Investment marketing by Ebriks

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-OnInvestment 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:

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

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

Salest    1  var   2  var  ...   t 1 t

Sales

2 t

MMM project process Set out objectives

Data preparation

-Define scope -Discuss data availability -Design data-warehouse

•Collect data •Validate, harmonize and consolidate data •Present exploratory analysis to client

Presentation

Model development

•Interpretation of results •Learning and recommendations

•Estimation •Diagnostics •Calculate ROIs, Price elasticity and response curves

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

2st time on air

25% of target televisions are tuned in

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 ADStockt ( )  GRPt    ADStockt 1 0  1

• Alpha is estimated iteratively using least squares. The estimate is then validated by media planners

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

August seasonal dummy 5% Discountt

Peaks every year in August

Sale promotion

Exploratory analysis Scatter plot

Unit root test

Histogram and desc stats Series: SALES Sample 1 209 Observations 209

28 24 20 16 12 8 4 0 130000

140000

150000

160000

170000

180000

Mean Median Maximum Minimum Std. Dev. Skewness Kurtosis

154403.1 153960.2 183102.5 125997.0 9476.290 0.053546 3.456209

Jarque-Bera Probability

1.912312 0.384368

Correlation matrix

Model development

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

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 16

Series: RESID Sample 1 209 Observations 209

14 12 10 8 6 4 2 0 -10000

-5000

ˆ  y  yˆ

5000

Mean Median Maximum Minimum Std. Dev. Skewness Kurtosis

-2.31e-11 -66.11295 8049.987 -11378.69 3612.711 -0.158326 2.624286

Jarque-Bera Probability

2.102443 0.349511

Durbin Watson = 1.69 DW>2 positive autocorrelation DW<2 negative autocorrelation

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 TV Contributiont(000â&#x20AC;&#x2122; Gallons) = coefficient *Adstock(TV)t

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

Estimated factors contribution to sales Peacks every year in August

Peaks every year in August

Fitted Salest = 167,412 + 168* TVt + 161*Radiot + estimated Intercept = Seasonaility 167,412 166* OOHt Equity + 580*= Promotion t + 6507* t Can be interpreted as Brand Equity

Estimated factors contribution to sales

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

Negative price effect

Marketing mix (sample output)

Estimated factors contribution to sales

TotSalesContribution  coeff   Factori i 1

Estimated factors contribution to revenue

Tot Re venueContribution  coeff   Factori  Pr icei i 1

ROI

ROI 

TOT Re venueContribution TOTCost

Does it really make sense?

TheDiminishing more I invest in media, returns the more I sell

Response curves NegExp  a  (1  exp(b  GRPs )) S  a  (1/(1  exp(b  (GRPs  mean(GRPs ))))

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

Avg Pr ice Elasticity  * coeff  0.12 AvgSales

A 10% drop in price increases sales by 1.2%

Dynamic price elasticity Elasticity changes with price

200,000

Weekly Volume and $ Sales vis-Ă -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

Elastic (>1): Demand is sensitive to price changes. Inelastic (<1): Demand is not sensitive to price changes

Price (750 ml)

20.0

Volume

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

Clientâ&#x20AC;&#x2122;s questions What is the return on investments of our media activities?

For each dollar invested in TV you get 3.5 dollars back

Clientâ&#x20AC;&#x2122;s questions Whatâ&#x20AC;&#x2122;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 ? Maximum Marginal Return

Optimal GRPs Over Optimal GRPs

Point of Saturation

Sub â&#x20AC;&#x201C;Optimal GRPs

Maximum Average Return

Invest more in Radio and less in OOH

Marketing Mix â&#x20AC;&#x201C; Sample Output

Marketing mix (sample output) 45

Diminishing Returns

5000

4500

Promo TV Saturation

3500 3000 2500

Current

2000

Optimal

Weekly GRPs

4000

Weekly Sales

Carry Over Effect

1500

25 20 15 10

1000

500 0

0 0

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 sales.

Simultaneous Effect Volume

Carry Over Effect (Ad Stock) relates to the residual effect of an ad.

Base/Seasonal

TV/Radio/Print

Direct Marketing

Time

Rates/Promotions

When all the components are layered on Base sales, it is clear what drivers contribute to sales and when and their Simultaneous Effect.

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:

Sales

Media

Income

– Either media causes sales – or sales causes media – or a third variable causes both sales and TV 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

Pooled regressions Sales

Local media

Nat media

Local Price

California

USA

California

+ ... + error

Nevada

USA

Nevada

+ ... + error

Oregon

USA

Oregon

+ ... + error

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  Sales   c O   21 c22

c13 c23

Media effect is also tested across regions

 c14   TVO   C      c24   RadioC   O    Radio O 

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 â&#x20AC;˘ A sample of the target audience is interviewed about brand awareness, consideration and choice â&#x20AC;˘ Research agencies provide awareness, consideration and purchase time series in % terms â&#x20AC;&#x201C; i.e. A purchase of 10% means that 10 out of 100 interviewed people purchased the product

Testing the purchase funnel Awareness

Media

Consideration

Purchase

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 a1,a2,a3 must be insignificant to confirm theory

 1  b  1  b2

 a1 1  b3

 a2   Awart   c11 c12  a3    Const   c21 0 1   Purcht  c31 0

c13 0 0

Const  c14     1t   TVt      0    2 t   Radiot  0     3t   OOH t 

References