measuring the effectiveness of display adverting: a time...

1
Related Work Methodology Results Measuring the Effectiveness of Display Adverting: A Time Series Approach Joel Barajas* 1 , Ram Akella 1 , Marius Holtan 2 , Jaimie Kwon 2 , Brad Null 2 1 UC Santa Cruz, Santa Cruz CA, USA 2 AOL Research, Palo Alto CA, USA References Introduction Online display advertising is an area of rapid growth and consequently of great interest as a marketing channel. Recent studies show that display advertising often triggers online users to search for information about commercial products [1]. Eventually, many of these users perform either online conversions at the advertiser's website or offline conversions at a physical store. Methodology [1] Atlas. Engagement mapping: A new measurement standard is emerging for advertisers, 2008. White Paper. [2] Zoubin Ghahramani and Geoffrey E. Hinton. Parameter estimation for linear dynamical systems. Technical report, 1996. [3] Google. CPA perfomance trends on the Google display network, 2010. White Paper. [4] Randall Lewis and David Reiley. Retail advertising works! measuring the effects of advertising on sales via a controlled experiment on Yahoo!, 2009. White Paper. [5] Mike West and Jeff Harrison. Bayesian forecasting and dynamic models (2nd ed.). Springer-Verlag New York, Inc., New York, NY, USA, 1997. [6] George Casella and Roger L. Berger. Statistical Inference (2nd ed.). Duxbury, 2002. Online display ad shown to a user Our goal is to measure the effectiveness of display advertising when users are exposed to multiple advertising channels. User is exposed to multiple advertising channels in time Eventually, the user performs commercial actions If a user performs a commercial action, how should the advertiser attribute credit for the conversion across these multiple channels and media impressions? This is particularly important for the Cost-Per-Action CPA business model when deciding how to allocate resources across advertising channels. CPA performance on a monthly level [3]. Aggregate study based just on the observed average performance A study about user features and performance on commercial actions is performed in [4]. However, the study was done with a small set of users and advertisers to have well-defined control and test groups. Our main goal is to measure the effectiveness of campaigns on a daily level. This is important for short campaigns and to provide dynamic online performance estimates. We follow a time-series approach to decompose the daily number of sales of a given product into a weekly seasonal component and a trend component using Dynamic Linear Models based on Kalman filtering and smoothing ideas [5]. Campaign Duration Days with positive significant change W N w w G V N F y t t t t t t t t , 0 ~ , 0 ~ ' 1 7 / 2 cos / 2 sin / 2 sin / 2 cos 1 0 1 1 T T T T T G G T p 2 / , , 0 , 1 , 0 , 1 , 0 , 1 ' T T p G G G blockdiag G F Number of commercial actions at time t t y State evolution of the model components (stochastic process) t By fixing G and F, we model the series evolution as a linear combination of a seasonal and a trend component [5] Our goal is to deseasonalize the series to associate the trend with a set of marketing campaigns. We find the MLE of the variances Φ = (V,W) through the EM algorithm [2] and smooth the series to analyze the trend component. We measure the effectiveness of campaigns by comparing sales occurring during the days of the campaign flight to a baseline. We test statistical significance of the change in the trend component against the baseline. 95% confidence intervals are used to detect an increase, decrease, or no change in the trend. E-step: , | log | : 1 : 1 , | : 1 : 1 T T y i y P E Q i T T i i Q | max arg 1 M-step: Kalman Filtering and Smoothing Optimization to update the variances (W,V) i Q | 1 i * 0 i T T y P , | : 1 : 1 Campaign Evaluation We use the previous day of the start of a campaign as the baseline. To evaluate the performance of these campaigns, we define four success criteria. 1. Average positive increase in sales during the campaign 2. Increase or no statistical significant change in sales on every day of the campaign. 3. More days with increasing sales than with decreasing sales. 4. More days with increasing sales or no significant change than with decreasing sales. We estimate the average change of sales during the campaign duration in the trend component. We use the delta method to approximate the distribution of the average change in sales respect to the baseline The approximation is based on the Taylor series decomposition of the function of the random variables [6]. Discussion Main goal: Measure the impact of advertising on sales near and long term. Difficulties: Advertisers typically use all available channels (TV, radio, print, online) and there are many vendors within each channel. Data is typically not integrated across channels or even within channels and thus the idea of following the online click-stream behavior of web users is generally not possible. Achievements and Gaps: High level study across several thousands of campaigns We do not have a detailed understanding of the exact goals of each campaign. Comparing sales against the sales generated during some period prior to the campaign might not always be appropriate (the campaign may focus on driving offline sales for instance or launched off-season). Negative sales changes as measured in this study do not necessarily mean that the campaign failed. Results Criterion 1 Criterion 2 Criterion 3 Criterion 4 Succesful Campaigns (%) 43% 65% 73% 84% Average Increase of Sales: Successful Campaigns 74% 42% 40% 31% Distribution of the average change in sales by campaign. This is broken down into campaigns with positive, negative and no statistically significant average change. We analyze 1,635 campaigns for 429 products during the period from July 1 st , 2009 to July 31 st , 2010. The following tables presents the successful campaigns according to the above criteria. Ongoing and Future Work Incorporate the number of impressions served as advertising actions in a marketing campaign. Does the number of impressions have an impact to predict the number of sales? 1 1 1 1 1 b b N t t c c c N 1 1 1 1 1 b b N t t c c E E E N E c c c c c N t t b c b N t t tb c b N t t c b N t j t j i t c N E E N E E N E 1 1 1 2 3 1 1 1 2 4 1 2 1 1 2 2 1 1 1 1 1 , cov 2 var , cov 2 var var c c c E N var , ~ 0 2 4 6 8 10 12 14 16 18 Percentage of Campaigns [-50%, -40%] [-40%, -30%] [-30%, -20%] [-20%, -10%] [-10%, 0%] [0%, 10%] [10%, 20%] [20%, 30%] [30%, 40%] [40%, 50%] [50%, 60%] [60%, 80%] [80%, 100%] [100%, 150%] [150%, 200%] [200%, 250%] Sales Percentage Change Distribution of Average Change in Sales Percentage for All Campaigns Separated into Positive, Negative and No-Significant Change Components Original Distribution Positive Change No Significant Change Negative Change * Contact Author

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Page 1: Measuring the Effectiveness of Display Adverting: A Time ...alumni.cse.ucsc.edu/~jbarajas/publications/Barajas-Measurement_letter.pdf · UC Santa Cruz, Santa Cruz CA, USA 2 AOL Research,

Rel

ated

Wor

k

Met

hodo

logy

Res

ults

Mea

surin

g th

e Ef

fect

iven

ess

of D

ispl

ay A

dver

ting:

A

Tim

e Se

ries

App

roac

hJo

el B

araj

as*1

, Ram

Ake

lla1 ,

Mar

ius

Hol

tan2

, Jai

mie

Kw

on2 ,

Bra

d N

ull2

1 UC

San

ta C

ruz,

San

ta C

ruz

CA

, US

A2 A

OL

Res

earc

h, P

alo

Alto

CA

, US

A

Ref

eren

ces

Intr

oduc

tion

•O

nlin

e di

spla

y ad

verti

sing

is a

n ar

ea o

f rap

id g

row

th a

nd c

onse

quen

tly

of g

reat

inte

rest

as

a m

arke

ting

chan

nel.

•R

ecen

t stu

dies

sho

wth

at d

ispl

ay a

dver

tisin

g of

ten

trigg

ers

onlin

e us

ers

to s

earc

hfo

r inf

orm

atio

n ab

out c

omm

erci

al p

rodu

cts

[1].

•E

vent

ually

, man

y of

thes

e us

ers

perfo

rm e

ither

onlin

e co

nver

sion

s at

th

e ad

verti

ser's

web

site

or o

fflin

e co

nver

sion

s at

a p

hysi

cal s

tore

.

Met

hodo

logy

[1]A

tlas.

Eng

agem

ent m

appi

ng: A

new

mea

sure

men

t sta

ndar

d is

em

ergi

ngfo

r adv

ertis

ers,

200

8. W

hite

Pap

er.

[2]Z

oubi

n G

hahr

aman

i and

Geo

ffrey

E. H

into

n. P

aram

eter

est

imat

ion

for

linea

r dyn

amic

al s

yste

ms.

Tec

hnic

al re

port,

199

6.

[3]G

oogl

e. C

PApe

rfom

ance

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ds o

n th

e G

oogl

e di

spla

yne

twor

k, 2

010.

W

hite

Pap

er.

[4]R

anda

ll Le

wis

and

Dav

idR

eile

y. R

etai

ladv

ertis

ing

wor

ks!m

easu

ring

the

effe

cts

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dver

tisin

gon

sal

es v

ia a

con

trolle

dex

perim

ent o

nYa

hoo!

, 20

09. W

hite

Pap

er.

[5]M

ike

Wes

t and

Jef

f Har

rison

. Bay

esia

nfo

reca

stin

g an

d dy

nam

ic m

odel

s (2

nd e

d.).

Spr

inge

r-Ve

rlag

New

York

, Inc

., N

ewYo

rk, N

Y, U

SA

, 199

7.

[6]G

eorg

eC

asel

laan

d R

oger

L. B

erge

r. S

tatis

tical

Infe

renc

e(2

nd e

d.).

Dux

bury

, 200

2.

Onl

ine

disp

lay

ad s

how

n to

a us

er

•O

ur g

oal i

s to

mea

sure

the

effe

ctiv

enes

s of

dis

play

adv

ertis

ing

whe

n us

ers

are

expo

sed

to m

ultip

le a

dver

tisin

g ch

anne

ls.

Use

r is

exp

osed

tom

ultip

le a

dver

tisin

g ch

anne

ls in

tim

e

Eve

ntua

lly, t

he u

ser

perf

orm

s co

mm

erci

al a

ctio

ns

•If

a us

erpe

rform

s a

com

mer

cial

act

ion,

how

shou

ldth

e ad

verti

ser

attri

bute

cre

dit f

or th

e co

nver

sion

acr

oss

thes

e m

ultip

le c

hann

els

and

med

ia im

pres

sion

s?

•Th

is is

par

ticul

arly

impo

rtant

for t

he C

ost-

Per

-Act

ion

CPA

busi

ness

mod

el w

hen

deci

ding

how

to a

lloca

te

reso

urce

s ac

ross

adv

ertis

ing

chan

nels

.

•C

PApe

rform

ance

on

a m

onth

ly le

vel [

3].

Agg

rega

te s

tudy

bas

edju

st o

n th

e ob

serv

ed

aver

age

perfo

rman

ce

•A

stud

y ab

out u

ser

feat

ures

and

per

form

ance

on

com

mer

cial

act

ions

is

per

form

ed in

[4].

How

ever

, the

stu

dy w

as d

one

with

a s

mal

l set

of

user

s an

d ad

verti

sers

to h

ave

wel

l-def

ined

con

trol a

nd te

st g

roup

s.

•O

ur m

ain

goal

is to

mea

sure

the

effe

ctiv

enes

s of

cam

paig

ns o

n a

daily

leve

l. Th

is is

impo

rtant

for s

hort

cam

paig

ns a

nd to

pro

vide

dy

nam

ic o

nlin

e pe

rform

ance

est

imat

es.

•W

e fo

llow

a tim

e-se

ries

appr

oach

to d

ecom

pose

the

daily

num

ber

of

sale

s of

a g

iven

pro

duct

into

a w

eekl

y se

ason

al c

ompo

nent

and

a

trend

com

pone

nt u

sing

Dyn

amic

Lin

ear

Mod

els

base

don

Kal

man

fil

terin

g an

d sm

ooth

ing

idea

s [5

].

Cam

paig

n D

urat

ion

Day

s w

ith p

ositi

ve

sign

ifica

ntch

ange

WN

ww

G

VN

Fy

tt

tt

tt

tt

,0~

,0~

' 1

7/

2cos

/2

sin/

2sin

/2

cos

10

11

T

TT

TT

GG

Tp

2/,

,0,1,0,1,0,1

'T

Tp

GG

Gblockdiag

GF

Num

ber o

f com

mer

cial

act

ions

at t

imet

tyS

tate

evo

lutio

nof

the

mod

el c

ompo

nent

s (s

toch

astic

pro

cess

)t

•B

y fix

ing G

and F,

we

mod

el th

e se

ries

evol

utio

n as

a li

near

com

bina

tion

of a

sea

sona

l and

a tr

end

com

pone

nt [5

]•

Our

goa

l is

to d

esea

sona

lize

the

serie

s to

ass

ocia

te th

e tre

nd w

ith a

se

t of m

arke

ting

cam

paig

ns.

•W

e fin

dth

e M

LE o

f the

var

ianc

es Φ

= (V,W

)thr

ough

the

EM

alg

orith

m[2

] and

sm

ooth

the

serie

s to

ana

lyze

the

trend

com

pone

nt.

•W

e m

easu

re th

e ef

fect

iven

ess

of c

ampa

igns

by

com

parin

g sa

les

occu

rrin

gdu

ring

the

days

of t

he c

ampa

ign

fligh

t to

a ba

selin

e.

•W

e te

st s

tatis

tical

sig

nific

ance

of th

e ch

ange

in th

e tre

nd c

ompo

nent

ag

ains

t the

bas

elin

e.

•95

% c

onfid

ence

inte

rval

s ar

e us

ed to

det

ect a

n in

crea

se, d

ecre

ase,

orno

cha

nge

in th

e tre

nd.

E-step:

,

|log

|:1

:1,

|:1

:1T

Ty

iy

PE

Qi

TT

i

iQ

|

max

arg

1M-step:

Kal

man

Filt

erin

gan

d S

moo

thin

gO

ptim

izat

ion

to u

pdat

eth

e va

rian

ces (W

,V)

i

Q

|

1

i

*

0

iT

Ty

P,

|:1

:1

Cam

paig

n E

valu

atio

n•

We

use

the

prev

ious

day

of t

he s

tart

of a

cam

paig

nas

the

base

line.

•To

eva

luat

e th

e pe

rform

ance

of t

hese

cam

paig

ns, w

e de

fine

four

succ

ess

crite

ria.

1.A

vera

ge p

ositi

ve in

crea

se in

sal

es d

urin

g th

e ca

mpa

ign

2.In

crea

se o

r no

sta

tistic

al s

igni

fican

t cha

nge

insa

les

onev

ery

day

ofth

e ca

mpa

ign.

3.M

ore

days

with

incr

easi

ng s

ales

than

with

decr

easi

ng s

ales

.4.

Mor

e da

ys w

ithin

crea

sing

sal

es o

r no

sig

nific

ant c

hang

eth

an w

ithde

crea

sing

sal

es.

•W

e es

timat

e th

e av

erag

e ch

ange

of s

ales

dur

ing

the

cam

paig

ndu

ratio

n in

the

trend

com

pone

nt.

•W

e us

eth

e de

lta m

etho

dto

app

roxi

mat

e th

e di

strib

utio

nof

the

aver

age

chan

ge in

sal

es r

espe

ct to

the

base

line

•Th

e ap

prox

imat

ion

is b

ased

on th

e Ta

ylor

serie

s de

com

posi

tion

of th

e fu

nctio

n of

the

rand

omva

riabl

es [6

].

Dis

cuss

ion

•M

ain

goal

:–

Mea

sure

the

impa

ct o

f adv

ertis

ing

on s

ales

nea

ran

d lo

ng te

rm.

•D

iffic

ultie

s:–

Adv

ertis

ers

typi

cally

use

all

avai

labl

ech

anne

ls (

TV, r

adio

, prin

t, on

line)

and

ther

e ar

e m

any

vend

ors

with

in e

ach

chan

nel.

–D

ata

is ty

pica

lly n

ot in

tegr

ated

acro

ss c

hann

els

or e

ven

with

in

chan

nels

and

thus

the

idea

of f

ollo

win

g th

e on

line

clic

k-st

ream

beha

vior

of w

eb u

sers

is g

ener

ally

not

pos

sibl

e.•

Ach

ieve

men

tsan

d G

aps:

–H

igh

leve

l stu

dy a

cros

s se

vera

l tho

usan

ds o

f cam

paig

ns–

We

dono

t hav

e a

deta

iled

unde

rsta

ndin

g of

the

exac

t goa

ls o

f ea

ch c

ampa

ign.

Com

parin

g sa

les

agai

nst t

he s

ales

gen

erat

ed d

urin

g so

me

perio

d pr

ior

to th

e ca

mpa

ign

mig

ht n

ot a

lway

s be

app

ropr

iate

(th

e ca

mpa

ign

may

focu

s on

driv

ing

offli

ne s

ales

for i

nsta

nce

orla

unch

ed o

ff-se

ason

).–

Neg

ativ

esa

les

chan

ges

as m

easu

red

in th

is s

tudy

do

not

nece

ssar

ily m

ean

that

the

cam

paig

n fa

iled.

Res

ults

Cri

teri

on

1C

rite

rion

2

Cri

teri

on

3C

rite

rion

4

Suc

cesf

ul

Cam

paig

ns (%

)43

%65

%73

%84

%

Ave

rage

Incr

ease

of

Sal

es: S

ucce

ssfu

l C

ampa

igns

74%

42%

40%

31%

•D

istri

butio

n of

the

aver

age

chan

ge in

sal

es b

y ca

mpa

ign.

This

is b

roke

n do

wn

into

cam

paig

ns w

ith p

ositi

ve, n

egat

ive

and

no

stat

istic

ally

sig

nific

anta

vera

ge c

hang

e.

•W

e an

alyz

e 1,

635

cam

paig

nsfo

r429

pro

duct

sdu

ring

the

perio

d fro

m J

uly

1st, 2

009

to J

uly

31st

, 201

0.

•Th

e fo

llow

ing

tabl

es p

rese

nts

the

succ

essf

ul c

ampa

igns

ac

cord

ing

to th

e ab

ove

crite

ria.

Ong

oing

and

Fut

ure

Wor

k•

Inco

rpor

ate

the

num

ber

of im

pres

sion

s se

rved

as

adve

rtisi

ng a

ctio

ns in

a

mar

ketin

g ca

mpa

ign.

•D

oes

the

num

ber

of im

pres

sion

s ha

ve

an im

pact

to p

redi

ct th

e nu

mbe

rof

sa

les?

1

11

11

b

b

N tt

cc

c

N

1

11

11

b

b

N tt

cc

E

EE

NE

c

c

cc

c

N tt

bc

b

N tt

tbc

b

N tt

cb

N tj

tj

it

cN

E

E

NE

E

NE

11

12

31

11

24

1

2

11

22

1

11

11

,cov

2var

,cov

2var

var

cc

cE

N

var

,~

024681012141618

Percentage of Campaigns

[-50%

, -40

%]

[-40%

, -30

%]

[-30%

, -20

%]

[-20%

, -10

%]

[-10%

, 0%

][0%, 1

0%][10

%, 2

0%][20

%, 3

0%][30

%, 4

0%][40

%, 5

0%][50

%, 6

0%][60

%, 8

0%][80

%, 1

00%

][100%

, 150

%]

[150%

, 200

%]

[200%

, 250

%]

Sal

es P

erce

ntag

e C

hang

e

Dis

trib

utio

n of

Ave

rage

Cha

nge

in S

ales

Per

cent

age

for

All

Cam

paig

nsS

epar

ated

into

Pos

itive

, Neg

ativ

e an

d N

o-S

igni

fican

t Cha

nge

Com

pone

nts

Ori

gina

l Dis

trib

utio

nP

ositi

ve C

hang

eN

o S

igni

fican

t Cha

nge

Neg

ativ

e C

hang

e

* Con

tact

Aut

hor