Mobile App Financial Model - 5-Year Forecast - Freemium

Description
Recent Updates: Full integration with a 3-statement model, cap table, and improved global assumptions as well as formatting conventions.

This general use 5-year financial forecast for a mobile app has three main drivers for revenue. They include:
1. Ad Revenue
2. No-Ad Service Revenue (freemium)
3. In-App Purchases

The logic that drives revenue can generally be applied to any business model that makes money by offering a free digital/online product and charges for in-app purchases. If the ad revenue piece doesn't fit, that can easily be zeroed out. There are levers to adjust how much traffic is coming in and what percentage of that traffic is downloading and engaging with the app.

To start, let's talk about traffic. There are two main sources:
1. Traffic from paid ads. This is a definable monthly spend. Based on a user defined CPA (cost per acquisition) that spend creates a certain amount of downloads per month.
2. Traffic from organic search or word of month/viral effects. This is a pre-defined figure that can be changed over the 5 year period. Also, a percentage exists in each year to define how many downloads result from that organic traffic.

In this model, there are two types of users:
1. A user that downloads the app and is subject to seeing ads.
2. A user that downloads the app and pays a monthly fee in order to remove all ads.

All users are then subject to a second percentage that defines how many of them will spend money on in-app purchases. There are up to 5 slots for different types of in-app purchases as well as a percentage to define how many users will purchase each type. These are not mutually exclusive and you can zero out any number of slots if it is not applicable. Most apps here will just have an average spend per month and that will drive this revenue piece, but I wanted to add flexibility with the extra slots in case that made sense for the users given scenario.

The user that is subject to seeing ads will then generate ad revenue for the app based on a predefined number of impressions per user per month and a CPM.

From there, we move on to expenses. I added general fixed expenses in a regular schedule that has 32 slots for G&A, R&D, and S&M as well as dynamic inputs for payroll taxes/benefits (i.e. you can mark if a given expense item is subject to that cost or not) and there is also assumptions for a few different cost of goods sold types. The first being an average cost per month per user (generally a hosting fee for servers) and the second being 6 slots to define monthly COGS values over 5 years if applicable.

The way the churn works is through two dynamic matrix tabs that are able to look at new users added per month against the average life per user (defined in each of the 5 years) and see the existing users over time per each monthly cohort.

There is a startup cost schedule, investor logic / valuation, and a high level executive summary to view the key financial line items of the 5-year forecast. There is also logic if you plan to raise money through traditional debt/bank with a p+i loan.

I included a summary DCF analysis for the project, investor pool if applicable, and the owner/operator as well as resulting ROI and equity multiples for each.

There are 15 visuals that display key metrics and results of the assumptions. Some of the key metrics included average CaC, LTV, month to pay back CaC, and average revenue / gross profit per user.

To reduce user errors, no lookup formulas were used. Instead, index/match was utilized to reference the assumptions dynamically.

This template is also included in two bundles:
- All Models Bundle: https://www.eloquens.com/tool/P8Y4TX4v/finance/financial-forecasting-models/financial-models-120-useful-and-usable-logic
- SaaS / Subscription Models: https://www.eloquens.com/tool/wmyQI0eE/finance/venture-capital/the-complete-venture-capital-bundle