Marketing
Research by
V. Seenu Srinivasan
Adams Distinguished Professor of Management
Stanford Graduate School of Business
Sang-Hoon Kim
Seoul National University
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What Makes Consumers Want to Buy the Latest Model?
May 2004
STANFORD GRADUATE SCHOOL OF BUSINESS—If you've everagonized over whether it's the right time to replace an old gadget with aspiffy new model—knowing that the new one may well become obsolete in afew months—you probably have an inkling of the kinds of decisionshigh-tech marketers must make in planning their products. And if you'remarketing such products yourself, you probably have puzzled over when totime each release. Which bells and whistles should you introduce first?And how do you price the upgrade to make it attractive to existing users?
To help planners of high-tech consumer products make these sorts ofdecisions, V. "Seenu" Srinivasan, the Adams DistinguishedProfessor of Management at the Stanford Graduate School of Business, andSang-Hoon Kim, assistant professor of marketing at Seoul NationalUniversity and a former student of Srinivasan's, created a mathematicalmodel that forecasts the sales path of a new version of an existingproduct.
"The model is quite simple," says Srinivasan. It is based onhow much the benefits of the new product (as compared to the old one)outweigh all the factors that typically hinder a customer's decision toupgrade. For example, a customer is more likely to buy a new PC if it issignificantly better than the one she already owns and if the upgradeseems painless and inexpensive. In this model, the hindrances include notonly the upgrade's various costs (financial, procedural, andpsychological), but also expectations about how quickly futuretechnological improvements will be made; consumer characteristics (such asinnovativeness); and the consumer's perceptions of the product in general(such as whether or not it saves time).
As might be expected, the greater the gap between the incrementalbenefit of the upgrade and its hindrances, the greater the probabilitythat the consumer will upgrade within a given month.
Applying this probabilistic model to their retrospective study of thePalm Pilot PDA, Srinivasan and Kim successfully predicted which volunteershad upgraded to a particular model within a given period with 76 percentaccuracy—significantly higher than the 53 percent accuracy expectedthrough random guesses.
Conducting such a study in the real world is far from simple. First, arandom sample of existing customers was asked to grade the importance ofvarious product features—say, price, size, and memory capacity. Customersthen filled out a personal questionnaire that measures about a dozenvariables such as how guilty they feel about discarding a product that'sstill working, expectation of how quickly new versions will continue tocome out, the percentage of friends and colleagues who use the product,the time it took the customer to buy the first generation of this product,and even whether the current product was a gift. All the answers feed intoa set of complex equations that generate probabilities that translate intotime-to-upgrade durations.
Srinivasan estimates the cost of conducting such a study in a realmarket setting at $100,000, but the bigger stumbling block may lieelsewhere. New releases of some products like laptops, printers, and cellphones may come so rapidly, says Srinivasan, that some technical managersbelieve there isn't time for this kind of market research.
But academicians are excited because the model is an innovative mix oftwo existing methodologies in marketing science: conjoint analysis andhazard rate modeling. Conjoint analysis, which involves asking a sample ofcustomers from the target market how important they deem differentfeatures, has long been used to determine which sets of product featuresto offer. But because conjoint analysis takes a static snapshot of themarketplace at a given moment, it alone doesn't answer the sorts ofquestions intrinsic to product upgrades. Hence the addition of hazard ratemodeling, which has traditionally been used to estimate the timedifference between a product's first purchase and subsequent, replacementpurchases.
Making only brief mention of his model in his Marketing 343 class, Customer-Focused Product Planning, Srinivasan explains it to students in away that avoids the hairy math. But in the future, the model could becomemore mainstream if the number-crunching can be automated. Vendors,including Sawtooth Software, already offer tools for performing conjointanalysis, he says, and there's no reason they couldn't eventually do thesame for this methodology.
Related Information
A Multiattribute Model of the Timing of Buyers' Upgrading toImproved Versions of High-Technology Products
Sang-Hoon Kim and V. Srinivasan
GSBResearch Paper #1720(R), September 2003
The Consumer Durable Replacement Buyer, Barry L. Bayus, Journal of Marketing, 1991, 55 (January), 42-51
The Effects of Expectations on Technology Adoption: SomeEmpirical Evidence, Allen Weiss, The Journal of Industrial Economics, 1994, 42 (4), 341-360
A Conjunctive-Compensatory Approach to the Self-Explication of Multiattributed Preferences, V. Srinivasan, Decision Sciences, 1988, 19, No. 2 (Spring),295-305