Presentation AAA 2016

A new approach to
the measurement of advertising effects
MODELING ADVERTISING EFFECTS
IN A MULTI-MEDIA ENVIRONMENT –
A LATENT CLASS LATENT MARKOV CHAIN APPROACH
Carsten Stig Poulsen, Professor, Ph.D., Copenhagen, Denmark
Pål Børresen, Schibsted Media Group, Schibsted ASA, Oslo, Norway
Presented at the AAA Annual Conference
Seattle, WA, March 17-20, 2016
1© Carsten Stig Poulsen & Schibsted Media Group

Plan of the presentation
• Short presentation of the illustrating case
• An overview of the modelling approach
• Focus on the underlying ideas and models –
no technical details
• Presentation of case results
• Future developments
• Q/A

3
• During the summer 2011 Our Brand Chips ran a
campaign in Norway using TV, the Web, and outdoor
• Target groups were:
– Private households
• KPIs of the campaign
– (Ad recall)
– Awareness of the new potato chip with a rifled surface
– Perceived as more spicy
– Intention-to-buy
Presenting Our Brand Chips case
© Carsten Stig Poulsen & Schibsted Media Group

Presenting Our Brand Chips case
• The purpose of the project was to track the development
of these KPIs over time as a function of the campaign
• We wanted 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

Media plan
5
GRPs according
to media
agency

Data collection
Panel design: same respondents across waves

– 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

dynamics
dynamics
©Carsten Stig Poulsen & Schibsted Media Group 8
Target groups
Goals of campaign (KPIs)
Budget
Media plan
Individual
media usage/habits
TV Print Web
Individual Exposures Model
(Imputed)
Exposures
TV
(Imputed)
Exposures
Print
(Imputed)
Exposures
Web
Individual Response Model
Segmentation
Simulation
model
What
-if

• Individual exposures (”OTS”) to advertising are the combined
result of
– Insertions from the media plan (under control at the aggregate level)
– Media usage/habits of the individual consumer (not under control)
• Individual exposures 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 time-varying
covariates
• Individual exposures sum up to (aggregate) GRPs as a control
for the GRPs provided by the media bureau
9
The Exposure model

10
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

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)
11
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

15
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
• 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, time-varying
covariates

Extending the model
Hierarcy-of-effects hypotheses & (unobserved) response heterogeneity
16
• (Latent) segments are formed by allowing people to have different preferences initially and
to 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 advertisement, 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%

©Carsten Stig Poulsen &
Schibsted Media Group
17
Extending the model
Short run and long run media effects
• Most advertising campaigns have effects that extend beyond the campaign
period
• Assuming underlying stationary processes these long run effects can be
projected
• Profitability of separate media and the entire campaign may change radically
when long run effects are accounted for

• With an individual, customer-centered model, predicting the
response to advertising, we are on the way to an optimization
model
• We will need
– The monitary 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
18
Optimization

Thank you for your attention!

Presentation AAA 2016

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