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