69% of organisations[1] monitor LTV, but they do it inefficiently. Instead, 81% of companies[1] that do a good job when measuring LTV increase their sales. The research involved marketers and consumers only across the UK, but the numbers are still impressive. We know, you don’t want to belong to those 69%. You would like to join those organisations who use LTV to gain more sales, optimize marketing expenses, increase customer retention and encourage brand loyalty. So that’s why we are here. Our team will tell you how to reach all these goals with the help of customer lifetime value (LTV) prediction using machine learning in finance and marketing.

What is Customer Lifetime Value (LTV) prediction  and Machine Learning are

Before we move forward, let us quickly explain what LTV and Machine Learning mean. LTV (also known as CLV and CLTV) stands for Customer Lifetime Value and measures all the potential profits a particular customer can bring to the organisation. For instance, you have an online shop selling bicycles and all the additional products, and a new customer has just bought one. In the future, they may buy a helmet, new tires, a basket, etc.. At some point they may come for another bike. All these potential purchases and revenues are LTV.

This value is one of the most important factors when it comes to maximizing the company’s efficiency. We have already mentioned some of the benefits of LTV. However, here is a more detailed example: when you know a total cash flow of a given customer, it is much easier for you to understand how far you have got with customer retention and maximise ROI (return on investment).

Machine Learning (ML) is the combination of study about algorithms and statistics. This approach is used by computer engines to do a specific job without any additional instructions, relying on patterns which are founded inside the big data sets. This method of data analysis is a branch of artificial intelligence (or AI, if short). It implies that systems can learn and make certain decisions while human intervention is reduced to a minimum. Together with predictive analytics, it can significantly simplify your life. And this is important, since when measuring LTV, you have to deal with a lot of numbers.

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Customer Lifetime Value (LTV) Prediction using Machine Learning: Where to Start

Now, when you are familiar with the terms and know what benefits you can get, it is time to proceed to practice. Let’s discover how CLV can help you to optimize your marketing expenses. Follow our guide!

1. Collect your customers’ data

Relevant data is a basis on any CLV prediction, and your case would not be an exception. Here are some questions you may want to answer:

  • How much money has every particular customer already spend at your shop? Obviously every customer matters but that one who has already spent $100 is more important and promising than a person who brought you only $5.
  • How long has every particular customer actually been your customer? For two years? For a month? Or only for a couple of days?
  • How old is every particular customer? Age matters — it is an important part of your customer’s portrait.
  • How did every particular customer respond to discounts and offers? When you know who is more likely to react, you can spend your marketing funds exactly on these people, and get a result.

Depending on the type of your business, you may need more data (country of residence, marital status, etc.) but we suggest you to start small. Business intelligence consulting will help you to deal with loads of data without any problems. But for the first time you need to focus only on the most important information — in this way, you will test the new system.

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2. Build a model

After you are done with collecting information, make sure your big data set is accurate and centralized. Otherwise, you will get the wrong predictions. Then, it is time to create a machine learning model, to validate it and to fine-tune, if necessary. As a result, this model will be used for predicting LTV.

3. Implement a machine learning solution for customer lifetime value prediction

Now implement machine learning algorithms for marketing, which will process the data and identify the patterns much faster than you can ever do. In case you don’t really know how to do this, we highly recommend you to call in an expert. Otherwise, your solution won’t work properly, and the whole idea of LTV will fail.

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What’s Now? Monetization!

When everything starts working, your machine learning algorithm will help you to understand the patterns. After that, it will categorize your customers according to their LTV predictions. In this way, you will know which categories are likely to spend more money than the others and respond to your offers and discounts with a greater frequency. These customers, with higher loyalty, are your main marketing target.

You won’t spend your entire budget on trying to promote your goods or services and face a low ROI index at the same time. Instead, you will focus your efforts on the most important and promising customers, while the machine learning algorithm will keep analyzing all of them, both existing and the new ones. Your marketing expenses will be under your total control, and the marketing itself will be much more efficient than before. And, as a result, you will have a very good chance to join that 81 % of companies which we mentioned at the beginning of this article.

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If you need more information on how to step into advanced analytics and machine learning world ping us a message.

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Data sources:

  1. https://www.criteo.com/wp-content/uploads/2018/03/Criteo-UK-Commerce-Marketing-Forum.pdf

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