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.

1. SOCIALMEDIA.ORG/SUMMIT2013ORLANDO
Motivations behind online
and offline WOM: The DNA
of talkable brands
MITCH LOVETT
UNIVERSITY OF ROCHESTER
DECEMBER 9–11, 2013

2. 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

3. 2
How predictable is the word of mouth brands get?

4. 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

5. 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
 . . .

6. 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

7. 6
What driver of WOM matters most?
Functional Social E ti lFunctional Social Emotional

8. 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

9. 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 …

10. 9
Word-of-mouth data
Online OfflineOnline Offline

11. 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

12. 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

13. 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

14. 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

15. 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

16. 15
Online-Offline differences
Online Offline
Social
Functional
Emotional
FunctionalF
Emotional
F
Social
Based on the log marginal likelihood

17. 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 ( )

18. 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 ( )

19. 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 ( )

20. 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

21. 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

22. 21
Building a better brand?

23. A Brand’s Expected Word-of-mouth
22

24. Online and Offline Performance
23
Need Not be the Same

25. 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

26. 25
THANK YOUTHANK YOU

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