Converting freemium to premium. Markov's chain system might be what you need.
Dimitris Adamidis
GTM Strategy | Vice President of RevOps | Head of Sales & Operations | Chief Strategy Officer | SaaS | Executive | Finance | Data & Analytics | Operational Excellence | Restructuring | SalesTech
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The 2023 G2 Crowd report highlights a prevailing trend: 70% of businesses leverage the freemium pricing model. While this model attracts numerous sign-ups, the real challenge for businesses is converting these free users into paying customers.? How can this be accomplished? The answer might be with the Markov Chain.
It's a powerful tool that can provide companies with insights into future revenue streams, thus aiding in planning and strategic decision-making. One intriguing mathematical model, often overlooked in this domain, is the Markov Chain (or at least not fully adopted). Let's get deeper into how this concept can help sales forecasting in the SaaS industry in the freemium to premium area.?
The SaaS industry has been at the forefront of digital transformation, reshaping how businesses operate, and consumers engage with technology. The challenge is the monetization struggles, as only limited free users will convert to paying customers. Typically, the conversion rate in the SaaS industry ranges between 2% to 5%. That particular problem is followed by the dilemma around the resource allocations required to support a massive free user base, which can strain resources, especially in smaller startup organizations.?
Many materials and models exist, but nothing stands out as a standard model that companies can follow. I'm pretty sure a few folks from #Slack, #HubSpot, or #zoom could weigh in with their experiences, which I think would be valuable, but I've tried to leverage something that maybe can get us going at a smaller scale and grow from that.?
Markov Chains serve as a bridge between the present and the future. They are mathematical models used to predict sequences of events, with each event's likelihood depending solely on the outcome of the previous event.
Of course, in the context of a SaaS company, envision tracking a user’s progression from sign-up to premium subscription. With the help of the concept, you can estimate the probability of a user moving through various engagement stages, such as 'Free Trial,' 'Regular Usage,' 'Engaged User,' and finally, 'Premium Subscriber.'?
Central to the Markov Chain is the Transition Matrix. It is a grid detailing probabilities. Think of it as the backbone of the entire process. Each row in this matrix represents a current state (like 'Free Trial'), while the columns depict potential future states.
It's vital to note that a Transition Matrix is not static. The matrix will need updates as user behaviors change and external factors evolve. Your regular analysis ensures that the matrix remains reflective of the current situation, making it a dynamic tool for decision-making. Practically, you will need an additional layer of files or analysis to monitor changes and update the model automatically (everyone knows why).?
The Transition Matrix acts as the navigational compass for the Markov Chain. It quantifies transition probabilities, guiding businesses in forecasting, strategizing, and making informed decisions. Understanding its intricacies can improve your conversion predictability, especially in products where your product needs time to adopt. User behavior is pivotal in shaping the product value validation, impacting the company's trajectory.?
Let’s take Slack, the famous collaboration platform, as an example. Slack provides a free version with limited features and a premium version that gives users access to advanced functionalities.
First, Slack would analyze user behavior patterns and construct a Transition Matrix. It might outline the likelihood of a user moving from:
We defined categories so that we can associate them with the numbers. Suppose Slack has 20,000 users currently in the 'Free Trial' stage. Using the Transition Matrix, Slack can predict how many users will transition to 'Regular Usage,' 'Engaged User,' and eventually become 'Premium Subscribers.'
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For instance, if the matrix predicts a 50% chance for a user to move from 'Free Trial' to 'Regular Usage,' Slack can anticipate around 10,000 users making this shift.
The next thing is the iteration. By continuously applying the Transition Matrix, Slack can further refine its predictions. For instance, out of the initial 20,000 users, they might eventually foresee 5,000 users becoming 'Premium Subscribers.' So, what's the big deal about that? Sounds like a typical conversion model. Not exactly. The typical conversions in your capacity or financial planning have a more extended iteration frequency and are set for a year, quarter, or, at best, every month. That's still under the assumption that you have a robust rollup process, which we know needs to be revised by people's judgment. Here is the other way around. Your model constantly fluctuates, giving you better predictions each time something converts. There is another benefit of that model that allows you to focus your effort on stimulating your freemium customers to premium or advancing the progressive categories to premium.?You can measure multiple product adoption steps between the stages listed. That's how this model will work in practical terms.
The real strength of Markov Chains is in iterative application. The more you apply the Transition Matrix, the closer you get to predicting your long-term market share. This iterative approach allows companies like Slack to visualize their potential revenue streams and understand how tweaking the user experience can impact conversions.
Freemium to Premium with Slack
Step 1: Slack would first construct its Transition Matrix based on extensive user data analysis. This matrix might predict:
Step 2: With an initial 20,000 users in the 'Free Trial' stage, after applying the Markov Chain:
Step 3: Iterations provide Slack with a stabilized user distribution over several months, enabling more accurate revenue predictions.
…rinse and repeat.?
The number of iterations in the model largely hinges on factors like business dynamics, data availability, computational resources, and desired forecast accuracy. A company like Slack, operating in a dynamic business environment, might benefit from weekly or bi-weekly iterations, giving them a competitive edge by allowing them to adapt rapidly.
Conclusions: Markov Chains is, in fact, your forecasting Maverick.
We know that there is no crystal ball in FCSTing. It's only a matter of getting close enough to your numbers and minimizing the impact of the randomness. Using this concept is helping you to stay on the path, offering systematic insights into customer progression and shaping sales forecasts.
For SAAS giants like Slack, Markov Chains are not just mathematical models but strategic tools. While paved with probabilities and matrices, the road to forecasting success is illuminated by the insights from this tool. The model that I've described above could apply the concept in several other areas:?