What is revenue forecasting?
Revenue forecasting is the process of estimating the future revenue of a business over a specific period.
This is based on assumptions and metrics such as customer acquisition, retention, and monetization rates.
Example:
Consider a D2C brand in India that specializes in sustainable fashion, and the following data points:
- The brand has 3,000 active customers, and no churn is expected in the upcoming month.
- It expects to acquire an additional 300 customers in the upcoming month.
- The average purchase value per customer is ₹2,000 per month.
- The customers are expected to buy only once in the month.
The revenue forecast for the next month would be:
Revenue = (3,000 active customers + 300 new customers) × ₹2,000
= 3,300 × ₹2,000
= ₹66,00,000.
[Note: For the purpose of modular learning, we have made 3 assumptions which won't hold true in a real-world scenario:
- Zero churn rate
- Purchase frequency being 1. Also, it being the same for new and returning customers
- Average order value being same for new and returning customers
We will be tackling more realistic scenarios later in the skilleton.]
Inputs required for revenue projection:
Revenue forecasting for an internet business, therefore, needs 3 input metrics:
1. Customer Acquisition Projection: Estimate new customer acquisitions based on historical trends and upcoming marketing initiatives.
2. Customer Retention Projection: Assess the retention rate from past trends.
3. Monetization Projection: Determine Average Revenue Per User (ARPU) or Average Order Value (AOV), considering historical trends and impact of new initiatives.
Revenue Calculation: Integrate these projections to estimate total revenue:
Revenue = (Existing Customers × Retention Rate x ARPU) + (New Customers × ARPU)
We will be covering these input factors one by one, in subsequent concept cards through this skilleton.