Freemium Apps: Five Data Strategies To Increase Revenue
Freemium apps offer a set of baseline functionalities for free and premium features with a fee, typically on a subscription basis. LinkedIn is a good example, where anyone can create a profile and add connections free of charge and buy for a subscription if you want to use features such as sending an InMail.
The key to building a successful freemium app is the fine balancing act between keeping your free users happy while pushing for monetisation. Data and customer analytics are your secret weapons to solve this equation! Here are five data strategies to help you increase revenue…
Optimise free vs paid features
Assume your app has 10 different features. Which ones should you offer for free? This is not a moment to be greedy. If your free features are not strong enough to create a buzz and bring in organic or relatively low cost traffic, you will have to compensate with large marketing budgets to acquire new users at scale. You also can’t afford to be too generous. If users get most what they need for free, there will be little left to monetise.
Apart from benchmarking your cost per acquisition, analyse new user behaviours. What proportion and how quickly do they convert to paid users? Are large percentages churning because they constantly hit the paywall and can’t do anything without taking out their credit card? If so, you have a leaking users and need to improve your free features. Vice versa, if your new user retention is remarkable but little convert to paid users, you are giving away too much.
No matter how attractive your paid features are, there will always be users who will never pay. Free users are as important especially if your growth relies on network effects, such as a messaging or dating apps. So make sure you monitor free user engagements throughout their customer journey and constantly reiterate your free features.
There are other ways to monetise free users too. If you have a large base of loyal free users, they probably wouldn’t mind seeing an ad or two. If you are worried about ads ruining the user experience, think of smart brand partnerships and sponsored offerings that will benefit all parties.
Clear Value Proposition
The No. 1 blocker to paid conversion is the lack of understanding on the value one will receive from paid features, especially if they are new to your app. Start by listing all the features included in your paid model and their unique selling points (USPs).
There are four key elements to test. First, the order of USPs that is presented to the user. Secondly, the wording of each USP. If you can quantify the value, people are more likely to convert. For example, an InMail receives over 50% higher reply rate than a cold connection request (the number is made up for demonstration purposes only).
Thirdly, you should also consider tailoring your approach based on different user segments. Different types of users will be interested in different selling points and while you don’t want to overcomplicate things, by doing some smaller testing on which messages resonate with which audiences you can improve results.
Timing is the fourth element to test here and it should be behaviour driven. If a user sees the InMail paywall multiple times, offer a free trial in exchange for signing up with payment details. Once they have experienced the value first hand, they are likely to stay on after the free version. If what you are offering has to be experienced by the user to realise its value, then a free trial is often your only solution to increase conversion.
Find your aha! moment
Another way to describe a freemium app is that it offers a useful free service and charges a fee to solve certain pain points or frictions experienced by users. If you can pinpoint and quantify these frictions, it is the Aha! Moment for your paid product. Data can guide you in this search. Gather all behaviour data for a user cohort, for example new users registered on the first week of December and their activities in the first 7 days. Run correlation analysis to identify behavioural patterns that lead to paid conversion. Follow up with A/B tests to confirm the causal impact of such patterns.
Analyse by cohort
If you can’t measure something, you can’t improve it. Make sure you have clear visibility for each step of the conversion funnel by cohort/segment. Your data warehouse should be efficiently modelled and optimised to provide you with cohort analysis that are 1) automatically refreshed at your desired time interval, and 2) easily manipulated so you can drill down further and examine through multiple lenses. If you are operating a subscription model, monitor retention cohorts on the health of your paid product and identify quickly any churning points.
Automate Customer Reviews
Pay attention to qualitative feedback. They come from passionate users speaking directly to you and sharing first-hand experiences on your product. A product they care about. Build natural language processing (NLP) scripts to convert large volumes of text to easily digestible insights, such as trending keywords, sentiment score and important topics. We have previously run NLP models that unlocked new paid features, doubling the revenue run rate to over $100 million. Text data is a gold mine most companies overlook.
How could 173tech help?
So there we have it, five effective ways to optimise freemium app revenue by applying your own customer data. The fundamental requirements to execute these strategies successfully are 1) an agile and data-informed startup culture, and 2) an efficient data pipeline to easily support all types of business requests. The power of data is unlimited in optimising user experience and conversion. We would love to help you scale your app through data, so get in touch today to see how we can help.