motivations behind online and offline wom: the dna of talkable brands, presented by mitch lovett
DESCRIPTION
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.TRANSCRIPT
SOCIALMEDIA.ORG/SUMMIT2013ORLANDO
Motivations behind online and offline WOM: The DNA of talkable brands
MITCH LOVETTUNIVERSITY 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 2013Mitch Lovett: Simon Business School, University of Rochester
Email: [email protected]: 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
thatencourage or enableencourage or enable
f d t l h ti ti t fundamental 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 lFunctionalAge
ComplexityType of Good
SocialDifferentiation
QualityPremium/Value
EmotionalExcitementSatisfaction
P i d Ri k*ypKnowledge
Perceived Risk*Involvement*
Premium/ValueVisibility
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 lFunctionalAge
ComplexityType of Good
SocialDifferentiation
QualityPremium/Value
EmotionalExcitementSatisfaction
P i d Ri k*ypKnowledge
Perceived Risk*Involvement*
Premium/ValueVisibility
Perceived Risk*Involvement*
Word of mouth Word-of-mouth mentions
(online or offline)
* Hybrid characteristics
8
The brands we considerA 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
1012141618
f men
tion
s
02468
007
007
007
007
007
007
007
007
008
008
008
008
008
008
008
009
009
009
009
009
009
Num
ber
o f
2W
/E J
an
072
W/E
Feb
25
2W
/E A
pr
152
W/E
Jun
03
2W
/E J
ul
222
W/E
Sep
09
2W
/E O
ct
282
W/E
Dec
23
2W
/E F
eb
102
W/E
Mar
30
2W
/E M
ay
182
W/E
Jul
06
2W
/E A
ug
242
W/E
Oct
12
2W
/E N
ov
302
W/E
Jan
18
2W
/E M
ar
082
W/E
Apr
26
2W
/E J
un
142
W/E
Aug
02
2W
/E S
ep
20
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
umbe
r of
men
tio
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
n u discussion groups
02/0
7/20
002
/08/
200
02/0
9/20
002
/10/
200
02/1
1/20
002
/12/
200
02/0
1/20
002
/02/
200
02/0
3/20
002
/04/
200
02/0
5/20
002
/06/
200
02/0
7/20
002
/08/
200
02/0
9/20
002
/10/
200
02/1
1/20
002
/12/
200
02/0
1/20
102
/02/
201
02/0
3/20
102
/04/
201
12
Brand characteristics dataDecipher IncComplexity, VisibilityInvolvementExcitementBrand familiarity
Brand Asset Valuator by Y&RyDifferentiation, Relevance, Esteem, KnowledgeUsage
Secondary data collectionAge, Value/Premium, Product/Service , Internet
InterbrandBrand equity –is part of the top 100?
ACSISatisfaction
13 Analysis: Bayesian Statistical ModelDecipher IncComplexity, VisibilityInvolvementExcitementBrand familiarity
Brand Asset Valuator by Y&R The Keller Fay groupOffline word of mouthy
Differentiation, Relevance, Esteem, KnowledgeUsage
Offline word-of-mouth
Nielsen BuzzmetricsOnline word-of-mouth
Secondary data collectionAge, Value/Premium, Product/Service , Internet
InterbrandBrand equity –is part of the top 100?
•Negative Binomial Model•Bayesian Hierarchical StructureB i M l i l I i•Bayesian Multiple ImputationACSI
Satisfaction
Results Overview14
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 OfflineSocial
Functional EmotionalFunctionalF
EmotionalF
Social
Based on the log marginal likelihood
The detailed characteristics16
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 characteristics17
Online Offline
Differentiation (+) Satisfaction (-)Stronger Esteem (+)
Premium (+)E i ( )
Visibility (+)Excitement (+)Complexity (+)Knowledge (+)
gOnline than
OfflineExcitement (+)
Satisfaction (-)Perceived Risk (+)
Differentiation (+)Esteem (+)Visibility (+)Knowledge ( )
( )
Complexity (-)
Knowledge (+)Differentiation (+)
Relevance (+)Age (-)g ( )
The detailed characteristics18
Online Offline
Differentiation (+) Satisfaction (-)Stronger Esteem (+)
Premium (+)E i ( )
Visibility (+)Excitement (+)Complexity (+)Knowledge (+)
gOnline than
OfflineExcitement (+)
Satisfaction (-)Perceived Risk (+)
Differentiation (+)Esteem (+)Visibility (+)Knowledge ( )Different
Effect Online and Offline( )
Complexity (-)
Knowledge (+)Differentiation (+)
Relevance (+)Age (-)g ( )
The detailed characteristics19
Online Offline
Differentiation (+) Satisfaction (-)Stronger Esteem (+)
Premium (+)E i ( )
Visibility (+)Excitement (+)Complexity (+)Knowledge (+)
gOnline than
OfflineExcitement (+)
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
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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-mouth22
Online and Offline Performance 23
Need Not be the Same
On brands and word of mouth24
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
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