Research shows that both online and off-line customer conversations drive purchase decisions — but they require separate marketing strategies.

Nordstrom, the Seattle-based retailer, had a memorable 2017. In early February, Donald Trump, then the newly elected U.S. president, took to Twitter to berate Nordstrom for dropping the Ivanka Trump clothing line, complaining that the company had treated his daughter “so unfairly … terrible!” The tweet set off a powerful reaction in social media. Our research showed the number of weekly mentions of the Nordstrom brand on Twitter and other sites surged by 1,700%, while the tone of those conversations (as measured using natural-language processing, which interprets meaning from adjacent words and context) swung sharply from positive to negative.1 However, in off-line conversations (measured via surveys), the sentiment stayed positive. Amidst these mixed signals, Nordstrom rolled through the 2017 holiday season with a 2.5% sales increase over the prior year.

Divergent conversations about brands are fairly common — and not only for brands that get caught up in controversies.2 Indeed, we studied more than 500 leading consumer brands and found that in most cases there was little correlation between what consumers said about the brands online and what they said off-line, even though both streams of conversation can have big effects on a company’s sales.3

Marketers have long recognized word of mouth as a powerful force affecting how well products perform. Since the advent of Twitter and Facebook, some people now think of social media as “word of mouth on steroids” — the conversation that represents what consumers are saying.4 Yet we found that online and off-line conversations matter for different reasons.

Most studies on social media marketing effectiveness have looked at how brand engagement on specific platforms such as Facebook or Twitter (for example, the likes, shares, retweets, and comments) responds to marketing initiatives as opposed to considering the social ecosystem as a whole. There is little research looking at off-line conversations — those that occur face-to-face at the office watercooler, over the kitchen table, or at a health club — because of the difficulty and cost of measuring them. However, we addressed that challenge by asking selected consumers to recall the product and service categories and brands they talked about the day before, including whether the brand conversations were positive or negative.

References

1. C. Manning and H. Schütze, “Foundations of Statistical Natural Language Processing” (Cambridge, MA: MIT Press, 1999).

2. B. Fay and R. Larkin, “Why Online Word-of-Mouth Measures Cannot Predict Offline Outcomes,” Journal of Advertising Research 57, no. 2 (June 2017): 132-143; B. Fay, “Dick’s Sporting Goods Proves the ‘Noise’ of Social Media Can Give an Incomplete Signal,” June 14, 2018, www.mediapost.com.

3. J. Morrissey, “Brands Closely Monitor Social Media, but Offline Chatter Is Just as Important,” The New York Times, Nov. 27, 2017; and “Return on Word of Mouth,” working paper, Word of Mouth Marketing Association, September 2015.

4. L. Geller, “Why Word of Mouth Works,” May 13, 2013, www.forbes.com; H. Conick, “‘Word of Mouth on Steroids’: Brands Find Success in Peer Endorsements, Study Finds,” Marketing Insights, April 5, 2016; and A. Lane, “Word of Mouth on Steroids — Understanding the Motives of Sharing Content,” Nov. 27, 2017, www.marketingmag.com.au.

5. Trading Economics and U.S. Bureau of Economic Analysis, total for four quarters ending July 2018, https://tradingeconomics.com.

6. M.J. Lovett, R. Peres, and R. Shachar, “On Brands and Word of Mouth,” Journal of Marketing Research 50, no. 4 (August 2013): 427-444; and A. Barasch and J. Berger, “Broadcasting and Narrowcasting: How Audience Size Affects What People Share,” Journal of Marketing Research 51, no. 3 (June 2014): 286-299.

7. B. Fay, “Dick’s Sporting Goods Proves the ‘Noise’ of Social Media Can Give an Incomplete Signal,” June 14, 2018; and W. Duggan, “Gun Restrictions Don’t Dampen Dick’s Stock,” May 30, 2018, 132-143, https://money.usnews.com.

8. B. Libai, E. Muller, and R. Peres, “Decomposing the Value of Word-of-Mouth Seeding Programs: Acceleration Versus Expansion,” Journal of Marketing Research 50, no. 3 (April 2013): 161-176.

9. D. Chmielewski, “Marketing Moms: Nintendo Reaches Out to a Relatively Untapped Segment of Potential Users in an Effort to Promote Its New Console,” Los Angeles Times, Dec. 25, 2006; and L. Richwine, “Disney’s Powerful Marketing Force: Social Media Moms,” Reuters, June 15, 2015.

10. This “two-step flow” is consistent with work that goes back to the 1950s, when it was devised by researchers at Columbia University and the University of Pennsylvania. See P. Lazarsfeld and E. Katz, “Personal Influence: The Part Played by People in the Flow of Mass Communications” (New York: Free Press, 1955); J. Bughin, J. Doogan, and O. Jørgen Vetvik, “A New Way to Measure Word of Mouth Marketing,” McKinsey Quarterly (April 2010); and M. Trusov, R.E. Bucklin, and K. Pauwels, “Effects of Word-of-Mouth Versus Traditional Marketing: Findings From an Internet Social Networking Site,” Journal of Marketing 73, no. 5 (September 2009): 90-102.

11. J. Bughin, J. Doogan, and O. Jørgen Vetvik, “A New Way to Measure Word of Mouth Marketing,” McKinsey Quarterly (April 2010); and M. Trusov, R.E. Bucklin, and K. Pauwels, “Effects of Word-of-Mouth Versus Traditional Marketing: Findings From an Internet Social Networking Site,” Journal of Marketing 73, no. 5 (September 2009): 90-102.

12. E. Keller and B. Fay, “How to Use Influencers to Drive a Word-of-Mouth Strategy,” WARC Best Practice, April 2016.

i. D. Hanssens, L. Parsons, and R.L. Schultz, “Market Response Models: Econometric and Time Series Analyses” (Boston: Kluwer Academic Publishers, 2003): 87-317.