Subscription Modeling Challenges: Rolling Revenue Forecast

 When you have a subscription-based business, one of the more difficult tasks to do is create an ongoing revenue forecast that consistently takes into account all existing subscribers, future subscribers, and the resulting retention of those cohorts. It is important to do for general financial planning, expansion, capex spending, and operational decision-making as well as strategizing.

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SaaS (Software as a Service) companies often face challenges when it comes to forecasting their rolling revenue, which is the projection of their expected revenue over a future period. Here are some of the main challenges:
  • Subscription model complexity: SaaS companies often have complex pricing models with different pricing tiers, add-ons, and discounts, making it difficult to accurately forecast revenue. Moreover, the company may also have a mix of monthly, quarterly, and annual subscriptions, each with different terms and renewal dates, which can further complicate revenue forecasting.
  • Churn rate uncertainty: Churn rate refers to the rate at which customers cancel their subscriptions. It's difficult to accurately predict churn rate because it can be influenced by various factors such as customer satisfaction, product quality, and market competition. A high churn rate can significantly impact revenue, making it challenging to forecast revenue accurately.
  • Market fluctuations: SaaS companies operate in a dynamic and rapidly evolving market, making it challenging to predict customer demand and competitive pressures. Changes in the market can significantly impact revenue, making it challenging to forecast revenue accurately.
  • New customer acquisition: The acquisition of new customers can be unpredictable and heavily influenced by marketing and sales efforts. It can be challenging to accurately forecast the rate at which new customers will be acquired, making it challenging to forecast revenue accurately.
  • Seasonality: SaaS companies may experience seasonality in their revenue due to factors such as holidays or industry-specific events. Seasonality can make revenue forecasting more challenging and may require additional analysis and adjustments.
To address these challenges, SaaS companies can use a combination of historical data, market research, and industry benchmarks to build more accurate revenue forecasts. Moreover, they can implement regular monitoring and adjustment of their forecasting models to reflect changes in the market and business environment.

I've seen a lot of clients have trouble doing this. They can usually piece together a forecasting model, but trying to combine forecasting and actuals gets really hard. It is not a perfect science, but following some guidelines for modeling this can help. You want to estimate the retention of existing customers, estimate new customers and their retention, and then run a monthly forecast based on that.

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