Dr. Peter Fader is a professor at Wharton Marketing Department, researching things such as: the lifetime value of the customer, sales forecasting for new products, using behavioral data to understand and forecast shopping/purchasing activities across a wide range of industries, and managerial applications with focus on topics such as customer relationship management.
Key Takeaways
(02:15–03:51) Combining Research & Business
(12:42–16:51) The Myth About CLV and Customer-Centricity
16:51–18:51) About the "Customer Centricity" Book
(18:51–23:08) How to Acquire the Right Customers
(25:54–28:37) The Role of Technology in Marketing
(29:23–32:30) About the Gaps in Behavioral Analysis
(32:30–34:20) What Does the Data Really Tell Us
(34:20–38:47) The Culture of Valuing Analytics Insights
(42:35–46:05) What Changed in the Way Business Schools Operate
(46:05–49:56) About the "Customer-Based Audit" Book
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Debunking Myths Around Customer Lifetime Value
In this insightful discussion, Peter Fader, a renowned expert in marketing, data analytics, and customer behavior, delves into his academic and professional journey. He explores the evolution of customer lifetime value (CLV) models, the role of technology in marketing, and the critical need for bridging marketing and finance to drive business success. Through his experiences, including groundbreaking research on subscription-based business valuation and the development of practical customer-centric strategies, Fader emphasizes the importance of applying data-driven insights to real-world business challenges. This conversation offers valuable lessons on the effective use of data, the impact of customer-centric thinking, and the strategic role of technology in modern business practices.
1. Combining research & business
Peter Fader explains that he didn’t initially plan to become a business school professor, as his interest was more in working in the industry. However, he has always enjoyed analyzing numbers, forecasting, and deriving insights. He acknowledges the "publish or perish" culture of academia but emphasizes that his true passion lies in observing people use marketing principles to make better decisions and improve their quantitative literacy. Despite the academic demands, Peter values the industry outreach his research allows and is grateful that he can pursue both without compromising either.
But I've always loved crunching numbers and forecasting things and coming up with algorithms and insights that arise from them.
— Peter Fader
2. The myth about CLV and Customer-Centricity
Peter emphasizes that many companies reject customer lifetime value (CLV) models by claiming their business is "different," but this is a myth. He’s focused on developing not only highly accurate but also simple and broadly applicable CLV models to eliminate excuses and encourage adoption. The idea is to strike a balance between computational rigor and practical accessibility. He argues that while different types of businesses—like subscription-based versus discretionary retailers—require different model structures, the underlying principles of CLV remain useful and can be standardized across contexts.
He also dispels the misconception that there’s a single CLV formula. Instead, it must be tailored to the business model. Another myth is that it's impossible to forecast customer behavior over the long term. Peter pushes back, noting that customer behavior is often far more predictable than people assume, even over extended periods. Drawing from his early actuarial training, he likens CLV modeling to insurance risk models, which use group-level behavior patterns to make reliable forecasts. This long-term predictability is foundational to understanding and applying CLV effectively in both marketing and finance.
What I'm trying to do is not only come up with the world's best lifetime value models but the world's simplest. Because I want to make them broadly applicable. I wanna basically knock down all the excuses. Why do you think this one wouldn't apply to you, or why you couldn't implement it yourself? It's that just right balance between state-of-the-art in terms of math and computation. But also, why wouldn't we give this a try?
— Peter Fader
3. The role of technology in marketing
Peter criticizes the recurring pattern of overinvesting in technology without a clear strategic purpose. He draws a parallel between past tech waves—scanner data, CRM, big data—and the current obsession with AI. In each case, companies pour money into building infrastructure without first determining what they're trying to achieve or how it will drive ROI.
He argues that this tech-first mindset is flawed. Instead, companies should begin by asking what business problems they're trying to solve and prioritize technology accordingly, especially through the lens of customer data and expected outcomes. Peter calls for a real partnership between marketing and technology, one that starts with clarity of objectives rather than excitement over the latest tool. Without this discipline, companies risk repeating the same mistakes, wasting money, and failing to realize the value of their tech investments.
It's the same thing every time, which is that we just throw a ton of money at the technology. It's just, “Let's build the thing, and then, you know, money will come raining down from the sky.” It never happens that way. It never has happened, and it never will happen.
— Peter Fader
4. Changes in the way business schools operate
Peter discusses the evolving nature of business schools, highlighting how knowledge is becoming more distributed across disciplines and industries. He notes that while finance remains dominant, there is increasing interest in fields such as entrepreneurship, marketing, and analytics. The traditional focus on Wall Street is shifting, with students now exploring roles in retail, media, entertainment, and sectors focused on environmental, social, and governance (ESG) issues, such as climate change. This shift reflects a broader, more diversified interest in business education, with students seeking knowledge that aligns with emerging global concerns and industries.
Moreover, Peter emphasizes the importance of global perspectives in modern business education. He points out that business schools are no longer confined to a U.S.-centric view. International collaboration is now a key component, with students and companies from around the world contributing to research and insights. This global exchange allows for a richer understanding of data structures and business models, particularly in fields like customer lifetime value (CLV). As a result, business schools are increasingly adapting to a world where knowledge is not only more distributed but also deeply interconnected across different cultures, industries, and geographies.
If you look at the enrollments in our courses, finance is still the big kahuna, let's not deny that. But marketing statistics operations are much fairer than they used to be, so that's one big part of it. They're also going to be much more distributed across different industries.
— Peter Fader
5. About the "Customer-Based Audit" book
Peter Fader explains that many companies fail in new markets because they assume new customers will behave like those in their core markets. Instead of recognizing the gradual evolution of customer behavior—such as early adopters being more loyal or higher spending—companies copy-paste their existing model without adjusting for local or temporal differences. This often leads to disappointing results and reactive strategy changes.
To avoid this, Peter recommends analyzing customer cohorts based on when or how they were acquired—by time, geography, or channel. This allows companies to detect patterns, like declining customer quality or shifting behaviors, and plan accordingly. Cohort analysis provides a forward-looking view that helps businesses better anticipate the characteristics of new customers and adapt their approach, rather than relying on flawed averages or assumptions.
First of all, in terms of just their overall goodness, are we acquiring better and better customers? Or, more frequently, slightly worse customers? And then let's do average on that basis as well. Why are they worse? Is it because they don't buy as often, don't spend as much, and don't stay as long, so the average ripples down?
— Peter Fader
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