In a nutshell:
- Predicting customer lifetime value (LTV) early on is crucial for identifying and nurturing high-value customers.
- Historical analysis of LTV has limitations, while a predictive approach offers more robust support for performance goals.
- Understanding LTV benefits businesses by focusing on future high-value customers and enhancing customer loyalty.
- Machine learning can personalize customer experiences and reveal novel predictive insights.
- Predicting LTV benefits customers by increasing return on ad spend, reducing customer acquisition costs, and focusing on high-value customers for outstanding results.
Two people begin a relationship with your business. One makes a single purchase and vanishes after a month, never to be seen again. The other makes purchases monthly and sticks around for years, enjoying your products and services for the long haul.
Wouldn’t it be nice to know, from the very first purchase, which one is which? The long-term customer who buys regularly and is devoted to your brand: That’s the customer you undoubtedly want to acquire, embrace, and retain.
Therefore, it’s imperative to identify those customers by predicting customer lifetime value (LTV) early in their journey with your business. LTV is sometimes known as CLTV or CLV and, in the mobile apps/games world, as pLTV, which stands for “predicted lifetime value.” Same concept!
Customer lifetime value is "the indispensable measure for marketers."
— Neil Hoyne, Chief Measurement Strategist, Google
Why Predict Customer Lifetime Value
It’s easy to get swept up in short-term considerations when there’s pressure to acquire as many customers as possible. But playing the long game by focusing on LTV is much more likely to drive better and more sustainable business growth.
Photo by billow926 on Unsplash
With innovative predictive methods that make the most of your customer data, you can be better equipped than ever. You can predict the future of high-value customers and nurture them for long-term success.
Whether companies explicitly recognize it or measure it, they are already in relationships with their customers. It’s just a question of strength and value of knowing how important you are to your various customers. Who’s a best friend? An acquaintance? A fling? Who are the customers who snapped up that 75 percent-off daily deal, never to return? Who are the long-term, committed partners who haven’t been getting the attention from you that they should? There’s actually a way to answer these questions, with the precision that only math can deliver. The metric we use to understand customer relationships is known as customer lifetime value, or CLV. A CLV model predicts how much each of your relationships will be worth over its lifetime. It’s quickly becoming the indispensable measure for marketers trying to understand if they are creating sustainable value for their business or merely positioning themselves between transactions.
Neil Hoyne
Chief Measurement Strategist at Google, in his book Converted: The Data-Driven Way to Win Customers’ Hearts
Neil Hoyne, Chief Measurement Strategist at Google, discusses predicting LTV at a Pecan webinar
Historical Analysis of LTV Offers Limited Value
A typical approach to calculating customer lifetime value is retrospective. In other words, it looks at past data sets to see which existing customers are worth the most.
Typically, you’d total up an individual customer’s purchases and divide that sum by the count of their purchases. That would tell you the average purchase value for that customer. A higher average purchase value would mean a customer was more valuable to your business over their entire lifetime.
What would you do with this lifetime customer value information? First, you might create customer segments by LTV and look for their demographic and purchasing behavior patterns. Next, you could do statistical analyses or create visualizations based on your gut feelings about which variables might set high-value customers apart. Finally, you could seek out more potential customers with those same profiles. But you’d assume (maybe incorrectly) that they’d become high-value customers.
As you might already notice, this approach has some serious limitations:
- What if a customer identified here as high-value just made their very last purchase from your business? You can’t tell from this approach whether that customer will continue a relationship. You might invest in nurturing customers who are already out the door, or spend too much on active customers who would stay with you anyway. (Hey, we’d also love to help you predict customer churn!)
- What about new customers? With no purchase history to look back on, there’s nothing to calculate with this retrospective approach. You might do some “look-alike” customer segmentation to guess their trajectories, but that’s probably not much better than a guess. Instead, you could efficiently identify future high-value customers and quickly act to solidify your relationship with them.
- Is your analysis of historical data the best way to find future high-value customers? Humans can stare at data all day and never see complex patterns, even using BI tools. But predictive models powered by artificial intelligence can spot the patterns in seconds.
A historical method for using LTV has limitations — while a predictive approach to LTV can offer far more robust support for your performance goals. Let’s explore how.
Photo by Icarus Chu on Unsplash
How Predicting Customer LTV Benefits Businesses
When marketing resources are limited, predicting and focusing on future high-value customers is critical to success. Building customer loyalty is everything in this time of rapid change, economic uncertainty, reduced brand allegiance, and unpredictable customer behavior. As a result, identifying and keeping customers happy — especially high-value customers — is more critical than ever.
Predicting customer lifetime value with machine learning can enhance and personalize customers’ experiences. Predictive analytics methods are far better at parsing complex data and determining how variables interact to shape different outcomes.
In addition, when humans look for patterns in data, their search is biased by their preexisting assumptions. Machine learning models bring an impartial perspective to the data and reveal novel predictive insights.
Personalization is a force multiplier — and business necessity — one that more than 70 percent of consumers now consider a basic expectation. Organizations able to build and activate the capability at scale can put customer lifetime value on a new trajectory — driving double-digit revenue growth, superior retention, and richer, more nurturing long-term relationships.
McKinsey report
Predicting LTV Benefits Customers, Too
When you identify your future high-value customers, you can dedicate marketing resources to campaigns designed to acquire and retain them. With that focused approach, you can increase your return on ad spend (ROAS) and reduce customer acquisition costs (CAC).
Identifying patterns in the data of your current high-value customer base is invaluable. This method allows you to understand which new customers will also likely evolve into high-value customers within a specific time period. That’s possible even early in your relationship with them.
Whatever industry you’re in, your current and future high-value customers are your greatest resource. Focusing on their needs and personalizing their experience can bring outstanding results.
Photo by Jorge Fernández Salas on Unsplash
Common Questions About Predicting LTV
Why is predicting customer lifetime value crucial for businesses?
Predicting customer lifetime value is crucial because it allows businesses to understand customer behavior and revenue potential. By identifying their most valuable customers, companies can tailor their marketing strategies accordingly. This leads to increased customer acquisition and retention, ultimately driving business growth. LTV predictions help businesses focus their resources on the customers who are likely to bring the most monetary value over time through future transactions.
How do I predict the lifetime value of a customer?
To predict a customer's lifetime value, you can use a formula or a machine learning model. The formula involves multiplying the customer's annual spend by the expected number of years they will stay with your business. By multiplying these two factors, you can estimate the monetary value a customer will bring to your business over their entire relationship with you. While this formula is helpful, a machine learning model can be far more accurate by incorporating many more nuances of your customers' profiles and behavior across your entire customer and transactional data. Pecan is an excellent tool for building these LTV models to predict high-value customers, without needing a data scientist.
How accurate are machine learning models in predicting CLV?
Machine learning models for CLV predictive analytics have been proven to be highly accurate and reliable when trained on large and diverse datasets. Machine learning algorithms can process vast amounts of customer data, identifying patterns and trends that might be invisible to human analysts. This accuracy allows businesses to make data-driven decisions with confidence and allocate their resources more effectively, leading to improved overall performance and ROI.
How does CLV benefit different areas of a business?
CLV provides valuable insights that benefit multiple areas of a business, including advertising, marketing, sales, support, and operations. By understanding customer preferences, behavior, and profitability, businesses can:
- Personalize campaigns to acquire customers who will be high value
- Develop marketing strategies that resonate with the most profitable segments and increase conversions
- Guide sales teams to focus on high-potential leads
- Improve customer support to retain valuable clients and bring about repeat purchases
- Optimize operations to better serve key customer groups
These insights enable businesses to fine-tune their strategies across departments, including sales and marketing teams, and improve overall performance.
What tools are available for accurate CLV analysis?
Tools like Pecan provide accurate predictions of customer lifetime value, using advanced algorithms and techniques to process customer data and generate actionable insights. With CLV predictions from Pecan, businesses can:
- Identify trends in customer behavior
- Predict future purchasing patterns and repeat purchases
- Carry out customer segmentation based on their potential value
- Optimize marketing and retention strategies to increase conversion
This allows businesses to make informed decisions and maximize their return on investment in customer acquisition and retention efforts.
Why is CLV considered a crucial metric for business decision-making?
CLV is a crucial metric because it provides a comprehensive understanding of customer monetary value and profitability over time. Unlike short-term metrics, these measures give businesses insight into the long-term potential of their customer relationships. By using CLTV and CLV, companies can:
- Prioritize their efforts and resources more effectively
- Make informed decisions about customer acquisition costs
- Develop strategies for long-term customer retention
- Identify opportunities for upselling and cross-selling
- Gauge overall business performance and growth potential
These metrics help businesses focus on building lasting, profitable relationships with their customers, leading to sustainable growth and success.
In a follow-up blog post, we explored exactly how this process works and discuss some examples of putting predicted LTV to work. Check it out now — or, if you’re already ready to learn more, get in touch today!
