Successfully reported this slideshow.
We use your LinkedIn profile and activity data to personalize ads and to show you more relevant ads. You can change your ad preferences anytime.

Market mix modelling

575 views

Published on

A case study

Published in: Data & Analytics
  • Be the first to comment

Market mix modelling

  1. 1. MARKET MIX MODELLING A CASE STUDY
  2. 2. INTRODUCTION  WHAT IS IT?  Market Mix Modelling is used to estimate the effectiveness of Investment in Media.  Statistical methods are applied to measure the impact of media investments, promotional activities and price tactics on sales  HOW IT WORKS?  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 .
  3. 3. STEP BY STEP APPROACH  Data understanding reveals that the media variables need some transformations using different Adstock , Power and Lag combinations.  This works on the concept that advertising has an effect extending several periods after the original exposure, which is generally referred to as advertising carry-over or ‘Adstock’.  Media effect is broken into two components: Current effect - change in sales caused by an advertising exposure occurring at the same time period as the exposure and Carry-over effect – change in sales that occurs in time periods following the pulse of advertising. The following APLs are tried: ADSTOCKS: .1,.3,.5,.7,.9 POWER: .1,.3,.5,.7,.9 LAG: 0,1,2 The sample data provided has 2 years of Monthly Sales data for 2 different regions. It also has the TV, RADIO, ONLINE, PRINT and OUTDOOR monthly spend for 2 years. The level of the data is : Region x Month DATA UNDERSTANDING DATA PREPARATION AND STRUCTURING EXPLORATORY DATA ANALYSIS MODEL FITTING USING SAS FINAL MARKETING MIX  After the structuring of the Data , the basic exploratory analysis was done on the data to check trends and observe relationships.  The "data exploration" tab provides a snippet of the data exploration that was conducted  Using Proc Corr bivariate relationship and correlations between sales_volume and different media activities viz TV, RADIO, ONLINE etc was conducted.  The test for autocorrelation was conduted using Proc Autoreg  In our data, the first-order Durbin-Watson test is insignificant, with p > .0001 for the hypothesis of no first-order autocorrelation.Thus, no autocorrelation correction is needed.  A 2 step modelling tecchnique was followed:  STAGE1: The Base /Seasonality was estimated using monthly dummies and SSN_BASE variable was created . This was incorporated in the stage 2 model as an independent variable along with other marketing/transformed marketing variables.  STAGE 2: Since here we have regional data of marketing activity as well as their corresponding sales at the regional level, we fit a linear mixed model with a random class structure as the region. The RANDOM option specifies the variables whose effect on sales is assumed to be random among the different regions.Proc Mixed was used for this.  It was observed that the contribution of SSN_BASE or the SEASONALITY is highest followed by Online and Radio contributions.  The Rsquare for the model was coming to be around 63%
  4. 4. DATA EXPLORATION 0 200 400 600 800 1000 1200 0 50 100 150 200 250 300 350 400 450 01Nov2012 01Jan2013 01Mar2013 01May2013 01Jul2013 01Sep2013 01Nov2013 01Jan2014 01Mar2014 01May2014 01Jul2014 01Sep2014 Sum of TV_000100 Sum of TV_000300 Sum of TV_000500 Sum of TV_000700 Sum of TV_000900 Sum of TV_100100 Sum of TV_100300 Sum of TV_100500 Sum of TV_100700 Sum of TV_100900 0 20 40 60 80 100 120 0 10 20 30 40 50 60 70 80 90 100 01Nov2012 01Jan2013 01Mar2013 01May2013 01Jul2013 01Sep2013 01Nov2013 01Jan2014 01Mar2014 01May2014 01Jul2014 01Sep2014 Sum of Radio_000100 Sum of Radio_000300 Sum of Radio_000500 Sum of Radio_000700 Sum of Radio_000900 Sum of Radio_100100 Sum of Radio_100300 Sum of Radio_100500 Sum of Radio_100700 Sum of Radio_100900 Sum of Radio_300100 Sum of Radio_300300 0 50 100 150 200 250 300 350 400 450 0 50000 100000 150000 200000 250000 300000 350000 400000 450000 Sum of TV Sum of Sales_Volume 0 10 20 30 40 50 60 70 80 90 100 0 50000 100000 150000 200000 250000 300000 350000 400000 450000 01Nov2012 01Dec2012 01Jan2013 01Feb2013 01Mar2013 01Apr2013 01May2013 01Jun2013 01Jul2013 01Aug2013 01Sep2013 01Oct2013 01Nov2013 01Dec2013 01Jan2014 01Feb2014 01Mar2014 01Apr2014 01May2014 01Jun2014 01Jul2014 01Aug2014 01Sep2014 01Oct2014 Sum of Radio Sum of Sales_Volume For Region R001 the original TV and Radio variables are plotted against the transformed TV and Radio variable For Region R001 the Sales Volume is plotted against original TV and Radio variables 0 200 400 600 800 1000 1200 0 50 100 150 200 250 300 350 400 450 01Nov2012 01Jan2013 01Mar2013 01May2013 01Jul2013 01Sep2013 01Nov2013 01Jan2014 01Mar2014 01May2014 01Jul2014 01Sep2014 Sum of TV_000100 Sum of TV_000300 Sum of TV_000500 Sum of TV_000700 Sum of TV_000900 Sum of TV_100100 Sum of TV_100300 Sum of TV_100500 Sum of TV_100700 Sum of TV_100900 0 20 40 60 80 100 120 0 20 40 60 80 100 120 01Nov2012 01Jan2013 01Mar2013 01May2013 01Jul2013 01Sep2013 01Nov2013 01Jan2014 01Mar2014 01May2014 01Jul2014 01Sep2014 Sum of Radio_00010 0 Sum of Radio_00030 0 Sum of Radio_00050 0 Sum of Radio_00070 0 Sum of Radio_00090 0 Sum of Radio_10010 0 Sum of Radio_10030 0 For Region R220 the original TV and Radio variables are plotted against the transformed TV and Radio vars 0 50 100 150 200 250 300 350 400 450 0 50000 100000 150000 200000 250000 300000 350000 400000 450000 01Nov2012 01Dec2012 01Jan2013 01Feb2013 01Mar2013 01Apr2013 01May2013 01Jun2013 01Jul2013 01Aug2013 01Sep2013 01Oct2013 01Nov2013 01Dec2013 01Jan2014 01Feb2014 01Mar2014 01Apr2014 01May2014 01Jun2014 01Jul2014 01Aug2014 01Sep2014 01Oct2014 Sum of TV Sum of Sales_Volume For Region R220 the Sales Volume is plotted against original TV and Radio variables 0 20 40 60 80 100 120 0 50000 100000 150000 200000 250000 300000 350000 400000 450000 Sum of Radio Sum of Sales_Volume
  5. 5. Modelling Process Autocorrelation test The Durbin-Watson test is a widely used method of testing for autocorrelation. The following statements perform the Durbin-Watson test for autocorrelation in the OLS residuals for orders 1 through 4. /*-- Durbin-Watson test for autocorrelation --*/ proc autoreg data=ads_regmon_wid_ssn outest=testdw; by region_code; model sales_volume= time / dw=4 dwprob; run; In our case, the first-order Durbin-Watson test is insignificant, with p > .0001 for the hypothesis of no first- order autocorrelation. Thus, no autocorrelation correction is needed. Proc Mixed PROC MIXED DATA=ads_regmon_wid_ssn METHOD=ML; CLASS region_code; MODEL SALES_VOLUME = SSN_BASE Print TV_100700 Radio_100701 Online_500301 Outdoor_100701 /NOINT SOLUTION; random Print TV_100700 Radio_100701 Outdoor_100701 /solution subject=region_code; ods output solutionf=est_fixed; RUN; ESTIMATES Effect Estimate StdErr DF tValue Probt ssn_base 0.9 0.2 36.0 5.4 0.0 Print 1560.2 1704.7 1.0 0.9 0.5 TV_100700 733.6 1105.5 1.0 0.7 0.6 Radio_100701 4332.1 3855.2 1.0 1.1 0.5 Online_500301 88466.6 64882.9 36.0 1.4 0.2 Outdoor_100701 -30867.5 12967.1 1.0 -2.4 0.3 STAGE1: The Base /Seasonality was estimated using monthly dummies and SSN_BASE variable was created . This was incorporated in the stage 2 model as an independent variable along with other marketing/transformed marketing variables. STAGE 2: Since here we have regional data of marketing activity as well as their corresponding sales at the regional level, we fit a linear mixed model with a random class structure as the region. The RANDOM option specifies the variables whose effect on sales is assumed to be random among the different regions.Proc Mixed was used for this. A noint model was chosen as SSN_BASE (seasonality ) is getting incorporated as an independent variable to account for the base. *****Please note that Since this is a dummy data and the modelling is developed just for illustration sake and to judge the approach I have not taken a lot of effort to improve the significance of parameter estimates
  6. 6. FINAL MIX For Region R001: ACTUAL VS PREDICTED At each month the stacked bars show the predicted contribution by each Marketing Driver for Region R001. Contribution of Seasonality is highest followed by Online and Radio At each month the stacked bars show the predicted contribution by each Marketing Driver for Region R220 Contribution of Seasonality is highest followed by Online and Radio For Region R220: ACTUAL VS PREDICTED
  7. 7. THANK YOU

×