aims2012 marketing associates quantifying the buzz effect

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Copyright©2012MarketingAssociatesLLC.Allrightsreserved. DecisionSciences Quantifyingthe"Buzz"Effect:IntegratingSocial MediawithLoyalty&DefectionModels MarketingAssociates: KeithShields,Director,DecisionSciences RoniLeibovitch,SeniorConsultant,DigitalIntelligence MindyDeatrick,SeniorConsultant,QuantitativeSolutions FordMotorCompany: MargaretKishore,PerformanceandMetricsManager CreateaBusinessBlueprint WithDataDrivenCustomerInsights

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Keith Shields Marketing Associates Managing Director of the Decisions Science Group“Quantifying the Buzz Effect: Integrating Social Media with Loyalty & Defection Models”

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Page 1: AIMS2012 Marketing Associates Quantifying the Buzz Effect

Copyright�©2012�Marketing�Associates�LLC.�All�rights�reserved.

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

Page 2: AIMS2012 Marketing Associates Quantifying the Buzz Effect

Copyright�©2012�Marketing�Associates�LLC.�All�rights�reserved.

Decision�Sciences

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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Copyright�©2012�Marketing�Associates�LLC.�All�rights�reserved.

Decision�Sciences

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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Copyright�©2012�Marketing�Associates�LLC.�All�rights�reserved.

Decision�Sciences

One�More�Item�of�Note…

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Copyright�©2012�Marketing�Associates�LLC.�All�rights�reserved.

Decision�Sciences

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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Copyright�©2012�Marketing�Associates�LLC.�All�rights�reserved.

Decision�Sciences

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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Decision�Sciences

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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Decision�Sciences

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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Copyright�©2012�Marketing�Associates�LLC.�All�rights�reserved.

Decision�Sciences

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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Copyright�©2012�Marketing�Associates�LLC.�All�rights�reserved.

Decision�Sciences

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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Decision�Sciences

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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Decision�Sciences

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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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�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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Decision�Sciences

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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Decision�Sciences

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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Decision�Sciences

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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Copyright�©2012�Marketing�Associates�LLC.�All�rights�reserved.

Decision�Sciences

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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Decision�Sciences

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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Decision�Sciences

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

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0

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Ford�Related�Twitter�Comments�"Binned"

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0

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Positive

Neutral

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Decision�Sciences

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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Decision�Sciences

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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Decision�Sciences

Re�Fit�the�Buzz�Index:�Attributing�the�Tweets�– You�Must�Retain�Data�

Data�Creation

Customer�Data�Warehouse�

Twitter

� 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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Decision�Sciences

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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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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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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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].