investment marketing

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

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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

Seasonality

Sale promotion 5% Discountt August seasonal dummy

Peaks every year in August

Exploratory analysis

0

4

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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

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-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


Fitted Salest = 167,412 + 168* TVt + 161*Radiot + 166* OOHt + 580* Promotiont

TV Contributiont(000’ Gallons) = coefficient *Adstock(TV)t


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


Negative price effect

Fitted Salest = 167,412 + 168* TVt + 161*Radiot + 166* OOHt + 580* Promotiont + 6507* Seasonailityt - 12631* Pricet

Marketing mix (sample output)


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


What is the return on investments of our media activities?

For each dollar invested in TV you get 3.5 dollars back


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

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500

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1500

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4000

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5000

0 20 40 60 80 100 120 140 160 180

Vo

lum

e

Time

0

5

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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

investment marketing

Documents