Morph the Web to Build Empathy, Trust and Sales

We’ve long been able to personalize what information the Internet tells us — but now comes “Web site morphing,” and an Internet that personalizes how we like to be told. For companies, it means that communicating — and selling — will never be the same.

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The leading question

What are the consequences if the Web can connect with users in the cognitive style they prefer?

Findings
  • As salespeople and anyone trying to communicate already knows, individuals process information in different ways. Messages delivered in the matched “cognitive style” will be more effective.
  • Advances in technology and behavioral science are beginning to enable an “empathetic Web” to emerge — a Web that can figure out for itself how a user wants to be talked to.

When we talk to someone, we often feel that communication is more effective if we are “on the same wavelength” with them. If they “get it,” we feel empathy and trust. We’re more likely to believe their statements or even buy what they’re selling. While this trust and empathy come from good communication, good communication is more than just content. It depends not only on what is in the message, but also on how the message’s content is delivered — in particular, how well the message’s delivery style matches the way the listener (or Web site visitor, or customer) thinks. We call these thinking styles “cognitive styles.” They define how people process information.

Some people are analytical and want to take messages apart and study each component in depth, while others look holistically at the message and react to it. Presenting an analytical case to someone who processes ideas holistically is not likely to be effective, and vice versa. Some people are deliberative and want to carefully consider ideas, while others are impulsive and “go with their gut.” Some people think with pictures, while others process information in words. Matching your presentation to the cognitive style of the Web site visitor or customer is critical for success, especially if you are trying to persuade that person to buy your product.

Good salespeople have known this for years, of course. The best ones carefully diagnose how the client thinks and then modify their pitch to match the customer. This sales approach, often instinctive, enables the salesperson to vary the presentation of information depending on the cognitive style of the customer.

Now, through Web site morphing, the Internet is beginning to do the same.

Morphing increases sales. A recent experimental study at MIT demonstrated that Web-originated purchase intentions for a large global telecommunications company’s broadband product could increase 20% after morphing the site to match individual cognitive styles.

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References

1. Urban, G.L. “The Emerging Era of Customer Advocacy,” MIT Sloan Management Review 45, no. 2 (2004): 77-82; and Urban, G.L., F. Sultan and W.J. Qualls, “Placing Trust at the Center of Your Internet Strategy,” MIT Sloan Management Review 42, no. 1 (2000): 39-48.

2. Gittins, J.C. “Allocation Indices for Multi-Armed Bandits” (London: Wiley, 1989).

3. Hauser, J.R., G.L. Urban, G. Liberali and M. Braun, “Website Morphing,” Marketing Science 28, no. 2 (March-April 2009): 202-223.

4. Allinson, C.W. and J. Hayes, “The Cognitive Style Index: A Measure of Intuition-Analysis for Organizational Research,” Journal of Management Studies 33, no. 1 (January 1996): 119-135; Frederick, S., “Cognitive Reflection and Decision Making,” Journal of Economic Perspectives 19, no. 4 (2005): 25-42; Riding, R.J. and S. Rayner, “Cognitive Styles and Learning Strategies: Understanding Style Differences in Learning and Behavior” (London, U.K.: David Fulton Publishers, 1998); and Paivio, A., “Imagery and Verbal Processes” (New York, New York: Holt, Rinehart and Winston, 1971).

5. Chickering, D.M. and T. Paek, “Personalizing Influence Diagrams: Applying Online Learning Strategies to Dialogue Management,” User Modeling and User-Adapted Interaction 17, no. 1-2 (2007): 71-91; Frias-Martinez, E., S.Y. Chen and X. Liu, “Automatic Cognitive Style Identification of Digital Library Users for Personalization,” Journal of the American Society for Information Science and Technology 58, no. 2 (2007): 237-251; and Santally, M.I. and S. Alain, “Personalisation in Web-Based Learning Environments,” International Journal of Distance Education Technologies 4, no. 4 (October-December 2006): 15-35.

6. Ansari, A. and C.F. Mela, “E-Customization,” Journal of Marketing Research 40, no. 2 (May 2003): 131-145; and Montgomery, A.L., S. Li, K. Srinivasan and J. Liechty, “Modeling Online Browsing and Path Analysis Using Clickstream Data,” Marketing Science 23, no. 4 (fall 2004): 579-595.

7. Hauser et al., “Website Morphing.”

8. Ibid.

9. Hofstede, G., “Culture’s Consequences: Comparing Values, Behaviors, Institutions and Organizations Across Nations,” 2nd ed. (Thousand Oaks, California: Sage Publications Inc., 2001); Steenkamp, J.-B.E.M. and H. Baumgartner, “Assessing Measurement Invariance in Cross-National Consumer Research,” Journal of Consumer Research 25, no. 1 (June 1998): 78-90; and Trompenaars, F. and C. Hampden-Turner, “Riding the Waves of Culture: Understanding Cultural Diversity in Business” (London, U.K.: Nicholas Brealey, 1993).

10. Tybout, A.M. and J.R. Hauser, “A Marketing Audit Using a Conceptual Model of Consumer Behavior: Application and Evaluation,” Journal of Marketing 45, no. 3 (summer 1981): 82-101; Wernerfelt, B., “Efficient Marketing Communication: Helping the Customer Learn,” Journal of Marketing Research 33, no. 2 (1996): 239-246: and Wright, P.L., “The Cognitive Processes Mediating Acceptance of Advertising,” Journal of Marketing Research 10 (February 1973): 53-62.

i. Hauser et al., “Website Morphing.”

ii. Hauser et al. give all equations and derivations as well as simulations that illustrate how the Gittins strategy works. Technically, with the Bayesian Inference Engine, we know only the probability that a visitor is in a cognitive state, so we need to use the expected value of the Gittins index rather than the raw indices. This solution is no longer optimal per se but is very, very close to optimal.

Acknowledgments

We would like to acknowledge financial support from BT, GM and Suruga Bank, which made this work possible. Special thanks to their managers, Robert Bordley, Jonathan Owen, Stephen Stokols, Takuya Sugiyama, David Vanderveen and Tokoro Yoshio, and our research assistants, Shirley Fung, Jong-Moon Kim, Tehilla Kalisky, Shelly Lau, Antonio Lorenzon, Clarence Lee, Erin MacDonald, Aman Narang, Ele Ocholi, Ashlee Rigel, Kevin Wang and Min Zhang.

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