Innovations in Market Mix Modelling

© AbsolutData 2013 Proprietary and Confidential

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www.absolutdata.com

20 January 2014

Meet-up Data Science Oxford

Our exploration into innovations in Marketing Mix Modelling

© AbsolutData 2013 Proprietary and Confidential 2

Friends do not let friends to use Excel for statistical analysis

Agenda

NLMixed & MCMC to automate the fitting of s-curves and more

The statistical Challenges and the curse of the s-curve

What is Marketing Mix Modeling?

VARx Modelling

Discussion



Strategic vs. tactical decisions

Spend Brand Equity

Engagement

Web

Purchase

Profile/segment

Preferences

Propensity Models

Strategic

How to spread the budget?

What campaigns?

Product design

Pricing strategy

? ??

Tactical

Who?

What?

When?

? ??

Decision support modelling and

tools



Measurement of effectiveness & efficiency by marketing driver –Answering key business questions

Identify brand/s’ volume growth/decline drivers

Explain what is driving year over year change in sales

Determine relative importance & estimates elasticity of different drivers

Identify the competition and assesses impact

Determine the key competitive levers that impact/interact with own brand

Build a robust framework for investment decisions for short term -brands that should be supported

Determine an optimal realistic investment plan for the future based on simulated scenarios



Magazine

Online

Print

Radio

TV

We would like to measure the direct and indirect impact of marketing investment at a granularity relevant to planning

Overall Sales

Affiliate Clicks

Paid Search Clicks

Display Clicks

DecisionSupportAnalysis



Marketing mix modeling & optimization @ AbsolutDatafocus on shift towards true attribution & predictive spend mix

Data Collection

From Disparate

Data Sources

Data Review –Confirming directional

movements of prominent variables

Review past performance to

determine CONTRIBUTION of

each marketing mix element

Traditional Regression Based

Modeling Techniques such as

OLS

DIYSimulator

What-If Scenario Planning

Optimized Media

Calendar

Objective Function & Constraints

Attribution & ROI

Measurement – Multi channel

influence and interactions

Advanced Modeling

Techniquessuch as

Auto Regressive, VARX, SEM

Added model

granularity at

customer segment

and regional

level



The key challenge it to find a statistically sound solution that can help the business

In most cases the data is highly noisy

The exogenous (independent) time series are highly inter-correlated

The business requires an impact estimate for the actions they can take in order to evaluate strategies (what if analysis)

The business would prefer not to hear about dimension reduction

S-curves



Ad-stock

Saturation Effects

Ad stock captures exponential decay effect of GRPs - TV

This depends on the temporal effect of GRPs, can be done for Print , Radio as well

Research suggests that immediate response generated by advertising follows an S-curve

It introduces three additional parameters: saturation rate, point of inflexion and ‘half-life’ parameter (for carryover effect) as unknowns in the model

Together this captures the diminishing return of advertising

Ad-stock impact which depends on the temporal effect of GRPs

Carry-Over Effect

Samp

le O

utp

ut

Business understanding is introduces through transformation



Various research indicated that immediate response generated by advertising is followed exponential decay

Ad-stock carry over is estimated and reported in terms of half-life

Current Effect

Half-Life K= Carry Over

Carry-Over Effect



0

200

400

600

800

1000

1200

1400

1600

1800

0 50 100 150 200 250 300 350 400 450

Re

ven

ue

Imp

act

(Exc

lud

ing

Car

ry-o

ver

eff

ect

)

$ Spent per Week

Inflexion Point

Saturation Level

Adstock Equation:

Where, X is actual spending, K is decay constant and determined by expression exp(ln(0.5)/t1/2); V as saturation parameter and Xd as diminishing return point (point of inflexion)

S-Curve Impact Carry-over ImpactAd-stock Impact

At= 1/(1+exp(-V*(X-Xd ))) + K*At-1

S-Curves transformation reflect the belief that Marketing spend reduces in its effectiveness at a certain point and it impact decays over time

Half-Life

Research suggests that immediate response generated by advertising can be modeled using an S-curve followed byexponential decay of effect which helps capture the diminishing return of advertising



What is more appropriate from a businesswise perspective: Univariate or Multivariate?

Standardize the variables

Select a subsetApply

s-curves

Fit a regression model

explaining the Revenue

Evaluatequality of fit

Multivariate – optimize s-curves to work together in a particular setting

Manually optimize

Univariate –fit a s-curve for each variable on its own

Standardize the variables

De-trend and account for seasonality effect

Fit an s-curve for each variable optimising it for regression to the residuals to the Revenue trend

Automate



Instead of calculating the s-curve from t=0, I concentrate on the last n lags:

I explored two sas procedures for fitting the s-curves

Fit aggregation model where S is the dependent and the residual to the Revenue trend is the independent (no intercept for now – might need to reconsider that)

NLMIXED MCMC



My code – if you must

%Macro HazSThree(SVar);

S_Mue=1/(1+exp(-sV*(&SVar._lag&Nlags.-sXd))) ;

%do l=%sysevalf(&Nlags.-1) %to 0 %by -1;

S_Mue=1/(1+exp(-sV*(&SVar._lag&l.-sXd))) + sK*S_Mue;

%end;

%mend

proc nlmixed data=HAZ.Detrend MAXITER=4000 maxfunc=4000;

ods select ParameterEstimates;

parms b1=1 se=1 sV=1 sXd=0 SK=0.25;

bounds se>0;

bounds sV>0;

bounds 0<SK<1;

bounds b1>0;

%HazSThree(&HazVar.);

Mue= b1*S_Mue;

model Detrended ~ normal(mue, se);

proc mcmc data=HAZ.Detrend &MCMCOptions.;

ods select PostSummaries ;

parms b1 se

sV sXd SK

;

prior b1 ~ normal(mean = 1, var = 0.5);

prior se ~ igamma(shape = 3/10, scale = 10/3);

prior SK ~ uniform(0,1);

prior sXd ~ uniform(-2,2);

prior sV ~ uniform(0,10);

%HazSThree(&HazVar.);

Mue=b1*S_Mue;

model Detrended ~ n(mue, sd = se);

For illustrative purposes the code shown here fits only one s-cure. The solution actually fits a baseline and all thecurves



Vector Auto Regression (VAR)

time

Steady stateprogression

Target

Sales/Signups

time

Co dependencyassociationIndigenous

Web search

time

Marketing impact & decay

Exogenous

campaigns



Phase I: Top down marketing mix modeling

Phase III: Reconcile MMM & Cookie Attribution

Phase IV: Reporting, Simulation and Optimization

Phase I: Marketing Mix Modeling

Phase II: Cookie-Based Attribution Algorithm

Search Clicks

AffiliatesDisplay

Impressions

TV Impacts

AffiliatesSecondary Relationships

Search Signups

Email Signups

Print Signups

Signups from Other

Factors

Previous Day’s

Baseline Signups

+TV GI Signups

Display Signups+ + + + +

Daily Signups=



Secondary attribution provides A refined view of the system

PaidSearch Clicks

Nonpaid search

CableTotal Impact

11.4%

9.0%

2.5%

3.8%

-1.0%2.2%

-0.1%

2.6%-3.8%

-2.2%

Actual TV Attribution taking into account indirect contribution of Search

Final Attribution 7.5% 5.7% 11.1%

Samp

le O

utp

ut

-0.1%



Applying VARx to a BIG

number of SKUs

How to choose priors

We have encountered interesting challenges

Challenges

Modelling short term and long

term effect

Cookie data allows to not only do attribution but

identify key sequences



Incomplete sales (Target) data

We do not know what is sold when by the distributers We sell to their stock


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AbsolutData provide analytics based solutions to address business critical issues

40% increase in profits through Conjoint based Pricing Optimization – A top SaaS company

$50MM increase in revenue by Market Mix Modeling across 4 geographies– A leading CPG Company

15% revenue growth through Multi Channel Attribution– A large ecommerce company

$23MM increase in Customer Loyalty and CRM marketing

revenue– A major Hotel chain

$9MM incremental revenue as a result of focused

promotional campaigns created

– A major Online Retail Discounter

Contribution of $78MM over the last few years to their margins

– A major Retailer

We are decision scientists who help decision makers take better and informed decisions

Thank You

Eli Y. KlingDirector - Analytics

Phone: +44 (0)7940094976Email: [email protected]: Uk.linkedin.com/in/elikling

mailto:[email protected]

Innovations in Market Mix Modelling

Marketing

Transcript of Innovations in Market Mix Modelling