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Marketing mix or attribution journey towards unified marketing analytics

Next-generation MMM is an integrated process that
leverages its understanding of overall demand drivers
(marketing and non-marketing) along with other research
external to MMM measurement to present a holistic
marketing strategy to target growth opportunities. Attribution modeling focuses on understanding the
impact of marketing or, more specifically, media
campaigns on each individual consumer decision or
“conversion.” It is a “bottom-up” approach, measuring
total impact by aggregating up from transaction-specific
impacts. Both MMM and attribution modeling are great for the
purpose they are intended for, with MMM guiding
overall marketing strategy and attribution modeling
guiding tactical campaign performance.

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Marketing mix or attribution journey towards unified marketing analytics

  1. 1. MARKETING PERFORMANCE MANAGEMENT: ATTRIBUTION OR MARKETING MIX? Thought Leadership by 2
  2. 2. Srishti Gupta President IRI Media Center of Excellence Joy Joseph Practice Leader, Marketing Productivity IRI Strategic Analytics TODAY’S PRESENTERS 3
  3. 3. TODAY’S AGENDA Why is this debate gathering momentum? The case for and against Attribution Pros and Cons with MMM [MMM + Attribution]= Marketing Productivity Excellence Vision for the future 4
  4. 4. TODAY’S AGENDA Why is this debate gathering momentum? The case for and against Attribution Pros and Cons with MMM [MMM + Attribution]= Marketing Productivity Excellence Vision for the future 5
  5. 5. INFORMATION EXPLOSION HAS CREATED A DIVERSE AND WELL INFORMED CONSUMER Health Benefits Occasions Lifestyle Democracy of information access has led to a highly evolved and diversified consumer landscape - targeted marketing has become indispensable for consumer relevancy Digital revolution has also created complex shopper journeys - imperative to integrate online, offline and touch points to engage across various stages of the Shopper journey 6
  6. 6. PROLIFERATION OF MEDIA TOUCH POINTS HAS LED TO A COMPLEX CONSUMER JOURNEY Digital ad optimization supported a far more complex engagement model, individual level sales conversion rather than aggregate sales contributions were required - enter Attribution Modeling A parallel measurement environment was initially OK because Digital stakeholders initially operated in a separate environment from traditional media, but not for long… Speed Granularity 7
  7. 7. THESE SEPARATE WORLDS COLLIDED AS ADVERTISERS RAMPED UP ON DIGITAL INVESTMENTS, FREQUENTLY AT THE COST OF TRADITIONAL MEDIA Digital and traditional media stakeholders began finding themselves in the same conference room with increasing frequency - a comparison and even bake-off between the two approaches was inevitable. DIGITAL AGENCIES PUBLISHERS/ AD NETWORKS/ EXCHANGES CRM/ DIGITAL MEDIA LEAD INSIGHTS BRAND MANAGEMENT TRADITIONAL AGENCIES MMM Sandbox Attribution Sandbox Buy-side Sell-side 8
  8. 8. TODAY’S AGENDA Why is this debate gathering momentum? The case for and against Attribution Pros and Cons with MMM [MMM + Attribution]= Marketing Productivity Excellence Vision for the future 9
  9. 9. IN AN INCREASINGLY ADDRESSABLE WORLD, ATTRIBUTION EPITOMIZES MICRO-MARKETING Un-Exposed EXPOSED Conversions (“Lift”) Display + Search Social + Search Social + Search Cross-PlatformSynergies Lifestyles Purchase BehaviorDemographics Post-campaign Diagnostics 10
  10. 10. HYPER-GRANULARITY UNCOVERS ARBITRAGE OPPORTUNITIES WITHIN THE LONG-TAIL OF DIGITAL OPTIONS Leaderboard Rectangle Skyscraper Internalize pay-for-performance: Align growth opportunities with the optimal execution mix 11
  11. 11. THIRD-PARTY MATCHING ALLOWS ATTRIBUTION PLATFORMS TO INTEGRATE ACROSS A WIDE RANGE OF DATA SOURCES HH 12
  12. 12. NEAR-REAL-TIME MEASUREMENT TO SUPPORT PROGRAMMATIC INITIATIVES Mid-campaign Attribution Read Optimize/Course- correct Post-campaign Diagnostics/ Lessons Learned/ Set-up for next campaign 4-8 Week Cycle-time DMP/ Conversion Metrics Programmatic/ DSP/Ad Exchanges DMP/ Conversion Metrics 13
  13. 13. ATTRIBUTION COMES WITH ITS OWN ISSUES THOUGH… • Attribution largely ignores offline touch points, resulting in potentially inflated ROAS estimates • Big data doesn’t mean “all data”- Integrating micro-data across multiple sources with varying quality and inconsistent matching across multiple third party sources potentially drive biased campaign lift estimates • Attribution can be too short-term focused, optimizing over weeks rather than quarters, so cannot address longer-term marketing planning decisions 14
  14. 14. TODAY’S AGENDA Why is this debate gathering momentum? The case for and against Attribution Pros and Cons with MMM [MMM + Attribution]= Marketing Productivity Excellence Vision for the future 15
  15. 15. MMM ENABLES PORTFOLIO STRATEGY BY OPTIMIZING ALLOCATION BY BRAND AS WELL AS BY PLATFORM 15 20 30 35 18 36 18 28 Brand A Brand B Brand C Brand D Current Optimal SpendAllocation Cross-PlatformAllocation16
  16. 16. BRAND CROSS-EFFECTS & HALO CAN BE LEVERAGED FOR EFFICIENT CROSS-BRAND CAMPAIGNS A B C D A B C D Halo Impact AdvertisedBrand Cross-BrandHalo SourcesofMktg.Sales 17
  17. 17. MMM ALSO SUPPORTS STRATEGIC PLANNING PROCESSES… Long-termBrandEquity DemandPlanning 18
  18. 18. DETERMINE OPTIMAL SPEND LEVELS ACCOUNTING FOR SATURATION AND SIMULATE MULTIPLE STRATEGIES FOR SUPPORTING INNOVATION… NewProductMarketing RightSpend 19
  19. 19. MOST IMPORTANTLY, MMM ALSO SUPPORTS OFFLINE MEDIA PLANNING… 20
  20. 20. MMM DISADVANTAGES • Digital can present challenges for MMM, especially where granular data (down to markets and trade areas) is infeasible • Hyper-targeted and addressable campaigns do not have the scale to register accurately in MMM • Pure MMM takes a one size fits all approach, but marketing responsiveness varies across consumer groups • Synergies, especially across digital campaigns are difficult to read without single source data (down to individual audience level), resulting in underestimation of cross-platform campaigns in MMM • Finally, the broad and encompassing demand measurement MMM undertakes comes at a cost; long cycle times - model updates can often take 8 or more weeks from data gathering to insights/execution 21
  21. 21. LACK OF INTEGRATION CAN IMPACT INSIGHTS AND STRATEGY IN MULTIPLE WAYS... Case Study: 12-week Cross-Media Campaign for a OTC Health Brand - MMM indicated the media portion was around 70 percent of the lift suggested by the attribution model (11.6 percent vs. 16.5 percent). A major in-store shopper initiative at a large regional retail chain was skewing Online Video up in the attribution study, while MMM was underestimating the highly targeted Display campaign. MTA Online Video Search TV Sales Lift 5.7% 3.6% 7.2% MMM Online Video Search TV Promo Sales Lift 2.1% 2.9% 6.6% 4.1% 22
  22. 22. WHAT DO YOU DO WHEN FACED WITH APPLES AND ORANGES? 23
  23. 23. TODAY’S AGENDA Why is this debate gathering momentum? The case for and against Attribution Pros and Cons with MMM [MMM + Attribution]= Marketing Productivity Excellence Vision for the future 24
  24. 24. DEFINE THE HORIZON & CADENCE… Both MMM and Attribution Modeling are great for the purpose they are intended for:  MMM guiding overall marketing strategy  Attribution Modeling guiding tactical campaign performance 25
  25. 25. DESIGNING THE INTEGRATED ANALYTIC MODEL • Offline and non-marketing drivers’ impact from MMM are leveraged as covariate controls in Attribution Modeling to more accurately quantify Digital/addressable campaign lifts • Lifts from Digital and other targeted campaigns measured in Attribution models are used in MMM-driven budget allocation and portfolio optimizations to more accurately represent these 26
  26. 26. SUCCESS DEPENDS UPON EXECUTION…AND ADOPTION
  27. 27. TODAY’S AGENDA Why is this debate gathering momentum? The case for and against Attribution Pros and Cons with MMM [MMM + Attribution]= Marketing Productivity Excellence Vision for the future 28
  28. 28. SOME PREDICTIONS FOR THE VERY NEAR FUTURE…  Immediate future will see a more cogent integration of MMM and Attribution  A greater focus on targeting will push measurement down to at least household- level, if not individual consumer level  Now that we have stopped being awestruck by the sheer amount of data that is creating “insights”, it is time to address issues like data bias  Focus will shift to predictive accuracy vs. backward-looking campaign scorecard  Advanced probability based models will be explored to compensate for data biases and for understanding non-addressable offline media
  29. 29. THANK YOU! Srishti.Gupta@IRIworldwide.com Joy.Joseph@IRIworldwide.com QUESTIONS?

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