predictive modelling of advertising awareness. a motivating example
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Predictive Modelling of Advertising AwarenessPredictive Modelling of Advertising Awareness
A motivating example
Key QuestionsKey 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?
• 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?
ActualAd Awareness
How advertisng is modelledHow advertisng is modelled
ModelledAd awareness
How advertisng is modelled...How advertisng is modelled...
Actual Tarps
How advertisng is modelled...How advertisng is modelled...
New
ActualAd Awareness
How advertisng is modelled...How advertisng is modelled...
Adstock Modelling 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
• 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
How is Adstock modelled
• . Adstockt = *Tarpst + (1-) . Adstockt-1 – where = decay rate usually about 10% or less
– Initial value taken to be Adstock1 = *Tarps1
• Exponentially smoothes Tarps so they become continuous
• Now have a memory component like recall
Motivating example revisited.How good is the model?
Current Situation
05
10
15202530
3540
30/4
/00
14/5
/00
28/5
/00
11/6
/00
25/6
/00
9/7/
00
23/7
/00
6/8/
00
20/8
/00
3/9/
00
17/9
/00
1/10
/00
15/1
0/00
29/1
0/00
12/1
1/00
26/1
1/00
date
EC
T
050100
150200250300
350400
TA
RP
s
Modelled NETT ECT NETT ECT TARPS
Motivating example Impact Indices
1.0%
1.5%
2.0%
2.5%
3.0%
3.5%
4.0%
4.5%
30/4
/00
21/5
/00
11/6
/00
2/7/
00
23/7
/00
13/8
/00
3/9/
00
24/9
/00
15/1
0/00
5/11
/00
Imp
act
Ad A
Ad B
Ad C
Ad D
Ad E
Average
Ads A & E return the best valueAds A & E return the best value
Future Media Spend - some scenarios
Proposed spend until June 2001(1500 TARPS in 10 weeks)
• 12% low builds slowly to 21% ECT
• Average ECT 19% after February
• 12% low builds slowly to 21% ECT
• Average ECT 19% after February
Proposed Spend
05
10152025303540
30/4
/00
28/5
/00
25/6
/00
23/7
/00
20/8
/00
17/9
/00
15/1
0/00
12/11
/00
10/1
2/00
7/01
/01
11/0
1/01
11/0
2/01
11/0
3/01
8/04
/01
6/05
/01
3/06
/01
date
EC
T
050100150200250300350400
TAR
Ps
Modelled ECT ECT TARPS
Alternative Spend Until June(Same Budget)
• Average ECT 21%• “Burst and hold’ Strategy• ECT higher longer - less variation
• Average ECT 21%• “Burst and hold’ Strategy• ECT higher longer - less variation
Alternative Spend
05
10152025303540
30/4
/00
4/6/
00
9/7/
00
13/8
/00
17/9
/00
22/1
0/00
26/11
/00
31/1
2/00
11/0
1/01
18/0
2/01
25/0
3/01
29/0
4/01
3/06
/01
date
EC
T
050100150200250300350400
TAR
Ps
Modelled ECT ECT TARPS
What’s been happening with this campaign lately?
ECT showing immediate increase following re-start of campaign
Modelled data and prediction
Actual and modelled ECT
05
1015202530354045
date
EC
T
050100150200250300350400
TA
RP
s
Modelled ECT ECT TARPS• Model adjusted to account for actual ECT and current spend
will see a return to average ECT of approximately 20-25%
Dynamic 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
Ad A - Impact (return/100 TARPs)
0.0%
0.5%
1.0%
1.5%
2.0%
2.5%
3.0%
Ad wearing out with time.
Ad. B - Impact ( return/100 TARPs)
Ad. B - Impact ( return/100 TARPs)
0
0.5
1
1.5
2
2.5
3
3.5
4
4.5
7/06
/199
721
/06/
1997
5/07
/199
719
/07/
1997
2/08
/199
716
/08/
1997
30/0
8/19
9713
/09/
1997
27/0
9/19
9711
/10/
1997
25/1
0/19
978/
11/1
997
22/1
1/19
976/
12/1
997
20/1
2/19
973/
01/1
998
17/0
1/19
9831
/01/
1998
14/0
2/19
9828
/02/
1998
14/0
3/19
9828
/03/
1998
11/0
4/19
9825
/04/
1998
9/05
/199
823
/05/
1998
6/06
/199
8
Same spend -different channels.
Key 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
Advertising modelling can be used to:
• Diagnose the effectiveness and current health of each execution
• Predict potential future scenarios
• find the optimal media expenditure strategy
The Relationship to Market Share
• Getting awareness up is first base– it doesn’t 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 -it’s a bloody hard problem!!!
Date
Bra
nd
Sha
re
0 20 40 60 80 100
78
910
Model Fit
33% of model fit due to adstock alone
51% of Brand share explained by what we measure
Execution A Execution B
A Market Share Model
• BRANDSHARE =
5.830053 initial
-2.16682*WINTER Opposition dumps!
+0.547*SOVLOTS SOV >=40%
+0.031*Adstock
+0.052*AdsExA Execution A lifts
Share
-0.0006*AdsExA2 Overspend on Ex A
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