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

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

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