aims2012 marketing associates quantifying the buzz effect
DESCRIPTION
Keith Shields Marketing Associates Managing Director of the Decisions Science Group“Quantifying the Buzz Effect: Integrating Social Media with Loyalty & Defection Models”TRANSCRIPT
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Decision�Sciences
Quantifying�the�"Buzz"�Effect:�Integrating�Social�Media�with�Loyalty�&�Defection�Models
Marketing�Associates:�Keith�Shields,�Director,�Decision�Sciences
Roni�Leibovitch,�Senior�Consultant,�Digital�IntelligenceMindy�Deatrick,�Senior�Consultant,�Quantitative�Solutions
Ford�Motor�Company:Margaret�Kishore,�Performance�and�Metrics�Manager
Create�a�Business�BlueprintWith�Data�Driven�Customer�Insights
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About�the�Title…
� “Buzz”�refers�to�the�amount�of,�and�sentiment�of,�the�Ford�related�comments�available�through�social�media�outlets.
� The�“Buzz�Effect”�refers�to�the�increase�or�decrease�in�brand�loyalty�/�defection�(measured�by�repurchase)�that�occurs�as�a�result�of�a�change�in�the�Buzz.
� “Quantifying�the�Buzz�Effect”�means�we�want�to�put�a�number�on�the�amount�of�that�increase�or�decrease.
� The�advantage�of�this�is�that�we�can�begin�to�put�a�dollar�value�on�salient,�publicly�known�events…such�as�refusing�to�take�government�bailouts.
� “Integrating�Social�Media�With�Loyalty�/�Defection�Models”�means�that�we�will:�� Extract�signals�of�future�vehicle�purchase�decisions�from�customer�comments found�
through�social�media�outlets AND� Capture�those�signals�in�the�form�of�predictive�variables�to�put�into�loyalty�models.� Those�variables�and�their�associated�model�coefficients�will�quantify�the�buzz�effect.� For�the�purpose�of�this�analysis�we�focus�our�efforts�on�Twitter.�
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Warnings�and�Disclaimers
� We�will�do�our�best�to�reveal�trends,�patterns,�and�findings�without�showing�actual�numbers�(but�for�some�cases).��Hiding�/�changing�of�numbers�is�done�to�protect�the�innocent�(Ford�Motor�Company�especially).
� In�the�course�of�the�presentation�we�will�share�many�Ford�related�“tweets”.��These�will�be�actual�tweets.��They�will�not�be�censored�because�their�informal�nature�highlights�a�point�we�want�to�make�about�text�mining.��Please�try�not�to�be�offended.
� We�use�“off�the�shelf”�techniques�when�it�comes�to�categorizing�sentiment.� Our�expertise�is�in�modeling�and�predicting�customer�behavior�based�on�all�
available�and�relevant�customer�data.�� We�see�social�media�as�a�potentially�rich�source�of�customer�data,�and�those�data�
just�happen�to�be�free�form�text.
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One�More�Item�of�Note…
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Background�on�Ford’s�Social�Media�Efforts…
� Measuring�the�“Consumer�Experience”� Alan�Mulally and�Apple…� The�Dealership�Experience:�Sales�and�Service� The�Ownership�Experience� How�do�people�share�experiences?��Traditionally�by�talking�to�each�other.��But�how�
much�today�is�done�through�Twitter,�Facebook,�Blogs?
� By�analyzing�the�comments�and�sentiment�expressed�through�Social�Media�outlets�can�we�glean�meaningful�insights�about�the�Ford�Consumer�Experience?��
� Can�we�make�inference�about�a�consumer’s�affinity�for�Ford…or�an�existing�customer’s�loyalty�to�Ford?
� If�no,�then�we’re�probably�not�trying�hard�enough.� Examples�next�2�slides.
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Google�Twitter�Search:�Ford�Comments
� Search:�“My�Ford�Focus�is�great.”
� I�love�my�Ford�Focus,�but�not�so�much�Ford�Service�in�Northampton�Mass.�Thieves.
� Got�my�new�computer�yesterday�and�can't�wait�to�get�my�new�2012�Ford�Focus SEL�in�4�6�weeks!�23�Apr
� Am�test�driving�Hondas�and�Fords 7�Apr
� We’d�like�to�have�a�mechanism�for�intervening�here.��On�April�7�this�person�indicated�he�was�facing�a�choice�between�buying�a�Honda�and�buying�a�Ford.
� Does�this�mean�we�can�simply�scrape�Twitter�for�the�words�“test�drive”?��Seems�like�it�would�be�predictive�of�future�behavior…
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Google�Twitter�Search:�Ford�Comments
� Search:�“I�don’t�like�my�Ford Escort.”
� The�ford�escort�texting�and�driving,�I�really�likemy�life�and�my�car,�please�don't�try�and�drive�into�us,�twice.�Close�call!
� My�old�'93�ford�escort�is�running�130k�and�runs�like�a�charm....�And�my�2003�ford ranger�truck�has�80k�without�problems.
� Again�this�seems�like�something�that,�if�captured�and�quantified�in�the�form�of�a�variable,�would�be�predictive�in�the�context�of�a�loyalty�model.
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Google�Twitter�Search:�Ford�Comments
� Search:�“Ford,�government�bailout”� This�weekend�my�wife�and�I�purchased�a�FORD.�Why?�Because�they�chose�not�to�accept�the�government funded�bailout.
� Ford didn't�accept�the�government�bailout � that's�pretty�awesome.� Wait�#Ford pulled�the�ad�that�was�critical�of�the�#Obama bailout�but�is�now�running�one�that�jokes�about�drinking�and�driving?�
� GM�CEO�wants�higher�gas�tax.�Buy�a�Ford car�or�truck.�Please�RT
� This�is�an�example�of�how�capturing�“influencers”�could�be�very�important.��This�person�happens�to�have�340�followers�and�routinely�tweets�about�auto�related�topics.�
� So�the�effort�to�mine�Twitter�for�Ford�sentiment�extends�beyond�improving�the�loyalty�and�defection�models…but�the�title�of�this�presentation�does�not.��That�said,�we�will�discuss�how�we�are�affecting�marketing�programs�with�our�existing�knowledge�of�influencers.��
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Background�on�Ford’s�Social�Media�Efforts…
� Measuring�the�“Consumer�Experience”� Alan�Mulally and�Apple…� The�Dealership�Experience:�Sales�and�Service� The�Ownership�Experience� How�do�people�share�experiences?��Traditionally�by�talking�to�each�other.��But�how�much�today�is�done�through�Twitter,�Facebook,�Blogs?
� By�analyzing�the�comments�and�sentiment�expressed�through�Social�Media�outlets�can�we�glean�meaningful�insights�about�the�Ford�Consumer�Experience?��
� Can�we�make�inference�about�a�consumer’s�affinity�for�Ford…or�an�existing�customer’s�loyalty�to�Ford?
� Yes!��So�what�can�we�do�capitalize�upon�good�sentiment�and�reverse�bad�sentiment?
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Start�With�the�Current�Infrastructure� The�Ford�Motor�Company�has�a�customer�data�warehouse�that�collects�relevant�data�from�all�customer�
touchpoints,�“customerizes”�it,�and�applies�a�suite�of�predictive�models�that�are�used�for�targeted�campaigns.
� More�importantly�the�warehouse�is�connected�to�many�customer�facing�and�dealer�facing�operational�systems,�and�it�passes�important�information�about�customer�behavior,�both�past�behavior�and�predicted�behavior,�to�operational�systems�when�decisions�regarding�the�customer�have�to�be�made�in�real�time.
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Fitting�in�to�the�Current�Infrastructure…
� Social�media�is�just�another�customer�touch�point.� The�text�we�mine�from�social�media�outlets�is�another�set�of�data�about�the�customer,�just�
like�the�call�center,�website,�or�the�customer�surveys.� We’d�like�to�use�that�data�just�like�the�rest�of�the�customer�data:�to�help�us�predict�customer�
purchase�behavior.
� In�some�sense�social�media�provides�a�source�of�unsolicited�surveys.
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A�Source�of�“Unsolicited�Surveys”…
� Why�do�we�survey�customers?��From�the�narrow�perspective�of�someone�who�predicts�customer�behavior,�the�graph�below�is�a�big�reason�why.
� How�much�do�we�spend�on�surveys?� Whatever�it�is,�our�feeling�is�that�if�we�can�establish�the�above�relationship�with�social�media�
sentiment�(use�it�as�your�X�axis),�and�cover�more�customers�for�less�than�what�we�currently�spend�on�surveys,�then�we�have�the�beginning�of�a�business�case�for�extracting�sentiment�from�social�media.
� What’s�more�compelling�is�that�relationship�between�a�customer’s�opinion�and�loyalty�holds�up�when�we�control�for�predicted�loyalty.��
� The�“Loyalty=1”�group�is�the�group�that�scores�in�the�lowest�20%�of�a�loyalty�model…a�model�built�without�survey�data.�
� The�results�remain�consistent�within�each�loyalty�tranche�so�much�so�that�customers�within�group�5�can�have�lower�repurchase�rates�than�those�in�group�3,�depending�on�survey�response.�
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� We�believe�that�there�are�three�“pillars”�for�the�business�case�to�actively�engage�consumers�through�social�media�outlets�(specifically�Twitter):1. Conquesting�new�customers2. Concern�resolution3. Voice�of�Customer
Introduce�Social�Media�as�an�additional�consumer�touch�point
1. Conquest$XX�mils�per�year
STRATEGY:
SUPPORTS�THESTRATEGY:
2. Concern�Res$XX�mils�per�year
3. VOC$XX�mils�per�year
A�Quick�Digression�on�Business�Cases…
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How�We�Use�Survey�Data�In�the�Models…
� Let�P�=�probability�a�Ford�customer�will�repurchase�another�Ford�upon�disposing�of�any�one�of�his�current�Fords.
� Logistic�regression�is�a�very�popular�way�to�model�and�predict�P.� ln[p�/�(1�p)]�=�b0 +�b1*x1 +�b2*x2 +�…�bn*xn� b0,�b1,�b2 are�parameter�estimates.��They�quantify�the�extent�to�which�x1,�x2,�…,�xn affect�the�
probability�of�repurchase.� x1,�x2,�…,�xn are�explanatory�variables,�e.g.�#�of�previous�Ford�purchase,�time�since�most�
recent�Ford�purchase,�miles�from�nearest�Ford�dealership,�etc…�
� Now�let�s1 =�1�if�“very�likely”,�0�otherwise� Let�s2 =�1�if�“likely”,�0�otherwise�…� Let�s5 =�1�if�“not�at�all�very�unlikely”,�0�otherwise� Refit�the�logistic�regression:
� ln[p�/�(1�p)]�=�b0 +�b1*x1 +�…�bn*xn +�bn+1*s1�+�bn+2*s2 +�bn+3*s3�+�bn+4*s4 +�bn+5*s5
Can�be�thought�of�as�the�VOC�(Voice�of�Customer)�Index,�but�it’s�based�on�just�survey�data,�which�may�only�be�available�on�10%�(roughly)�of�the�customers.This�is�a�nice�metric,�because�it,�by�design,�predicts�loyalty.
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How�We�Use�Twitter�Data�In�the�Models…
� Let’s�treat�the�Ford�customer’s�“tweets”�the�same�way�we�treat�survey�data.� Go�back�to�our�logistic�regression:
� ln[p�/�(1�p)]�=�b0 +�b1*x1 +�…�bn*xn +�bn+1*s1�+�bn+2*s2 +�bn+3*s3�+�bn+4*s4 +�bn+5*s5� And�let�t1 =�1�if�we�can�identify�a�“Ford�positive”�tweet�for�the�customer,�0�otherwise.� Let�t2 =�1�if�we�can�identify�a�“Ford�neutral”�tweet�for�the�customer,�0�otherwise.� Let�t3 =�1�if�we�can�identify�a�“Ford�negative”�tweet�for�the�customer,�0�otherwise.� Refit�the�model:
� ln[p�/�(1�p)]�=�b0 +�b1*x1 +�…�bn*xn +�bn+1*s1�+�bn+2*s2 +�bn+3*s3�+�bn+4*s4 +�bn+5*s5+�bn+6*t1�+�bn+7*t2 +�bn+8*t3
Can�be�thought�of�as�the�BUZZ�INDEX,�and�it�comes�directly�from�what�ford�customers�are�saying�on�Twitter.��This�metric�also,�by�design,�predicts�loyalty.��So�this�is�a�quantification�of�the�BUZZ�EFFECT.
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Interpreting�the�“Twitter�Enhanced”�Model…
� ln[p�/�(1�p)]�=�b0 +�b1*x1 +�…�bn*xn +�bn+1*s1�+�bn+2*s2 +�bn+3*s3�+�bn+4*s4 +�bn+5*s5+�bn+6*t1�+�bn+7*t2 +�bn+8*t3
� When�we�fit�this�model,�we�get�an�intuitive�result:�� bn+6�>�bn+7 > bn+8 =>�good�tweets�lead�to�higher�loyalty�than�do�neutral�tweets,�
neutral�tweets�lead�to�higher�loyalty�than�bad�tweets.
� Not�as�intuitive�(but�interesting�nonetheless):�� All�three�parameters�are�greater�than�0��(implying�ANY�tweeting�is�better�than�no�
tweeting).� bn+8 (the�parameter�for�bad�tweets)�is�NOT�SIGNIFICANT.��There�is�not�a�sufficient�
volume�of�bad�tweets�to�support�a�significant�result.��The�large�majority�of�FLM�tweets�are�good.�
� We�think�that�the�upshot�of�all�of�this�is�that�tweeting�about�Ford,�irrespective�of�sentiment,�signifies�a�high�level�of�customer�engagement.��This�has�implications�beyond�our�efforts�to�better�predict�loyalty.
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The�Buzz�Variables�Improve�the�Loyalty�Model:�So�What?
� More�data�and�better�data�yield�models�that�do�a�better�job�of�“separating”�loyalists�and�non�loyalists.��
� One�way�this�manifests�itself:�ranking�the�population�of�customers�with�a�better�model�will�yield�higher�repurchase�rates�in�the�top�decile (or�demi�deciles…depending�on�how�many�groups�you�want�to�establish),�and�lower�repurchase�rates�in�the�bottom�decile.
� So�a�marketing�campaign�that�increases�everyone’s�likelihood�of�repurchase�by�15%�(not�an�uncommon�number),�does�so�on�a�larger�base�of�loyalists�within�the�top�decile,�and�thus�creates�more�incremental�sales�for�the�same�amount�of�mailings.
1 2 3 4 5 6 7 8 9 10
Repu
rcha
se�Rate
Model�DecileLow�Loyalty�to�High
Old�ModelModel�w/VOC�&�Buzz
� Say�the�difference�between�these�two�bars�is�200�bps.�� Some�of�the�incremental�sales�from�the�campaign�
noted�in�the�bullet�above�(top�decile only),�are�attributable�to�having�a�better�model.��
� How�many?�.02�*�.15�*�top�decile population� If�the�population�of�interest�is�250,000�customers,�then�
the�impact�of�the�better�model�is�750�incremental�repurchases.
� If�a�repurchase�is�worth�$5,000�profit,�then�the�case�for�the�“buzz�variables”�is�substantial:�$3.75�million.
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Why�We�Will�Regularly�Re�Fit�the�Buzz�Index� We�have�several�reasons�to�believe�that�results�may�change�when�we�re�fit�our�
enhanced�loyalty�model�(twitter�sentiment�data�being�the�enhancement):1. The�number�of�Tweeters�is�increasing�all�the�time.��Ford’s�customer�email�capture�isn’t�great�
but�it�is�improving,�and�there�is�evidence�that�Ford�customers�are,�relatively�speaking,�very�active�on�Twitter.
2. Attribution�of�tweets�to�customers�is�difficult�and�unsure;�finding�the�Twitter�names�of�Ford�customers�is�difficult�and�painstaking.
3. Classifying�the�sentiment�of�Tweets�is�an�imprecise�exercise,�especially�when�using�off�the�shelf�tools�and�software.
4. The�content�of�the�“Ford�tweeting”�population�leads�to�potentially�biased�results;�it�is�biased�toward�a�demographic�that�naturally�tends�to�be�less�Ford�loyal:
� Females� Young� Used�vehicle�owners� Appear�to�be�more�service�loyal,�which�is�a�good�thing
5. The�tweeting�population�also�happens�to�be�geographically�biased,�but�this�does�not�concern�us�as�much�as�#4.
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Re�Fit�the�Buzz�Index:�The�Increasing�Number�of�Tweeters�
� According�to�GIGAOM�(http://gigaom.com/),�Twitter�had�175�million�users�in�December�2010,�and�was�growing�by�370,000�new�users�every�day.��Also�as�of�12/2010:
� 65%�of�those�users�lived�outside�the�US.� Roughly�6%�of�all�Americans�were�active�on�Twitter.��More�recent�studies�indicate�the�number�is�9%�10%�(or�
13%�of�internet�users).
� Of�the�Ford�customers�active�as�of�12/2010�(who�had�a�valid�email),�we�were�able�to�find�9%�of�them�active�on�Twitter.
� Not�surprisingly�we�have�found�the�number�of�Ford�related�tweets�to�be�increasing�over�time.��� Good�and�neutral�tweets�have�increased�whereas�bad�tweets�have�stayed�flat.��Seems�like�a�good�thing,�but�we�
have�some�thoughts�on�comment�classification.�
Frequency�of�Ford�Related�Comments�Found�on�Twitter
0
10,000
20,000
30,000
40,000
50,000
60,000
May�08
Jul�0
8
Sep�08
Nov�08
Jan�09
Mar�09
May�09
Jul�0
9
Sep�09
Nov�09
Jan�10
Mar�10
May�10
Jul�1
0
Sep�10
Nov�10
Jan�11
Mar�11
Ford�Related�Twitter�Comments�"Binned"
0
1000
2000
3000
4000
5000
6000
May�08
Jun�08
Jul�0
8Au
g�08
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Jan�09
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Jun�09
Jul�0
9Au
g�09
Sep�09
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Jan�10
Feb�10
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Apr�10
May�10
Jun�10
Jul�1
0Au
g�10
Sep�10
Oct�10
Nov�10
Dec�10
Jan�11
Feb�11
Mar�11
Postive,�Negative
0
10000
20000
30000
40000
50000
60000
Neu
tral
Negative
Positive
Neutral
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Re�Fit�the�Buzz�Index:�Attributing�the�Tweets�� Twitter�names�can�be�found�if�you�can�supply�an�email.
� With�an�email�address�you�can�find,�through�the�Twitter�API,�a�Twitter�name,�a�first�name,�and�a�last�name�associated�with�that�email�address.��It�will�not�return�the�email.
� Ford�has�11�million�bought�new�still�retained�customers.��We�have�emails�on�several��million�of�them.
� We�cannot�run�several�million�emails�through�the�Twitter�API.��Even�if�they�could�be�processed,�we�would�not�be�able�to�get�back�the�email.��We�would�only�get�back�the�thousands�of�Twitter�names�associated�with,�but�not�matched�to�the�emails.�
� The�only�sure�way�to�attribute�emails�to�Twitter�names�is�to�go�through�the�API�one�email�at�a�time. Can�we�off�shore�this?��We�can,�but�we�still�will�not�be�done�until�2013�if�we�go�this�route.��So�we�had�to�be�more�clever…
� Whatever�method�we�choose,�we�need�to�recognize�the�Twitter�name�as�useful�customer�data,�and�as�such,�store�it�in�the�data�warehouse.��Next�slide…
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Re�Fit�the�Buzz�Index:�Attributing�the�Tweets�– You�Must�Retain�Data�� What�comes�out�of�the�attribution�process�is�a�table�that�looks�like�this:
� We�have�another�process�(using�RADIAN6)�that�scours�Twitter�for�comments�that�contain�words�in�our�“start�list”�(e.g.�Ford,�Lincoln,�Mercury,�Taurus,�Mustang,�Fusion,�etc…).���It�produces�a�table�that�looks�like�this:
EMAIL TWITTER_NAME FIRST_NAME [email protected] Keith Shields
[email protected] ronedog Roni [email protected] Mindy Deatrick
TWITTER_NAME COMMENT SENTIMENTronedog My�new�Ford�Focus�is�also�imported�from�Detroit. Good
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Re�Fit�the�Buzz�Index:�Attributing�the�Tweets�– You�Must�Retain�Data�
Data�Creation
Customer�Data�Warehouse�
� We�have�to�integrate�this�into�existing�data�warehouse�processes�(which�should�be�easy�enough,�if�we’re�treating�this�like�just�another�source�of�customer�data):
EMAIL TWITTER_NAME FIRST_NAME [email protected] Keith Shields
[email protected] ronedog Roni [email protected] Mindy Deatrick
Customer�Touchpoint
TWITTER_NAME COMMENT SENTIMENTronedog My�new�Ford�Focus�is�also�imported�from�Detroit. Good
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Re�Fit�the�Buzz�Index:�Classifying�the�Tweets…�� Prepackaged�comment�binning�algorithms�are�not�as�accurate�as�we’d�like…they�result�in�
a�high�instance�of�inappropriate�comment�binning�(“Positive”,�“Neutral”,�or�“Negative”).��Here�are�some�actual�examples�of�inappropriately�binned�comments:
� Positive:�“Classic�Car�For�Sale�2001�FORD�EXPLORER�� Mt.�Royal�NJ:�Runs�and�Looks�Great!!!”� Negative:�“You�have�insulted�my�Ford�Fiesta,�shame�on�you.”�AND�“Just�drove�a�Ford�Fiesta�
getting�30�mph.�Not�bad!”�AND�“Just�dropped�my�car�off�at�the�FORD�Dealership�.�I�want�a�FORD�Fusion�soooooo BAD.”
� Neutral: “Ford�Escape!!!”�AND�“Smart,�easy,�&�fun�ride�with�the�new�Ford�Focus�with�Ford�Sync's�help!”
� In�order�for�us�to�get�the�intuitive�results�we�showed�on�slide�12�we�had�to�depart�from�sentiment�classification�algorithms�and�do�a�“brute�force”�classification.� Interns,�Cornerstone�Schools,�Detroit,�Mi.�(http://www.cornerstoneschools.org/)� There�are�other�inexpensive�ways…all�of�which�we�believe�to�be�more�accurate�than�existing�
“machine�intelligence”…albeit�not�as�scalable:• Existing�call�center�personnel• Mechanical�Turks�(https://www.mturk.com/mturk/welcome)
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Copyright�©2012�Marketing�Associates�LLC.�All�rights�reserved.
Decision�Sciences
Re�Fit�the�Buzz�Index:�The�Biased,�But�Changing,�Population�of�Tweeters…�
� See�the�graph�on�the�right.��The�fastest�growing�population�of�tweeters�is�18�34�year�olds.��About�56%�of�tweeters�are�34�years�or�younger.
� The�challenge�for�Ford:�the�median�age�for�Ford�customers�is�well�above�34,�despite�some�recent�strong�entries�in�the�small�car�market.��
� The�most�“tweeted�about”�Ford�is,�not�surprisingly,�the�Focus.
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Decision�Sciences
Re�Fit�the�Buzz�Index:�Geographically�Biased�Population�of�Tweeters…�
� The�numbers�represent�how�much�higher�the�Twitter�use�per�capita�is�in�that�state�versus�the�nation�as�a�whole.�
� For�example:�if�the�national�usage�rate�is�10%,�then�Michigan�is�11%�lower�than�that:�.10�� .11(.10)�=�8.9%.�
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Copyright�©2012�Marketing�Associates�LLC.�All�rights�reserved.
Decision�Sciences
Given�the�Worries�About�Attribution,�Classification…�
� The�real�opportunity�could�lie�in�the�business�experts�using�intuition�and�common�sense�to�tailor�campaigns�and�programs�to�tweeters�based�on�their�most�recent�comments.
� This�would�only�rely�upon�a�good�mechanism�for�scraping�relative�comments�from�Twitter�and�reacting�procedurally�and�appropriately.
� If�we�look�at�the�comments�as�unsolicited�survey�responses�we�see�opportunities�for�customized�offers�and�programs�(no�models�needed�– the�comment�reveals�the�customer’s�intent):
� Private�Sales�Offer�and/or�Pre�approval:�“Just�dropped�my�car�off�at�the�FORD�Dealership.� I�want�a�FORD�Fusion�soooooo BAD.”
� Rewards�Program�Offer:��“My�moms�taking�me�to�get�this�Ford�explorer�in�the�mornin tho i�should�have�a�new�whip�before�July�then�im haulin ass�to�the�A”
� Offer�for�trade�in:�“Ford�focus�sucks.�Very�uncomfortable�vehicle.”� Offer�for�service�discount�/�extended�warranty:�“My�car�is�running�rough�and�
keeps�blowing�the�injector�and�on�plug�coil�fuses,�its�a�2006�3.0�V6�Ford�Fusion.�HELP!!”�
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Decision�Sciences
� We�believe�that�there�are�three�“pillars”�for�the�business�case�to�actively�engage�consumers�through�social�media�outlets�(specifically�Twitter):1. Conquesting�new�customers:�pay�attention�to�influencers2. Concern�resolution3. Voice�of�Customer
Introduce�Social�Media�as�an�additional�consumer�touch�point
1. Conquest$XX�mils�per�year
STRATEGY:
SUPPORTS�THESTRATEGY:
2. Concern�Res$XX�mils�per�year
3. VOC$XX�mils�per�year
So�We�Revisit�Our�Three�Pillars…
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Decision�Sciences
Conquest�New�Customers:�Influencers
Fact: 93.6%�of�Twitter�users�have�less�than�100�followers,�while�98%�of�users�have�less�than�400�followers.�Meanwhile,�1.35%�of�users�have�more�500�followers,�and�only�0.68%�of�more�than�1,000�followers.�
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Copyright�©2012�Marketing�Associates�LLC.�All�rights�reserved.
Decision�Sciences
Conquest�New�Customers:�Influencers
Fact: As�Twitter�users�attract�more�followers,�they�tend�to�Tweet�more�often.�This�is�particularly�evident�once�someone�has�1,000�followers�the�average�number�of�Tweets/day�climb�from�three�to�six.�When�someone�has�more�than�1,750�followers,�the�number�of�Tweets/day�rises�to�10.�
Fact: A�small�group�of�Twitter�users�account�for�the�bulk�of�activity.�Sysomos discovered�that�5%�of�users�account�for�75%�of�all�activity,�10%�account�for�86%�of�activity,�and�the�top�30�account�for�97.4%�of�activity.
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Decision�Sciences
Conquest�New�Customers:�The�Opportunity
� Find�out�who�is�expressing�in�market�sentiment�and�send�them�a�targeted�offer.� We�estimate�that�through�Twitter�alone,�roughly�35,000�customers�per�year�express�inclination�
to�buy�Ford.�� Marketing�Associates�built�the�process�to�find�the�tweets�and�measure�the�back�end�results.��
Here�are�some�great�“Focus�tweets”…just�from�the�last�couple�of�weeks:� “I�think�I�want�a�2012�Ford�Focus.” 3/12/2012� “I�want�a�2012�ford�focus...just�because�it�parks�itself.�:\”��3/20/2012� “2012�Ford�Focus�ST�or�2013�Dodge�Dart?�I�dunno,�the�Dodge�Dart�it�is�just�a�Neon�that�
mated�with�an�Alpha�Romeo�but�the�Focus�ST�looks�pretty�promising�and�I�always�loved�my�buddies�SVT�Focus..�Decisions..�Decisions..”��3/1/2012
� Applying�a�result�from�an�analysis�of�"handraiser campaigns",�we�assume�15%�of�the�35,000�will�purchase�FLM.��This�is�35,000�*�15%�=�5,250�sales.
� Assuming�20%�lift�from�a�targeted�offer�to�in�market�customers�(not�an�uncommon�number),�we�estimate�that�a�conquesting campaign�directed�at�in�market�"social�media�leads“.��This�is�5,250�*�.2�=�1,050�incremental�sales.
� INTEGRATION�will�be�through�the�customer�data�warehouse�and�EXECUTION�through�the�concern�resolution�center.
� At�this�point�we�won’t�trouble�you�with�another�infrastructure�diagram.
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Decision�Sciences
Some�Recommendations…
� Treat�social�media�as�another�source�of�relevant�customer�data.��� Comments�about�your�product�are,�in�some�sense,�unsolicited�surveys.��They�can,�like�surveys�
do,�improve�your�ability�to�predict�the�behavior�of�your�own�customers.
� Pay�careful�attention�to�the�integration�of�social�media�data.��Integration�requires�“customerization”,�so�subsequent�customer�behavior�can�be�tracked.
� Attributing�comments�to�customers�is�tricky.��It�can�also�be�painstaking.��The�good�news�is�that�it�can�be�done�cheaply.
� Correct�classification�of�comments�is�essential�to�understanding�the�true�signal�in�the�comments.��
� The�most�accurate�means�of�classification�may�also�be�the�least�scientific:�have�an�English�speaker�(who�preferable�understands�colloquialisms)�read�the�comments,�and�bin�them.
� Categories�can�be�“good”,�“bad”,�“in�market”,�“service�issue”,�or�whatever�aligns�with�the�differentiated�treatments�and�offers.
� Retain�data,�and�integrate�intelligently�into�a�data�warehouse.��Test�and�measure�several�tactical�approaches�to�customers�and�prospects�who�are�commenting�about�your�products.
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Decision�Sciences
Quantifying�the�"Buzz"�Effect:�Integrating�Social�Media�with�Loyalty�&�Defection�Models
Thanks�for�your�time�and�attention.
Questions?��
Want�a�copy�of�this�presentation?Text�KEITH to�30241
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Marketing�Associates�Alerts:�Receive�up�to�2�msgs per�month.�Msg&Data rates�may�apply.�Text�STOP�to�stop.�For�more�info,�email�[email protected].