New Approach Measures Ad Effects for Media Group

A new approach to
the measurement of advertising effects,
developed for Schibsted Media Group
MODELING ADVERTISING EFFECTS
IN A MULTI-MEDIA ENVIRONMENT –
A LATENT CLASS LATENT MARKOV CHAIN APPROACH
Carsten Stig Poulsen, Aalborg University, Aalborg, Denmark
Pål Børresen, Schibsted Media Group, Schibsted ASA, Oslo, Norway
Presented at the ART Forum, Santa Fe, NM
June 23, 2014
1
©Carsten Stig Poulsen &
Schibsted Media Group

2
An overview of the approach

3
• During the summer 2011 Our Brand Chips run a
campaign using TV, the Web, and outdoor
• Target groups are:
– Private households
• KPIs of the campaign
– Awareness of the new potato chip with a rifled surface
– Perceived as more spicy
– Intention-to-buy
Presenting Our Brand Chips case

Presenting Our Brand Chips case
• The purpose of the project is to track the development of
these KPIs over time as a function of the campaign
• We want to track the effects of media, TV and the Web
• Examine if interactions between the media exist
• Propose a tool for predicting effects of changes in media
impact
• See whether the response to advertising is different
across various groups
4

Media plan
5
GRPs according
to media
agency

Data collection
Panel design
6

– Eating habits of chips
– Brands ever tasted
– Brand associations/attributes
• Well-spiced
• Has introduced a new type of rifled chip
• Tempting package design
• Crisp
• Best taste
– Purchase probability
– Bought last week
– Recall of ads in various media
– Media habits/usage
– Demographics (age, sex, geography)
7
Data collection
Questionnaire design

Seen ad in TV
last week
Response
Current
panel wave
Seen ad in TV
last week
Response
Previous
panel wave
The Effects model
A step-by-step development
8
The effect of the current
period’s state of exposure on
the current response state

Seen ad
last week
Response
Current
panel wave
Seen ad
last week
Previous
panel wave
Response
9
The effect of previous
state of exposure on
the current state of
exposure
The Effects model

Seen ad
last week
Response
Current
panel wave
Seen ad
last week
Response
Previous
panel wave
10
Cross-lagged effects of
the previous period’s
state of exposure on the
current response state.
The Effects model

Seen ad
last week
Response
Current
panel wave
Seen ad
last week
Response
Previous
panel wave
11
The effect of previous
state of response on
the current state of
response
The Effects model

Seen ad
last week
Response
Current
panel wave
Seen ad
last week
Response
Previous
panel wave
12
Cross-lagged effects of the
previous period’s state of
response on the current state of
exposure
The Effects model

Seen ad
last week
Response
Current
panel wave
Seen ad
last week
Response
Previous
panel wave
Seen ad
last week
Response
Initial state
distribution
13
Conclusion:
The response to advertising at
any given point consists of 6
different effects:
• 3 direct effects
• 3 indirect effects
The response process evolve
over time from the initial state
distribution according to these
The Effects model

14
Distinction between
measured state and true
state
Correction for
response
uncertainty
True effect, i.e. the effect
when the variables have
been corrected for
measurement error
The point is that effects are more
easily uncovered when errors in
measurements are taken into account!
The Effects model

15
Current
panel wave
Previous
panel wave
Seen ad
last week
Seen ad
last week
ResponseResponse
Measured state
of exposure
True state of
exposure
True state of
association
Measured state
of association
The Effects model

Current
panel wave
Previous
panel wave
Seen ad in TV
last week
Seen ad in TV
last week
ResponseResponse
GRP_TV(t)
GRP_Web(t)
GRP_TV(t)
GRP_Web(t)
16
Including the control
variables, GRPs, makes the
model suitable for ’what-if’
analyses, i.e. decision support
This can be done in two
ways:
• As aggregate GRPs that vary
over time
• As individual GRPs that vary
over time and respondents
However, they may give very
different information about
media effectiveness
The Effects model

• Recall of advertising is known to be unreliable
• Recall is not directly linked to the control variables in the media plan
• (Individual) exposures (”OTS”) to advertising are the combined result of
– Insertions from the media plan (under control)
– Media usage/habits of the (individual) consumer (not under control)
• Individual exposures to media are modelled by a latent Markov model,
allowing for variation over individuals and time
• The states of the Markov chain are joint reading/viewing probability for all
media/media groups
• Individual exposures for each media group (TV, Print, Web, …) are
introduced into the Effect model as covariates
• Individual exposures sum up to (aggregate) GRPs as a control for the GRPs
provided by the media bureau
17
The Exposure model

The 9-state latent Markov model
for viewing TV Channel 1 and 2 jointly
Initial frequency states
for Channel 1 and Channel 2
Area proportional to size
Frequency states for Channel 1 and
Channel 2 in 'steady state'
Area proportional to size
1
2
3
4
5
6
7
8 9
-0,20
0,00
0,20
0,40
0,60
0,80
1,00
-0,20 0,00 0,20 0,40 0,60 0,80 1,00
Viewingprob.Channel2
Viewing prob. Channel 1
1
2
3
4
5
6
7
8 9
-0,20
0,00
0,20
0,40
0,60
0,80
1,00
-0,20 0,00 0,20 0,40 0,60 0,80 1,00
Viewingprob.Channel2
Viewing prob. Channel 1
| | |
1 1
i t t i i
t
S J
tj s j s it tj tj
s j
z q   
 
     y y y
1 1
2
2

The Brand Manager’s dream come true?

Campaign effects
21
 
 
, 0,0
0
0
TV Web
t t
t
TV Web T
TV Web
t t
t
KPI KPI
GRP GRP










  
 
, 0,0
0
0
TV Web TV Web
t t t t
t
TV Web T
TV Web
t t
t
KPI KPI KPI KPI
GRP GRP





  




 ,0 0,0
0
0
TV
t t
t
TV T
TV
t
t
KPI KPI
GRP








 ,0 0,0
0
0
Web
t t
t
Web T
Web
t
t
KPI KPI
GRP









22
0,00%
2,00%
4,00%
6,00%
8,00%
10,00%
7 17 27 37 47 57 67
Proportion
Week
Contribution of the TV and Web campaign
TV and Web - Null
0,00%
2,00%
4,00%
6,00%
8,00%
10,00%
7 17 27 37 47 57 67
Proportion
Week
Contribution of TV advertising
TV alone - Null
0,00%
0,20%
0,40%
0,60%
7 17 27 37 47 57 67
Proportion
Week
Contribution of Web advertising
Web alone - Null
-0,20%
0,00%
0,20%
0,40%
7 17 27 37 47 57 67
Proportion
Week
Synergy between TV and Web advertising
(TV and Web - Null) - (TV alone + Web alone)
Campaign effects

Media effectiveness
23
 ,0 0,0
0
0
1,3904
5,4%
25,54093
TV
t t
t
TV T
TV
t
t
KPI KPI
GRP





  


 
 
, 0,0
0
0
1,4894
3,8%
39,28518
TV Web
t t
t
TV Web T
TV Web
t t
t
KPI KPI
GRP GRP






  



 ,0 0,0
0
0
0,0612
0,4%
13,74425
Web
t t
t
Web T
Web
t
t
KPI KPI
GRP





  


  
 
, 0,0
0
0
0,0378
0,01%
39,28518
TV Web TV Web
t t t t
t
TV Web T
TV Web
t t
t
KPI KPI KPI KPI
GRP GRP





  
  




• Each level in the hierarchy is seen as a separate process
• They work separately and interactively in creating the state of response
• Impacts of the media from the mediaplan work as individual covariates
24
Extending the model
Hierarchy-of-effects hypotheses
GRP
GRP
GRP
Awareness
Knowledge
Preference

25
Extending the model
Hierarchy-of-effects hypotheses
0%
20%
40%
60%
80%
100%
7 8 9 10 11 12 13 14 15
Proportion
Week
Recall Association Purchase intent

Extending the model
Hierarcy-of-effects hypotheses & consumer response heterogeneity
26
• (Latent) segments are formed by allowing people to have different preferences initially and
react differently to TV advertising (different impact of GRP)
• We see very marked differences in the response to advertising
• The largest segment1 (73%) has barely noticed the campaign
• The preference segment 2 (27%) has seen the advertsiment, understood the message, but
not received the new chip very well
• The two segments can be profiled in terms of standard criteria
0%
20%
40%
60%
80%
100%
7 8 9 10 11 12 13 14 15
Proportion
Week
Segment 1 - 73%
0%
20%
40%
60%
80%
100%
7 8 9 10 11 12 13 14 15
Proportion
Week
Segment 2 - 27%

• With an individual, customer-centered model, predicting the
response to advertising, we are on the way to an optimization
model
• We will need
– The money value of the response, e.g. association of Our Brand Chips
to KPIs
– The costs of the elements in the media plan
– An objective function, e.g. maximizing the discounted stream of net
profits
• Currently, we are working on these and other extensions of
the model
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Optimization

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To repeat:
An overview of the approach

Thank you for your attention!
29

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