Editor's note: Sanjay K. Rao is vice president, Strategic Research Insights, a Princeton, N.J., marketing research and consulting firm.
Understanding how consumers make purchase decisions is crucial to assessing a product’s value. Equally important is a rational method to allow value to be parsed into constituent components of the product so that strategic decisions about product component value are enabled.
This article outlines a marketing research approach to determine how customers of a smartphone value its specific features as well as that of its competitors, what value they impute to such features and how a marketer can monetize such valuations.
A version of the method discussed in this article was implemented by the author in a landmark litigation matter (Apple Inc. v Samsung, case No. 11-CV-01846-LHK 2014) to determine component valuations for smartphones, tablets, digital media players and laptops. The jury awarded damages to one party according to estimates derived by the method.
Vital for strategic purposes
Assessing the monetary value of a product, its components, a portfolio of products or a business entity with a collection of such assets is vital for a number of strategic purposes such as creating valuable products, pricing products on the basis of value, structuring deals for mergers and acquisitions, in/out licensing assets, estimating royalty rates, damages and verifying claims in intellectual property litigation.
In the rapidly evolving digital age the science of assessing such value has demanded new methodological approaches. The reason for this stems from the unique nature of relatively complex product offerings that are becoming available. Smart digital devices, online product and service offerings, telecommunication packages, consumer financial products, specialty biotechnology products or medical devices, for example, contain a multitude of components, many of which represent complex, patented innovations.
Even in traditional industries such as automobiles, food and retail, one key trend is the availability of products that are custom designed by customers who choose from a range of component options, each of which represents finite value. The sellers of such menu-based products thus are faced with the question of pricing selected options to accurately reflect such valuations.
Companies in the biotechnology, high-technology and telecommunications hardware and software industries frequently compete on the basis of innovation, much of which relies on patented technologies and processes. It is not uncommon for such companies to engage in litigation that calls for reasonable component valuations toward setting reasonable royalty rates, determining patent infringement or estimating damages.
Awareness and usage of complex products or their components vary widely among consumers, differentially impacting their utility, usage and value. Consumers of such complex entities are often unaware of some components in the products they have already purchased and many consumers use only a small subset. Some don’t much care for some components in relationship to other more ubiquitous components. For example, smartphone users who frequently transfer audio and video files to external receivers are rarely aware of proprietary software embedded in their devices that enables such activities. However, the value of such software to its users can hardly be overemphasized.
Mixed success
For purposes of addressing such issues, prevalent methodologies relying upon techniques borrowed from marketing science and econometrics have had mixed success at best. One major barrier is the fact that newer technologies, complex product offerings and technology-driven products have far more features than those considered appropriate for such methods to provide reliable descriptions of a consumer’s buying process, much less predictions of what product configuration would be bought and why.
In addition, many features considered vital to product success are invisible and thus prima facie immaterial to consumer value perceptions. This compounds accurate value measurement through the use of such methods. Even if a method was designed to expose a potential consumer to a product as defined by all of its features (such as by using a prototype), the all-too-real fact of consumer fatigue in processing the information available to him/her has been known to confound attempts at estimating value. This is a key issue when the goal is to estimate value of features that are less ubiquitous to the potential consumer, such as a chipset and software combination in a telecommunications device that enables instantaneous transfer of audio/video content to an external speaker or screen.
The most common method for assessing value of features in a product typically involves use of a time-based database of transactions containing information on consumer purchases of one or more products and other ancillary information as may be appended (such as prices and discounts) compiled by a manufacturer or an independent data supplier.1 Such a database is used to derive estimates of utilities consumers may have placed on features of products included in it. Such feature utilities are then assumed to represent feature values, either in and of themselves or in some proportion. This approach, while convenient in that it uses data that are already compiled and available, is fraught with limitations. For one, feature value estimates are necessarily a function only of products included in the database, the frequency with which relevant transactions occur per product, the prices consumers would have paid for any product at the point of purchase and the impact any marketing promotions may have had on the purchase decision during the transaction – regardless of intrinsic value.2
Further, the use of a retrospective transactional database, by definition, precludes a rational assessment of value imputed to features of a new product yet to be available in the market – particularly one that its manufacturers consider to be unique and highly differentiated from what was available in the past. The use of such a retrospective database for value estimation is, also by definition, unable to capture the decision-making process in which a customer would reasonably engage to make a purchase decision – information that is vital to assessing the value placed on key features of a new product and, by extension, on the new product as a whole. Such business and marketer needs are best captured by marketing research that is customized to the new product, its potential customers and the decision-making process in which they will likely engage to make a purchase decision in the context of a realistic competitive landscape.
Case study
Consider a business situation where a global smartphone manufacturer is interested in assessing the monetary value of patented technology innovations that are part of an audio-video suite of features in one of its flagship products. Most, if not all, such features (although essential to the totality of benefits provided by the product and undoubtedly contributory in some way to its perceived differentiation) go unnoticed or are too technical for a mainstream smartphone consumer to impact its demand per se.
The manufacturer, however, thinks otherwise. Indeed, the product’s marketing team believes that if and when the benefits provided by such features are clearly communicated, they would form the basis for sustainable differentiation. Additionally, an accurate consumer-based valuation would define a range of price premiums likely to capture the inherent, incremental value of such features over and above what is offered by prevailing competition.
Given that the product can conceivably be defined by at least 50-100 features (of which the features under examination are but a small set), the use of traditional stated response-based consumer decision modeling exercises such as full-profile conjoint/choice model analyses is inappropriate for estimating consumer utilities. While secondary data on consumer transactions are available for the product and its competitor, they suffer from common biases idiosyncratic to retrospective scanner-like data, key among which is the inability to represent the decision-making process that leads a consumer (or more likely, consumer segments) to determine the value of any feature and make a product purchase subsequently.
To tackle such challenges as specifically and rigorously as possible (and so to provide recommendations that could be implemented for developing reliable pricing and differentiation strategies), the author and his colleagues developed, tested and successfully made recommendations from a custom, survey-based framework and process.
In essence, the framework is based on a new approach to measuring consumer imputations of the relative worth of two or more characteristics in a product or service. The mathematical basis for the approach was first postulated by Thurstone (1927, 1959) and later modified by David (1969) and Louviere (1991).3 Variations of the approach have since been used in marketing research applications enabling product development and optimization in the automobile, food, computer hardware and services industries.
For the case outlined here, the basic framework was modified and a random sample survey of a statistically representative sample of consumers was designed and executed to collect data. The questionnaire contained a decision modeling experiment, responses to which were subsequently modeled through random utility models to determine consumer valuations of the features of interest. Such valuations were then monetized to develop component value stacks.4 Such value stacks were then used to determine pricing strategies that capture full or part of the estimated value.
At its core, the method relied upon the conceptualization of Thurstone et. al. of how a consumer may attribute utility to one feature over another in any collection of features that constitutes a product or a service.
Consider a product in which a respondent evaluates four features A, B, C and D. If the respondent says A has the most value and D has the least value, these two responses provide information on five of six possible implied paired comparisons, i.e., A>B, A>C, A>D, B>D, C>D, where “>” implies “has more value.” Showing multiple sets of four features and eliciting similar value preferences provides sufficient data to ascertain individual value comparisons among all features and their pairings. Controlling for the number of feature exposures, the number of feature pairings and the position of feature exposure enables an unbiased assessment of the relative value of each feature.
The scientific survey of smartphone consumers designed to address challenges outlined in the case study elicited such evaluations, producing sufficiently rich and representative data on consumer feature valuations. Such data were then calibrated and modeled to estimate feature valuations using random utility modeling. Value estimates, after validation, were used to make reliable predictions of how consumers value any one feature, a collection of features or an entire product or service offering comprised of many features.
Results
Figure 1 illustrates one of several key outputs from an application of the new method to the case outlined above. It presents summary details of how value may be imputed to a set of patented innovations in a single smartphone product. The patented innovations represent audio, video, synchronization and other functionality features in the product.
The methodology outlined in this article was able to impute a monetary value to each patented feature. Such monetization was aggregated over a relevant set of consumers in a market or one or more of its segments.
The results enabled marketers considering alternative differentiation strategies to focus on innovations that were more valuable and currently underemphasized. In-market pricing strategies could now be developed or modified by considering price changes, discounts or premiums that were proportional to such estimated valuations.
Pricing that better captured the value presented by such innovations was more likely to tally with customer perceptions of value, and – provided differentiation strategy and communication were aligned similarly – likely to present incremental revenue generating opportunities.
When value, differentiation and pricing were thus integrated, marketers could set the stage for increasing sustainable demand, immune to competitive promotional activities that were likely to impact perceptions temporarily at best, rather than cause fundamental changes in how customers intrinsically value what they buy.
Footnotes
1 Databases that contain information on contracts governing products – such as royalty rates and terms for volume based pricing – are also used to determine the financial value of a product or, by imputation, of its constituent features. Such databases also are limited in their applicability to new, innovative and highly differentiated products that are yet to be introduced in the market.
2 It is often the case in digital device purchases that consumers who do not have a desired feature in their current digital device – but who do impute value to such a feature – will purchase another device containing it in the future. It is also possible that consumers who are not aware of a feature, and therefore do not consider it in making a purchase decision at current market prices, may nevertheless frequently use that feature and inherently value it highly.
3 See Thurstone, L. L., (1927), "A law of comparative judgment," Psychological Review, 4, 273-286; and Thurstone, L.L. (1959), "The measurement of values," Psychological Review. Chicago: The University of Chicago Press; and David, H.A., (1969), The Method of Paired Comparisons, Charles Griffin and Company Ltd. London; and Louviere, J.J. (1991), "The best-worst or maximum difference measurement model: applications to behavioral research in marketing," The American Marketing Research Association’s 1993 Behavioral Research Conference, Phoenix, Arizona.
4 See Rao, Sanjay K. (2016), "Expert analysis: A new survey-based method for valuations in IP cases," LAW360, May 4.