- Published on
The Unpredictability of Revenue with Usage-Based Pricing
- Authors
- Name
- Chandrika Maheshwari
- @chandrika633
In my last post, I discussed the future of enterprise software and the significant shifts we're experiencing. One thread that stands out is the unpredictability of revenue. Let’s delve deeper into this issue, especially in light of the shift towards usage-based and hybrid pricing models.
The Unpredictability of Revenue
One of the most significant shifts in this new era of software is the move towards usage-based and hybrid pricing models. A few years ago, Bain published a report on evaluating whether usage-based (or consumption-based) pricing is suitable for your software. Since then, there's been a significant increase in software companies favoring consumption-based pricing across the board.
For instance, Zendesk is navigating this shift. With the disruption in customer success teams, Zendesk could potentially lose substantial revenue if they continued with a seat/license-based pricing model. To stay ahead, they recently announced a new pricing plan for their AI agents product.
Usage-based or hybrid pricing models offer the flexibility and potential for growth necessary to adapt to the new way of building software. They accommodate the variability in costs of serving customers and the revenue resulting from their usage. However, they also introduce unpredictability, making revenue forecasting and strategic planning more challenging.
The Importance of Accurate Revenue Forecasts
Accurate revenue forecasts are crucial to any successful business strategy. They inform budgeting, resource allocation, investor relations, and market positioning. In traditional pricing models, where revenue often stems from 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 reasonable 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, effective when pricing and customer behavior are consistent.
- Sales Pipeline Forecasting: Leveraging sales pipeline data to estimate future revenue based on the probability of closing deals, commonly used in B2B sales.
- Market Trends Analysis: Incorporating broader market trends and economic indicators to adjust revenue forecasts, helping align forecasts with external factors that could impact business performance.
Challenges with Usage-Based and Hybrid Pricing
The adoption of usage-based and hybrid pricing models introduces new complexities in revenue forecasting. Since revenue is directly tied to the customer's use of the product or service, it can fluctuate significantly month-to-month.
- Variable Revenue Streams: Usage-based or hybrid pricing results in variable revenue streams, complicating accurate revenue forecasting.
- Customer Behavior: Predicting customer behavior becomes crucial. Understanding when and how customers will use the product requires sophistication that many companies currently lack.
- Market Volatility: Economic conditions, market trends, and 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 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 use the service. This adds complexity to revenue recognition and reporting.