BPM Analytics
Maximising customer lifetime value (CLV) with AI and machine learning - a strategic imperative in modern data-driven marketing
The Harvard Business Review reports, "Getting a new customer is 5 to 25 times more expensive than retaining an existing one.”
Clearly, the secret to long-term success in today’s market isn’t just attracting customers—it’s keeping them.
One of the key metrics that help businesses retain customers is customer lifetime value or CLV. CLV helps marketers measure how much a customer contributes to a business over time, helping companies prioritise loyalty over one-time transactions. The higher the CLV, the stronger the customer relationship, and the greater the profitability. According to Frederick Reichheld of Bain & Company, “An increase in the customer retention rates by 5% can increase profits by 25% to 95%.”
But building these relationships isn’t as straightforward as it used to be. Customers expect personalised experiences, tailored offers, and seamless service, which isn’t easy to deliver without the right tools. This is where artificial intelligence (AI) and machine learning (ML) come into the game.
AI and ML aren’t just buzzwords. They are tools that can transform how businesses approach customer lifetime value (CLV). While AI provides the broader framework for systems to simulate human intelligence, ML focuses on training algorithms to learn and improve from data without being explicitly programmed. In simpler terms, AI drives the “what,” and ML delivers the “how.” Together, they help businesses predict customer needs, personalise experiences, and optimise retention strategies, eventually creating happier customers who stay loyal for longer.
What makes AI and ML so powerful is their ability to personalise experiences on a massive scale. These tools can analyse purchase histories to recommend products that align with a customer’s values or lifestyle, making every interaction feel relevant and meaningful.
In addition, artificial intelligence and machine learning also power predictive analytics, which can help businesses anticipate customer behaviour. A subscription service, for instance, thanks to AI and ML, might notice that a user hasn’t logged in for weeks, signalling potential churn, and could create a tailored offer to re-engage that customer.
Dynamic pricing is another way AI and ML can transform CLV. By adjusting prices based on demand, competition, and customer segments, businesses can optimise their revenue without alienating their audience. Ride-sharing apps, for example, can increase fares during peak hours, while e-commerce platforms can personalise discounts based on browsing habits. These strategies don’t just maximise revenue—they ensure customers feel they’re getting value for their money.
Even customer support has seen a major upgrade with AI and ML. In fact, according to Gartner:
- Generative AI will be leveraged by 80% of customer service organisations by 2025 to enhance their operations.
AI chatbots and virtual assistants have already started helping businesses handle routine queries, providing instant responses and freeing up human agents for complex issues. And, when coupled with ML, they can also learn from each customer interaction, refining their responses to deliver better support over time.
Clearly, the benefits of artificial intelligence and machine learning in modern-day marketing are more significant than ever. These tools don’t just help businesses understand their customers—they enable them to act on those insights in ways that drive loyalty and growth. However, it's important to note that integrating these technologies takes time and requires a foolproof strategy.
Here are some tips that can help businesses integrate AI and ML without problems.
- Audit the data
- Begin with clear, measurable goals
- Focus on system interoperability
- Empower cross-functional collaboration
AI and ML projects often fail because teams work in silos. Encourage data scientists, IT teams, and customer service departments to collaborate on implementation, ensuring solutions align with both technical and business needs.
- Establish robust fail-safes
Both artificial intelligence and machine learning thrive on data, but not all data is useful. Start by assessing the quality, quantity, and organisation of the data. Remove duplicates, standardise formats, and ensure compliance with data privacy regulations. Tools like automated data cleansing software can streamline this process.
Instead of attempting large-scale implementation right away, focus on a specific, impactful use case—like predicting customer churn or optimising pricing. Define measurable outcomes for these projects, such as a 10% reduction in churn rate or a 5% increase in upsells, to evaluate their success.
Ensure the AI and ML tools integrate smoothly with the existing CRM, ERP, or other operational systems. This avoids siloed implementations and enables insights to flow freely across departments, enriching all customer touchpoints.
No AI or ML system is perfect. Build in manual override options for critical decisions, especially in customer-facing interactions, to avoid negative experiences if the system produces an error or anomaly.
To sum up, AI and ML, when integrated thoughtfully, can maximise CLV and revolutionise how businesses retain customers, turning every interaction into an opportunity for loyalty and lasting growth.
These tools are both the present and future of modern-day marketing!
How can Infosys BPM help?
Infosys BPM's analytics services empower businesses to boost customer lifetime value (CLV) using AI and machine learning. Our solutions provide actionable insights, optimise strategies, and improve customer retention, driving profitability and growth.
