I am an Associate Professor of Marketing at the University of Maryland's Robert H. Smith School of Business. I popularized customer-based corporate valuation, a "bottom-up" approach to valuing firms by assessing the value of their customers. My other areas of research include data fusion, data privacy, missing data problems, machine learning, and causal inference.

My research has been accepted and published in top-tier academic journals such as the Journal of Marketing Research, Marketing Science, Journal of Marketing, Journal of the American Statistical Association: Theory and Methods, and Annals of Applied Statistics. My work has also been featured in media outlets such as the Harvard Business Review, Wall Street Journal, FT, Fortune, The Economist, Inc, USA Today, Barron's, CBS, CNBC, Slate, Business Insider, and CFO Magazine. I have won numerous awards, including the Don Lehmann Award, Robert J. Lavidge Award, MSI Alden G. Clayton Award, ISMS Award, Shankar-Spiegel Award, and Gary Lilien ISMS-MSI-EMAC Practice Prize. I was also a finalist for many others, including the Paul Green, John A. Howard/AMA, JM Hunt/Maynard, and MSI H. Paul Root Awards.

In 2015, I co-founded a predictive analytics company, Zodiac, where I served as the Chief Statistician. Zodiac was acquired by Nike in March 2018. I subsequently co-founded Theta to commercialize my work on customer-based corporate valuation.

Before joining the University of Maryland, I was an Assistant Professor of Marketing at Emory University's Goizueta School of Business. I hold a Ph.D. in Statistics from The Wharton School of the University of Pennsylvania. I also earned a BSc in Economics with a concentration in Statistics and Finance from The Wharton School and a BAS in Systems Science Engineering from the School of Engineering and Applied Science, as part of the Jerome Fisher Program in Management and Technology at the University of Pennsylvania.

I've been putting together materials for a new course I'm teaching this spring at University of Maryland - Robert H. Smith School of Business as part of the Interdisciplinary Business Honors (IBH) Program program. It's a section for undergraduate honors students, and I'm genuinely excited about it.

Part of what makes this group special is that they're early in their program. Many haven't had statistics or finance yet. So when I needed to introduce customer lifetime value (CLV), I couldn't just point them to the standard stuff. Those tend to assume you already know discounting, understand margin calculations, have some intuition for retention curves, etc etc.

Instead, I wanted to build the whole thing from the ground up. Start with a story (Blue Apron's IPO troubles). Work through why revenue isn't the same as profit. Explain fixed vs. variable costs with a $5 latte. Show how small differences in retention compound over time. Get to the time value of money. Then pull it all together. Oh, and have some fun with pictures :)

After a bunch of iterations, I'm pretty happy with where this landed. I'm calling it "A Playful Introduction to Customer Lifetime Value."

May pair well with my CLV definition songs: 
-Rap: https://lnkd.in/e76TqbFy
-Cabaret: https://lnkd.in/eMSzmP_P

I'm making it available in case it's useful to others who teach this material, or for practitioners who want a refresher that doesn't assume too much background. If you have thoughts or feedback, I'd love to hear them. Otherwise, enjoy!

https://lnkd.in/etYn9JRZ

Daniel McCarthy

Associate Professor of Marketing at the Robert H. Smith School of Business, University of Maryland, College Park

My OpenAI unit economics post from last week sparked great discussion. One point worth expanding on is that the CAC calculation (the CACulation 😀 ) I used was simple. There are better approaches.

The key issue is that there's often a lag between when you acquire a free user and when they convert to paid. My calculation took total free tier cost / paid conversions in the same period. This works as a rough estimate but ignores timing.

The more rigorous approach is to model the trial-to-conversion curve by cohort. Value free users at the time they start using the free plan based on conversion probability and expected PAV conditional on conversion.

Spotify published incredible data on this (see chart). Over 50% of MAUs eventually become subscribers, but it takes 2-4 years to fully play out and has a very particular curvature, which basically invalidates what people often shorthand this to, through the "conversion rate". There's no rate. There's a curve and it varies by trialist cohort. This was in an Investor Day presentation - one of my favorite disclosures ever in a filing. If only we had that for ChatGPT! Only gripe with it is it didn't use a color gradient to make cross-cohort dynamics more visually salient (probably would need to ditch the dark background to do this).

For those interested in measuring CAC correctly given dynamics like this, I'm sharing a short deck I put together on the topic for my CLV class at University of Maryland - Robert H. Smith School of Business. We spend a lecture on this. The core idea is that spend today leads to conversions tomorrow and that informs what proper CAC measurement should be.

Link to Spotify presentation with beautiful chart: https://lnkd.in/erNTpMCK

CAC deck from last year: https://lnkd.in/edBGZRpg

ChatGPT CLV analysis: https://lnkd.in/ey5zK_7w

A trillion dollar question right now revolves around what the unit economics are for the major LLMs - CAC, CLV, payback, CBCV, etc. Let's think about ChatGPT.

Post-acquisition value (PAV) is the NPV of contribution profits. CLV is PAV less CAC. CBCV bakes in future acquisition and overhead. Nothing new here. But the data is opaque, and we need to be careful with cost allocation. 

What's CAC? OpenAI doesn't buy FB ads, but let's remember what Slack correctly did in their S-1 - they acknowledged the biggest acquisition cost (and engine!) is running their free tier. Using subscriber count data and publicly reported inference costs, I estimated the implied CAC.

The free tier accounts for about 62% of active users but only 18% of compute utilization. OpenAI's 2024 inference costs were roughly $2 billion. So free tier inference plus overhead is ~$400 million.

Now here's where you have to be careful with the math. Paid subscribers went from 5.8 million to 15.5 million in 2024. But that 9.7 million is NET additions, not GROSS acquisitions. Some of that starting 5.8 million churned during the year, and new subscribers acquired early in the year also experienced churn before December.

I built a cohort model that applies a monthly retention curve to each acquisition cohort (Excel below!). Gross adds come out to around 14.7 million. Divide through: implied CAC is around $28 per acquired customer (range: $20-34 depending on assumptions).

At $28 CAC with $14 monthly contribution margin (that's 70% compute margin times $20 subscription price), payback happens in ~2 months.
The free tier is a more efficient acquisition engine than I'd have thought! The substantive conclusion doesn't change if we bump CAC to $35 or even $40.
Now combine this with the retention data. ~75% retained at Q1, ~66% at 6 months, ~59% at 1 year. Claude is similar. Note: external retention estimates are higher than these figures so PAV could be even higher.

With those retention curves and a 70% contribution margin, I estimate:
1-year PAV ~$116 
3-year PAV ~$260 
5-year PAV ~$349

That puts PAV/CAC at roughly 4x after one year and 9x after three years.
These are excellent unit economics for a consumer subscription business. CAC payback is fast, retention is strong, and contribution margin is now in software-like territory.

To me, the big challenge is whether they can cap overhead growth. Training new models, building compute infrastructure, competing with Google and Anthropic. That R&D treadmill is where the losses come from. Not the variable economics.

Think of it like a drug company: high margins on the drugs themselves, but massive R&D overhead. Thankfully, seems like many people will happily pay for multiple drugs if the "boosts" are non-overlapping enough!

I've made the Excel model available to play with the numbers - check it out: https://lnkd.in/exRzZE2e

Sources for data points provided in comments.

OpenAI's CFO Sarah Friar just published a piece on how their business scales with intelligence. The core thesis is that compute and revenue move in lockstep. 0.2GW => 0.6GW => 1.9GW of compute in 2023, 2024, and 2025 respectively. $2B => $6B => $20B in ARR over the same time frame. More compute, more revenue. Simple and powerful.

This is a top-down view. Infrastructure drives capacity, and capacity in turn drives monetization.

There's a complementary bottom-up view that I find equally useful (you knew it was coming - ha!) through customer-based corporate valuation. Instead of compute => revenue, expand it to customers => compute consumption => revenue.

Who are the heaviest users of compute? Why are some subscribers barely using it? What are people actually doing with it? What features could most effectively expand compute given our customer segments? How does usage evolve over time within a cohort? How are things changing across the cohorts?

It's like retail. Yes, stores drive revenue. But we can also say that customers visit stores, and THAT'S what actually drives revenue. The product on those shelves doesn't sell itself. Understanding the customer layer helps you see same-store growth, saturation risk, and where the real leverage is. This is what I teach about at University of Maryland - Robert H. Smith School of Business and bring to life with clients through Theta.

I'm sure OpenAI thinks about this internally. The questions matter because they point to different levers. The top-down view says "get more compute." The bottom-up view asks "how do we acquire customers who use compute heavily, retain them, and expand their usage over time?"

These are complementary perspectives. Friar's piece tells us demand is strong and revenue scales with infrastructure. The customer-based view (which I've written about before, links below) tells us that the unit economics at the customer level look solid, though overhead is high.

Together they paint a picture of a promising foundation, so long as they can keep scaling infrastructure "sub-linearly to revenue", keep the R&D treadmill from eating the gains, and bring in enough new customers / develop the existing ones to get overall contribution profit to hurdle those sources of overhead expenses.

https://lnkd.in/ewiHzrHY

Relevant links in comments

Open article: A business that scales with the value of intelligence by openai.com

A business that scales with the value of intelligence

We're hiring a new Sales Lead at Theta!

We ended 2025 with substantial sales growth, and entered 2026 with great momentum for further success. Our CLV work with category leaders in global retail and online marketplaces, enterprise SaaS firms, and some of the largest PE funds in the world has driven a step change in our sales and recurring revenue. The productization of CLV Ultra is also coming together, so our focus is now on accelerating commercial expansion. It is a very exciting and very important phase for us.

This next phase calls for a different sales motion to augment the project-oriented approach we've emphasized to date. We're looking for someone with a background in analytical solutions who can own the conversation with marketing and finance leaders who want to actually understand what their customers are worth. 

You'll be driving our GTM efforts, managing our pipeline, articulating the value of CLV to a diverse client base, and helping them understand how to leverage it most effectively. Remote position, so flexible in terms of location.

If you or someone you know are energized by what Peter Fader and I have been building in the CLV/CBCV space, and could be a good fit for this role, we'd love to hear from you. DMs are open, and we're also grateful for shares and likes to help get the word out more broadly. Details on the position below!

Open article: Sales Lead at Theta by theta-576709235054.breezy.hr

Hear, hear. This post brought a smile to my face!

One thing I’d add: moving from “growth at all costs” to sound economics doesn’t mean you need to be profitable today. This isn’t preaching extreme financial austerity. What it means is that when you acquire that next customer, the profit you’ll eventually earn from them exceeds what you spent to acquire them.

The goal is to earn a high overall rate of return on your customer investments. Not profitability on day one per se. Not revenue growth for its own sake. it’s just acknowledging that what actually puts food on the table is marginal profitability: is each new customer you bring in worth more than they cost?

The timing on this couldn’t be better. I just delivered my lecture on what customer lifetime value is (and isn’t) to one of my sections at University of Maryland - Robert H. Smith School of Business  this week, with a couple more to go. So while Neil is preaching the good word of CLV / CBCV to executives in Milan, I’m doing my small part with the next generation of future leaders here in College Park 😊

What would make me fly across the world to Milan? The people? The food? Always. 🙂  But yesterday evening, it was an invite-only gathering of the Google Executive Forum - where I was discussing ideas that can reshape how companies operate. Here's a quick peek...

➡️ The death of "growth at all costs." The macro environment has made that  playbook obsolete. Investors and boards want to see sustainable unit economics. But, the real question is how to execute when the CFO is talking EBITDA and capital allocation, and the CMO talking about ROAS and conversions? They both see the goal, but can't carry the conversation. 🫤 

➡️ If you're building with AI, you're especially exposed here. Your compute costs scale differently, your customer behavior patterns are still being understood and traditional valuation models break down. The companies that figure out their customer economics first will be the ones that can drive their transformation forward.

➡️ Metrics like Customer Lifetime Value (CLV), Customer Equity and methodologies like Customer-Based Corporate Valuation (CBCV) are emerging as the likely winners here. When you stop measuring campaigns and start measuring what a customer is actually worth over time, something inevitably shifts. Finance stops seeing marketing as a cost center to minimize, and starts treating it like an investment portfolio to manage. Isn't that where you want to be? 🤔 

A warm thank you to Melissa Ferretti Peretti and the Google Italy team 🇮🇹 for all of their hospitality in bringing this distinguished group together.

And, yes, there's more to unpack here. I'll be writing about the specific frameworks that came out of this, but feel free to toss any questions below. 

Ciao! 

#google #lifeatgoogle #technology #data #strategy

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