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Revenue forecast: why do you need to predict the future of your business?

Imagine you have made an app, installed tons of useful features for your customers, and have doubts if data analysis is correct to plan the future of your project. Generally marketing and product departments go a long way to arrive at the best forecasts. This involves both testing different forecasting models and assembling the right team to create an internal tool.

However, it is not always possible to assemble a tool that can fully address these tasks and satisfies the needs of marketing. Many companies risk losing time and money due to errors in metrics and forecasts. However, there is a way to avoid it.

Our tool, Mad Curve, was developed with three things in mind: simplicity, predictability, and accuracy. You will always know how, when, and why you need to spend money (or not to do it at all), what attracts your customers, and how to scale your business.

Given that our Mad Curve platform only works with mobile projects at the moment, we decided to showcase the need for forecasting for them. The described principles and goals apply to business at any time and do not depend on the type of mobile project you are developing.

What are the main goals of forecasting in the business?

  • Business valuation. Revenue forecast is valuable to businesses because it gives you better understanding of your money flows when implementing different monetization methods. Thus, you can plan your expenses more accurately and find products that can be scaled with less risk. Making informed decisions is a must when it comes to financial growth.
  • Customer acquisition. When you base revenue predictions on data from different countries and types of monetization, it gives a better and more accurate understanding of whether you can optimize campaigns to achieve the desired performance. Additionally, given the new era of super privacy, you can calculate all the necessary cost of targets metrics for your purchases, and the effectiveness of sources can be checked based on short cohort intervals in an aggregated form.
  • Product management. Whether you are a marketing specialist, a producer, or a product manager, you will implement new monetization campaigns, new content, change retention through various product enhancements, and engage users in the project. It’s crucial to measure all the changes that occur in the product for further implementation of those that actually influence the product.
  • Product development. Most business owners are always faced with changing conditions of stores and tax rates in different countries. These factors have become a game changer in advertising along with the business itself, so it makes no sense to focus on the average metrics or the previous year's report metrics. The most important thing nowadays is to keep abreast of your project: have a clear idea of its possibilities, test advertising funnel, and assess the balance of your LTV and CAC. By learning how to work with the balance of these metrics and when and how to influence them, you can develop a successful project.
  • Getting funding. Given the constant crises in the global financial environment, investment funds, lenders and publishers have started to meticulously curate the numbers and are less willing to invest in so-called projects of a better tomorrow. Projects that can successfully monetize their audience in the long term with the potential to scale it up through user acquisition are attractive to investors/lenders/publishers.

Important principles for working with forecasts

  • None of the models can predict your product development and live ops.Any forecast is a probabilistic event based on an analysis of the previously obtained data and cannot represent a 100% financially guaranteed future. No model can consider your future content plans, new monetization approaches, changes in the market and availability of your app in different countries, working with your community and returning users.
  • You need enough time and events to predict revenue.Before you work with a predictive model, you must collect data to train it. In one of the predictive models for in-app monetization, at least 500 payments/events are required. Note that time is of the essence: these 500 payments are no use for a model if they happened in one day. You need to monitor users for 3–4 weeks to collect data based on the actions of customers who came to the project, began to return, pay, reengage, and participate in promotions. After some time, you will accumulate the necessary number of events, which you can use in forecasts.
  • Manage forecasts to catch opportunities.Products under development are characterized by frequent changes in the monetization model. Here, it’s better to train the model for a period of one last month. This will allow you to detect changes in the project more quickly. If, in contrast, you want the forecasts to be constant and the figures to be more conservative, you can increase the model's training period to 3–6 last months.

It's worth noting that you should constantly monitor the LTV dynamics in your forecast models. You must be confident in your ability to keep momentum at a high level through content, monetization campaigns, community outreach, and reengaging users if you want to reach your target ROI and payback period painlessly.

Sign up for Mad Curve beta testing and try working with revenue forecasts yourself. Mad Curve is the best solution for those who want to get accurate insights into their business and make data-driven decisions. We provide marketing and product teams with the ability to track, measure, and optimize all of their efforts in one place. Apply for beta today: we'll help you achieve your marketing and business goals!

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