Title: Predictive Modelling of Advertising Awareness
1Predictive Modelling of Advertising Awareness
2A motivating example
3Key Questions
- How do you know you are using your media budget
to maximum effect - Which executions are working best?
- Are some wearing out?
- is our sceduling right?
- What is the best flighting strategy?
- Does this lead to an increase in market share?
4How advertisng is modelled
5How advertisng is modelled...
6How advertisng is modelled...
New
7How advertisng is modelled...
8 Adstock Modelling
- Poor correlation with Ad recall and TARPS
- Much better correlation with Adstock
- Adstock gives TARPS memory
- So Recall and Adstock are comparable
- Ad recallt Legacy Impact . Adstockt
- Legacy long term memory
- Decay rate at which people forget
- Impact rate of return of recall/100 TARPS
9How is Adstock modelled
- . Adstockt dTarpst (1-d) . Adstockt-1
- where d decay rate usually about 10 or less
- Initial value taken to be Adstock1 dTarps1
- Exponentially smoothes Tarps so they become
continuous - Now have a memory component like recall
10Motivating example revisited.How good is the
model?
400
350
300
250
ECT
200
TARPs
150
100
50
0
9/7/00
6/8/00
3/9/00
28/5/00
11/6/00
25/6/00
23/7/00
20/8/00
17/9/00
1/10/00
15/10/00
29/10/00
12/11/00
26/11/00
date
Modelled NETT ECT
NETT ECT
TARPS
11Motivating example
Impact Indices
4.5
4.0
Ad A
3.5
Ad B
3.0
Impact
Ad C
2.5
Ad D
2.0
Ad E
1.5
Average
1.0
2/7/00
3/9/00
30/4/00
21/5/00
11/6/00
23/7/00
13/8/00
24/9/00
5/11/00
15/10/00
Ads A E return the best value
12Future Media Spend - some scenarios
13Proposed spend until June 2001(1500 TARPS in 10
weeks)
- 12 low builds slowly to 21 ECT
- Average ECT 19 after February
14Alternative Spend Until June(Same Budget)
- Average ECT 21
- Burst and hold Strategy
- ECT higher longer - less variation
15Whats been happening with this campaign lately?
ECT showing immediate increase following re-start
of campaign
16Modelled data and prediction
- Model adjusted to account for actual ECT and
current spend will see a return to average ECT of
approximately 20-25
17Dynamic Adstock Modelling
- Impact can be evaluated on a weekly basis to see
if it changes with time. This can indicate when - An ad is wearing out
- Or if some other external factor is influencing
awareness e.g. - Better flight / channelling
- Increased clutter in the market
18Ad A - Impact (return/100 TARPs)
Ad wearing out with time.
19Ad. B - Impact ( return/100 TARPs)
Same spend -different channels.
20Key Learnings
- Thresholds of under/overspending exist
- Avoid 15 second executions
- Do not run multiple creative executions
- SOV is critical
- As executions may appear to be wearing out when
in fact competition consumers ear has increased - Burst and maintain strategy works best in the
markets analysed to date
21Advertising modelling can be used to
- Diagnose the effectiveness and current health of
each execution - Predict potential future scenarios
- find the optimal media expenditure strategy
22The Relationship to Market Share
- Getting awareness up is first base
- it doesnt necessarily result in increased share
- however, chances are that the client will notice
the effects when the ad is not on - In other words, it is a composite of optimal
spending on advertising and what is happening in
terms of distribution/sales and service. - Or -its a bloody hard problem!!!
2351 of Brand share explained by what we measure
Model Fit
33 of model fit due to adstock alone
10
9
Brand Share
8
7
Execution A
Execution B
0
20
40
60
80
100
Date
24A Market Share Model
- BRANDSHARE
- 5.830053 initial
- -2.16682WINTER Opposition dumps!
- 0.547SOVLOTS SOV gt40
- 0.031Adstock
- 0.052AdsExA Execution A lifts Share
- -0.0006AdsExA2 Overspend on Ex A