Motivations behind online and offline WOM: The DNA of talkable brands, presented by Mitch Lovett
 

Motivations behind online and offline WOM: The DNA of talkable brands, presented by Mitch Lovett

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In his Brands-Only Summit presentation, University of Rochester's Assistant Professor of Marketing, Mitch Lovett, provides insights into the DNA of "talkable" brands. ...

In his Brands-Only Summit presentation, University of Rochester's Assistant Professor of Marketing, Mitch Lovett, provides insights into the DNA of "talkable" brands.

He uses a unique data set to measure online social media, offline WOM, and brand characteristics to demonstrate the fundamental consumer motivations for spreading the word about brands online and offline.

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Motivations behind online and offline WOM: The DNA of talkable brands, presented by Mitch Lovett Motivations behind online and offline WOM: The DNA of talkable brands, presented by Mitch Lovett Presentation Transcript

  • SOCIALMEDIA.ORG/SUMMIT2013ORLANDO Motivations behind online and offline WOM: The DNA of talkable brands MITCH LOVETT UNIVERSITY OF ROCHESTER DECEMBER 9–11, 2013
  • M B h d O l d Offl WOMMotivations Behind Online and Offline WOM: The DNA of Talkable Brands December 2013 Based on “On Brands and WOM,” JMR 2013 Mitch Lovett: Simon Business School, University of Rochester Email: mitch.lovett@simon.rochester.edu website: https://sites google com/site/mitchlovettprof/ , website: https://sites.google.com/site/mitchlovettprof/ Renana Peres: The Hebrew University of Jerusalem Ron Shachar: Arison School of Business (IDC) and NYU Stern
  • 2 How predictable is the word of mouth brands get?
  • What makes a brand more talkable? 3 Brand characteristics that encourage or enableencourage or enable f d t l h ti ti tfundamental human motivations to communicate
  • Which fundamental human motivations? 4  Need for esteem Need for esteem  Need for information/understand Avoidance of risk or embarrassment Avoidance of risk or embarrassment  Desire to converse and connect N d b l d Need to balance and express emotions  Need to express ones uniqueness  . . .
  • 5 The Brand DNA of Word of Mouth Functional Social E ti lFunctional Age Complexity Type of Good Social Differentiation Quality Premium/Value Emotional Excitement Satisfaction P i d Ri k* yp Knowledge Perceived Risk* Involvement* Premium/Value Visibility Perceived Risk* Involvement* * Hybrid characteristics
  • 6 What driver of WOM matters most? Functional Social E ti lFunctional Social Emotional
  • 7 The Brand DNA of Word of Mouth Functional Social E ti lFunctional Age Complexity Type of Good Social Differentiation Quality Premium/Value Emotional Excitement Satisfaction P i d Ri k* yp Knowledge Perceived Risk* Involvement* Premium/Value Visibility Perceived Risk* Involvement* Word of mouthWord-of-mouth mentions (online or offline) * Hybrid characteristics
  • 8 The brands we consider A list of 700 most talked about US brands from 16 categories (most mentioned during 2007-2010)g ( g )  52 Beauty products – Always, Clinique, Pantene …  66 Beverages – 7up, Coors, Red Bull ….  47 Cars – Acura, Toyota, Jiffy Lube …  19 Children's products – OshKosh, Gerber, Lego …  50 Clothing products – Adidas, Gap, Gucci …  15 Department stores – Walmart, Kmart, Target ….  39 Financial services - AIG, Etrade, HSBC …  105 Food and dining - Albertsons, Frito Lay, Burger King …  28 Health products and services – CVS, Blue Cross, Tylenol …  14 Home design and decoration – Home Depot, Ikea, Pottery Barn …  24 Household products – Lysol, Palmolive, Tide …p y  103 Media and entertainment – BBC, American Idol, Indiana Jones …  21 Sports and hobbies – Boston Celtics, NBA, Curves …  56 Technology products and stores – Apple, Sony, IBM …  25 Telecommunications – Blackberry, Virgin Mobile, Nokia …y, g ,  34 Travel services – Amtrak, Hilton, Expedia …
  • 9 Word-of-mouth data Online OfflineOnline Offline
  • 10 Word-of-mouth data Offline from Keller Fay databaseOffline from Keller Fay database  The award winning TalkTrack® project  Ongoing survey of 700 people each week Ongoing survey, of 700 people each week  A representative sample of the US population  Respondents report on all brand–related conversations for the st 24 h spast 24 hours  Open self-report  Not constrained to a brand list  Weekly number of mentions since Jan 2007 10 12 14 16 18 fmentions 0 2 4 6 8 007 007 007 007 007 007 007 007 008 008 008 008 008 008 008 009 009 009 009 009 009 Numberof 2W/EJan07 2W/EFeb25 2W/EApr15 2W/EJun03 2W/EJul22 2W/ESep09 2W/EOct28 2W/EDec23 2W/EFeb10 2W/EMar30 2W/EMay18 2W/EJul06 2W/EAug24 2W/EOct12 2W/ENov30 2W/EJan18 2W/EMar08 2W/EApr26 2W/EJun14 2W/EAug02 2W/ESep20
  • 11 Word-of-mouth data Online from Nielsen McKinsey Incite  User-generated content search engine  Search for brand mentions through blogs, ((("banana republic" OR "banana republics") AND NOT (fruit OR fruits OR produce OR food OR government Online from Nielsen-McKinsey Incite Search for brand mentions through blogs, discussion groups and microblogs (tweets)  Search through screening queries OR governments)) OR NEAR7((br OR brs OR banana OR bananas) AND (clothes OR store OR clothing OR retailer OR retailers OR outlet OR outlets OR retail OR merchandise OR pants OR jeans OR shirt OR shirts OR  D il b f ti i J l 2008 pants OR jeans OR shirt OR shirts OR skirt OR skirts OR shorts OR dress OR dresses OR shoe OR shoes OR sandals OR outerwear OR coat OR coats OR jacket OR jackets OR blouse OR blouses OR belt OR belts OR  Daily number of mentions since July 2008 2000 2500 3000 ons jewelry))) 1000 1500 2000 umberofmentio Blogs Micro Blogs 0 500 08 08 08 08 08 08 09 09 09 09 09 09 09 09 09 09 09 09 10 10 10 10 nu discussion groups 02/07/200 02/08/200 02/09/200 02/10/200 02/11/200 02/12/200 02/01/200 02/02/200 02/03/200 02/04/200 02/05/200 02/06/200 02/07/200 02/08/200 02/09/200 02/10/200 02/11/200 02/12/200 02/01/201 02/02/201 02/03/201 02/04/201
  • 12 Brand characteristics data Decipher Inc Complexity, Visibility Involvement Excitement Brand familiarity Brand Asset Valuator by Y&Ry Differentiation, Relevance, Esteem, Knowledge Usage Secondary data collection Age, Value/Premium, Product/Service , Internet Interbrand Brand equity – is part of the top 100? ACSI Satisfaction
  • 13 Analysis: Bayesian Statistical Model Decipher Inc Complexity, Visibility Involvement Excitement Brand familiarity Brand Asset Valuator by Y&R The Keller Fay group Offline word of mouth y Differentiation, Relevance, Esteem, Knowledge Usage Offline word-of-mouth Nielsen Buzzmetrics Online word-of-mouth Secondary data collection Age, Value/Premium, Product/Service , Internet Interbrand Brand equity – is part of the top 100? •Negative Binomial Model •Bayesian Hierarchical Structure B i M l i l I i•Bayesian Multiple ImputationACSI Satisfaction
  • Results Overview 14  All three sets of drivers play a role All three sets of drivers play a role  Most brand characteristics play a role B t th i l ti i t diff But their relative importance differs between online and offline
  • 15 Online-Offline differences Online Offline Social Functional Emotional FunctionalF Emotional F Social Based on the log marginal likelihood
  • The detailed characteristics 16 Online Offline Differentiation (+) Satisfaction (-) Esteem (+) Premium (+) E i ( ) Visibility (+) Excitement (+) Complexity (+) Knowledge (+) Excitement (+) Satisfaction (-) Perceived Risk (+) Differentiation (+) Esteem (+) Visibility (+) Knowledge ( ) ( ) Complexity (-) Knowledge (+) Differentiation (+) Relevance (+) Age (-)g ( )
  • The detailed characteristics 17 Online Offline Differentiation (+) Satisfaction (-) Stronger Esteem (+) Premium (+) E i ( ) Visibility (+) Excitement (+) Complexity (+) Knowledge (+) g Online than Offline Excitement (+) Satisfaction (-) Perceived Risk (+) Differentiation (+) Esteem (+) Visibility (+) Knowledge ( ) ( ) Complexity (-) Knowledge (+) Differentiation (+) Relevance (+) Age (-)g ( )
  • The detailed characteristics 18 Online Offline Differentiation (+) Satisfaction (-) Stronger Esteem (+) Premium (+) E i ( ) Visibility (+) Excitement (+) Complexity (+) Knowledge (+) g Online than Offline Excitement (+) Satisfaction (-) Perceived Risk (+) Differentiation (+) Esteem (+) Visibility (+) Knowledge ( ) Different Effect Online and Offline( ) Complexity (-) Knowledge (+) Differentiation (+) Relevance (+) Age (-)g ( )
  • The detailed characteristics 19 Online Offline Differentiation (+) Satisfaction (-) Stronger Esteem (+) Premium (+) E i ( ) Visibility (+) Excitement (+) Complexity (+) Knowledge (+) g Online than Offline Excitement (+) Satisfaction (-) Perceived Risk (+) Differentiation (+) Esteem (+) Visibility (+) Knowledge ( ) Different Effect Online and Offline( ) Complexity (-) Knowledge (+) Differentiation (+) Relevance (+) Age (-) Unique To One Channel g ( )One Channel
  • The conversation matches the h i i 20 characteristics  When people talk about an exciting brand When people talk about an exciting brand do they express excitement?  Method: Method:  Subsample of 41 brands over 365 days  Differentiation excitement and esteem Differentiation, excitement, and esteem  Netbase’s Insight Workbench Tool  Results: The differentiation and excitement brand The differentiation and excitement brand characteristics relate to topic of WOM
  • 21 Building a better brand?
  • A Brand’s Expected Word-of-mouth 22
  • Online and Offline Performance 23 Need Not be the Same
  • On brands and word of mouth 24  Brand characteristics are associated with Brand characteristics are associated with the quantity, channel, and content of WOM  The importance of understanding the The importance of understanding the fundamental human motivations behind WOM  The role of functional, social, and emotional drivers online and offline differ  Our results shed light on how to better  Build talkable brands  Identify a brand’s WOM potential  Invest in the appropriate channel for the brand
  • 25 THANK YOUTHANK YOU
  • SOCIALMEDIA.ORG/SUMMIT2013ORLANDO Learn more about past and upcoming events DECEMBER 9–11, 2013 SOCIALMEDIA.ORG/EVENTS