Innovations in Market Mix Modelling
 

Innovations in Market Mix Modelling

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Innovations in Market Mix Modelling Innovations in Market Mix Modelling Presentation Transcript

  • © Absolutdata 2014 Proprietary and Confidential Chicago New York London Dubai New Delhi Bangalore SingaporeSan Francisco www.absolutdata.com April 28, 2014 Our exploration into innovations in Marketing Mix Modelling Meet-up Data Science Oxford
  • © Absolutdata 2014 Proprietary and Confidential 2 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   
  • © Absolutdata 2014 Proprietary and Confidential 3 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
  • © Absolutdata 2014 Proprietary and Confidential 4 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
  • © Absolutdata 2014 Proprietary and Confidential 5 Magazine Online Print Radio TV Overall Sales Affiliate Clicks Paid Search Clicks Display Clicks Decision Support Analysis We would like to measure the direct and indirect impact of marketing investment at a granularity relevant to planning
  • © Absolutdata 2014 Proprietary and Confidential 6 Marketing mix modeling & optimization @ Absolutdata focus 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 DIY Simulator What-If Scenario Planning Optimized Media Calendar Objective Function & Constraints Attribution & ROI Measurement – Multi channel influence and interactions Advanced Modeling Techniques such as Auto Regressive, VARX, SEM Added model granularity at customer segment and regional level
  • © Absolutdata 2014 Proprietary and Confidential 7 In most cases the data is highly noisy The key challenge it to find a statistically sound solution that can help the business 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
  • © Absolutdata 2014 Proprietary and Confidential 8 Business understanding is introduces through transformation 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 SampleOutput
  • © Absolutdata 2014 Proprietary and Confidential 9 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
  • © Absolutdata 2014 Proprietary and Confidential 10 0 200 400 600 800 1000 1200 1400 1600 1800 0 50 100 150 200 250 300 350 400 450 RevenueImpact (ExcludingCarry-overeffect) $ 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 Half-Life Research suggests that immediate response generated by advertising can be modeled using an S-curve followed by exponential decay of effect which helps capture the diminishing return of advertising S-Curves transformation reflect the belief that Marketing spend reduces in its effectiveness at a certain point and it impact decays over time
  • © Absolutdata 2014 Proprietary and Confidential 11 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 What is more appropriate from a businesswise perspective: Univariate or Multivariate? Standardize the variables Select a subset Apply s-curves Fit a regression model explaining the Revenue Evaluate quality 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 Automate
  • © Absolutdata 2014 Proprietary and Confidential 12 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
  • © Absolutdata 2014 Proprietary and Confidential 13 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 the curves
  • © Absolutdata 2014 Proprietary and Confidential 14 Vector Auto Regression (VAR) time Steady state progression Target Sales/Signups time Co dependency associationIndigenous Web search time Marketing impact & decay Exogenous campaigns
  • © Absolutdata 2014 Proprietary and Confidential 15 Phase I: Top down marketing mix modeling Phase III: Reconcile MMM & Cookie Attribution Phase IV: Reporting, Simulatio n and Optimization Phase I: Marketing Mix Modeling Phase II: Cookie- Based Attribution Algorithm Search Clicks Affiliates Display 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=
  • © Absolutdata 2014 Proprietary and Confidential 16 Secondary attribution provides A refined view of the system Paid Search Clicks Non paid search Cable Total 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% SampleOutput -0.1%
  • © Absolutdata 2014 Proprietary and Confidential 17 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
  • © Absolutdata 2014 Proprietary and Confidential 18 Incomplete sales (Target) data We do not know what is sold when by the distributers We sell to their stock
  • © Absolutdata 2014 Proprietary and Confidential 19 http://www.linkedin.com/groupItem?view=&gid=130238&item=233249172&type=member&commentID=5810144471664320512&trk=hb_ntf_COMMENTED_O N_GROUP_DISCUSSION_YOU_FOLLOWED#commentID_5810144471664320512 Friends do not let friends to use Excel for statistical analysis
  • © Absolutdata 2014 Proprietary and Confidential 20 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
  • Eli Y. Kling Director - Analytics Phone: +44 (0)7940094976 Email: Eli.Kling@absolutdata.com LinkedIn: Uk.linkedin.com/in/elikling Follow us on: