Customer Lifetime Value (LTV) Prediction using Machine Learning

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 [1] monitor LTV, but they do it inefficiently. Instead, 81% of companies[1] 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. [5]

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.

What is the 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.

Product recommendation

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.

Financial analysis

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.

Read more: Machine Learning. What it is and why it is essential to business?

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. [1]

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. [2] 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.

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. [4]

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. [4]

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. [4]

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. [8]

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. [7]

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 have collected 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 it, if necessary. This model will be used to predict 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.

How to measure customer lifetime value?

There are many ways to measure a customer’s lifetime value, and the option depends on the resources and the company. Customer lifetime value can be measured as follows:

• Identify the touchpoints where the buyer creates value.
• Integrate records to create a customer interaction cycle.
• Measure revenue at each point of interaction.
• Add together over the lifetime of that customer

Source: Qualtrics.com

The customer’s lifetime value can also be interpreted in different ways. By simply calculating lifetime value, you can improve your business in all directions. From LTV research you will learn how to increase customer loyalty and increase your sales. If you leave satisfied customers, they will stay longer and will continue to buy from you.

How to increase customer lifetime value?

As Dale and Ben Midgley write in Golden Circle Secrets: “Do what makes people feel good and they will continue to buy from you and refer others to you who will also continue to buy from you”.[3] So, what tactics will increase the chance that a customer will buy more from you?

Here are some basic techniques:

1. Detailed buyer persona and customer segmentation

Learn more about your customers through feedback channels, customer analysis, and market research.

2. A unique offer helps to distinguish you from the competition

Offer a special gift or deal based on a customer’s unique preferences. Be creative. When you’re working with increasing customer lifetime value, investing in something special to impress a customer.

3. Loyalty programs

According to Hotel Business Review, companies more often use loyalty programs as an effective tool for improving customer lifetime value. Over the past decade, the number of loyalty program participants has increased by 20%. [3]

Read more: Easy To Implement Customer Loyalty Program Ideas

4. Expand your online presence

Constantly posting content on your site and being active on social media will let your customers know that your brand exists when they need you.

5. Value-driven cross-selling

Cross-selling increases value for your customer, realize this option. By increasing the average cost of transactions, you increase your customer lifetime value. Make it profitable for your customers, and in the process, you can earn some customer loyalty points. [3]

What’s next? 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 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.

Customer lifetime value prediction using machine learning: In summary

The most important part of the value of a customer’s lifetime is that it does not apply to any particular industry, but extends to all industries. And now you know how valuable and helpful it can be.

However, if you have any questions, do not wait to contact us. We would be pleased to provide you with more detailed information about CLV and machine learning consulting. The more effort you put into increasing customer lifetime value, the more your business will generate revenue.

See our machine learning solutions to find out more.

Customer Lifetime Value (LTV) Prediction using Machine Learning: FAQ

What is the customer lifetime value (LTV), and why is it important?

Customer Lifetime Value (LTV) measures the total potential revenue a customer can bring to a business over their entire relationship with that business. It’s crucial because it helps companies understand the value of their customer base, prioritize resources, and tailor marketing strategies to maximize profits.

How can machine learning predict customer lifetime value?

Machine learning algorithms analyze large datasets to identify patterns and behaviors indicative of customer value. By leveraging historical data on customer transactions, interactions, and demographics, machine learning models can predict future customer spending and lifetime value.

What are the benefits of predicting customer lifetime value using machine learning?

Predicting LTV with machine learning enables businesses to optimize marketing strategies, increase customer retention, personalize experiences, and improve overall profitability. It helps in targeting high-value customers, reducing churn, and enhancing the effectiveness of marketing campaigns.

How does customer satisfaction (CSAT) relate to customer lifetime value?

Customer satisfaction plays a significant role in determining LTV. Satisfied customers are more likely to remain loyal, make repeat purchases, and contribute higher lifetime value to a business. Investing in excellent customer service can lead to increased customer satisfaction and, subsequently, higher LTV.

What industries benefit from customer lifetime value prediction?

Various industries, including retail, finance, insurance, e-commerce, and healthcare, can benefit from predicting customer lifetime value. Any business with a customer base can use LTV prediction to optimize marketing strategies, improve customer experiences, and drive long-term profitability.

How can businesses increase customer lifetime value?

Businesses can increase LTV by implementing strategies such as developing detailed buyer personas, offering unique incentives, implementing loyalty programs, expanding online presence, and utilizing value-driven cross-selling. These tactics aim to enhance customer satisfaction, encourage repeat purchases, and maximize customer lifetime value.

What role does machine learning play in digital marketing?

Machine learning revolutionizes digital marketing by enabling personalized recommendations, improving customer segmentation, automating processes, and providing data-driven insights. It optimizes marketing efforts, enhances customer experiences, and increases ROI by leveraging predictive analytics and automation.

This article is an updated version from May 25, 2021.

References

[1] Spd.Group. Artificial Intelligence for Customer Behavior Analysis: A Practical Use Case. URL: https://spd.group/artificial-intelligence/ai-for-customer-behavior-analysis/ Accessed May 24, 2021.
[2] Appsflyer. Machine learning in a digital age: The future is now. URL: https://www.appsflyer.com/blog/machine-learning-digital-marketing/#data-driven-analysis. Accessed May 25, 2021.
[3] Marketingsidegroup. 6 Powerful Methods to Maximize Customer Lifetime Value (CLTV). URL:https://marketinginsidergroup.com/content-marketing/6-powerful-methods-maximize-customer-lifetime-value-cltv. Accessed May 25, 2021.
[4] Forbes. 10 Examples Of Predictive Customer Experience Outcomes Powered By AI. URL: https://www.forbes.com/sites/blakemorgan/2018/12/20/10-examples-of-predictive-customer-experience-outcomes-powered-by-ai/. Accessed May 25, 2021.
[5] Blog.Hubspot. How to Calculate Customer Lifetime Value. URL: https://blog.hubspot.com/service/how-to-calculate-customer-lifetime-value#:~:text=Customer%20lifetime%20value%20(CLV%2C%20or,the%20company’s%20predicted%20customer%20lifespan. Accessed May 25, 2021.
[6] Qualtrics. What is customer lifetime value (CLV) and how do you measure it?. URL:https://www.qualtrics.com/experience-management/customer/customer-lifetime-value/. Accessed May 25, 2021.
[7] Levohealth. Calculating Patient Lifetime Value & Why It Matters. URL:
https://levohealth.com/calculating-patient-lifetime-value-why-it-matters/  Accessed May 25, 2021.