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1. Market Mix Modelling
Estimate the effectiveness of
investment in media

7. Agenda
• Business application of Marketing Mix
modelling
• A case study
• Strengths and weaknesses
• Brief introduction to more advanced
approaches: pooled regressions and structural
equations

8. Making BP’s media dollars work harder
• “Mindshare helped BP to make the most of their media
investments across the many states of the USA.”
• “BP engaged Mindshare to develop enhanced media
investment strategies to maximise sales and boost revenue
performance.”
• “Drivers of performance were quantified (e.g. media,
promotions, distribution, competitor effects) in seven USA
states, over three years”
• “Return on investment figures were calculated - both short
and long term - for 40 campaigns.”

9. Marketing Mix modelling
• Statistical methods applied to measure the impact of
media investments, promotional activities and price
tactics on sales or brand awareness
• Used to assist and implement a marketing strategy by
measuring:
– Effectiveness: contribution of marketing activities to sales
– Efficiency: short term and long term Return-On-
Investment of marketing spend
– Price elasticity
– Impact of competitors

10. MMM How does it work?
• A statistical model is estimated on historical data with sales as
a dependent variable and list of explanatory variables as
marketing activities, price, seasonality and macro factors
• The simplest and broadly used model is linear regression:
Salest    1  var 1t   2  var 2t  ...   t
• The output of the model is then used to carry out further
analysis like media effectiveness, ROI and price elasticity and
to simulate what-if scenarios

11. Factors that could drive sales
Advertising Promotions
Competition
TV Sponsorships
Seasonality
Radio Events
Weather
Print Price
Economic
Outdoor Adv quality
Demographic
Internet Distribution
Industry data
Merchandising
Salest    1  var   2  var  ...   t
1
t t
2
Sales

12. MMM project process
Set out objectives Data preparation
-Define scope •Collect data
-Discuss data •Validate, harmonize
availability and consolidate data
-Design data-warehouse •Present exploratory
analysis to client
Presentation Model development
•Interpretation of •Estimation
results •Diagnostics
•Learning and •Calculate ROIs, Price
recommendations elasticity and response
curves

13. Case study
• An energy company SPetrol wants to evaluate the advertising
investments of its retail business in the US from 2001 until
2004.
• Client’s questions:
• How much have we made through advertising?
• What is the return on investments of our media activities?
• Which marketing drivers have had the greatest effect?
• What’s the influence of price on our sales?
• Are we optimally allocating our budget across products ?

14. Target variable

15. Advertising data
• The performance of TV and radio advertising is expressed in
terms of Gross Rating Points (GRPs) . A rating point is a
percentage of the potential audience and GRPs measure the
total of all rating points during and advertising campaign.
– GRPs (%) = Reach * Frequency
– Example: Let’s assume a commercial is broadcasted two
times on TV
1st time on air 2st time on air GRPs
25% of target 32% of target
57%
televisions are tuned in televisions are tuned in

16. Advertising data
• Spetrol has deployed 5 TV campaigns over the
sample with a total expenditure of 300 million $
• Each campaign lasted from 4 to 8 weeks
• Is there any relationship between sales and TV
advertising?

17. Carry over effect of TV

18. Carry over effect of TV
• The exposure to TV advertising builds awareness,
resulting in sales.
• ADStock allows the inclusion of lagged and non
linear effects
ADStockt ( )  GRPt    ADStockt 1
0  1
• Alpha is estimated iteratively using least squares.
The estimate is then validated by media planners

19. Advertising data
300 M 164 M 160 M
TV Spend Radio Outdoor

20. Below the line promotions
• It may include
– sponsorship
– product placement
– sales promotion
– merchandising
– trade shows
• Usually represented by dummies (variables
equal to 1 when a promotion takes place and
0 otherwise)

21. Below the line promotions
Sponsorship
World Rally
Championship
Sale promotion
Sale promotion
5% Discountt

22. Price

23. Seasonality
August seasonal dummy
5% Discountt
Peaks every year
Sale promotion
in August

24. Exploratory analysis
Scatter plot Unit root test
32
Histogram and desc stats Correlation matrix
Series: SALES
28 Sample 1 209
Observations 209
24
Mean 154403.1
20 Median 153960.2
Maximum 183102.5
16 Minimum 125997.0
Std. Dev. 9476.290
12
Skewness 0.053546
Kurtosis 3.456209
8
Jarque-Bera 1.912312
4
Probability 0.384368
0
130000 140000 150000 160000 170000 180000

25. Model development

26. Estimated equation
Salest = 167412 +
168* AdStock(GRPsTVt,0.75) +
161* AdStock(GRPsRadiot,0.35) +
166* AdStock(Outdoort,0.15) +
580* PromotionDummyt +
6507* Seasonalityt +
-12631* Pricet + Errort

27. Model diagnostics
• Model:
– Significant F-stat and high R-squared
• Variables:
– Significant T-stats
– Coefficients must make sense
– Variance inflation factor low
• Residuals:
– Normality (Jarque-Bera)
– Absence of serial correlation ( Durbin Watson,
correlogram)

28. Residuals diagnostics
16
Series: RESID
14 Sample 1 209
Observations 209
Durbin Watson = 1.69
12
Mean -2.31e-11
DW>2 positive autocorrelation
10 Median
Maximum
-66.11295
8049.987
DW<2 negative autocorrelation
8 Minimum -11378.69
Std. Dev. 3612.711
6 Skewness -0.158326
Kurtosis 2.624286
4
Jarque-Bera 2.102443
2
Probability 0.349511
0
-10000 -5000 0 5000
  y y
ˆ ˆ

29. Estimated factors contribution to sales
Fitted Salest = estimated Intercept = 167,412
Can be interpreted as Brand Equity:
•Volume generated in absence of any marketing
activity
•Indicator of the strength of the brand and users’
loyalty

30. Estimated factors contribution to sales
TV Contributiont(000’ Gallons) =
coefficient *Adstock(TV)t
Fitted Salest = 167,412 + 168* TVt + 161*Radiot +
166* OOHt + 580* Promotiont

31. Estimated factors contribution to sales
in August
Peaks every year
Peacks every year
in August
Fitted Salest = 167,412 + 168* TVt + 161*Radiot +
166* OOHt Equity = Promotiont + 6507* Seasonailityt
+ 580* estimated Intercept = 167,412
Can be interpreted as Brand Equity

32. Estimated factors contribution to sales
Fitted Salest = 167,412 + 168* TVt +
161*Radiot +
166* OOHt + 580* Promotiont + 6507*
Seasonailityt - 12631* Pricet
Negative price
effect

33. Marketing mix (sample output)

34. Estimated factors contribution to sales

35. Estimated factors contribution to sales
N
TotSalesContribution  coeff   Factori
i 1

36. Estimated factors contribution to
revenue
N
ibution  coeff   Factori  Pr icei
Tot Re venueContr
i 1

37. ROI
TOT Re venueContr
ibution
ROI 
TOTCost

38. Does it really make sense?
TheDiminishing in
more I invest
media, returns I sell
the more

39. Response curves
NegExp  a  (1  exp(b  GRPs ))
S  a  (1/(1  exp(b  (GRPs  mean(GRPs ))))
Taking into account
diminishing returns

40. Price elasticity
• Assumption: constant elasticity across the sample which
implies a linear relation between volume and price
• By using the coefficient of the regression, it is possible to
derive an estimate for price elasticity:
– Price coefficient = -12631
– Average price = 1.51 $
– Average volume sales = 154,000 Gallons
Avg Pr ice A 10% drop in price
Elasticity  * coeff  0.12 increases sales by 1.2%
AvgSales

41. Dynamic price elasticity Elasticity changes with price
200,000
Weekly Volume and $ Sales vis-à-vis price of 1.75L
180,000
Volume (9L Cases)
160,000
140,000
120,000
100,000
80,000
60,000
40,000
20,000
0
Price (750 ml)
9
20.0
11
13
16
18
25
27
29
10
12
14
15
17
19
21
22
23
24
26
28
30
Volume
Elastic (>1): Demand is sensitive to price changes.
Estimated through non
Inelastic (<1): Demand is not sensitive to price changes linear regressions

42. Client’s questions
How much have we made through advertising?
• 1 billion $ driven by TV
• 500 million $ due to radio
• 200 million $ generated by Outdoor and
promotional activities
Investments in media generated 1.7
billion $ in revenue

43. Client’s questions
What is the return on investments of our media
activities?
For each dollar invested in TV you get 3.5 dollars
back

44. Client’s questions
What’s the influence of price on our sales?
A 10% drop in price
increases sales by 1.2%

45. Are we optimally allocating our
budget across products ?
Maximum Optimal
GRPs
Marginal
Return Over Optimal GRPs
Point of
Saturation
Sub –Optimal GRPs
Maximum
Average Return
Invest more in Radio
and less in OOH

46. Marketing Mix – Sample Output
Marketing mix (sample output)
45
Carry Over Effect
5000 Diminishing Returns 40
4500 35
4000 30
Promo TV Saturation
3500
Weekly GRPs
Weekly Sales
25
3000
20
2500
Current Optimal 15
2000
1500 10
1000
5
500
0
0
0 20 40 60 80 100 120 140 160 180 Week1 Week2 Week3 Week4 Week5
Avg. Weekly GRPs
Diminishing Returns is the point were spending
additional GRPs does not results in additional
Simultaneous Effect sales.
Carry Over Effect (Ad Stock) relates to the
Volume
residual effect of an ad.
When all the components are layered on Base
Base/Seasonal TV/Radio/Print Direct Marketing Rates/Promotions
sales, it is clear what drivers contribute to sales
Time and when and their Simultaneous Effect.

47. Pros and cons
• Simple and intuitive • Correlation doesn’t imply
• The outcome is backed by causality
qualitative expertise and in • Risk of spurious regressions
field research especially when modelling
• Constructive way of running in levels
different scenarios and • Model highly depends on
evaluating past variables chosen
performance • Poor in forecasting
• Better with granular data
• Very successful method –
high turnover

48. Spurious statistics
• A high correlation
between sales and TV
could mean:
Sales Media – Either media causes
sales
– or sales causes media
– or a third variable causes
Income both sales and TV
What is the truth?

49. Non sense correlations
• Some spurious • On the other hand, a
correlations: low correlation doesn’t
– death rate and rule out the possibility
proportion of marriages of a strong relation:
Corr = 0.95
Corr = 0.0
– National income and
sunspots Corr = 0.91
– Inflation rate and
accumulation of annual
rainfall
•Correlations must support a theory
•Calculate correlations both in levels and differences
•Always look at scatter plots

50. What variables should have been
included?

51. New media
• Digital Marketing
– Display Marketing
– Search Engine Marketing (SEO & PPC)
– Affiliate Marketing
– Mobile Marketing
– Social Media

52. New media
• Data availability
– Impressions
– Clicks
– Post event activity
– Bespoke engagement metrics
• Example of a tracking centre:
– Double-click

53. Alternative methods
• Linear regression
• Logistic regression
• Discriminant analysis
• Factor analysis
• Cluster analysis
• Structural equations modelling

54. Pooled regressions
Sales Local media Nat media Local Price
California California USA California + ... + error
sa
Nevada Nevada USA Nevada + ... + error
Oregon Oregon USA Oregon + ... + error

55. Pooled regressions example
1. SalesCalifornia = c11*TVCalifornia +
c12*TVOregon+c13*RadioCalifornia +c14*RadioOregon +
ErrorColifornia
2. SalesOregon = c21*TVCalifornia +
c22*TVOregon+c23*RadioCalifornia +c24*RadioOregon +
ErrorOregon  TVC 
 SalesC   c11 c12 c13 c14   TVO   C 
 
 Sales   c    Radio    
 O  21 c22 c23 c24  C  O
 
 RadioO 
Media effect is also tested across regions

56. How advertising effects consumers?
Understanding:
– the process by which advertising affects
consumers
– How the effects of advertising are spread over
time
– The role of different media
– The role of competitors

57. The purchase funnel
• A basic process that
leads to the purchase of Awareness
a product consists in:
– Awareness – costumer is
aware of the existence of
a product Consideration
– Consideration – actively
expressing an interest in
the company
– Purchase
Purchase

58. Working on survey data
• A sample of the target
audience is interviewed
about brand awareness,
consideration and choice
• Research agencies provide
awareness, consideration
and purchase time series in
% terms
– i.e. A purchase of 10% means
that 10 out of 100 interviewed
people purchased the product

59. Testing the purchase funnel
Awareness Consideration Purchase
Advertising first exercise its
influence on awareness. Via
awareness there is an effect on
Media consideration which drives the
consumer to purchase

60. Testing the purchase funnel
• Awarenesst=c11+c12*TVt+c13*radiot+c14*OOHt+error1t
• Considerationt = b1*awarenesst + c21 + error2t
• Purchaset = b3*Considerationt + b2*Awareness +c31 +
error3t
a1,a2,a3 must be insignificant to confirm theory
Const 
 1  a1  a2   Awart   c11 c12 c13 c14      1t 
 b 1  a3    Const   c21 0 0 0    TVt    
 1      Radiot   2 t 
 b2
  b3 1   Purcht  c31 0
    0  OOH   3t 
0   
 t 

61. Agenda
• Business application of Marketing Mix
modelling
• A case study
• Strengths and weaknesses
• Brief introduction to more advanced
approaches: pooled regressions and structural
equations

62. References