Best Practices in Forecasting & Optimization

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M/A/R/C's Amy Barrentine-EVP General Manager, Randy Wahl-EVP Advanced Analytics, and Scott Waller-VP Business Development, co-presented at Quirk's event in March 2011.

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Best Practices in Forecasting & Optimization

  1. 1. Presented by M/A/R/C Research® Sponsored by Quirk’s ® Best Practices in Forecasting and Optimization March 9, 2011 Page 1
  2. 2. Presenters Amy Barrentine EVP – General Manager  Randy WahlEVP ‐ Advanced Analytics Scott Waller Vice President – Business Development Page 2
  3. 3. Who is M/A/R/C Research?46 years of research service and innovationIndustry experience includes… Consumer Packaged Goods Pharmaceuticals and Healthcare Telecommunications and Technology Dining and Hospitality Retail and Financial ServicesPart of the Omnicom Group Page 3
  4. 4. ObjectivesToday we will discuss… …the range of volumetric forecasting approaches that marketers use …key requirements in a custom, buyer‐based system …things to avoid in forecasting …big opportunities to fine tune through optimization Page 4
  5. 5. Today’s Agenda Frequently Asked Questions Range of Methods Key Requirements Big Opportunities to Optimize Things to Avoid Forecasting Fiction Q&A Page 5
  6. 6. Frequently Asked QuestionsCan I get a copy of today’s presentation? Yes, a copy will be emailed to you Is today’s webinar being recorded? Yes, downloadable Can I ask questions during the event? A Q&A session will commence at the end of the  presentation Page 6
  7. 7. Range of Volumetric  Forecasting  Approaches  Marketers Use Page 7
  8. 8. Forecasting Approaches Primary Examples Pros      Cons Methods Assessment is fast No road map  Qualitative Anecdotal Easy buy in Easily understood  Adjustment for offering  Analog discrepancies Observable Historical Can encompasses large  Backward looking Econometric/Time  # of variables Unexplained variables  Series disregarded Provides Marketing  Mix  Direction Estimate across many  Predictive within test range Choice launch scenarios Calibration requiredSurvey Response Norm Comparison Easy to understand Static context (inflexible) Based Collective history Limited to experience/  category availability Decision‐Driver, Adaptable Hurdles provided, but  Self Calibrating Innovative offerings/ norms don’t apply emerging categories Page 8
  9. 9. Key Requirements  in a Custom,  Buyer‐Based  System Page 9
  10. 10. Key RequirementsValidated Methodology “Well, most of the time we’re right!”Competitive Context Included ~ Consideration of new offerings vs. other in‐market  options is important… Page 10
  11. 11. Key RequirementsMarketing Spend Levels Estimated and Incorporated Marketing Spend (MM) $12.5 $18.0 $27.0 Advertising (MM) $12.0 $17.5 $25.0 30" HH GRPs 816 1044 1400 15"  HH GRPs 669 854 1200 Online (MM) $0.5 $0.5 $2.0 Banner Ads TRPs 75 75 200 Emails 2MM 2MM 2MM Facebook (75k fans) Ad Ad Promo AWARENESS 19% 23% 35% Page 11
  12. 12. Key Requirements Ability to Integrate Cross‐Channel Purchasing~ Ability to account for purchasing through alternative channels within one  respondent – avoiding double counting volume and keeping costs down  Page 12
  13. 13. Key Requirements Ability to integrate multiple layers of influence  and decision making ~ Kids impact moms…insurance decisions made  jointly…office managers,  patients influence physicians and vice‐versaInputs % Lives Reimbursed Formulary  Decisions Copay Price Prior Authorization Reimbursed Physicians  Physicians Share of Share of  20% mark-up Prescriptions Prescriptions Copay Scripts Scripts Retail price Patient  Patient Complex DTC Marketing Awareness Decisions Decisions Interaction Page 13
  14. 14. Key RequirementsAbility to identify offer acceptors at the individual  level for targeting and offer optimization (penalty analysis). Sample Trier Profile Profile IndexAge % %18-24 11 11 10625-34 20 15 7435-49 41 47 11450-64 28 27 96Outside Franchise 17 11 66Light Users 24 36 153Heavy Users 39 45 115Super‐Heavy Users 21 8 39 Too Hot Just Right Too Cold Temperature 23 50 27 Repeat Index 96 120 72 Page 14
  15. 15. Key Requirements Adaptable to Accommodate Complex Launches~ Methodology should be flexible enough to address alternative launch scenarios:  staged introduction?  discounting?  potential for added features? UNITS  78.9 73.8 62.2 65.8 17.6 28.8 Product Y 22.2 21.5 Product X 62.22 Current 47.25 43.62 39.81 Current Current & Current & Current, Product X Product Y Product X & Y Page 15
  16. 16. Key Requirements:  Flexibility~ BACKGROUND: Multiple generations of an offering were  under consideration – each one delivering more than the  previous one and the client had a desire to price each  commensurately with the added benefit.~ OBJECTIVE:  Forecast each new offering and determine   the opportunity for price escalation and coexistence.~ OUTCOME:  Able to identify price thresholds for each  generation, when to phase out previous offerings and  where opportunities for enhanced margins resided. Page 16
  17. 17. Big Opportunities to  Fine Tune through  Optimization Page 17
  18. 18. Alternative Strategy Assessment  Variations of: PricingVolume BrandingForecast Features Portfolio Choice Set 1 Respondent evaluates Choice Set 2 alternatives in competitive Choice Set 3 context Choice Set 4 Page 18
  19. 19. Product Optimization – Best Product Offerings Retail  Factory  Legitimizing  Competitive  Sales  Retail Sales  Sales # Bundle Claim Claim Price Form (MM) Index to Base (MM)1 A M A Low X $194.2 139 $119.52 A M B Lower X $187.3 134 $115.23 A N A Lower X $183.1 131 $112.74 A O C Lower X $178.9 128 $110.15 A N C Lower X $177.5 127 $109.96 C M A Low X $174.7 125 $107.57 E M D Low X $171.9 123 $105.88 D N B Lower Y $167.7 120 $103.29 B M A Low Y $167.3 120 $102.910 E M B Lower X $166.3 119 $102.3 Page 19
  20. 20. Simulating Outcomes Page 20
  21. 21. Things to Avoid in  Forecasting Page 21
  22. 22. Forecasting PitfallsIgnoring a model, believing a model~ Forecasting is part science, part art – experience counts! Page 22
  23. 23. Forecasting PitfallsRelying solely on one measure  Purchase Interestto predict in‐market outcomes Likeability/Benefit~ Decision making is complex ValueFailing to incorporate a measure  Uniquenessof differentiation Competitive Context~ The offering must provide a meaningful  unfulfilled benefit~ This benefit can’t be ignored Trial Page 23
  24. 24. Forecasting Pitfalls: Ignoring Differentiation ~ TESTED: New line of cookies that were co‐branded  with current brands of candy bars. ~ OUTCOME:  Utilizing a forecasting methodology  that utilized differentiation only as a diagnostic  measure, the lift in volume a truly differentiated  offering could deliver was lost; hence, revenue  projections were way‐underestimated. Page 24
  25. 25. Forecasting Pitfalls Sampling: Too broad/Too narrow ~ Too broad = Waste Target (F 21-36) Non-Target 80 70 60 50~ Too narrow = Missed Volume 40 30 71 53 20 47 29 10 0 % of Sample % of Volume Page 25
  26. 26. Forecasting PitfallsTesting non‐executable offerings ~ Products that over‐promise, are over‐communicated  or have an over stimulating concept drives volume  that will never be achievedIgnoring cannibalization (source of business) $1016 Sourced From  $691 Cannibalized 68% Current Product Line Current Product 1 35% Incremental $325 Current Product 2 15% Current Product 3 18% Revenue Page 26
  27. 27. Forecasting Pitfalls: Ignoring Cannibalization ~ PLAN:   NEW PIZZA offering was  going to be successful because it  would just capture new occasions – parties, get‐togethers ~ OUTCOME: Traded current buyers  down from “2 for 1” which generated  higher margins and revenues Page 27
  28. 28. Forecasting  Fiction Page 28
  29. 29. Forecasting FictionX “Homeruns capture 10% market share”In most categories 3 to 5% is more realistic…fragmented categories more like .5 to 1% Parent Line extensions typically garner 10 to 30%  Child SOM of parent – cannibalizes parent at 2 to  3 times “fair share.” Page 29
  30. 30. Forecasting FictionX “A restage can drive 25% growth” ~ 10% is the most yr1 growth expected ~ Primary objective should be to hold SOM or re‐capture lost share  RestagingSOM Assessor: An Overview “Real” Gain 2009 2010 2011 Page 30
  31. 31. Forecasting FictionX“Not to worry – they will learn to like it!” ~ Most trial occurs within first 6 months – typically peaking at month 4 Cumulative 75% 100 Trial (%) 80 Assessor: An Overview60 30 40Monthly 20 20Trial (%) 10 10 2 4 6 8 10 12 Page 31
  32. 32. Forecasting FictionX “This product will be everywhere!” ~ Maximizing distribution is critical to success ~ Impact has an almost linear impact on volume ~ Rate of distribution build is also important ~ Disappointed potential buyers ~ Less time for repeat Page 32
  33. 33. So, what is important in choosing a  Forecasting Methodology?Competitive Context and Differentiation incorporatedMarketing spend fairly representedSource of volume consideredFlexible enough to accommodate complex launchesAccount for multiple layers of influence, cross‐channel buyingAbility to profile identified triers (targeting, penalty analysis) Page 33
  34. 34. Scott Waller Vice President 1660 North Westridge Circle   Irving, TX 75038-2424 M/A/R/C® Research Strong brands start with tel: 972-983-0412 fax:972-983-0444 strong research   Scott.Waller@MARCresearch.com www.MARCresearch.com     Amy Barrentine Executive Vice President, General Manager Randy Wahl 1660 North Westridge Circle   Irving, TX 75038-2424 Executive Vice President M/A/R/C® Research Strong brands start with strong research tel: 972-983-0476 fax:972-983-0444 1660 North Westridge Circle   Amy.Barrentine@MARCresearch.com   Irving, TX 75038-2424 www.MARCresearch.com M/A/R/C® Research   Strong brands start with tel: 972-983-0469 fax:972-983-0444  strong research   Randy.Wahl@MARCresearch.com www.MARCresearch.com   Page 34

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