Presented in conjunction with the Chartered Institute of Marketing at Aston Science Park on 1st November 2011

1. Maximising returns on your
communications investment
Marketing measurement and econometrics
1st November 2011

2. We know that we need to measure marketing
• To increase its effectiveness
• To reduce risk
• To justify the marketing budget

3. But marketing measurement is hard
• Marketing doesn’t always work quickly
• The effects are often not felt immediately
• So we end up not being sure if it’s working at all

4. Week-to-week sales movements are affected by many
factors other than marketing
• Short-term sales movements due to advertising are
difficult to pull apart from other factors
Scale of weekly sales movements typically measured by an FMCG model
30-50% Promotions and discounts
30% Distribution change (+30%)
10-20% Price change (+10%)
5-10% Above the line advertising
5-6% Competitor activity
3- 5% Seasonality
1-2% Random ‘noise’
~ 1% Weather

5. TVRs TVRs / Sales Impact
0
20
40
60
80
100
120
140
0
20
40
60
80
100
120
140
180
160
07-Jan-08
28-Jan-08
Week 1
18-Feb-08
Week 2
10-Mar-08
31-Mar-08 Week 3
21-Apr-08 Week 4
12-May-08 Week 5
TVRs
02-Jun-08 Week 6
23-Jun-08 Week 7
14-Jul-08 Week 8
04-Aug-08
Week 9
25-Aug-08
Week 10
Yorkshire TVRs
15-Sep-08
06-Oct-08 Week 11
27-Oct-08 Week 12
17-Nov-08 Week 13
08-Dec-08 Week 14
Illustrated impact of TV airtime on sales
29-Dec-08 Week 15
19-Jan-09 Week 16
09-Feb-09
Week 17
Effect on Sales
02-Mar-09
Week 18
23-Mar-09
13-Apr-09 Week 19
04-May-09 Week 20
25-May-09 Week 21
15-Jun-09 Week 22
06-Jul-09 Week 23
27-Jul-09 Week 24
17-Aug-09
Week 25
07-Sep-09
Week 26
28-Sep-09
19-Oct-09 Week 27
09-Nov-09 Week 28
30-Nov-09 Week 29
Shape of Impact on Sales (Index)
21-Dec-09 Week 30
11-Jan-10 Week 31
01-Feb-10 Week 32
22-Feb-10
Week 33
15-Mar-10
Week 34
05-Apr-10
26-Apr-10 Week 35
Continuous TV airtime and shape of impact on sales (Brilliant Media client example)
17-May-10 Week 36
07-Jun-10 Week 37
28-Jun-10 Week 38
19-Jul-10 Week 39
09-Aug-10 Week 40
30-Aug-10
Week 41
20-Sep-10
Week 42
11-Oct-10
01-Nov-10 Week 43
22-Nov-10 Week 44
13-Dec-10 Week 45
03-Jan-11 Week 46
24-Jan-11 Week 47
14-Feb-11 Week 48
07-Mar-11
Week 49
28-Mar-11
Week 50
18-Apr-11
09-May-11 Week 51
30-May-11 Week 52
0
1
0.2
0.4
0.6
0.8
1.2
1.4
1.6
The full impact of marketing is also not felt immediately
Indexed Impact on Sales

6. New Customers
0.00
50.00
100.00
150.00
200.00
250.00
01-Oct-10
05-Oct-10
09-Oct-10
13-Oct-10
17-Oct-10
21-Oct-10
25-Oct-10
29-Oct-10
02-Nov-10
06-Nov-10
New Customers
10-Nov-10
14-Nov-10
18-Nov-10
22-Nov-10
26-Nov-10
Example of a highly impactful TV burst
30-Nov-10
Immediate TV Contribution
04-Dec-10
08-Dec-10
12-Dec-10
16-Dec-10
20-Dec-10
24-Dec-10
28-Dec-10
01-Jan-11
05-Jan-11
09-Jan-11
13-Jan-11
17-Jan-11
21-Jan-11
25-Jan-11
29-Jan-11
TV Carryover contribution
02-Feb-11
06-Feb-11
10-Feb-11
14-Feb-11
18-Feb-11
22-Feb-11
26-Feb-11
02-Mar-11
06-Mar-11
10-Mar-11
14-Mar-11
18-Mar-11
22-Mar-11
26-Mar-11
30-Mar-11
03-Apr-11
07-Apr-11
11-Apr-11
15-Apr-11
19-Apr-11
23-Apr-11
27-Apr-11
The effect of a TV burst can last well beyond the timing
of the spots

7. Year on Year Change
-5%
-15%
-10%
0%
5%
15%
20%
25%
30%
35%
10%
03-Jan-11
10-Jan-11
17-Jan-11
24-Jan-11
31-Jan-11
07-Feb-11
14-Feb-11
21-Feb-11
28-Feb-11
07-Mar-11
14-Mar-11
21-Mar-11
28-Mar-11
04-Apr-11
11-Apr-11
18-Apr-11
25-Apr-11
02-May-11
09-May-11
16-May-11
23-May-11
30-May-11
06-Jun-11
13-Jun-11
20-Jun-11
27-Jun-11
04-Jul-11
Year on year sales. Client was looking for the reason that sales increased from June 2011
11-Jul-11
18-Jul-11
25-Jul-11
01-Aug-11
08-Aug-11
15-Aug-11
22-Aug-11
29-Aug-11
increases in sales, works occasionally
Trying to identify marketing impact by looking for

8. Year on Year Change
100%
-40%
-20%
0%
20%
40%
60%
80%
03-Jan-11
10-Jan-11
17-Jan-11
24-Jan-11
31-Jan-11
07-Feb-11
14-Feb-11
21-Feb-11
28-Feb-11
07-Mar-11
14-Mar-11
TV Ratings Year on Year
Sales Sales Year on Year
21-Mar-11
28-Mar-11
04-Apr-11
11-Apr-11
18-Apr-11
25-Apr-11
02-May-11
09-May-11
16-May-11
23-May-11
30-May-11
06-Jun-11
13-Jun-11
20-Jun-11
Year on year sales. Client was looking for the reason that sales increased from June 2011
27-Jun-11
04-Jul-11
11-Jul-11
18-Jul-11
25-Jul-11
01-Aug-11
08-Aug-11
15-Aug-11
22-Aug-11
29-Aug-11
Unfortunately, more often it leads to confusion.
Sales increased here, after TV was returned to normal.

9. Weekly sales tracking that tries to pin down marketing,
is rarely effective unless marketing uplifts are very large
• Tracking can lead to a singular focus on trying to explain the
previous week’s sales
– Trying to explain away the movements that aren’t marketing is
very difficult
– Often we end up blaming everything on the weather
A weekly retail sales tracking dashboard with a singular focus on weather (Brilliant Media client example)
Total sales TY 2,534.42 3,347.83 2,321.96 2,384.24 2,320.83 2,623.11 3,429.65
Total sales LY 2,167.29 3,138.10 3,061.70 3,110.99 3,446.56 3,739.47 4,942.17
Total budget sales 2,194.87 3,357.76 3,249.18 3,275.26 3,657.56 3,955.02 5,119.55
Sales vs budget 15.5% -0.3% -28.5% -27.2% -36.5% -33.7% -33.0%
Sales vs LY 16.9% 6.7% -24.2% -23.4% -32.7% -29.9% -30.6%
2011 Weather
Temp 7.1°C 6.7°C 6.1°C 5.6°C 5.9°C 5.9°C 5.7°C
2010 Weather

10. We can achieve a lot, without complex statistics
• Track what we can measure
– Take care not to only spend on what we can track…
• Acknowledge the issues with marketing measurement

11. Direct response tracking completes a part of the picture
Econometrics
Return on investment
Budget allocation
Budget setting
Sales forecasting
Test and learn
Consumer Research Direct response tracking
Brand tracking Optimisation within a marketing
Brand perception channel, including:
Creative tuning Colour vs. B&W
Target audience Ad size
Segmentation Newspaper titles
Competitor benchmarks Web display placement

12. Direct response has problems, but it’s a good step for
advertisers with a product that’s suited to being tracked
• Several mechanics allow us to track response
– Bespoke numbers
– Direct mail
– Competitions
– ‘Where did you hear about us?’
• Compares within channels only
– What about brand TV, or if your brand has a memorable
telephone number that you don’t want to change?
– Many brands – such as FMCG - have no response
mechanic

13. Direct response data lets us optimise within press,
search or other channels with response metrics
• Database technology makes this type of reporting quick
and relatively easy
– Tool for both agencies and clients

14. ‘Brand’ might be driving a lot of your direct activity
• True cost per acquisition is a combination of brand and
direct
– Most advertisers don’t analyse to this depth (yet)
Large numbers of search clicks can be driven by TV (Brilliant Media client example)
Base Driven by TV
Organic
Brand
CPC
Organic
Cheap
CPC
Product 2
Organic
CPC
Product 1
Organic
CPC
0 10,000 20,000 30,000 40,000 50,000 60,000
Number of clicks

15. But if you’re not careful, it can all get a little complicated

16. There’s a simple rule of thumb that avoids a lot of
brand measurement issues
• If direct channels (including search) bring in sales at a
cost lower than TV, then they’re making your marketing
more efficient
– TV generates the interest in your product, whether you
run search ads to convert it, or not
• But we’ll need econometrics to find out the cost per
acquisition from TV

17. Econometrics solves some of our direct response
measurement issues

18. WARC case studies incorporating econometrics
Number of WARC case studies referencing econometrics (to October 2011)
Food 146
Retail 67
Drink and beverage 54
Pharmaceutical and healthcare 43
Household and domestic 39
Toiletries and cosmetics 33
Financial services 33
Telecomms 29
Leisure and entertainment 27
Travel, transport and tourism 20
Media and publishing 20
Motor and auto 18
Govt. and non-profit 16
Wearing apparel 14
Business and industrial 5
Utilities and services 4
Tobacco 1

19. Econometrics adds a new set of information, that we
can’t get from direct response tracking alone
Econometrics
Return on investment
Budget allocation
Budget setting
Sales forecasting
Test and learn
Consumer Research Direct response tracking
Brand tracking Optimisation within a marketing
Brand perception channel, including:
Creative tuning Colour vs. B&W
Target audience Ad size
Segmentation Newspaper titles
Competitor benchmarks Web display placement

20. Econometrics measures and then improves the
effectiveness of advertising
Econometrics uses statistical models of sales to…
– Measure the effectiveness (return on investment) of past
advertising campaigns
– Split marketing campaigns into their individual parts
(TV, radio, outdoor etc.) and measure the effectiveness of
each part of the marketing Press Radio
mix TV
Seasonality
Store Openings
Actual
Model
250
– Forecast the effectiveness of
200
future advertising campaigns
150
Sales (£ '000s)
100
50
– Use forecasts to produce a 0
more effective marketing mix -50
Week 1
Week 4
Week 7
Week 10
Week 13
Week 16
Week 19
Week 22
Week 25
Week 28
Week 31
Week 34
Week 37
Week 40
Week 43
Week 46
Week 49
Week 52
Week 55
Week 58
Week 61
Week 64
Week 67
Week 70
Week 73
Week 76
Week 79
Week 82
Week 85
Week 88
Week 91
Week 94
Week 97
Week 100
Week 103
Week 106
Week 109
Week 112
Week 115
Week 118
Week 121
Week 124
Week 127
Week 130
Week 133
Week 136
Week 139
Week 142
Week 145
Week 148
Week 151
Week 154

21. In marketing, econometrics usually means…
• Proving the effectiveness of advertising in driving sales
• Measuring return on investment (ROI)
• Building a mathematical model of two to three years of
historical sales data
• Concentrating hardest on major above the line spends
• Aiming to produce a more efficient media budget
allocation

22. A wider definition is much more useful
“Econometrics is a toolbox that
helps you to test theories about
your marketing
”

23. What’s the output?
1. Measurement of past advertising campaigns, split
into the different media channels that were used.
Proof that past advertising added to sales and
(hopefully!) was profitable
Return on investment calculations showing the individual
profitability of each marketing channel
2. Forecasting and improvement of future campaigns
The really useful bit and why it’s worth investing in
econometrics
We can use the model to forecast the effectiveness of
potential media schedules and then choose the one with
the highest returns.

24. How a (standard) model actually works
• The maths that goes into a model is complicated…
Sti Pti
   1   2 (Tti  Ti )   ti
Si Pi
• But you really don’t need to understand it, to get a
feeling for how econometrics works

25. Sales (£ '000s)
50
0
-50
100
150
200
250
Week 1
Week 4
Week 7
Week 10
Week 13
Sales
Week 16
Week 19
Week 22
Week 25
Week 28
Week 31
Week 34
Week 37
Week 40
Week 43
Week 46
Week 49
Week 52
Week 55
Week 58
Week 61
Week 64
Week 67
Week 70
Week 73
Week 76
Week 79
Week 82
Week 85
Week 88
Week 91
Week 94
Week 97
Week 100
Week 103
Week 106
Week 109
Week 112
Week 115
Week 118
Week 121
Week 124
Week 127
Week 130
Week 133
Week 136
Week 139
Week 142
Week 145
Week 148
Week 151
Week 154
weekly sales data
We start with a sales history; two to three years of

26. Sales (£ '000s)
50
0
-50
100
150
200
250
Week 1
Week 4
Week 7
Week 10
Week 13
Sales
Week 16
Model
Week 19
Week 22
Week 25
Week 28
Week 31
Week 34
Week 37
Week 40
Week 43
Week 46
Week 49
Week 52
Week 55
Week 58
Week 61
Week 64
Week 67
Week 70
Week 73
Week 76
Week 79
Week 82
Week 85
Week 88
Week 91
Week 94
Week 97
why sales have moved in the past
Week 100
Week 103
Week 106
Week 109
Week 112
Week 115
Week 118
Week 121
Week 124
Week 127
Week 130
Week 133
Week 136
Week 139
Week 142
that tracks actual sales as closely as possible – explaining
Week 145
The aim of model building is to produce a model (red line)
Week 148
Week 151
Week 154
and a model, that at this stage doesn’t know anything
at all about sales movements

27. Sales (£ '000s)
50
0
-50
100
150
200
250
Week 1
Week 4
Week 7
Week 10
Week 13
Week 16
Actual
Week 19
Model
Week 22
Week 25
Seasonality
Week 28
Week 31
Week 34
Week 37
Week 40
Week 43
Week 46
Week 49
effect of Christmas
Week 52
Week 55
Week 58
Week 61
Week 64
Week 67
Week 70
Week 73
Week 76
Week 79
Week 82
Week 85
Week 88
Week 91
Week 94
Week 97
Week 100
Week 103
Week 106
Week 109
Week 112
Week 115
Week 118
Week 121
that the model is measuring. This variable captures the
Week 124
The bars show how many sales are driven by each factor
Week 127
Week 130
Week 133
Week 136
Week 139
Week 142
Week 145
Week 148
Week 151
Week 154
Add large, easy to measure factors
to the model
Step 1:

28. Sales (£ '000s)
50
0
-50
100
150
200
250
Week 1
Week 4
Week 7
Week 10
Week 13
Week 16
Week 19
Week 22
Actual
Model
Week 25
Week 28
Seasonality
Week 31
Week 34
Store Openings
Week 37
Week 40
Week 43
Week 46
Week 49
Week 52
Week 55
Week 58
Week 61
Week 64
Week 67
Week 70
Week 73
Week 76
Week 79
Week 82
Week 85
Week 88
Week 91
Week 94
Week 97
Week 100
Week 103
Week 106
Week 109
Week 112
Week 115
Week 118
Week 121
Week 124
Week 127
Week 130
Week 133
Week 136
Week 139
Week 142
Week 145
Week 148
Week 151
Week 154
Step 2:
Identify and model major trends in sales

29. Sales (£ '000s)
50
0
-50
100
150
200
250
Week 1
Week 4
Week 7
TV
Week 10
Week 13 Model
Week 16
Week 19
Seasonality
Week 22
Week 25
Week 28
Week 31
Week 34
Week 37
Week 40
Week 43
Week 46
Actual
Week 49
Week 52
Week 55
Week 58
Store Openings
Week 61
Week 64
Week 67
Week 70
Week 73
Week 76
Week 79
Week 82
Week 85
Week 88
Week 91
Week 94
Week 97
Week 100
Week 103
Week 106
Week 109
Week 112
Week 115
Week 118
Week 121
Week 124
Week 127
Week 130
Week 133
Week 136
Week 139
Week 142
Week 145
Week 148
Week 151
Week 154
Once the basic model is built, we can get a first
estimate for larger marketing spends
Step 3:

30. At every stage, diagnostic statistics tell us how well
the model is working
• We get a lot of information from a model
1. The sales impact of each factor that we have included
(ROI)
2. How sure we are that each individual measurement is
accurate (confidence)
3. How sure we are that the overall model is robust
R2, t and F; diagnostic statistics that only econometricians find interesting

31. Sales (£ '000s)
50
0
-50
100
150
200
250
Week 1
Week 4
Week 7
Week 10
Week 13
TV
Week 16
Press
Week 19
Model
Week 22
Week 25
Seasonality
Week 28
Week 31
Week 34
Week 37
Week 40
Week 43
Week 46
Week 49
Week 52
Week 55
Week 58
Week 61
Radio
Actual
Week 64
Week 67
Week 70
Week 73
Week 76
Store Openings
Week 79
Week 82
Week 85
Week 88
Week 91
Week 94
Week 97
Week 100
Week 103
Week 106
Week 109
Week 112
Week 115
Week 118
Week 121
Week 124
Week 127
Week 130
Week 133
Week 136
Week 139
Week 142
Week 145
Week 148
Week 151
Week 154
The final model is a good fit for sales and
includes all major marketing investments
Step 4:

32. It’s about asking the right questions
• There is a ‘standard’ econometric analysis, but modelling
works much better, when we set it up from the start to
answer specific questions
Finance are threatening to cut
the ad budget; I need to prove
that advertising is profitable.
Can my media mix be made [Online banking]
more efficient? Do I really need
[this is a ‘standard’ analysis] ‘brand’ TV, or can
search and DM do
Should I transfer some of
the job alone?
my ATL budget online?
[Car insurance]
[High Street banking]
What will it cost to hit
this sales target? How much budget do I
[Automotive] need for a store re-launch?
[Supermarket retail]

33. Finding the most effective marketing mix is more than
ROI measurement
• Response curves measured using econometrics forecast the
effect of changing marketing budgets
Example response curves
TV
Press
1. The most effective marketing mix
350%
Additional Sales
allocates budget first to TV…
300%
250% Beyond a £400k campaign, additional TV spend
generates few extra sales
200%
150%
2. …and then the remaining
100% budget to press
50%
0%
100 200 300 400 500 600 700 800 900 1000
£'000s

34. Bringing together the three elements of marketing evaluation
1. Monitoring the market
– Tracking competitor activity
– Benchmarking (share of voice etc.)
2. Response tracking
– Immediate indicators of consumer behaviour
– Web traffic, Click through, Cost per click, Call volume, store footfall
and more…
– Awareness & consideration tracking
3. Modelling
– Filling in the gaps that can’t be measured by direct response

35. The flow of campaign evaluation
Post Campaign Long-term
Plan Campaign
Analysis analysis
Flow of results Econometrics
In-campaign Clicks (web response)
‘tuning’ Phone #s
‘Multiplier’ adjustments
Direct sales
- Adjustments to direct
response Measures
Market Monitoring
Share of voice etc. - Click path analysis
Weekly / Monthly updated
Direct response tracking
Cost per acquisition etc.
Budget setting, forecasting
Daily / Weekly updated
and optimisation

36. Sales
0.00
200.00
400.00
600.00
800.00
1,000.00
1,200.00
1,400.00
01-Oct-10
05-Oct-10
09-Oct-10
13-Oct-10
17-Oct-10
21-Oct-10
25-Oct-10
29-Oct-10
02-Nov-10
06-Nov-10
10-Nov-10
14-Nov-10
18-Nov-10
National sales revenue projections
22-Nov-10
26-Nov-10
30-Nov-10
04-Dec-10
08-Dec-10
12-Dec-10
16-Dec-10
Sales minus Central TV burst
20-Dec-10
24-Dec-10
28-Dec-10
01-Jan-11
05-Jan-11
09-Jan-11
Actual sales (including Central TV burst)
13-Jan-11
17-Jan-11
21-Jan-11
25-Jan-11
29-Jan-11
02-Feb-11
06-Feb-11
10-Feb-11
14-Feb-11
Projected Sales if Central burst had been run nationally
18-Feb-11
22-Feb-11
26-Feb-11
02-Mar-11
06-Mar-11
10-Mar-11
14-Mar-11
18-Mar-11
22-Mar-11
26-Mar-11
30-Mar-11
03-Apr-11
07-Apr-11
11-Apr-11
15-Apr-11
19-Apr-11
23-Apr-11
27-Apr-11
Sales Forecasts and Projections

37. Year on Year Change
-5%
-15%
-10%
0%
5%
15%
20%
25%
30%
35%
10%
03-Jan-11
10-Jan-11
17-Jan-11
24-Jan-11
31-Jan-11
07-Feb-11
14-Feb-11
21-Feb-11
28-Feb-11
07-Mar-11
14-Mar-11
21-Mar-11
28-Mar-11
04-Apr-11
11-Apr-11
18-Apr-11
25-Apr-11
02-May-11
09-May-11
16-May-11
23-May-11
30-May-11
06-Jun-11
13-Jun-11
20-Jun-11
27-Jun-11
04-Jul-11
11-Jul-11
18-Jul-11
25-Jul-11
01-Aug-11
08-Aug-11
15-Aug-11
22-Aug-11
29-Aug-11
Strong year on year sales performance raised the question: What was going right? (Brilliant Media client example)
their recent performance had been so good
A Brilliant client (with econometric models) asked why

38. Year on year analysis provided strong evidence that
the overall market was improving
• Year on year is good for evidence, but it doesn’t tell you what to
track. We could only draw this chart because we already knew
Google searches were important
Google search activity closely matched overall sales (Brilliant Media client example)
Year on Year Sales (5wk MA)
Google Searches for product term (non-brand)
20%
Year on Year Change
15%
10%
5%
0%
-5%
-10%
06-Jun-11
13-Jun-11
20-Jun-11
27-Jun-11
04-Jul-11
11-Jul-11
18-Jul-11
25-Jul-11
07-Feb-11
14-Feb-11
21-Feb-11
28-Feb-11
04-Apr-11
11-Apr-11
18-Apr-11
25-Apr-11
01-Aug-11
08-Aug-11
15-Aug-11
22-Aug-11
17-Jan-11
24-Jan-11
31-Jan-11
07-Mar-11
14-Mar-11
21-Mar-11
28-Mar-11
02-May-11
09-May-11
16-May-11
23-May-11
30-May-11

39. What about test and control?

40. What makes a ‘good’ media test?
1. Clear objectives
• What, exactly, are we trying to find
out?
2. Designed to generate a measure
• What uplift do we expect that the
test might generate …?
• … So what scale does the test need
to be for this effect to be
measurable?
3. Useful negative results
• If the test finds no significant uplifts,
are we sure the activity doesn’t
work?

41. Why is econometrics Important?
1. Controls for external factors
2. Lets us measure smaller effects
3. Helps to specify an appropriate scale for the test

42. A ‘bad’ media test… Real world example
• An advertiser wanted to find the effect of a combined TV and
Radio campaign on various brand preference metrics
• The campaign was run over four weeks in the Central and
Granada BARB regions
• Pre and Post survey dips in four cities provided the
awareness data
– Three test cities: Manchester, Carlisle & Birmingham
– One control: Norwich
• 600+ respondents in the pre-campaign dip and 700+ in the
post campaign
• What’s wrong with that… ?

43. What’s wrong with that?
1. No clear objective for the campaign
• ‘Run a campaign and see which metric moves’ is not an ideal
starting point
2. No prior knowledge of whether the test is likely to be
big enough to make a difference (and so be measured)
• In order to generate the 10% sales uplift that we will need to get a
solid measure, should the test be run for longer? Or with more
GRPs?
• One control city only is very, very risky
3. Post campaign measurement using a simple average
vs. Control
• Leaves the test exposed to unforeseen events that have a different
impact in the test and control regions
• Econometrics gives a much better chance of a useable result

44. What actually happened?
The test was inconclusive…
Spontaneous Awareness
Awareness Metric
Two test cities increased. One
decreased.
68%
61% 63%
57% 55%
53% 54% 52% The two that increased were in
47%
41% different BARB regions
Control also increased
(by more than the test regions)
Inconclusive result.
Manchester
Birmingham
Carlisle
Control
Overall Test
Pre Post

45. What actually happened?
The test was inconclusive…
Purchase Intent
Purchase intent
Purchase intent fell in two
test regions and fell very
43% heavily in Birmingham
36%
33%
28%
31%
31% 31% The control region declined
23%
15%
Manchester stayed at
31%, bucking the decline of
8%
the control region
Overall, were our test regions
Manchester
Birmingham
Carlisle
Control
Overall Test
better? Or worse?
Pre Post

46. Briefing an analysis

47. A variety of companies provide econometrics and
bring different strengths to the analysis
Independent Semi-Independent Media Agencies
Completely impartial Generally impartial Risk of conflict of interest
Little internal data Strong internal data Strong internal data
Smaller (riskier) Large analytical teams Mid to large analytical teams
May not be media Can be expensive Media measurement specialist
measurement specialist
Weak ties to planning Strong ties to planning
Very weak ties to planning

48. Automated tools can help (and sometimes reduce
analysis costs) but need a lot of care
• Automated tools are seductive, but we need to be
aware of their limitations
• Analysis is only ever an aid to decision making

49. Briefing an econometric analysis
1. Why do you want the work done?
2. What data exists and who is responsible for it?
3. When is the decision deadline that the work informs?
4. How will the work inform future decisions?
5. Who are your project team?
6. Interim meetings
7. What was your marketing designed to achieve?
8. everyone who will use the results needs to be involved
from the start

50. We haven’t mentioned consumer research, but it’s the
final piece of the puzzle
Econometrics
Return on investment
Budget allocation
Budget setting
Sales forecasting
Test and learn
Consumer Research Direct response tracking
Brand tracking Optimisation within a marketing
Brand perception channel, including:
Creative tuning Colour vs. B&W
Target audience Ad size
Segmentation Newspaper titles
Competitor benchmarks Web display placement

51. Contact
Neil Charles
Head of Econometrics
Brilliant Media
1 City Square
Leeds
LS1 2FF
+44(0)113 394 0078
+44 (0)7508 269965
neil.charles@brilliantmedia.co.uk