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How Market Mix Modeling Can Impact Cross-Channel Budget and Business Planning

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How Market Mix Modeling Can Impact Cross-Channel Budget and Business Planning

  1. 1.
  2. 2. Cross Channel StrategyHow Market Mix Modeling Can Impact Cross-Channel Budget and Business Planning<br />Speakers:<br />Dhiraj Rajaram, Mu Sigma<br />Craig Kronzer, UnitedHealth Group<br />
  3. 3. Session Objectives<br />Learn approaches to Market Mix modeling – how it enables measurement of multi-channel activities<br />Discover the advanced framework to quantify ‘true’ cost of acquisition, netting out cross channel effects and cannibalization<br />Evaluate tools and platforms for budget scenario planning and optimize marketing budget allocation<br />
  4. 4. background<br />
  5. 5. Organization Overview<br />Insurance Solutions<br />Established in 1998 as a AARP/UHG relationship<br />Nation's largest supplemental insurance program focusing on people age 50 and over<br />Distribution: DTC, Employer, Agent, Web <br />Largest provider of pure-play decision sciences and analytics services <br />30 Fortune 500 Clients in 10 Industry Verticals<br />Headquartered in Chicago IL with presence all over the US<br />
  6. 6. Business Problem<br />Background<br />Business Hypotheses<br />Insurance Solutions uses multiple marketing channels to attract members<br />Operational constraints result in less than complete attribution of sales to marketing efforts<br />Several sales are not attributable to any of the marketing channels<br />The business wanted to test the hypothesis that unattributed sales are driven by marketing<br />In particular, there was a need to understand the impact of DRTV on sales<br />The solution framework required to measure cross-channel impacts<br />
  7. 7. The Challenge of Measurement<br />Attribution by Channel<br />A major portion of sales is unattributed to any advertising channel<br />Sales attributed to DRTV are low compared to proportion of investment<br />Business wants to measure the true effect of TV advertising by understanding the “halo effect” <br />
  8. 8. The Need for Measurement<br />Due to relatively low attribution of sales to DRTV, the apparent cost of acquisition for the channel is high<br />There is a need for improved measurement to calculate the ‘true’ cost of acquisition<br />Cost of acquisition is a key component in marketing planning<br />Cost of Acquisition<br />
  9. 9. Solution approach<br />
  10. 10. Problem Solving Framework<br />SCQFinal<br />SCQInitial<br />Factor<br />Network<br />Hypothesis<br />Matrix<br />SCQInitial<br />SCQFinal<br />The Mu Sigma Problem DNA ensures appropriate emphasis on design and hypothesis leading to right representation<br />
  11. 11. Solution Approach<br />Mapping the exhaustive set of factors enables testing of all relevant hypotheses<br />
  12. 12. The Market Mix Framework<br />The Market Mix Framework decomposes total sales into contributions by advertising vehicles and external factors<br />Contributions from different channels enable calculation of ROI<br />
  13. 13. MMX Modeling Approaches<br />Contribution<br /><ul><li>Percentage of enrollments due to each promotional program</li></ul>Total and Marginal ROI for each program<br /><ul><li>Cost per Sale
  14. 14. Lifetime Value</li></ul>Optimization<br /><ul><li>Promotional spend allocation at aggregate program level taking into account diminishing marginal
  15. 15. Portfolio level optimization for all products</li></ul>Direct Marketing<br />Direct Response TV<br />Marketing Mix Model<br />Sales = f(DM, DRTV, Print, Web, Events…)<br />Print<br />Promotional Activity<br />Web<br />Agent<br />Unattributed Sales<br />Additive<br />Multi Target<br />Multiplicative<br /><ul><li>Measurement of individual contributions
  16. 16. Measurement of cross channel effects
  17. 17. Measurement of diminishing returns</li></li></ul><li>Ad stock – Lagged effects<br />xx<br />Adstock transformation methodology<br />At= Tt + λ At-1<br />Where:<br /><ul><li>Tt is the value of the marketing variable at time t
  18. 18. λis the decay or lag weight parameter
  19. 19. At-1 is the carryover of Advertising at time t-1 </li></li></ul><li>“Halo” Effects and reattribution<br />
  20. 20. Multi-target Model<br />Each of the target sales modeled on all advertising inputs as well as external factor<br />
  21. 21. Reattributed Sales <br />Original Attribution<br />Post Modeling Reattribution<br />The Market Mix models are able to measure the contribution of advertising to previously unattributed sales<br />
  22. 22. Improved measurement<br />Reattributed CPS <br />Original CPS<br />Due to higher level of attribution in sales, the effective cost per sale reduces significantly<br />
  23. 23. Halo Effect<br />Self Contribution<br />The ‘halo’ effect of advertising channels enables quantification of cross-channel contribution<br />Halo Effect<br />
  24. 24. Impact of the initiative<br />Pre-MMX Modeling<br />Post MMX Modeling<br />Cost of sale calculated based on direct attribution used in budget planning<br />Member lifetime value calculations biased by high cost of acquisition in some channels<br />“Dark Test” conducted to verify impact of TV on unattributed sales<br />The optimization process for allocating budget across channels refined by using ‘true’ cost of acquisition<br />Budget allocation across marketing channels changed significantly<br />“Bright Test” conducted to test additional advertising opportunities <br />
  25. 25. SPEAKER BIOs<br />
  26. 26. Speaker Bios<br />Dhiraj Rajaram<br />Founder and CEO of Mu Sigma, an analytics services company that helps clients such as Microsoft and Dell institutionalize data-driven decision making.  Prior to founding Mu Sigma, he advised senior executives across a variety of verticals as a strategy and operations consultant at Booz Allen Hamilton and PricewaterhouseCoopers.<br />Craig Kronzer<br />Leads a Data Analytics team for UnitedHealthcare. Team is responsible for enterprise-wide analytics including building predictive models, designing and analyzing marketing tests, and claim data analytics. Previously, was with Carlson Marketing Group and Lands' End.  Craig holds an MS in Statistics from the University of Minnesota and BS in Computer Science from the University of Wisconsin.<br />

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