Consumption based pricing: what is it, and why is it hard to forecast.

What is Consumption-Based Pricing?

Consumption-based pricing charges customers based on their actual usage of a product or service. This model provides flexibility and aligns costs with actual usage, making it popular across industries like SaaS, telecommunications, and fintech.

Why Adopt Consumption-Based Pricing?

  • Flexibility: Customers pay only for what they use, which can be more appealing than traditional fixed pricing.
  • Scalability: Easily adapts to customer usage patterns, allowing businesses to scale services to meet demand.
  • Cost-Effectiveness: Reduces wasted resources by aligning expenses with actual consumption.

How do you forecast with consumption based pricing?

Consumption forecasting involves predicting future revenue based on anticipated customer usage patterns. This approach goes beyond traditional sales forecasting by not just projecting potential sales, but also by estimating the revenue each customer will generate over time.

Why is it so hard?

  • Dual Forecasting Needs: Forecasting the revenue obtained at customer sign-up separately from ongoing consumption.
  • Variable Consumption Patterns: Usage and, consequently, revenue can fluctuate significantly, making forecasting challenging.
  • Seasonality, by industry: usage may fluctuate across the year dependant on the customers’ customer. 
  • Data Integration: Effective forecasting requires integrating data across CRM, finance, product development, and data warehouses.

How to solve it

  • Leverage Advanced Analytics: Use machine learning to improve forecast accuracy by identifying usage patterns and adapting to data changes over time.
  • Enhance Data Integration: Automate data collection and normalization across systems (CRM, billing, usage monitoring) to ensure accuracy.
  • Real-Time Monitoring: Implement systems for ongoing, real-time data analysis to quickly adjust forecasts and operations.
  • Customer Segmentation: Improve forecasting precision by categorizing customers based on distinct usage behaviors.
  • Cross-Functional Collaboration: Form teams across sales, finance, operations, and IT to integrate diverse insights into forecasting efforts.
  • Update Forecasting Models Regularly: Continually revise models to reflect current data, market trends, and consumer behavior.
  • Focus on Education and Change Management: Train staff on new forecasting methods and manage the transition smoothly with clear communication and support.

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