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I'm looking at ideas to see how I can forecast contract-based revenue. For example, I can have customers who have purchased a monthly mobile plan, an annual plan, or a 3-year plan. How can I use that information to predict revenue? I have to lock a customer, say on month 5 with a 1-year contract such that the customer doesn't contribute to revenue until the next renewal. (There is a possibility of churn or a change of plan.)

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  • $\begingroup$ The aim is to predict customers' revenues, right? (you've written "my revenue") $\endgroup$ Nov 29, 2022 at 14:26

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One approach you could take is to use time series forecasting techniques to predict the revenue from each individual contract. You can use the historical data on when customers signed up for their contracts and how long the contract is for to train a model that can forecast the revenue for each contract.

Another approach is to use a survival analysis model to predict the likelihood that a customer will renew their contract or churn. This can help you identify the contracts that are most at risk of not contributing to revenue in the future.

Once you have predicted the revenue for each contract and the likelihood of renewal or churn, you can use these predictions to forecast your overall revenue. This will allow you to take into account the fact that some contracts will not contribute to revenue until they are renewed.

In order to make more accurate predictions, it may be helpful to incorporate additional factors into your model, such as the customer's usage of the service or any promotions that are offered. It may also be helpful to use multiple forecasting models and combine their predictions using an ensemble approach to improve the accuracy of your forecasts.

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