How to Use AI to Uncover Customer Lifetime Value ?

Customer is the king, but which one? How does the business know what the consumer thinks about its brands and what they don’t like? All customers are not equal. The answer lies in customer lifetime value, or CLV. Any organization that understands the importance of providing exceptional customer experiences ranks high in customer satisfaction. Understanding the customer lifetime value is essential for getting a holistic view of the customer journey and driving long-term profitability, identifying pain points, and making informed business decisions. 

As businesses adapt to CLV, artificial intelligence (AI) has emerged as a significant change for predicting, calculating, and enhancing CLV to identify the opportunities for improvement. This guide explores the use of artificial intelligence to uncover and maximize customer lifetime value. 

Why is Customer Lifetime Value Important?

Building long-term relationships is crucial in the dynamic digital world, and artificial intelligence helps cope up by giving access to a wealth of data and making CLV more important than ever.  

Did you know that attracting a new customer is five times costlier than keeping an existing one? In fact, retention is the bottom line. Just a 5% increase in retention can increase profits from 25% to 95%. Over 80% of your company’s future revenue will come just from 20% of the existing customers. 

What is Customer Lifetime Value?

An insight on the purchase patterns uncovers the tips to help improve customer satisfaction and elevate their experience with your brand. Calculating CLV varies by industry, company, and region, and hence there is no set metric for it. 

By definition, the customer lifetime value is the total revenue a business can expect from a customer or the total worth of a customer to a business throughout their relationship with the company. It is calculated on a number of factors such as 

• Purchase frequency

• Referrals

• Average transaction value

• Customer retention rate 

• Length of customer relationship 

CLV is the net project profit over the specific period or entire relationship. It takes into account the history and infers the future value. CLV is essential for evaluating

• Customer loyalty

• Shaping marketing budget

• Prioritizing resource allocation

The use of AI makes the process simpler and goes beyond the traditional CLV calculations by giving advanced insights and predictions for profitable and strategic decisions. 

Customer Lifetime Value and Artificial Intelligence 

AI can be easily integrated in all stages of the customer life cycle. There are four stages of CLV where AI is used popularly 

• Acquisition i.e., identifying the prospects and allocating sales and marketing resources accordingly. 

• Onboarding, i.e., activating the customers in a way that future retention is less challenging. 

• Engagement, i.e., identifying customers with the highest lifetime value by offering right product at the right time. Improves cross-sell and upsell.

• Retention, i.e., building satisfied and loyal customers leading to referrals and influencer sales.

Correct CLV is the most important metric for improving ROI. It helps in understanding the long-term value of a customer, boosting customer satisfaction and loyalty, making informed decisions, and ultimately increasing the revenue and improving the products and services. Customer lifetime value is necessary in everything from planning the product to selling it successfully. 

The Role of Artificial Intelligence in Customer Lifetime Value

Artificial intelligence is the modern-day marvel that uses different tools like machine learning, NLP, and AR to access, analyze and collate large amounts of data to present accurate and actionable insights into customer behavior and value. Here are some ways AI helps CLV:

Enhanced Data Processing and Integration

Artificial intelligence works seamlessly and in the background without any interruptions to combine structured and unstructured data from multiple sources- including CRM, social media, feedback programs, website analytics, purchase histories, and other touchpoints – into a unified view giving a comprehensive CLV prediction. This quick data analysis helps the business understand the long-term worth of each individual customer.

Predictive Modelling

AI delves deeper into the historical customer behavior, and machine learning tools help predict the future actions and outcomes. Unearthing the complex consumer behavior helps with accurate customer lifetime value, and an AI dynamically updates CLV data to indicate even a subtle churn or loyalty. 

Proactive Customer Retention 

One of the most valuable uses of AI in customer lifetime value calculation is to identify customers that are likely to reduce purchases with the brand. AI can analyze the behavior patterns of the customers and predict which are likely to decrease their spending, reduce the purchase frequency, or stop interacting with the company. This helps the business proactively devise the retention strategies, thus reducing the customer churn and maximizing CLV. 

Enhanced Personalization at Scale

Personalization is the key to winning more customers. Consumers expect it from the brand, and studies have shown that 37% of customers will end their relationship with the brand if personalization is absent. Artificial intelligence analysis gives an insight into customer churn and helps maximize CLV over time through early intervention. Understanding the customer lifetime value helps tailor marketing campaigns, design offers and recommendations, loyalty programs to address the need and interest of the consumer. Hyper personalization through generative artificial intelligence ultimately increases customer lifetime value. 

Segmentation

Artificial intelligence is instrumental in segmenting customers on basis of their potential future behaviors. This means micro-segments like high-value customers are prioritized for loyalty programs, exclusive offers, and hyper-personalized marketing efforts, precise targeting, etc. AI based segmentation with CLV builds loyalty and profitability. 

Real-Time Insights

Advanced generative AI tools provide real-time predictions or updates on customer lifetime value metrics so that the businesses can quickly respond to changing customer behavior and adjust the strategies accordingly. Real-time insights on CLV also enable the businesses to make changes according to changing market conditions and optimize the customer journeys for maximum long-term value.

Proactive Scenario Stimulation

A major advantage of using AI to uncover customer lifetime value is to test and stimulate various customer scenarios to forecast how the CLV strategies will pan out. AI helps with CLV by 

Measuring campaign impact, i.e., predicting how the proposed campaigns will impact customer retention, spending, or offer uses, etc. 

Product pricing changes i.e., how the changes in the pricing structure will affect the customer purchasing patterns. 

Cost Efficiency

Artificial intelligence is able to quickly work out customer acquisitions and retention costs for different segments automatically. This makes predicting CLV more cost optimized and resource-intensive. 

Steps to Use AI to Uncover Customer Lifetime Value

Collect and Centralize Customer Data

Ensure that data across all touchpoints, such as sales, marketing, customer service, and social media, can be accessed by generative AI tools. 

Chose the Right AI Tools and Algorithms

Select the right artificial intelligence tool that aligns with the business goals. The important tools to look for are predictive analytics, machine learning, and NLP. 

Build Predictive Models

Use AI output to analyze customer data and identify the trends. This includes average transaction value, customer churn rates, and purchase frequencies. This will help forecast the future customer lifetime value of customer segments or individual customers. 

Measure and Refine CLV Strategies

Use the AI tool to evaluate the performance of the CLV strategies by measuring the retention rates, customer satisfaction scores, and marketing ROI, and keep refining your strategies based on the results.

Concluding Thoughts

Using advanced artificial intelligence tools, it is simpler to refine the customer lifetime value models. A business can predict customer behavior and maximize conversion rates by using personalized recommendations and forging lasting relationships to drive sustainable growth. Using AI to uncover CLV brings in

• More accuracy by eliminating human error and considering multiple variables. 

• More efficiency by saving time and resources.

• Improved personalization by boosting lifetime value.

• Scalability because AI can handle large scale data easily without asking for additional resources. 

• Better strategic decision making by giving detailed insights about customer acquisition, retention, and resource allocation recommendations. 

Conclusion

The future of artificial intelligence in customer lifetime value is going to get more inclusive and precise with the inclusion of emerging technologies such as blockchain, IoT, AR/VR, etc., to create more immersive and data rich customer experiences that are secure and boost CLV. 

Read Also: The Role of Marketing Automation in Optimizing Conversion Rates Across the Customer Journey

Written By – Amit Bhateja