How the energy sector can leverage AI to maximize customer lifetime value

AI can be a game-changer for energy companies looking to maximize customer lifetime value (CLV). In today's data-rich environment, AI's sophisticated data processing capabilities allow energy providers to predict future customer behavior with remarkable accuracy, shaping marketing campaigns and personalizing experiences in real-time and at scale.

In part 4 of our four-part series on AI in the energy sector, we’ll take a look at the areas AI can maximize the customer lifetime value and share an example of how Credera has helped a client do that very thing.

Join us for our "Scaling AI: Transforming the Oil & Gas Industry with AI Solutions" webinar on November 12!

CLV: Where AI does—and does not—help

A CLV strategy typically comprises these four components:

1. Understanding/reducing the cost of retaining customers

2. Understanding/reducing the cost of acquiring customers

3. Determining the most and least lucrative (valuable) customers

4. Maximizing the profitability of your most valuable customers while minimizing investment into your least valuable customers

While AI is not uniquely helpful in solving for the first three, it can be useful for solving for the fourth. Credera has used AI and machine learning (ML) solutions to understand at which points our clients’ customers opt-out of their buying journey and which incentives compel them to remain within that journey and ultimately convert into a transaction.

When we prioritize this analysis for the highest value customers, AI becomes a tool that increases enterprise value by improving the experience of customers with the highest lifetime value categories.

Case study: Leveraging AI to increase conversion rates for highly valuable customers

Here’s an example that elucidates the step change that AI brings to CLV, particularly when it comes to creative ways to maximize the value of top customer segments.

Credera partnered with a top streaming entertainment company to more intelligently target a high-value customer segment. While this customer profile was known to be the most profitable for the company, it had one of the poorest trial-to-subscriber conversion rates.

Credera used machine learning to determine that subscribers within this category who watched multi-episode shows often cancelled their trial subscription before viewing the sixth episode. However, if the client could compel the viewer to watch the sixth episode, they were 68% more likely to retain their trial and convert into a paying subscriber.

Equipped with this insight, the company’s marketing team could time different tactics to entice these viewers to watch the sixth episode at the moment they finished viewing the fifth through emails, push notifications, etc. As a next step, AI agents could create self-determined tasks based on which marketing tactics worked best to move the viewer into the sixth episode.

How much would a company have spent five years ago trying to increase conversion rates for this highly profitable though somewhat elusive customer? How many focus groups, consultative engagements, loyalty perks, and new content would have been budgeted for? Fast-forward to today. A small team can gain even better insights and deploy automated solutions that yield better results at a fraction of the cost of legacy techniques.

The result is that the client has not only acquired more of its most valuable customers but also has further increased their lifetime value by reducing their cost of acquisition.

Maximizing CLV within the energy sector

Marketing leaders in the energy sector can use AI to identify upsell and cross-sell opportunities by analyzing historical data, similar customers, and similar projects. Examples include:

• Dynamic pricing for utility customers: Based on customer behavior and market conditions, AI can adjust prices during the configure-price-quote process to ensure competitive and attractive pricing for high-value customers.

• Loyalty program attrition within fuel retail: By understanding consumer opt-outs, AI can recommend marketing tactics to reduce fuel loyalty attrition, decreasing cost of retention and increasing revenue longevity.

• Next-best product or service: AI can predict the next-best product or service for each customer (B2B or B2C) based on their past behavior and preferences, increasing the chances of successful upsells and cross-sells.

The bottom line

Our specialists have developed repeatable, actionable plans you can follow to quickly leverage the power of AI. Schedule a call with us to see how we can help you increase customer lifetime value with AI, and check out the rest of the content in this series:

• Part 1: Empowering the energy industry with AI

• Part 2: 4 ways AI helps the energy sector enhance customer service while cutting costs

• Part 3: How AI gives energy leaders the right information at the right time

• B2b
• B2c
• AI Strategy
• Oil And Gas
• AI Products
• Power & Utilities
• AI
• Artificial Intelligence
• Energy Sector
• Energy Industry
• Energy companies
• Energy
• Machine Learning
• Customer lifetime value
• Customer Experience