Usage Based Pricing from Financial & Data Analysis points of view

We are all increasingly aware of the new buzz word of 2021 – ״usage based pricing” – as more and more unicorns (such as Snowflake, Twilio, AWS, Datadog, Stripe, etc) have adopted this model. Usage based pricing means that you pay for what you use, or in other words you pay for the value you get. This could be based on a variety of metrics such as the number of transactions, the amount of data used, the amount of time (hours) consumed, etc. Sounds like a dream, doesn’t it? – low entry barrier for customers, flexibility to expand as customers grow their product offering combined with never-ending highly granular feedback from customers.

But usage based pricing brings with it some potential pain points that you need to be aware of and that need to be resolved early on:

1. Billing:
Billing can be a real issue. In traditional SaaS, you bill once a year / month a fixed amount, while with usage based pricing, you bill the customer monthly for their variable usage. This can be complicated to calculate and can be difficult to demonstrate transparently to your customer.

  • Traditional CRM tools (like Salesforce or hubSpot) do not have solutions for this yet, and you will need to develop APIs or develop a tailor-made billing system for your business.

2. Reporting Actuals:
Actuals reporting is also challenging for usage based pricing. Every month you have different amounts of revenue from each of your customers and to track and report main KPIs like ARR, Net $ retention, and CAC recovery, you will need to work with average 3, 6, and-12 month Revenues and take into account seasonality factors, etc.
In Fact, you will need to replace ARR (annual recurring revenues) with a new version of ARR (Annual run rate).

  • A solution to the challenges of this calculation can be to connect your CRM to a BI tool like Power BI, Looker, or Tableau, and to define an actuals data-base report in BI.

3. Forecasting:
Forecasting with usage based pricing becomes a matter of data science. With fluctuating revenues, it can be hard to make reliable predictions.

  • To resolve this, you will need to:
    1. breakdown your customers according to segments that reflect their behavior (e.g., according to churn, expansion, or ASP)
    2. forecast on the basis of customer cohort’s month of activation.
    3. make patterns of actuals that are updated every month with options for improvement factors.

Our customers refer to our usage based forecast spreadsheets as Learning Machines.

In conclusion:
Usage based pricing is a great model for customer acquisition, but it requires a lot of management effort. In this blog we touched on the kind of effort required in relation to finance and data management, but the same can also be said of the effort required in relation to product management, R&D, S&M and Administrative Support.
In summary, with usage based pricing, you are fighting customer churn on a daily basis, so it can become very complicated to manage, but given that it benchmarks higher expansions and retention, and therefore leads to better valuations compared to other models, it is probably worth the effort.


Avner Shier, FP&A

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