Data Fusion: Grabbing Information “When You Can” Is Your Friend" T$I$M$E$ T$I$M$E$

IT’S NOT ABOUT BIG DATA:

IT’S ABOUT BIG DATA “COMPRESSION”!"

AMA#$#ECMI#$#EMAC## Marke,ng#&#Innova,on# Symposium#

Eric T. Bradlow" K.P. Chao Professor; Professor of Marketing, Statistics and Education Co-Director, Wharton Customer Analytics Initiative Vice-Dean and Director, Wharton Doctoral Programs"

The Wharton School The “Faucet of Big Data” Is Turned On: And, We Are Never Going Back!" GPS$Loca)on$ Social$Network$ (“ac)on$and$space”)$ (“mine$and$others”)$

Eye$Tracking$$ Data$Fusion$ (“fast$and$frequent$measurement”)$ (“Many$Variables”)$

The Wharton School Eric T. Bradlow" 2" The More We Learn, The More We Forget “Smart Data Compression”"

“Data”$

The Wharton School Eric T. Bradlow" 3" Sampling: The √⁠" is Your Friend"

T$I$M$E$

U$ N$ Big$Data$Just$Became$Smaller$Data$ I$ T$ S$

The Wharton School Eric T. Bradlow" 4" Meta-Analysis: 1 + 1 < 2 is Your Friend" T$I$M$E$

U$ N$ I$ T$ S$

$*$Wang,$P.,$Bradlow,$E.T.,$and$ George,$E.I.$(2014),$“MetaTAnalysis$ Big$Data$Just$Became$Smaller$Data$ Using$Informa)on$Reweigh)ng:$An$ Applica)on$to$Online$Adver)sing”$

The Wharton School Eric T. Bradlow" 5" Getting Rid of Old Data: Time is Your Friend"

T$I$M$E$

Big$Data$Just$Became$Smaller$Data$

U$ N$ $*$Gopalakrishnan,$A.,$Bradlow,$ I$ OLD$DATA$ E.T.,$and$Fader,$P.$(2014),$“A$CrossT T$ Cohort$Changepoint$Model$for$ S$ CustomerTBase$Analysis”$

The Wharton School Eric T. Bradlow" 6" Data Aggregation: The Information in the Margins is Your Friend" T$I$M$E$

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Musalem,$A.,$Bradlow,$E.T.,$and$Raju,$J.$(2008),$$ “Who’s$got$the$coupon?$$ U$ Es)ma)ng$Consumer$Preferences$and$Coupon$$ Usage$from$Aggregate$Informa)on”$ N$ *$ I$ *$ T$ *$ S$

Big$Data$Just$Became$Smaller$Data$ And$You$Did$It$On$Purpose!$

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S*1$ *$*$*$$ S*T$

The Wharton School Eric T. Bradlow" 7" Data Fusion: Grabbing Information “When you can” is Your Friend" T$I$M$E$ T$I$M$E$

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U$ N$ *$ =$WOW!$ I$ +$ *$ T$ *$ S$

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S*1$ *$*$*$$ S*T$ Feit,$E.,$Wang,$P.,$Bradlow,$E.T.$and$Fader,$P.S.$(2013),$$ “Fusing$Aggregate$&$Disaggregate$Data$with$an$Applica)on$$ to$Mul)TPlaeorm$Media$Consump)on”$

The Wharton School Eric T. Bradlow" 8" Function Approximation: Math Is Your Friend"

$*$Bradlow,$E.T.,$Hardie,$B.G.S.,$and$ Fader,$P.S.$(2002),$“Bayesian$ Inference$for$the$Nega)ve$Binomial$ Distribu)on$Via$Polynomial$ Expansions”$$

$*$Everson,$P.J.$and$Bradlow,$E.T.$ (2002),$“Bayesian$Inference$for$the$ BetaTBinomial$Distribu)on$via$ Polynomial$Expansions”$$$ TTTT$=$Truth$ Computa)on$=$36$Hours$ $*$Miller,$S.J.,$Bradlow,$E.T.,$and$ $ Dayartna,$K.$(2006)$“ClosedTForm$ $ Bayesian$Inferences$for$the$Logit$ Model$via$Polynomial$Expansions”$ TTTT$=$Approxima)on$ Computa)on$=$2$seconds$ Time$Just$Became$Smaller$

The Wharton School Eric T. Bradlow" 9" The Next Frontier: Statistical Sufﬁciency/Data Compression is Your BEST FRIEND!

X$ X$ X$ X$ X$ X$

Recency(R): the last time the customer purchased." Frequency(F): number of purchase occasions." Monetary Value(M): average dollar value spent."

$ Data$=$[date$stamp,$amount]$for$each$transac)on$$ $ Three$Numbers$(R,F,$and$M) $

The Wharton School Be Careful, You May Be Throwing Away Valuable Information"

Do$these$Customers$Look$the$Same$to$You???$ C$

$*$Zhang,$Y.,$Bradlow,$E.T.,$and$ $*$Zhang,$Y.,$Bradlow,$E.T.,$and$ Small,$D.S.$(2013),$“New$Measures$ Small,$D.S.$(2014),$“Capturing$ of$Clumpiness$for$Incidence$Data”,$$ Clumpiness$when$Valuing$ Customers:$From$RFM$to$RFMC$

The Wharton School Eric T. Bradlow" 11" Clumpiness Matters: Be Careful!"

The Wharton School Eric T. Bradlow" 12" What Does The Future of Data Compression Look Like?"

Answer: I don’t know, but here is what my future looks like" "* Fast Computational Methods" "* Data Sufﬁciency" "* Aggregate Information Analyses (Link to Privacy)"

The Wharton School Eric T. Bradlow" 13" Thanks! [email protected]"

The Wharton School