Predictive Modelling of
Advertising Awareness
A motivating example
Key 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 advertisng is modelled
Actual
Ad Awareness
How advertisng is modelled...
Modelled
Ad awareness
How advertisng is modelled...
Actual
New Tarps
How advertisng is modelled...
Actual
Ad Awareness
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
How is Adstock modelled
• . Adstockt = d*Tarpst + (1-d) . Adstockt-1
– where d = decay rate usually about 10% or less
– Initial value taken to be Adstock1 = d*Tarps1
• Exponentially smoothes Tarps so they
become continuous
• Now have a memory component like
recall
Motivating example revisited.
How good is the model?
40
35
30
25
20
15
10
5
0
400
350
300
250
200
150
100
50
0
Modelled NETT ECT
date
NETT ECT
TARPS
TARPs
ECT
Current Situation
Motivating example
4.5%
Impact Indices
4.0%
Ad A
3.5%
Impact
Ad B
3.0%
Ad C
2.5%
Ad D
2.0%
Ad E
1.5%
Average
1.0%
Ads A & E return the best value
Future Media Spend
- some scenarios
Proposed spend until June 2001
(1500 TARPS in 10 weeks)
400
350
300
250
200
150
100
50
0
Modelled ECT
date ECT
• 12% low builds slowly to 21% ECT
• Average ECT 19% after February
TARPS
TARPs
40
35
30
25
20
15
10
5
0
30
/4
28 / 00
/5
25 / 00
/6
23 / 00
/7
20 / 00
/8
17 / 00
/
15 9/ 0
/1 0
12 0 /0
/1 0
10 1 /0
/1 0
2
7/ /00
0
11 1/ 0
/0 1
11 1 /0
/0 1
11 2 /0
/0 1
3/
8/ 01
04
6/ / 01
05
3/ / 01
06
/0
1
ECT
Proposed Spend
Alternative Spend Until June
(Same Budget)
400
350
300
250
200
150
100
50
0
Modelled ECT
date ECT
• Average ECT 21%
• “Burst and hold’ Strategy
• ECT higher longer - less variation
TARPS
TARPs
40
35
30
25
20
15
10
5
0
30
/4
/0
4/ 0
6/
00
9/
7/
13 00
/8
17 /00
/
22 9/00
/1
26 0 /0
/1 0
31 1 /0
/1 0
11 2 /0
/0 0
18 1 /0
/0 1
25 2 /0
/0 1
29 3 /0
/0 1
4
3/ /0 1
06
/0
1
ECT
Alternative Spend
What’s been happening with this
campaign lately?
ECT showing immediate increase following re-start of campaign
Modelled data and prediction
45
40
35
30
25
20
15
10
5
0
400
350
300
250
200
150
100
50
0
TARPs
ECT
Actual and modelled ECT
01
6/
/0 1
24 5/0
/0 1
27 4/0
/0
29 /01
04
1/ /01
03
4/ /01
02
4/ /01
01 0
7/ 2/0
/1 0
10 1/0
/1 0
12 0/0
/1
15 /00
/9
17 /00
/8
20 /00
/7
23 /00
/6
25 /00
/5
28 /00
/4
30
Modelled ECT
date
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
/0
9
11 /97
/1
0
25 /97
/1
0/
8/ 97
11
22 /97
/1
1/
9
6/ 7
12
20 /97
/1
2/
3/ 97
01
17 /98
/0
1
31 /98
/0
1
14 /98
/0
2
28 /98
/0
2
14 /98
/0
3
28 /98
/0
3
11 /98
/0
4
25 /98
/0
4/
9/ 98
05
23 /98
/0
5/
9
6/ 8
06
20 /98
/0
6/
9
4/ 8
07
18 /98
/0
7/
1/ 98
08
/9
8
27
Ad A - Impact (return/100 TARPs)
3.0%
2.5%
2.0%
1.5%
1.0%
Ad wearing out with time.
0.5%
0.0%
7/
06
21 /19
/0 97
6/
1
5/ 99
07 7
19 /19
/0 97
7/
1
2/ 99
08 7
16 /19
/0 97
8
30 /19
/0 97
8
13 /19
/0 97
9
27 /19
/0 97
9
11 /19
/1 97
0
25 /19
/1 97
0/
1
8/ 99
11 7
22 /19
/1 97
1/
1
6/ 99
12 7
20 /19
/1 97
2/
3/ 199
01 7
17 /19
/0 98
1
31 /19
/0 98
1
14 /19
/0 98
2
28 /19
/0 98
2
14 /19
/0 98
3
28 /19
/0 98
3
11 /19
/0 98
4
25 /19
/0 98
4/
9/ 199
05 8
23 /19
/0 98
5/
6/ 199
06 8
/1
99
8
Ad. B - Impact ( return/100 TARPs)
4.5
4
3.5
3
2.5
2
1.5
1
Same spend -different channels.
0.5
0
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!!!
10
51% of Brand share explained
by what we measure
Model Fit
8
7
Brand Share
9
33% of model fit due
to adstock alone
Execution A
0
20
Execution B
40
60
Date
80
100
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
A
Overspend on Ex