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Working Paper
| HBS Working Paper Series
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2006
The Value of a 'Free' Customer
by
Sunil Gupta, Carl F. Mela and Jose M. Vidal-Sanz
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Abstract
Central to a firm's growth and marketing policy is the revenus and profit potential of its customer assets. As a result, there has been a recent proliferation of work regarding customer lifetime value. However, extant research in this area is silent regarding how to assess the profitability of customers in a networked setting. In such settings, the presence of one type of customer can affect the value of another. Examples of such settings include job agencies (whose customers include both job seekers and listers), realtors (whose clients include home sellers and purchasers), and auction houses (whose
customers include buyers and sellers). Customers such as buyers of an auction house pay no fees to the firm making their value difficult to compute. Yet these customers generate value to the firm because their presence attracts fee-paying sellers. In this paper we consider the value of a customer in these types of networked setting.
We compute the value of customers by developing a joint model of buyer and seller growth. This growth comes from three sources — marketing actions (price and advertising), direct network effects (e.g., buyer to buyer effects), and indirect network effects (e.g., buyer to seller effects). Using this growth model we concurrently solve the firm's problem of choosing optimal pricing and advertising subject to constraints on customer growth. By relaxing constraints on growth by one customer, we can then impute their lifetime value to the firm. We apply our model to data from an auction house.
Our results show that there are strong direct and indirect network effects present in our data. We find that in the most recent period buyers have a value of about $550 and the sellers have a value of around $500. We also find that our approach leads to estimates of firm value that are more accurate than models that fail to consider network effects. Finally, price and advertising elasticities are low (-0.16 and 0.006) and decrease over time as network effects become increasingly important.
Keywords: Customer Value and Value Chain;
Auctions;
Network Effects;
Business Strategy;
