Date post: | 10-May-2015 |
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Technology |
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Market Mix Modelling
Estimate the effectiveness of investment in media
Agenda
• Business application of Marketing Mix modelling
• A case study
• Strengths and weaknesses
• Brief introduction to more advanced approaches: pooled regressions and structural equations
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.”
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
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:
• 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
ttttSales ...2var1var 21
Factors that could drive sales
Advertising TV
Radio Print
Outdoor Internet
Promotions Sponsorships
Events Price
Adv quality Distribution
Merchandising
Competition Seasonality
Weather Economic
Demographic Industry data
Sales
ttttSales ...varvar 2
2
1
1
MMM project process
Set out objectives -Define scope -Discuss data
availability -Design data-warehouse
Data preparation •Collect data
•Validate, harmonize and consolidate data •Present exploratory
analysis to client
Model development •Estimation •Diagnostics
•Calculate ROIs, Price elasticity and response
curves
Presentation •Interpretation of
results •Learning and
recommendations
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 ?
Target variable
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 25% of target
televisions are tuned in
2st time on air 32% of target
televisions are tuned in
GRPs 57%
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?
Carry over effect of TV
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
• Alpha is estimated iteratively using least squares. The estimate is then validated by media planners
10
)( 1
ttt ADStockGRPADStock
Advertising data
300 M TV Spend
164 M Radio
160 M Outdoor
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)
Below the line promotions
Sponsorship
World Rally Championship
Sale promotion Sale promotion 5% Discountt
Price
Seasonality
Sale promotion 5% Discountt August seasonal dummy
Peaks every year in August
Exploratory analysis
0
4
8
12
16
20
24
28
32
130000 140000 150000 160000 170000 180000
Series: SALES
Sample 1 209
Observations 209
Mean 154403.1
Median 153960.2
Maximum 183102.5
Minimum 125997.0
Std. Dev. 9476.290
Skewness 0.053546
Kurtosis 3.456209
Jarque-Bera 1.912312
Probability 0.384368
Correlation matrix Histogram and desc stats
Scatter plot Unit root test
Model development
Salest = 167412 +
168* AdStock(GRPsTVt,0.75) +
161* AdStock(GRPsRadiot,0.35) +
166* AdStock(Outdoort,0.15) +
580* PromotionDummyt +
6507* Seasonalityt +
-12631* Pricet + Errort
Estimated equation
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)
Residuals diagnostics
0
2
4
6
8
10
12
14
16
-10000 -5000 0 5000
Series: RESID
Sample 1 209
Observations 209
Mean -2.31e-11
Median -66.11295
Maximum 8049.987
Minimum -11378.69
Std. Dev. 3612.711
Skewness -0.158326
Kurtosis 2.624286
Jarque-Bera 2.102443
Probability 0.349511
Durbin Watson = 1.69 DW>2 positive autocorrelation DW<2 negative autocorrelation
yy ˆˆ
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
Estimated factors contribution to sales
Fitted Salest = 167,412 + 168* TVt + 161*Radiot + 166* OOHt + 580* Promotiont
TV Contributiont(000’ Gallons) = coefficient *Adstock(TV)t
Estimated factors contribution to sales
Equity = estimated Intercept = 167,412 Can be interpreted as Brand Equity
Fitted Salest = 167,412 + 168* TVt + 161*Radiot + 166* OOHt + 580* Promotiont + 6507* Seasonailityt
Peacks every year in August
Peaks every year in August
Estimated factors contribution to sales
Negative price effect
Fitted Salest = 167,412 + 168* TVt + 161*Radiot + 166* OOHt + 580* Promotiont + 6507* Seasonailityt - 12631* Pricet
Marketing mix (sample output)
Estimated factors contribution to sales
Estimated factors contribution to sales
N
i
iFactorcoeffntributionTotSalesCo1
Estimated factors contribution to revenue
i
N
i
i iceFactorcoeffibutionvenueContrTot PrRe1
ROI
TOTCost
ibutionvenueContrTOTROI
Re
Does it really make sense?
Diminishing returns
The more I invest in media, the more I sell
Response curves
))exp(1( GRPsbaNegExp
))))((exp(1/(1( GRPsmeanGRPsbaS
Taking into account diminishing returns
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
12.0*
Pr coeff
AvgSales
iceAvgElasticity
A 10% drop in price increases sales by 1.2%
Dynamic price elasticity Elasticity changes with price
0
20,000
40,000
60,000
80,000
100,000
120,000
140,000
160,000
180,000
200,000
9 10 11 12 13 14 15 16 17 18 19
20.0 21 22 23 24 25 26 27 28 29 30
Volume (9L Cases)
Price (750 ml)
Weekly Volume and $ Sales vis-à-vis price of 1.75L
Volume
Elastic (>1): Demand is sensitive to price changes. Inelastic (<1): Demand is not sensitive to price changes
Estimated through non linear regressions
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
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
Client’s questions
What’s the influence of price on our sales?
A 10% drop in price increases sales by 1.2%
Are we optimally allocating our budget across products ?
Over Optimal GRPs
Optimal GRPs
Sub –Optimal GRPs
Maximum Marginal
Return
Maximum Average Return
Point of Saturation
Invest more in Radio and less in OOH
Marketing Mix – Sample Output
Promo TV Saturation
Avg. Weekly GRPs
Wee
kly
Sale
s
Optimal Current
0
500
1000
1500
2000
2500
3000
3500
4000
4500
5000
0 20 40 60 80 100 120 140 160 180
Vo
lum
e
Time
0
5
10
15
20
25
30
35
40
45
Week1 Week2 Week3 Week4 Week5
Wee
kly
GR
Ps
Carry Over Effect
Base/Seasonal TV/Radio/Print Direct Marketing Rates/Promotions
Simultaneous Effect
Diminishing Returns
Diminishing Returns is the point were spending additional GRPs does not results in additional sales. Carry Over Effect (Ad Stock) relates to the residual effect of an ad. When all the components are layered on Base sales, it is clear what drivers contribute to sales and when and their Simultaneous Effect.
Marketing mix (sample output)
Pros and cons
• Simple and intuitive
• The outcome is backed by qualitative expertise and in field research
• Constructive way of running different scenarios and evaluating past performance
• Better with granular data
• Very successful method – high turnover
• Correlation doesn’t imply causality
• Risk of spurious regressions especially when modelling in levels
• Model highly depends on variables chosen
• Poor in forecasting
Spurious statistics
• A high correlation between sales and TV could mean:
– Either media causes sales
– or sales causes media
– or a third variable causes both sales and TV
Sales Media
Income
What is the truth?
Non sense correlations
• Some spurious correlations:
– death rate and proportion of marriages Corr = 0.95
– National income and sunspots Corr = 0.91
– Inflation rate and accumulation of annual rainfall
• On the other hand, a low correlation doesn’t rule out the possibility of a strong relation:
Corr = 0.0
•Correlations must support a theory •Calculate correlations both in levels and differences
•Always look at scatter plots
What variables should have been included?
New media
• Digital Marketing
–Display Marketing
– Search Engine Marketing (SEO & PPC)
–Affiliate Marketing
–Mobile Marketing
– Social Media
New media
• Data availability
– Impressions
– Clicks
– Post event activity
– Bespoke engagement metrics
• Example of a tracking centre:
– Double-click
Alternative methods
• Linear regression
• Logistic regression
• Discriminant analysis
• Factor analysis
• Cluster analysis
• Structural equations modelling
sa
Pooled regressions
California California USA California
Nevada USA Nevada Nevada
Oregon Oregon USA Oregon
+ ... + error
+ ... + error
+ ... + error
Sales Nat media Local Price Local media
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
O
C
O
C
O
C
O
C
Radio
Radio
TV
TV
cccc
cccc
Sales
Sales
24232221
14131211
Media effect is also tested across regions
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
The purchase funnel
• A basic process that leads to the purchase of a product consists in:
– Awareness – costumer is aware of the existence of a product
– Consideration – actively expressing an interest in the company
– Purchase
Awareness
Consideration
Purchase
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
Testing the purchase funnel
Awareness Consideration Purchase
Media
Advertising first exercise its influence on awareness. Via awareness there is an effect on consideration which drives the consumer to purchase
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
t
t
t
t
t
t
t
t
t
OOH
Radio
TV
Const
c
c
cccc
Purch
Cons
Awar
bb
ab
aa
3
2
1
31
21
14131211
32
31
21
000
000
1
1
1
a1,a2,a3 must be insignificant to confirm theory
Agenda
• Business application of Marketing Mix modelling
• A case study
• Strengths and weaknesses
• Brief introduction to more advanced approaches: pooled regressions and structural equations
References