Web-Based Consumer Decision Tools: Motivations and Constraints

Kieran Mathieson

Mukesh Bhargava

Mohan Tanniru

April 14, 1999

School of Business Administration
Oakland University
Rochester, MI 48309
USA

OU SBA Working paper 1999-004

Please send comments to Kieran Mathieson at mathieso@oakland.edu.

Introduction

The Web offers new ways for consumers and firms to interact. Much has been written about using the Internet for traditional persuasion-oriented marketing (e.g., Ellsworth and Ellsworth, 1997), but there is less work on use of the Web to enhance relationships between individual consumers and individual firms.

This paper examines the motivations for the construction of consumer decision tools (CDTs). CDTs are Web-based decision support systems aimed at improving the quality of consumer decisions. They are organized around the decisions themselves, rather than around the content being presented.

We will start by examining some important attributes of the consumer choice context. We then briefly describe a CDT we built and tested. Finally, the main benefits of CDTs for consumers and firms are enumerated.

Consumer Choice Context

Several factors define relationships between consumers and firms in a modern economy. First, the net utility from a transaction is the total utility from the product, service or payment received, less the costs of the transaction. For consumers, transaction costs include information search time, travel to a distribution point, sales or consumption tax, and so on. For a firm, transactions costs most obviously include the costs of the supply chain, such as shipping and storage.

In one view, marketing is a transaction cost as well. Advertising and promotion try to attract and retain customers, that is, people who will engage in transactions with the firm. Marketing is not a trivial activity, with advertising and promotion costing $479 billion in the United States alone in 1997 (Cassino, 1997).

Much of this expenditure is wasted. Every day, marketers in the US expose consumers to 12 billion display ads, three million radio ads and more than 300,000 television commercials (Hagel and Singer, 1999). The average US consumer receives about a million marketing messages per year, across all media. Response rates are usually between 0.5% to 2%. Marketers drown consumers in a river of information, more than they can possibly handle.

A second issue is that consumers face more decision complexity than ever before. Some of this is because of greater choice. A consumer choosing a vacation, for instance, has options ranging from a rental cottage in Tuscany to a packaged tour in Disneyland. The choice set is huge.

Much of the effort consumers spend in selecting high-involvement products is not classical "decision making," where they weigh information about known alternatives. Instead, consumers spend time learning, about their own needs, available products, transaction costs and so on. Learning has received less attention than it deserves from consumer behaviour researchers (Hoch and Deighton, 1989).

Third, choice processes are complicated. O'Keefe and McEachern (1998) present a simple consumer choice model, but notes that it is not followed in practice. Given variance in individual consumer knowledge, goals, motivation, expectations, available time, and so on, it is not feasible to predict every mental operation a consumer engages in while selecting a product. In constructing a CDT, it might be more useful to provide a set of context-specific tools consumers can use as they wish, plus some decision guidance (cf. Silver, 1991).

In considering the benefits that CDTs offer, we make two assumptions. First, we assume consumers are motivated to expend effort in making a good decision. Most consumers devote little effort to most decisions (cf. Dickson and Sawyer, 1990; Stewart et al., 1989). They would only seek Web-based support for decisions that are relatively important and non-routine, such as buying a house, selecting a university, or choosing a health plan. We limit ourselves to high involvement choices since (a) these are the decisions that have the greatest effects on consumers' lives and (b) consumer interest could justify a firm's investment in CDTs.

Second, we assume that the firm offering decision support wants to help consumers make decisions that are good from consumers' perspectives. Some companies deliberately hide information about consumer choices that are not maximally profitable. This study is limited to situations where firms desire mutually beneficial exchanges, and are willing to support long run profitability at the expense of short term losses in sales.

This is not to say that a firm providing a CDT would, for example, recommend a competitor's product. However, within the constraints of its own product line, the firm can help consumers make good choices, even when the choice that is best for a particular situation leads to lower short-term profits. This philosophy is key to building long-term relationships with customers (Glazer, 1991). Current practices based on "persuasion" are expensive. Replacing these with model of exchange based on communication provide a basis of reducing marketing costs and developing a sustainable advantage (Wernerfelt, 1996).

Consumer Decision Tools

A CDT is a Web-based decision support system aimed at improving the quality of consumer decisions. CDTs can be developed by firms offering products, or by information intermediaries. CarPoint (http://www.carpoint.com) is an example of the latter.

CDT designers should analyze the decision goals of consumers, and build systems to match. For instance, we constructed a CDT to help consumers select a manufactured home (Mathieson, 1998). Focus groups and interviews identified the main challenges consumers face:

  1. They don't know what they want (i.e., their requirements are unclear)
  2. They don't know what products are available
  3. They don't know how to make a decision (i.e., the method that will maximize their utility)
  4. They don't know how to handle the large amount of information that is available

The home CDT included features designed to address each problem. Figure 1 shows part of a home worksheet that helped structure the choice (problem 3), and provided a decisions record for the consumer (problem 4). The worksheet emphasized the importance of defining requirements (problem 1). Advice and consumer scenarios helped with requirements definition (problem 1). A catalog (Figure 2) showed the products (problem 2), with links to a grid that made direct problem comparisons easier (problem 3 - see Figure 3). Figure 4 shows the payment calculator (problem 1) and Figure 5 the mortgage calculator (problem 2). The home CDT also let consumers add a personal note to any page in the site, and then summarized those notes in a single location (problem 4 - see Figure 6).

Two themes are evident in the design of the home CDT. The first is consumer learning, about goals (Figure 1), decision methods (Figure 1), and products (Figure 2). The page notes (Figure 6) and worksheet (Figure 1) help consumers remember what they have learned. The second theme is choice, supported by the grid (Figure 3), later stages of the worksheet, and tools that help select home options, like appliances and carpets. We classify the calculators as learning tools. The payment calculator (Figure 4) helps consumers learn about their financial situation, while the mortgage calculators (Figure 5) translates a relatively meaningless product attribute (home price) to a relatively meaningful one (monthly payment).

Forty subjects used the CDT to choose a home. Pre- and post-test surveys showed that using the CDT dramatically improved subjects' product knowledge. They also believed they knew more about manufactured homes, and were more confident about their (a) ability to make a good decision and (b) knowledge of their housing requirements.

Analysis of system logs showed that the systems' decision support features were used heavily. Subjects spent an average of 2.25 hours using the CDT, 46% of it on either the home worksheet or payments calculator pages. Qualitative analysis of the log files yielded some interesting insights into consumer decision processes. For instance, we provided advice and consumer scenario pages to help with product requirements definition. They were amongst the least frequently used pages. Instead, subjects tended to infer their requirements from a few product descriptions.

The system is available at http://w3.sba.oakland.edu/home_cdt. The reader is invited to use it, but please read the technical notes on the site first, since some browser configuration options must be set.

Benefits from CDTs

Benefits to Consumers

  1. Improved decision making
  2. The utility generated by a product depends on its fit with consumer needs. CDTs help consumers understand their requirements, understand products, and fit the two together.

  3. Reduced cost of future decision making

CDTs help consumers learn about (a) their needs, (b) product attributes and (c) choice processes. Even if a consumer never buys a manufactured home again, the home CDT will have shown them how to go about making a good choice. They can apply that lesson to other decisions they make.

Benefits to Firms

  1. Insight into consumer choice processes
  2. Using the Internet for a store front does not by itself provide a sustainable advantage to the firm, since these innovations are easily imitated. However, knowing how customers make decisions (Glazer, 1991) can supply a competitive advantage. Most consumer behavior research uses artificial product evaluation situations (e.g., focus groups) that provide equivocal information. What a consumer says in a focus group often does not match what they do in the marketplace. CDTs let firms peek inside real decisions by real customers as they are being made, offering deeper insights into decision processes. This information is captured on the firm's Web servers and, unlike a normal Web site, is not visible to competitors. CDTs can provide a sustainable competitive advantage (Barney, 1991).

  3. Customer loyalty
  4. With traditional advertising or personal selling, firms tightly control information flow. CDTs reverse this situation, creating a qualitatively different type of interaction with consumers. CDTs let customers explore a company's claims about its products for themselves, and tailor their exploration to their own requirements. Tailoring information to individual consumers increases persuasion (Cassell, Jackson and Cheuvront, 1998.)

    CDTs help consumers become more confident about their own decisions, and about the good faith of the firm. As their trust grows, they are more likely to accept the firm's product recommendations, and will tell their friends about their good experiences.

  5. Reduced marketing costs

CDTs can reduce marketing costs in several ways. First, increased customer loyalty from CDT use could reduce customer turnover. This can result in significant savings, since on average it costs 5 times as much to get a new customer than to retain an existing one (Kotler and Armstrong, 1997, p. 16). Second, positive word-of-mouth has a powerful effect on persuasion, yet costs the firm little. Third, CDTs allow message tailoring at a reasonable cost, potentially resulting in greater persuasion.

Conclusions

CDTs are quite different from traditional Web sites. They're active decision support systems. They focus on specific problems consumers have in making decisions, providing tools for both learning and choice. CDTs help consumers make good decisions, and help firms learn about consumer choice processes.

The main barrier to CDT adoption will be the relationships firms want with their customers. The customer-as-cow is a familiar metaphor is sales meetings, where a docile herd beast is lead to the barn for milking - or perhaps to the abattoir for butchering. Sales representatives are taught to "overcome objections" (see Kimball, 1994, for example), rather than truly listen to customers. Such firms will want to avoid CDTs, not embrace them.

It is possible that this short-term approach leads to greater profits, though Wenerfelt (1996) argues otherwise. However, it will not maximize the utility generated by the billions of transactions that make up an economy. Further, it will not build the trust that is part of a sound, long-term relationship. Nor is it sustainable in industries such as healthcare, where society demands positive consumer outcomes from private companies.

The results from our initial work have been encouraging. CDTs have proved technically feasible, and have value for both consumers and firms.

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References

Barney, J. "Firm Resources and Sustained Competitive Advantage," Journal of Management (17:1), 1991, pp. 99-120.

Cassell, M., Jackson, C. and Cheuvront, B. "Health Communication and the Internet: An Effective Channel for Health Behavior Change?", Journal of Health Communication (3:1), 1998, pp. 71-79.

Cassino, K.D. "A World of Advertising," American Demographics (19:11), 1997, 57-60.

Dickson, P.R. and Sawyer, A.G. "The Price Knowledge and Search of Supermarket Shoppers," Journal of Marketing (54), July 1990, pp. 42-53.

Ellsworth, J. and Ellsworth, M. Marketing on the Internet, 2nd ed., Wiley, New York, 1997.

Glazer, R. "Marketing in an Information-Intensive Environment: Strategic Implications of Knowledge as an Asset," Journal of Marketing (55), October 1991, pp. 1-19.

Hagel, J. and Singer, M. "Private Lives," The McKinsey Quarterly, (1), 1999, pp. 6-15.

Hoch, S.J. and Deighton, J. "Managing What Consumers Learn from Experience," Journal of Marketing (53), April 1989, pp. 1- 20.

Kimball, R. AMA Handbook for Successful Selling, American Marketing Association, Chicago, Illinois, 1996.

Kotler, P. and Armstrong, G. Marketing: An Introduction, Prentice-Hall, Upper Saddle River, New Jersey, 1997.

Mathieson, Kieran, "A Menage a Trois in a Manufactured Home: Improving Decisions and Building Relationships," presented at the First Oakland University Conference on Information Technology, Oakland University, Rochester, Michigan, September, 1998.

O'Keefe, R.M. and McEachern, T. "Web-based Customer Decision Support Systems," Communications of the Association for Computing Machinery (41:3), March 1998, pp. 71-78.

Silver, M. "Decision Guidance for Computer-Based Decision Support," MIS Quarterly (15:1), pp. 105-122.

Stewart, D.W., Hickson, G.B., Pechmann, C., Koslow, S. and Altemeier, W.A. "Information Search and Decision Making in the Selection of Family Health Care," Journal of Health Care Marketing (9), June 1989, pp. 29-39.

Wenerfelt, B. "Efficient Marketing Communication: Helping the Customer to Learn," Journal of Marketing Research (33), May 1996, pp. 239-246.

Biographies

Kieran Mathieson, Ph.D. (Indiana University) is associate professor of information systems at Oakland University. His interests include Internet technology, Web marketing and Web-based decision support systems. He entertains visitors to his office with a disco ball and a life-size Xena. Really!

Mukesh Bhargava, Ph.D. (University of Texas at Austin) is associate professor of marketing at Oakland University. He is interested in the evolution of marketing practices in non-traditional settings. He continues to work on enlarging the application of marketing practices in the (perhaps vain) hope that they can better the world.

Mohan Tanniru Ph.D. (Northwestern University) is professor of information systems at Oakland University. His research interests are in systems development methodology, decision support, expert/knowledge based systems, and information technology/systems planning. He has published in just about every technology journal known to man. Perhaps that's why he smiles so much.

 

Figure 1. The Home Worksheet

 

 

Figure 2. Floor Plan Catalog

 

 

(a) Checkbox to add to the grid

(b) Checked floor plans

Figure 3. The Plan Grid

 

. . .

Figure 4. The Payment Calculator

 

 

Figure 5. The Mortgage Calculator

 

 

(a) Single Page Note entry

(b) Page Note summary

Figure 6. Page Notes

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Copyright, 1999.