Connecting brands and word of mouth
Brands and word of mouth are cornerstones of marketing, yet their relationship has been largely ignored. In this report, the authors explore the relationship between brand characteristics and the word of mouth (WOM) they generate, in order to better understand WOM drivers and provide marketers with insights as to managing the WOM on their brands.
Connecting brands and word of mouth
The authors present a theoretical framework that argues that consumers spread the word on brands as a result of three drivers: functional, social, and emotional. The functional driver relates to the need to obtain information and the tendency to share information; the social driver to the need to express uniqueness, self-enhance, and socialize; and the emotional driver to emotion sharing. These drivers are mapped into a set of brand characteristics that stimulate WOM—age, complexity, type of good, and knowledge for the functional driver; differentiation, quality, and visibility for the social driver; and excitement and satisfaction for the emotional driver. Finally, the characteristics of involvement and perceived risk relate to multiple drivers. These brand characteristics are expected to stimulate WOM through (at least) one of the fundamental drivers. For example, a highly differentiated brand is easier to use as a signal of one’s uniqueness and, therefore, is likely to generate greater WOM.
A unique dataset of the 697 most-talked-about national U.S. brands (covering 16 categories) include information on both WOM and brand characteristics. WOM data came from the Keller Fay Group for offline conversations and Nielsen-McKinsey Incite Buzzmetrics for online brand mentions. Brand characteristic perceptions were collected via a large-scale survey via Decipher Inc. and enriched with Young and Rubicam’s Brand Asset Valuator panel.
Overall, the authors find that brand characteristics influence WOM with a strong channel effect:
1. Brand characteristics play an important role in generating WOM. All the characteristics (but one—involvement) have a significant effect on WOM.
2. Their influence differs for offline conversations and online brand mentions. For example, new and more complex brands are talked about more offline, but such a relationship is not found online. In contrast, highly differentiated brands have significantly more online brand mentions while such a relationship is not found offline.
3. While the social and functional drivers are the most important for stimulating online WOM, the emotional driver is the most important for stimulating offline WOM.
These results shed light on the link between investments in brands and their market outcomes:
How can managers create “talkable” brands? Knowing how brand characteristic perceptions influence WOM, brand managers can craft the brand’s characteristics to enhance WOM on their brands (e.g., increase visibility, emphasize differentiating attributes).
Does the actual WOM on a brand fulfill its potential? By comparing the level of WOM as predicted by the model to the actual level, one can see if the actual level is above or below the expected WOM. For example, the authors find that Pillsbury, Swanson, Zest, AOL, Motorola, Dell, Microsoft, and Mercedes Benz all underperform for at least one channel compared to what they would expect based on their brand characteristics.
How important is WOM in the overall marketing communications mix for the brand? The average expected level of WOM for a brand indicates how important WOM is to the brand’s communication strategy. Furthermore, some brands should expect to have a significant WOM online or offline, but not on both channels. Knowing this can help marketing managers plan more effective integrated marketing communications.
Renana Peres is Assistant Professor, School of Business Administration, Hebrew University of Jerusalem. Ron Shachar is Dean, Arison School of Business, IDC Herzliya, Israel. Mitchell J. Lovett is Assistant Professor of Marketing, Simon Graduate School of Business, University of Rochester.
We thank our industry collaborators: Brad Fay from the Keller Fay Group, Nina Stratt from NMIncite, and Ed Lebar from Young and Rubicam Brand Asset Valuator for sharing their data. We thank Kristin Luck and the Decipher Inc. team for programming and managing the survey. We gratefully thank Barak Libai and Eitan Muller for constructive discussions and also our research assistants, at Wharton: Christina Andrews, Linda Wang, Chris Webber-Deonauth, Derric Bath, Grace Choi, Rachel Amalo, Yan Yan, Niels Mayrargue, Nathan Pamart, and Fangdan Chen; at the Hebrew University: Yair Cohen, Dafna Presler, Oshri Weiss, and Liron Zarezky. This research was supported by the Kmart International Center for Marketing and Retailing at the Hebrew University of Jerusalem, the Israel Science Foundation, the Marketing Science Institute, and the Marketing Department at the Wharton School.
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