The Unpredictability of Revenue with Usage-Based Pricing

I talked about how I see the future of enterprise software evolving in my last post. I have been pulling on one of the threads I mentioned in that - Unpredictable Costs and Revenue.?

Let’s go into the details of the unpredictability of revenue. One of the most significant shifts we're seeing with this new era of software is the move towards usage-based and hybrid pricing models. A couple of years ago, Bain published this report on how to think about whether usage-based (or consumption-based) pricing is right for your software.?

Since then, the number of software companies that now have a preference for consumption based pricing has grown significantly across the board. Take Zendesk as an example. Customer success teams are inevitably getting disrupted and Zendesk would be leaving a ton of revenue on the table if they continued to charge based on seats/licenses. So they’re getting ahead of it and recently announced their new pricing plan for their AI agents product.?

The usage based or hybrid pricing models offer the flexibility and potential for growth necessary to adapt to the new way of building software, to accommodate for the variability in costs of serving customers and the revenue resulting from their usage. They also introduce a level of unpredictability that makes revenue forecasting and strategic planning challenging.?

The Importance of Accurate Revenue Forecasts

Accurate revenue forecasts are the backbone of any successful business strategy. They inform everything from budgeting and resource allocation to investor relations and market positioning. In traditional pricing models, where revenue is often based on fixed subscriptions or seats/licenses, forecasting has been relatively straightforward. Finance teams could rely on historical data and predictable patterns to project future revenue with a reasonable degree of accuracy.

Traditional Revenue Forecasting Models

Historically, finance teams have used various models to forecast revenue, including:

• Historical Analysis: Using past revenue data to predict future trends. This method works well when there is consistency in pricing and customer behavior.
• Sales Pipeline Forecasting: Leveraging data from the sales pipeline to estimate future revenue based on the probability of closing deals, most common in B2B sales.
• Market Trends Analysis: Incorporating broader market trends and economic indicators to adjust revenue forecasts. This helps in aligning forecasts with external factors that could impact business performance.

Challenges with Usage (or Consumption) Based and Hybrid Pricing

The adoption of usage-based and hybrid pricing models brings a new level of complexity to revenue forecasting. Since revenue is tied directly to the customer's use of the product or service, it can fluctuate significantly month-to-month.?

• Variable Revenue Streams: Unlike fixed pricing, usage-based or hybrid pricing results in variable revenue streams. This variability makes it difficult to forecast revenue accurately.
• Customer Behavior: Predicting customer behavior becomes crucial. Understanding when and how customers will use the product requires a level of sophistication a lot of companies lack today.
• Market Volatility: Factors such as economic conditions, market trends, and even regulatory changes can impact customer usage patterns, further complicating forecasts.
• Cash flow issues: Usage-based pricing often involves charging customers at the end of a billing period based on their actual usage. This "use now, pay later" approach can create cash flow challenges, as companies may need to cover operational costs before receiving payment.
• Complex revenue recognition: With usage-based pricing, revenue may be recognized at different times depending on when customers actually use the service. This adds more complexity to revenue recognition and reporting.

I’m curious to learn more about how companies implementing usage-based or hybrid pricing are navigating these challenges. If you are involved in solving this or have thought about this topic deeply before, I’d love to chat with you.