Subscribed Podcast: Prof Daniel McCarthy on CLV, valuations, and subscription pitfalls

By Aarthi Rayapura December 4, 2018

Select excerpts from Subscribed Podcast with Professor Daniel McCarthy

Professor Daniel McCarthy is Assistant Professor at Emory University. He is a marketing scientist and a methodologist, and his research interests include customer lifetime value (CLV) and marketing/finance interface. Professor McCarthy has authored papers on valuations of subscription businesses and analyzes B2C subscription companies.

We talk to Professor McCarthy about subscription business valuation, CLV, and pitfalls to avoid.

Select excerpts:
Tell us a little about your research, particularly around subscription businesses.

The main thing that I focus on is overall financial valuation. I think it has really struck a chord with a lot of people because whether you’re a CEO and investors, valuation is central. Ultimately, almost any decision that a company makes, the north star should be what effect is this going to have on the valuation of my business?

My work explicitly relates financial valuation to measures of underlying unit economic health: customer acquisition, retention, orders, etc. If you have a good ability to make predictions for each of those processes mathematically, that has to give you a good idea of what future sales will be. It’s more predictive, it’s more accurate, and it’s more diagnostic because it gives you a few yardsticks to be able to assess your performance.

CLV (Customer Lifetime Value) is a favorite topic for us and our audiences. You’ve done some work on the topic, particularly around the issue of variance in CLV. What would you say is the main takeaway for businesses that measure CLV?

The one that has seemed to be very reliable is CLV to CAC (Customer Acquisition Cost) where I define CLV to be the net present value of all future profit streams, subtracting the CAC, and then dividing that by the CAC. That is very directly a measure of return on investment. I find it to be a very helpful yardstick that there are some companies that have negative CLV to CAC, and regardless of the business, that can’t be a good thing.

You can have many reasons that companies are very unprofitable. If that unprofitability is because they have a lot of fixed costs, but whenever they acquire a customer, they’re making a lot of variable profits on top of that, then they can grow out of that. But if they don’t have great retention, and their CLVs are low, they will never be able to grow out of that. I think that’s a fundamental distinction that people are finally starting to wake up to.

Can you tell us a little about how you value, particularly growth versus profitability, growth versus some of the unit economics, and how to balance those out?

If the lack of profitability is just because you’re building infrastructure and that makes total sense. I think where companies can run into trouble is when, on every customer, their variable profitability is negative.

For a company like Blue Apron, once you acquire a customer, you’re getting a pretty steady stream. The best you can hope for is that you’re going to continue to get that monthly check, but it’s probably not going to grow very much.

In a SaaS setting, we’ll often see a lot more monetization growth. They’ll upsell you into different products or expand within the organization. So, it might make a lot of sense to spend a very large amount of money to acquire a customer because you know that if you bring them in the door, you’ll make many multiples of that even though, on a GAP basis, you will be unprofitable.

I think the key is that you have a very good model for the value of customers and how it’s changing over time. It’s especially tricky with newly acquired customers. They’re the ones that are probably the most relevant in terms of the profitability of the customers that you’ll acquire in the future, but they’re the ones that you, by definition, have the least amount of information on.

Blue Apron is often talked about as a cautionary tale. What do you think went wrong?
I’ve got some crisp data on what I think the issue was. I built this statistical model for their customer acquisition, retention, orders, and spend, using data from their S-1 filing. Even though they didn’t include any information about what their churn was, I was able to back into what their retention curve was. It basically suggested a really steep drop-off in customers after they’ve been acquired.

I had estimated that about 70% of their customers had churned six months after acquisition. It’s very hard to make the economics of the business work when you have that high of a rate of churn. The second issue is their CAC. Back in the olden days when their marketing was more referral-based, their CAC was between $50 to $80 per customer. In the period right up to the IPO, say that last six months, they started spending a ton of money on marketing, and I inferred that their CAC had gone up above $130 a customer.

So, whereas the value of a customer after they’d been acquired was somewhere around $130, they were starting to lose money on the customers in that last short window of time. I really think that they were trying to show good top-line numbers before the IPO and they sacrificed a lot to be able to hit them. I think that was a very big mistake.

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