What’s the value of a customer who doesn’t pay you anything? If you’re running a hot dog stand, the answer is probably “zero.” But if you’re running a two-sided market – a market, like eBay or Monster.com or AdWords or YouTube or Digg or even Second Life, that needs to attract both buyers and sellers (or content generators and content consumers) – the answer may be “a lot.” EBay, for instance, earns most of its money from its sellers, who pay the company a fee whenever they sell something through the auction site. The buyers don’t have to pay when they make their purchases. But while eBay receives no direct revenue from the buyers, the buyers nevertheless represent a crucial set of customers for the company – without buyers, there’d be no sellers and hence no business.
Clearly, each buyer in such a networked business has value to the company – but how much value, exactly? That’s where things get tricky. Because traditional approaches to determining the economic value of a customer don’t work when the customer isn’t generating any direct revenue, there hasn’t been any good way to estimate customer value in these sorts of two-sided markets. That means companies have to fly blind when determining how much they should be spending on marketing to attract the non-paying customers. And that, in turn, likely means they’re spending either too much or too little.
But a recent paper by three business-school professors – Sunil Gupta, Carl Mela, and Jose Vidal-Sanz – offers a new approach for estimating the value of nonpaying, or, as the professors term them, “free,” customers. The authors created a mathematical model of a hypothetical firm, with a business similar to Monster’s, and used it to calculate how much every new buyer joining the company site is worth and how that value changes over time.
Some of the results are fascinating. The professors found, for instance, that the value of each nonpaying customer (buyer) was actually slightly higher than the value of each paying customer (seller) – even though there were far more buyers than sellers in the company’s marketplace. (To put it another way, the network effect of a buyer on a seller was far stronger than the network effect of a seller on a buyer.) The research also demonstrates that it’s possible to estimate optimal marketing expenditures as a two-sided business grows. While heavy markering spending is required in the early days to attract a critical mass of buyers, the network effect itself becomes a larger attractant than marketing as the business grows, allowing a company to cut back its marketing budget over time. Knowing the optimum spending amount with some precision at different points in time would help businesses maximize their profits. The information would also, the authors argue, allow a company’s founders, managers, and investors to gain a more accurate understanding of the firm’s overall value.
In an interview about the study, Gupta notes that the model applies only to fairly simple two-sided markets. But on the Net today, of course, nonpaying, “free” customers are also critical to other businesses with more complex network structures, from YouTube to MySpace to Skype to an open-source software company like Red Hat or MySQL. If you have a “community,” you likely have “free customers.” Gupta says that he’s currently
working on understanding and modeling complex network structures such as those of MySpace. Here the issue that we are grappling with is the tangible and intangible value of customers. In other words, customers provide tangible value to a firm through direct purchases but they also provide intangible value through network effects or word of mouth. It is quite possible that some customers have low tangible but high intangible value. Traditional models would label such customers as low value and would miss a huge opportunity for a firm.
This promises to be a particularly fruitful, and practical, area of study in the years ahead.