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Kaushansky-Data Driven Marketing
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Kaushansky-Data Driven Marketing



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  • Bonnie
  • Bonnie
  • Matt
  • Matt
  • All three
  • Joe
  • Mike
  • Mike (Matt voice over from campaign knowledge)
  • Mike
  • Joe
  • Joe
  • Mike
  • Joe
  • Joe
  • Matt
  • All three
  • Bonnie


  • 1. Automotive Marketing Mix Model – Case Study
    Moving to Results-Driven Investment!
    August 4th 2011
  • 2. The Assignment
    How can data and analytics be used to make the smartest possible marketing decisionsby defining attributable channels, using results to optimize advertising spend and help manage the growth of the business
    Defined KPIs across stages of the purchase funnel to monitor the progression of prospects and understand linkages between advertising spending and sales volume
    Developed a marketing mix model to optimize total marketing investment
    Developed 4 models which explain key drivers for each stage of the marketing funnel, i.e. what drive search, leads, sales…
  • 3. Discussion Overview
    • Objective
    • 4. Recommended approach
    • 5. Detailed Findings & Implications
    • 6. Future Enhancements
    • 7. Discussion
  • 8. Our Modeling Capability – Moving Data Forward
    Marketing Effectiveness System
    • Established marketing mix practice with global presence; we thrive on being the strategic partners.
    • 9. Recent modeling accomplishments: Sears, Oppenheimer, and Evian Water.
    • 10. Our strength is evident by our proximity to large amounts of data and our deep understanding of media planning and buying.
    • 11. Experienced network of analytics worldwide. Led by statisticians, researchers, analysts, and strategic planners.
  • 12. Brand Awareness
    Modeling Process
    We modeled each stage of the funnel
    • Our specialty is understanding Online and Offline
    Sales, Dealerships, Promotions
    Search Volume
    Paid Search Impressions
    Paid Search Clicks
    Business Impact
    Online and Offline Sales
  • 13. Analysis Path
  • 14. Modeling Approach
    • We modeled one mid-sized model, since most of our US media was focused on the re-launch
    • 15. Aggregated weekly data from multiple online/offline sources
    • 16. We used 2009/2010 performance to ensure the learnings are recent and representative
    • 17. Total showroom traffic was our main primary performance indicator (KPI)
    • 18. To account for each stage of the conversion funnel we developed four econometric modelsto accommodate US and eventually global communication efforts
  • 19. Comprehensive Data Collection
    • Defined and aggregated 12+ unique data sources
    • 20. Including: all media, site engagement, social buzz, and sales activity data
    Structured Equation
    = r2 > .87
    Yt= α + β1Mt + β2Et + β3St + β4Tt + εt
    Y (represents the dependent variable, e.g.,sales)
    M (media), E (engagement), S (searches), T (store traffic), etc…
  • 21. Focused on understanding the impact of marketing communications at five stages of the purchase funnel
    • National level analysis/data
    • 22. 41weeks (broadcast weeks 9/13/10 through 6/20/11)
    • 23. Marketing communications: GRPs, impressions, clicks, dollar spending
    * KPIs are: (a) representative of the stage in the purchase funnel, (b) ultimately linked to purchase behavior and (c) we understand how to affect variation
  • 24. Hierarchical Modeling was used to Assess Impact at Each Stage
    Four Independent Models
    Working our way up the funnel
    Strong Existing Correlation
  • 25. Detailed Findings & Implications
  • 26. Total Brand Google Searches
    • TV is with branded Display are a primary driver of Google search (natural search volume)
    • 27. Over 11% contribution but with diminishing returns with higher spend
    • 28. So how do consumer initiated searches contribute to site traffic?
    Model explains 86% of variation in Total Leads
  • 29. Unique Page Views
    • TV aligned with Display Clicks are key drivers of Unique Pages
    • 30. Display ads and natural search contribute almost 60% of all unique page views; YouTube’s augmented reality drove a significant spike in March
    • 31. So how do pageviews contribute to online leads?
    YouTube Masthead
    Model explains 98% of variation in Total Leads
  • 32. Online Leads
    • Google searches/visits & TV are primary drivers of Total Leads
    • 33. Google searches lead to page views and represent 65% of contribution, a function of its role in online exploration of cars
    • 34. So how do leads contribute to showroom traffic?
    Model explains 90% of variation in Total Leads
  • 35. Showroom Traffic
    • Online Leads and TV GRPs are primary drivers of Showroom Traffic and TV is a key driver of Online Leads, which includes: Configuration, quick-quote, test-drive, and 3rd party leads
    • 36. So how does showroom traffic contribute to sales?
    Model explains 90% of variation in Total Leads
  • 37. Showroom Traffica Useful Proxy for Unit Sales
    • Advertising drives showroom traffic
    • 38. Model showroom traffic as success criterion
    • 39. Unit sales ultimately tie to product availability, price, other non-advertising factors
  • 40. Topline conclusions
    • The hypothesized purchase funnel is supported by this analysis. Tight linkage exists between each of the stages and ultimately showroom traffic.
    • 41. The impact of TV is strong and present throughout each stage of the funnel.
    • 42. Other media work with TV exerting disproportionate influence at different stages of the funnel.
    • 43. A strong statistical correlation is seen to sales as a result of cumulative media impact.
    Online Leads
    Total Showroom Traffic
    Unit Sales
  • 44. Implications for Investment
    • TV is a consistent driver of search volume, due to search’s important impact on total online leads – continue to invest
    • 45. Online Display is a proven awareness driver and influencer of search activity resulting in site traffic activity, most effective when in conjunction with TV is most effective – invest and align with TV
    • 46. Media’s impact lasts 3-4 weeks before it loses most of its effect – going dark with media may cost more to recapture share
    • 47. Online leads supported by TV and searches, leads drive showroom traffic – ensure search budgets are proportional to TV investment and align creative as needed
    • 48. Planning implication: maintaining a continuous mix of TV & online display remains essential for a steady flow of showroom traffic
  • 49. Attribution
    • Strong brand equity results in high 88% top-level brand search
    • 50. Display Ads contributes 26% and paid search 34% to site traffic
    • 51. Paid search contributes to 65% of the online leads, TV adding another 20%
    • 52. Online leads are responsible for 16% of showroom traffic with TV adding a 5% contribution;
    • 53. Showroom traffic has a 0.88 R-squared correlation to sales
  • 54. Spending Optimization Tool
    • Budget Planning and Scenario Optimization Tool
    • 55. To aid in the planning process, the model equations were coded into a tool to provide scenario and budget planning
    • 56. Run unlimited number of scenarios to set budgets by channel
  • Next Steps and Future Enhancements
    • Control for competitive and external influences (e.g. economy)
    • 57. Evaluate further impact .COM and 3rd party site engagement
    • 58. Include granular sales data (e.g. sales, profit)
    • 59. Test synergy between all channels
    • 60. Consider other auto models
    • 61. Deployment strategy
    • 62. Integrating data into planning decisions (e.g. next dollar invested)
    • 63. Socialization of data-driven direction
  • 64. Thank you
  • 65. Discussion
    • Today we’ve given you a first look at how we would approach the big picture question using modeling
    • 66. Havas Digital Analytics’ rigor and 360° approach to communications modeling ultimately provides more-usable outputs (offline/online +retail +competitive)
    • 67. We believe:
    • 68. Analytics are best done by our strategic brand/media owners given our deep brand understanding and local ability to respond in real-time
    • 69. The best multi-market analytic systems combine local market intelligence and interpretation with a shared global platform
    • 70. Next steps:
    • 71. Further analyses can be developed pending your further direction