed to boost freemium conversion even slightly. Those who are capable of reaping substantial benefits. They’ll get there faster if they make better use of product analytics.
Product Features Tell the Customer Story
The amount of data generated by software users is enormous. Through using product analytics linked to the cloud data warehouse, product teams can better grasp each customer’s journey. Actually, the amount of data has never been an issue. Giving product teams access to data and allowing them to ask questions and glean actionable information is a different story altogether.
Although marketers may use well-established campaign analytics tools and conventional BI can be used to look at a few historical metrics, product teams can’t always easily mine the data to ask (and answer) the consumer journey questions they want to investigate. What are the most common features? When does the use of a function start to dwindle until it becomes disengaged? How do users respond to changes in the free vs. paid tiers’ feature selection? Design analytics allows teams to ask better questions, develop better hypotheses, test their hypotheses, and execute product and roadmap changes more quickly.
This allows product teams to look at segments based on feature use, how long customers have had the app or how much they use it, feature popularity, and more. For example, you might discover that users in the free tier are over-indexing on a particular function. So, add the function to a paid tier and monitor the impact on both paid tier updates and free tier churn. For rapid analysis of such a transition, a conventional BI tool will fall short.
The Qualms of Free-Tier
The free tier’s aim is to encourage people to try it out before upgrading. Users that do not upgrade to a paid plan either become a cost center or simply stop using the service. Neither earns income from subscriptions. Product analytics will help you achieve both of these goals. Product teams will determine how products were used (down to the feature level) differently between users who disengaged quickly vs. those who participated in certain activities over time, for example, in the case of disengaged users.
Even in the free tier, consumers must see an immediate benefit from the product to avoid dropping out. If features aren’t being used, it could indicate that the tools’ learning curve is too steep for certain users, lowering their chances of ever upgrading to a paid tier. Product analytics will assist teams in evaluating feature use and creating improved product experiences that are more likely to convert customers.
Without product analytics, product teams will struggle (if not impossible) to understand why customers are leaving. Traditional business intelligence wouldn’t tell them anything more than how many customers had become disengaged, and it wouldn’t explain the how and why of what was going on behind the scenes.
Users that continue to use restricted features when on the free tier face a different challenge. The product clearly provides value to its consumers. The question is how to take advantage of their current affinities to get them to upgrade to a paid tier. Product analytics may help distinguish distinct segments within this community, ranging from infrequent users (who aren’t a high priority) to users who are testing the limits of their free access (a good segment to focus on first). A product team may try a different communication strategy to emphasize the advantages of the paid tier, or see how these users respond to more restrictions on their free access. Product analytics, in any case, allows teams to track the customer experience and duplicate what works in a larger group of customers.
Adding Value To The Customer Experience
Ideal segments and personas become more noticeable as the product improves for consumers, offering inspiration for promotions that can target customers that are similar to them. Product analysts will continue to glean information from consumer data while consumers use apps over time, mapping the customer path from engagement to disengagement. Understanding what causes consumers to leave — what features they used and didn’t use, and how their usage shifted over time — is crucial knowledge.
Test various interaction opportunities to see how effective they are in keeping users onboard and getting them into paid plans when at-risk personas are established. In this way, analytics is at the center of product success, prompting functionality enhancements that contribute to more customers, assisting in the retention of existing customers for longer periods of time, and assisting in the creation of a better product roadmap for both current and potential users. Product teams can ask any question, shape a hypothesis, and test how users react with product analytics linked to the cloud data warehouse.
