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May 2012 - Marketing Roundtable - Jeff Ewald
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May 2012 - Marketing Roundtable - Jeff Ewald

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This the presentation from Jeff Ewald, from of the Marketing Roundtable-Marketing Return on Investment program.

This the presentation from Jeff Ewald, from of the Marketing Roundtable-Marketing Return on Investment program.

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Transcript

  • 1. Why Conduct a ROMI Analysis? Jeff Ewald1
  • 2. • Why are you considering conducting a ROMI analysis?• What do you want to do with the results?2
  • 3. Typical Objectives• Convince CEO that I’m doing a good job!3
  • 4. Typical Objectives• Convince CEO that I’m doing a good job!• Determine optimal level of overall spend• Optimize mix: allocate $ to tactics• Understand appropriate marketing response to environmental changes• Predict outcomes – determine KPIs4
  • 5. Interpreting Correlation Coefficients  The scale used to evaluate the strength of correlation coefficients in this analysis is: 1.0 “Perfect” .76-.99 “Very strong” .51-.75 “Strong” .26-.50 “Moderate” .11-.25 “Weak” .01-.10 “Very Weak” 0.0 “No relationship” Source: Losh (2004), cited in Communication Research Statistics, by J. Reinard, 20065
  • 6. Determine optimal level of overall spend6
  • 7. Where Are We on the “S-Curve”? • In this case, because of changes in spend levels month-to-month, a log transform helps visualize the relationship between spend and sales • Curve fitting suggests that the 2010 spend (test levels) is on the upward slope of the classic “S-Curve” Classic S Curve Best Fit Curve (2nd order polynomial) Fall 2010 Current State r=.5407
  • 8. Optimal Level of Overall Spend Total Spending vs.. Contracts Traditional Media + Emerging + Internet + Events + DM + Mktg Ops Contracts *8
  • 9. Optimize Mix: Allocate $ to Tactics9
  • 10. Positive Impact on Leads Each of these variables contribute to lead generation at > 85% CL Total Spend General Mkt Spend TV Spend 70000 70000 70000 60000 60000 60000 50000 50000 50000 40000 40000 Leads Tot Act Enl Nat 40000 Leads Tot Act Enl Nat Leads Tot Act Enl Nat 30000 30000 30000 20000 20000 20000 10000 10000 -1000000 1000000 3000000 5000000 -1000000 0 1000000 2000000 3000000 4000000 5000000 10000 0 2000000 4000000 6000000 -2000000 0 2000000 4000000 6000000 8000000 Television Spending non-Production General Radio Spend Internet Spend Internet CPL 1600 70000 70000 60000 1400 60000 50000 50000 1200 40000 Leads Tot Act Enl Nat 40000 1000Leads Tot Act Enl Nat Cont Act Enl Nat 30000 30000 800 20000 20000 600 10000 0 100000 200000 300000 400000 10000 300000 500000 700000 900000 1100000 -200000 200000 600000 1000000 1400000 400000 600000 800000 1000000 Int CPL 0 400000 800000 1200000 1600000 Cat Internet Radio 10
  • 11. Catalog Circulation  Higher volumes of catalogs correspond with higher sales volumes – Diminishing returns at approximately 160,000 pieces – Suggests that higher cost catalogs (i.e. CPM) produce results 400000000 300000000 200000000 ALL SMB 100000000 1000000 2000000 300000011 CATQTY
  • 12. Marketing Communications Variable TreeShare ofvoice, print,online, and Prod B Printdirect mail all Share of voice Out of pockethave anaffect onsales Sales Shipments Prod A Share of voice Direct Mail Note how Print has an impact by itself Print AND in combination with Direct Mail Online costs Out of pocket 12
  • 13. Marketing and the External Environment13
  • 14. How Does Weather Impact Sales? Minneapolis Albany Denver St. Louis14
  • 15. Variable Importance is Similar Across DMAs Albany Denver Consumer Sentiment Index 100 Marketing Spend 100 Labor Force 88 Consumer Sentiment Index 86 Employment 76 Ave Monthly Temperature 72 Ave Monthly Temperature 19 Marketing Spend 56 0 20 40 60 80 100 120 0 20 40 60 80 100 120MARS Analysis MARS AnalysisAdj R2 =.551 / r = .742 Adj R2 =.648 / r = .805 St. Louis Minneapolis Marketing Spend 100 Feeder Cattle Futures 100 S&P 500 98 Ave Monthly Temperature 90 Wheat Futures 53 S&P 500 89 Marketing Spend 50 Consumer Sentiment Index 83 Ave Monthly Temperature 37 Precipitation 46 Agriculture Employment 35 CPI 32 Live Cattle Futures 34 MARS Analysis 0 20 40 60 80 100 120 MARS Analysis 0 .6740 20 60 80 100 12015 Adj R2 =.760 / r = .872 Adj R2 =.780 / r = .883
  • 16. Predicting Outcomes Simulations and “What If” Analysis16
  • 17. B2B “Sales Support” Situation Media Influences LEADS Radio General Market Spend Overall Spending TV Internet CPL Direct Marketing Media Events CONTRACTS 1717
  • 18. Summary: What do you want to do?• Convince CEO that I’m doing a good job!• Determine optimal level of overall spend• Optimize mix: allocate $ to tactics• Understand appropriate marketing response to environmental changes• Predict outcomes – determine and track against KPIs18
  • 19. Intersecting marketing, science and technology™ • Contact information: Jeff Ewald Phone: 248-459-1194 Email: jewald@optimizationgroup.com19