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Disney Marketing ROI Case Study                DMA Conference           Presented by Disney & SAS                        O...
Defining A Marketing ROI Solution                             Reach the right audience                             Through...
Presentation Agenda • Introduction   – Disney Management Science & Integration   – SAS   – The Science Behind Marketing RO...
Disney Management Science and Integration    4 employees - 2008               30 employees - 2012 • Consulting support for...
SAS® Company Overview                                       SAS® is the largest                                  independe...
Science Behind Marketing ROI – Modeling                   Marketing Effort                    Response   Measurement      ...
Science Behind Marketing ROI – The “Right” Model   Selecting the right modeling approach is critical for success!  Regress...
Science Behind Marketing ROI – Measurement          Analysts pay careful attention to data considerations and          cho...
Science Behind Marketing ROI – Optimization    Planners leverage model output and their insights to adjust      and optimi...
Case Study Overview      A television network is seeking decision science      support to improve return on investment for...
Case Study Challenges     Previous attempts to answer these questions have yielded     valuable insights, but have not cre...
Disney and SAS® Partnership Project Management    15%    15% Data Management       30%    15% Science Integration   30%   ...
Project Timeline  Established a separate timeline for each work stream, inclusive         of milestone and reports out to ...
Data Collection Overview     Data collection ultimately took four times longer than   originally planned, due in large par...
Data Collection Challenges Model database changed 17 times during a 1-year span, most often due to missing data or data co...
Data Visualization  Showing clients the relationship between impressions and costs helped   to identify likely errors in t...
Data Visualization (cont.)  Exploring flights enabled us to recognize the need to model          certain media types diffe...
Data Transformation     Often necessary to transform the data for measurement      variables in our models to avoid creati...
Data Handoff to Science    Key milestone was the go/no-go decision on beginning         the development of the measurement...
Data Handoff to Science (cont.)   Future iterations of the model will incorporate new data that is either     unavailable ...
Science Integration                  Integration between the team managing                  data collection and model deve...
Overview of Planning & Optimization Tool The tool is designed to become self-sustaining to support updates  to the measure...
Optimization Goals   Objective is to maximize total ratings for the premiere episodes         of all shows within a market...
Evaluating Media Plans     Ability to compare different plans by measuring the number of     new households generated for ...
Key Lessons Learned                         Designing a      Creating Clear                        Structured QA      Requ...
Questions and Answers
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Disney: The Magic of Marketing Mix Analytics & Optimization

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Disney: The Magic of Marketing Mix Analytics & Optimization

  1. 1. Disney Marketing ROI Case Study DMA Conference Presented by Disney & SAS October 2012
  2. 2. Defining A Marketing ROI Solution Reach the right audience Through the right channel Maximize Return on investment for At the right time marketing spend With the right frequency At the right price Stand-alone studies often fail to achieve long-term success—trying to implement a project instead of a process!
  3. 3. Presentation Agenda • Introduction – Disney Management Science & Integration – SAS – The Science Behind Marketing ROI • Case Study Overview – Project Goals & Organization – Data Management – Science Integration – Tool Development • Lessons Learned • Questions & Answers
  4. 4. Disney Management Science and Integration 4 employees - 2008 30 employees - 2012 • Consulting support for analytics, data and reporting needs • Technology integration for reporting and data tools • Development and management of decision science tools
  5. 5. SAS® Company Overview SAS® is the largest independent software vendor in the world SAS Annual Revenue 1976-2011• 2011 & 2010 Fortune Magazine: #1 Place to Work• 2011 Revenue: $2.73 billion• SAS® reinvests ~25% of annual revenue into R&D• 90 of top 100 companies on FORTUNE Global 500® use SAS®
  6. 6. Science Behind Marketing ROI – Modeling Marketing Effort Response Measurement Model For Each Channel vs. Variable (spend, impressions, etc.) (sales, leads, etc.) More TV effective Sales Less Radio effective Marketing Spend
  7. 7. Science Behind Marketing ROI – The “Right” Model Selecting the right modeling approach is critical for success! Regression / Time Series Model Econometric / Panel Model R2 = 97% R2 = 67% Sales (t) = … Sales (t) = … +0.7 * Sales (t-1) +0.2 * Sales (t-1) -0.2 * Price -1.0 * Price +0.06 * TV +0.1 * TV -0.005 * Online +0.02 * Online +… +… Heavy weight on lagged sales; sales not Less weight on lagged sales; price & responsive to price & media changes media elasticities more reasonable Better for FORECASTING Better for MEASUREMENT
  8. 8. Science Behind Marketing ROI – Measurement Analysts pay careful attention to data considerations and choice of models to robustly fit the data for measurement Impressions by Saturation Media Type Curves Goodwill Cable Impressions Print Ratings Ratings Model Radio Time Spend Time Model Input Model Output
  9. 9. Science Behind Marketing ROI – Optimization Planners leverage model output and their insights to adjust and optimize marketing plans per business constraints Impressions by Saturation Media Type Curves Goodwill Cable Impressions Print Ratings Ratings Model OptimalRadio Optimal Media Mix Flighting Radio Cable Time Spend Time Impressions Print Ratings Spend Radio Spend Time
  10. 10. Case Study Overview A television network is seeking decision science support to improve return on investment for the marketing of primetime television shows • How effective is our current marketing spend? • Which shows should get more marketing dollars? • Which channels are the most effective? Most efficient? • Based on current practices, where are we over-saturated?
  11. 11. Case Study Challenges Previous attempts to answer these questions have yielded valuable insights, but have not created sustained changes• Avoid the temptation to answer all questions with a single model• Ensure inputs into the solution are readily available and cost effective• Avoid bundling decisions that are controlled by separate teams Limited data availability prevents the network from getting accurate measures of performance for marketing efforts• Data is warehoused in multiple systems, with few connection points• Impression-level data is extremely difficult to capture, with actualized data existing in combinations of spreadsheets, e-mails, and faxes• Given the state of the data, common reports can take days to generate
  12. 12. Disney and SAS® Partnership Project Management 15% 15% Data Management 30% 15% Science Integration 30% 30% Tool Development 25% 40%
  13. 13. Project Timeline Established a separate timeline for each work stream, inclusive of milestone and reports out to key stakeholders
  14. 14. Data Collection Overview Data collection ultimately took four times longer than originally planned, due in large part to data quality issues• Identified over 30 potential data sources and almost 250 variables• Data sources ranged from databases, spreadsheets, e-mails, and faxes• Established weekly meetings with key stakeholders and implemented dashboards to review data collection progress• Placed an analyst in the media agency office for four weeks to speed data collection and improve understanding of the data Data collection is never really over—continue to find errors or missed opportunities even months later!
  15. 15. Data Collection Challenges Model database changed 17 times during a 1-year span, most often due to missing data or data collection errors Bad circulation Magazine Cume based estimate for on all publications Entertainment instead of purchased Weekly Nielsen P3 vs. C3 Duplication from Misclassified OOH SQL Errors support as Events “Week 53” Issue
  16. 16. Data Visualization Showing clients the relationship between impressions and costs helped to identify likely errors in the data (e.g., misclassification of spending)
  17. 17. Data Visualization (cont.) Exploring flights enabled us to recognize the need to model certain media types differently than others 15% 70% 15%
  18. 18. Data Transformation Often necessary to transform the data for measurement variables in our models to avoid creating misleading insights or recommendations Episode Promos in Promos in Air Date Calendar Week Past 7 Days Sunday S M T W R F S S Monday S M T W R F S S M Tuesday S M T W R F S S M T Wednesday S M T W R F S S M T W Thursday S M T W R F S S M T W R Friday S M T W R F S S M T W R F Saturday S M T W R F S S M T W R F S Transform to a full week
  19. 19. Data Handoff to Science Key milestone was the go/no-go decision on beginning the development of the measurement model NIELSEN PROMOS & MARKETING AWARENESS Program Name On-Air Promos Survey Respondents TRPs, Seconds, # of Spots Air Date Digital Aware Respondents Impressions & Clicks Start Time Cinema % Aware Impressions, Seconds Per Spot Unaided & Aided Duration National Cable Intent to View TRPs Top Box, Top 2 Box, Non Committed, Bottom Box Program Type Newspaper Impressions & Circulation Program Rating Magazine Total & Weekly Impressions Lead-in Rating Spot Cable TRPs & Impressions Competition Spot Radio TRPs OOH Impressions
  20. 20. Data Handoff to Science (cont.) Future iterations of the model will incorporate new data that is either unavailable right now or represents a higher level of complexity MISSING DATA MISSING COMPLEXITY DATA RECONCILIATION Network Radio On-Air Promos On-Air Promos Day-of-Week, Promo Length Synergy Cable Nielsen Digital Impressions Reach, Share, HUT, PUT Synergy Online Print Size, Placement, Inserts Emails & Newsletters National Cable Channel, # of Spots, Promo Length MODEL EXPANSION Public Relations Spot Cable & Radio # of Spots, Seconds of Promo Geo-Panel Data Local Market Ratings and Marketing Affiliate Promotions OOH # of Units, Size, Media Form On-Air Promo Precision Minute-by-Minute Ratings Digital Size, Placement, Pillar Efficiency Costs for Marketing & Promotions Social Media Facebook, Twitter, Blog Mentions
  21. 21. Science Integration Integration between the team managing data collection and model development is critical to the success of the projectScience Data When it doesn’t work well—each revision of the data model would delay the science timeline by 3 weeks! Critical to integrate science team with tool developers to ensure alignment with the expected input and outputs of the modelsScience Tool
  22. 22. Overview of Planning & Optimization Tool The tool is designed to become self-sustaining to support updates to the measurement model and to allow media plan comparisonsHistorical Data Measurement Data Model Model Model (one time) Adjustments Actualized Optimization Goals & Media Plans Model Constraints Agency Approved Recommended Media Media Plans Media Plans Plans
  23. 23. Optimization Goals Objective is to maximize total ratings for the premiere episodes of all shows within a marketing campaign portfolio• Provide recommended spending by channel for each show/week combination• Allow users to input constraints on total spending by show/channel/week• Define spend thresholds that reflect minimum purchase amounts for each channel• Compare optimal recommendations against manually created plans Critical to understand relationship between spend and impressions; some channels have a significant delay between purchase and delivery!
  24. 24. Evaluating Media Plans Ability to compare different plans by measuring the number of new households generated for each incremental unit of spendRecommended Plan: Week Cable Radio Print Outdoor Cinema(balanced by optimization) t = -5 20 N/A 20 20 20 t = -4 20 N/A 20 20 20 t = -3 20 N/A 20 20 20 t = -2 20 20 20 20 20 t = -1 20 20 20 20 20 t= 0 20 20 20 20 20Media Agency Plan: Week Cable Radio Print Outdoor Cinema(incremental opportunities) t = -5 70 N/A 80 110 10 t = -4 105 N/A 5 170 4 15 t = -3 160 N/A 5 3 5 30 t = -2 240 5 5 10 80 t = -1 355 1 75 2 25 30 125 t= 0 25 10 50 50 150 5
  25. 25. Key Lessons Learned Designing a Creating Clear Structured QA Requirements Process & Team Having a Test “Shadow” Environment Implementation
  26. 26. Questions and Answers

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