How to Predict Customer LTV Using Machine Learning and Python

The business development process is multifaceted and requires attention at every stage. Among the mandatory conditions for conducting profitable activities are the presence of a mobile application, studying the needs of the target audience and building an effective advertising campaign based on the preferences of the target audience. The first task can be solved in the following ways: gain access to Google console Developer or the iOS software developer platform, or use the services of third-party specialists (this can be a freelancer or a specialized digital agency). It is not always possible to create an Apple Developer Account; in particular, difficulties arise for users from Russia. If it didn’t work out once, or you don’t want to waste time, then you can get a ready-made profile of the desired type, which has already been verified and paid for a year in advance. On the Nova.Shop website you can conclude a deal profitably and quickly. As soon as you have access to your account, you can start working on the software.

Monitoring the pain of your target audience allows you to clearly understand what your customers need and give it to them before they go to competitors. Research needs to be carried out not only at the initial stage, when an application was created with the help of Apple, Google or Developer Huawei console, and the website was set up, but also during further commercial activities. No less attention should be paid to calculating Lifetime Value – an indicator that reflects the likely profit from a particular client. In this review, we will figure out how to use Python and machine learning to achieve accurate and quick results when determining this parameter for a given period, and also why you need to know LTV.

Who needs to calculate LTV and why?

Anyone who develops an application for a business and publishes it using the Apple Developer Certificate to increase sales, it makes sense to understand the mechanism for determining the value of individual customers. At least if he conducts commercial activities himself and does not involve third-party specialists. The main goal of forecasting is to achieve maximum attention from the target audience to your brand. Mastering the scheme of using machine learning and Python to determine LTV is definitely necessary for:

• ensuring maximum customer satisfaction by learning about their preferences;
• highlighting the categories of services or goods on which a person will spend the most money;
• clearer customer segmentation due to additional measurement in the database;
• timely adoption of measures to optimize commercial activities.

Of course, doing the calculations yourself is more difficult than becoming an Apple Developer and publishing a mobile application in the official store, but this is a very useful skill that allows you to carry out competent strategic planning and rationally use the advertising budget.

Workflow for Predicting User Value

1. At the very beginning, it is necessary to collect and combine customer data into one database, namely the amount of transactions made, the date and time of the transaction, as well as a personal identifier. You can add other categories of transactions.
2. The next stage is the transformation of the collected information into special functions through the use of RFM. This technique provides a meaningful quantitative assessment, in particular, to find out how long ago the last purchase was made, how often a person makes orders and for what amount. The average check amount is also calculated. For the convenience of further work, it makes sense to divide into the current and future periods. You can forecast for a month, quarter, year, etc., you just need to enter a cutoff between the observed period of time and the next period. The goal is to use a recursive method to calculate functions that allow the model to track changes in customer behavior over different time periods.
3. When the necessary data has been obtained and systematized, the moment comes to select the optimal model for subsequent making the right management decisions. It is better to use Random Forest Regressor for this – this tool is easy to use and gives fairly accurate results.

The described instructions are indispensable for marketers and TOP managers, but it can also be used by Apple Store Developer, who expands knowledge and improves skills. For example, the results of the analysis can be used to make changes to the functionality of the company’s application, send special offers and push messages suitable to the user. When managing software, the iOS Developer must remember the subscription limit. Once a year, you must pay $99 to use the platform (or $299 if you are a member of the Apple Developer Enterprise program), otherwise most of the functions will be blocked. If you have any difficulties with payment, passing security checks, obtaining a personal ID, or you simply do not know how to register an Apple developer account, please contact our specialists.