In today’s customer-focused market, it is very important to know the customer lifetime value (LTV). LTV helps companies focus their business around the most “profitable” customers and predict customer lifetime value through the use of machine learning.
Nowadays, 69% of organizations  monitor LTV, but they do it inefficiently. Instead, 81% of companies that do a good job when measuring LTV increase their sales.
According to the HubSpot survey, 55% of developing companies believe that it is “very important” to invest in customer service programs. Another study by Bain & Company showed that a 5% increase in the retention rate could result in a 25% to 95% increase in profits. 
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 that 69%. You would like to join those organizations that 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.
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What is Customer Lifetime Value (LTV) and how can it be predicted by Machine Learning?
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 organization. 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 the total cash flow of a given customer, it is much easier for you to understand how far you have got with customer retention and maximize 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 that are found 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.
Besides the customer lifetime value prediction, other key ways ML can help your business are listed here:
Eliminates Manual Data Entry
Duplicate and inaccurate data are some of the biggest problems faced by businesses today. Predictive modeling algorithms and ML can significantly avoid any errors caused by manual data entry.
Machine Learning algorithms use a customer’s purchase history and match it to a large inventory of products to uncover hidden patterns and group similar products together. These products are then offered to customers, thus motivating the purchase of the product.
Due to a large amount of quantitative and accurate historical data, ML can now be used in financial analysis. Machine Learning is already used in finance for portfolio management, algorithmic trading, credit insurance, and fraud detection.
Increasing Customer Satisfaction
Ml can help increase customer loyalty as well as provide excellent customer service. This is achieved by using previous call records to analyze customer behavior, predict customer lifetime value, and based on this requirement, customers will be appropriately assigned to the most appropriate customer service manager.
Customer Lifetime Value Prediction in Digital Marketing
Customer lifetime value is essential in e-commerce applications. In digital marketing, it gained notoriety with the development of software as a service but quickly found its place in e-commerce as well.
LTV is the most important metric to measure gross margin and success over time. Knowing the customer’s lifetime value will help you find the perfect balance between customer retention and customer acquisition.
Customer lifetime value prediction provides real information about customer retention strategies. The ever-increasing average LTV shows that your efforts to maintain and increase sales are paying off and affect your customer’s chances of returning.
Now, in the era of data-driven insights extracted through artificial intelligence and machine learning algorithms, it is possible to get information like customer sentiment analysis based on social media and predict behavior for months ahead. Some companies are just starting to use these technologies, but others such as Netflix are already actively benefiting from them.
One important thing to consider when working with customer lifetime value is that customer satisfaction (CSAT) plays a huge role. The better customer service, the higher the lifetime value of each customer will be. Excellent customer service increases the likelihood that they will buy from you in the future and remain as a customer.
80% of the content that Netflix subscribers watch is already created by an artificial intelligence-based recommendation system. By some estimates, the algorithms help to predict customer lifetime value and save $1 billion in customer retention each year. Amazon is also using AI to predict where products will be stored and place them as close as possible to potential buyers. 
Besides customer lifetime value prediction, there are many other ways to use machine learning in digital marketing to make the most of your data: improved customer segmentation, a more personalized customer experience, automation of processes, etc.
It allows you to make quick decisions based on big data. The automation achieved with machine learning can be used to better optimize your campaign. Algorithms can quickly and accurately process and analyze campaign data and send you notifications when certain trends or insights appear.  Customer path analysis using artificial intelligence finds all relationships in existing data. It can predict the likelihood of future behavior with great accuracy while detecting drivers and obstacles in the work of customers.
Read more about machine learning solutions for retail and e-commerce.
Other user cases of customer lifetime value prediction:
Harley Davidson – Motorization
The popular motorcycle company relies on predictive analysis of the customer’s lifetime value to guide potential customers, generate leads, and close sales. Harley Davidson uses an artificial intelligence program called Albert to identify high-value potential customers who are willing to make a purchase. Then, the sales representative can then contact the customers directly and guide them through the sales process to find the perfect motorcycle. 
Royal Bank of Scotland – Banking
The number of opportunities for using machine learning in global banking is constantly growing. Banking is all about numbers and patterns. Royal Bank of Scotland uses these templates to predict what products a customer might want and what problems might arise. Real-time data analysis helps the company track complaints so that it can understand serious issues and predict what questions or complaints customers may have. Artificial intelligence and machine learning in the financial sector can increase the profits of these organizations and increase customer confidence.
Progressive Corporation – Insurance
The potential of artificial intelligence in various aspects of the insurance industry is very large. Applications such as insurance advice, claims to process, and fraud detection can reap huge benefits from ML.
For example, customers of the Progressive insurance company can share their driving data with it. The company puts the collected driving data into an algorithm that predicts which customers are at higher risk of accidents. Thanks to this, Progressive can better understand the insurance market and adjust its offers based on predicted trends. 
Stitch Fix – Fashion Industry
Thanks to artificial intelligence, retailers selling physical goods also have more and more opportunities for individual personalization on a large scale. Stitch Fix, for example, uses machine learning algorithms and data analysis to examine shoppers ‘ tastes in clothing through personal style profiles that they fill out online. The Stitch Fix system then provides recommendations to help stylists make personalized choices for clients, using stylists ‘ feedback to improve their own suggestions over time. The quality of retail customer service will likely develop at a stimulated pace in the coming years. To compete effectively in this new era, retailers will need to master two important resources: data and people. 
LTV in Healthcare
The demand for patients from the healthcare sector is growing, so patient involvement is key to the development of any medical practice. Lifetime value is crucial for medical practice because it allows healthcare professionals to narrow down their interests to specific clients and gives an idea of how profitable their office is. The use of the patient pathway is an important part of the interaction. Creating a warm atmosphere in the office, welcoming feedback from patients, and sending helpful reminders about appointments will help patients stay up to date with their health and feel comfortable in the office. 
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 the basis of 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 spent at your shop? 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 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 start small. Business intelligence 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.
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, validate it, and 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 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.