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 Approach Defined KPIs across stages of the purchase funnel to monitor the progression of prospects and understand linkages between advertising spending and sales volume Methodology Developed a marketing mix model to optimize total marketing investment Results Developed 4 models which explain key drivers for each stage of the marketing funnel, i.e. what drive search, leads, sales…

4. Recommended approach

5. Detailed Findings & Implications

6. Future Enhancements

7. Discussion3

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.4

13. Analysis Path

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 efforts7

20. Including: all media, site engagement, social buzz, and sales activity dataStructured Equation 8 = 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…

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 9

24. Hierarchical Modeling was used to Assess Impact at Each Stage Four Independent Models Working our way up the funnel Strong Existing Correlation 10

25. Detailed Findings & Implications

27. Over 11% contribution but with diminishing returns with higher spend

28. So how do consumer initiated searches contribute to site traffic?12 Model explains 86% of variation in Total Leads

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 13

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?14 Model explains 90% of variation in Total Leads

36. So how does showroom traffic contribute to sales?15 Model explains 90% of variation in Total Leads

38. Model showroom traffic as success criterion

39. Unit sales ultimately tie to product availability, price, other non-advertising factors16

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.TV Display Searches Online Leads Total Showroom Traffic Unit Sales 17

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 traffic18

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 sales19

55. To aid in the planning process, the model equations were coded into a tool to provide scenario and budget planning

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 direction21

64. Thank you 22

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 direction23