Pricing & Monetization
3 min read
Monetization: Price is right — Monetization strategy (Part III)
Written by
Vinay Roy
Published on
19th Jan 2020

In the previous article, we discussed pricing myths, how to create a monetization strategy and how to approach pricing for a new product. Let us discuss in detail how to measure the success of the monetization strategy.

How to measure the success of a monetization strategy?

Once we have implemented or crafted the monetization strategy, there are a few metrics that we could use to measure the success:

  1. CLTV: Customer Lifetime value is the most widely used metric among all. Some marketers and product teams use the basic LTV formula (Average Revenue per Customer/Average Customer churn rate) for its simplicity. However, the formula ignores some other important components such as Customer acquisition cost (CAC) — the money spent to acquire a customer, the actual lifetime of a user — assuming perpetuity is just an approximation and may signal higher appetite on customer acquisition than what actually is, discount rate — the time value of money. Also not all users are identical but taking an average means we acquire some customers at an unprofitable point. This is where cohort analysis or grouping users based on attributes could be helpful. A more detailed formula to calculate CLTV is given here
  2. Payback period: Time required to recover the cost of an investment and is calculated as Investment/cash flow per period. Cash flow in this case is not discounted for the time value of money. We would want this to be as short as possible. However, there is a tradeoff that needs to be taken into account. The shorter the payback period is, the higher is the barrier to entry or price point for the prospective customer. So payback period should be considered along with other metrics.
  3. ARPPU (Average revenue per paying user): Monthly recurring revenue divided by the total number of active paying customers.
  4. % upgraded to paying users: What % of registering users are converting to paid users? or if we have multiple packages, then what % of users are upgrading their packages? This will help us evaluate the relative importance of each packages. We could also dissect the users for each package and see what behavioral or observable traits define the users of each package. By looking at this, we are taking a closer look at the value proposition of our product for our user base.

While there are many ways we can break down our cohort data to gain more perspective, we will look at three ways we can glean the data and generate some insights:

1. Revenue — non cumulative analysis: What is the revenue from the paying users per month

2. Revenue — Cumulative Analysis: What is the cumulative revenue from the paying users

3. Conversion (or upgrade) analysis: What % of new users are paying for the product

The best part of this analysis is that we don’t need a lot of data to evaluate the success. We can draw the above table within first few weeks of launch and see how we are doing.

Pricing is an iterative process. Best way to get it right is to keep doing qualitative research, quantitative analysis, and roll out A/B testing to confirm your hypothesis.

Hope this was helpful. I write on growth and monetization as I really enjoy understanding these levers to unlock growth potential. Let me know if you would like me to explore a specific aspect.

Read our other articles on Product Leadership, Product Growth, Pricing & Monetization strategy, and AI/ML here.

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