34
Page 1 Best Practices in Forecasting and Optimization March 9, 2011 Presented by M/A/R/C Research® Sponsored by Quirk’s ®
| Date post: | 29-Nov-2014 |
| Category: |
Business |
| Upload: | marc-research |
| View: | 744 times |
| Download: | 6 times |
Page 1
Best Practices in Forecastingand Optimization
March 9, 2011
Presented by M/A/R/C Research®
Sponsored by Quirk’s ®
Page 2
Randy WahlEVP ‐ Advanced Analytics
Amy BarrentineEVP – General Manager
Scott WallerVice President – Business Development
Presenters
Page 3
46 years of research service and innovation
Industry experience includes…Consumer Packaged Goods
Pharmaceuticals and Healthcare
Telecommunications and Technology
Dining and Hospitality
Retail and Financial Services
Part of the Omnicom Group
Who is M/A/R/C Research?
Page 4
Objectives
Today 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 5
Frequently Asked QuestionsRange of MethodsKey Requirements
Big Opportunities to OptimizeThings to Avoid
Forecasting FictionQ&A
Today’s Agenda
Page 6
Is today’s webinar being recorded?Yes, downloadable
Can I get a copy of today’s presentation?Yes, a copy will be emailed to you
Frequently Asked Questions
Can I ask questions during the event?A Q&A session will commence at the end of the
presentation
Page 7
Range of Volumetric Forecasting Approaches
Marketers Use
Page 8
PrimaryMethods
Examples Pros Cons
Qualitative Anecdotal Assessment is fastEasy buy in
No road map
Historical
Analog
Econometric/Time Series
Easily understood Observable
Can encompasses large # of variablesProvides Marketing Mix Direction
Adjustment for offering discrepancies
Backward lookingUnexplained variables disregarded
Survey ResponseBased
Choice
Norm Comparison
Decision‐Driver,Self Calibrating
Estimate across many launch scenarios
Easy to understandCollective history
AdaptableInnovative offerings/emerging categories
Predictive within test rangeCalibration required
Static context (inflexible)Limited to experience/ category availability
Hurdles provided, but norms don’t apply
Forecasting Approaches
Page 9
Key Requirements in a Custom, Buyer‐Based
System
Page 10
Validated Methodology
“Well, most of the time we’re right!”
Competitive Context Included~ Consideration of new offerings vs. other in‐market
options is important…
Key Requirements
Page 11
Marketing 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 140015" HH GRPs 669 854 1200
Online (MM) $0.5 $0.5 $2.0
Banner Ads TRPs 75 75 200Emails 2MM 2MM 2MMFacebook (75k fans) Ad Ad Promo
AWARENESS 19% 23% 35%
Key Requirements
Page 12
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
Key Requirements
Page 13
Inputs
CopayPrior Authorization
20% mark upReimbursed
Copay
Retail priceComplex
DTC Marketing Awareness InteractionPatient
Decisions
Physician's Prescriptions
Share of Scripts
% Lives Reimbursed
Price
-
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‐versa
Formulary Decisions
Share of Scripts
Physicians Prescriptions
Patient Decisions
Page 14
Ability to identify offer acceptors at the individual level for targeting and offer optimization
(penalty analysis).
Key Requirements
Sample TrierProfile Profile Index
Age % %18-24 11 11 10625-34 20 15 7435-49 41 47 11450-64 28 27 96
Outside Franchise 17 11 66Light Users 24 36 153Heavy Users 39 45 115Super‐Heavy Users 21 8 39
23 50 27Temperature
Too Hot Just Right Too Cold
72Repeat Index 96 120
Page 15
Adaptable to Accommodate Complex Launches~ Methodology should be flexible enough to address alternative launch scenarios: staged introduction? discounting? potential for added features?
Key Requirements
62.2247.25 43.62 39.81
28.821.522.2
17.662.273.8
65.8
78.9
Product Y
Product X
Current
UNITS
Current Current &Product X
Current &Product Y
Current,Product X & Y
Page 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.
~ OUTCOME: Able to identify price thresholds for each generation, when to phase out previous offerings and where opportunities for enhanced margins resided.
~ OBJECTIVE: Forecast each new offering and determine the opportunity for price escalation and coexistence.
Page 17
Big Opportunities to Fine Tune through
Optimization
Page 18
Alternative Strategy Assessment
VolumeForecast
Variations of:Pricing
BrandingFeaturesPortfolio
Choice Set 1
Choice Set 2
Choice Set 3
Choice Set 4
Respondent evaluates
alternatives in competitive
context
Page 19
# BundleLegitimizing
ClaimCompetitive
Claim Price Form
Retail Sales (MM)
Retail Sales Index to Base
Factory Sales (MM)
1 A M A Low X $194.2 139 $119.5
2 A M B Lower X $187.3 134 $115.2
3 A N A Lower X $183.1 131 $112.7
4 A O C Lower X $178.9 128 $110.1
5 A N C Lower X $177.5 127 $109.9
6 C M A Low X $174.7 125 $107.5
7 E M D Low X $171.9 123 $105.8
8 D N B Lower Y $167.7 120 $103.2
9 B M A Low Y $167.3 120 $102.9
10 E M B Lower X $166.3 119 $102.3
Product Optimization – Best Product Offerings
Page 20
Simulating Outcomes
Page 21
Things to Avoid in Forecasting
Page 22
Ignoring a model, believing a model~ Forecasting is part science, part art – experience counts!
Forecasting Pitfalls
Page 23
Forecasting Pitfalls
Trial
Purchase Interest
Likeability/Benefit
Value
Uniqueness
Competitive Context
Relying solely on one measure to predict in‐market outcomes~ Decision making is complex
Failing to incorporate a measure of differentiation~ The offering must provide a meaningful
unfulfilled benefit~ This benefit can’t be ignored
Page 24
~ 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.
Forecasting Pitfalls: Ignoring Differentiation
Page 25
Sampling: Too broad/Too narrow
~Too broad = Waste
~Too narrow = Missed Volume
Forecasting Pitfalls
29
7153
47
0
10
20
30
40
50
60
70
80
% of Sample % of Volume
Target (F 21-36) Non-Target
Page 26
Forecasting Pitfalls
Testing non‐executable offerings~ Products that over‐promise, are over‐communicated
or have an over stimulating concept drives volume that will never be achieved
Ignoring cannibalization (source of business)
68%
35%
15%
18%
Sourced From Current Product Line
Current Product 1
Current Product 2
Current Product 3Revenue
$325
$691
$1016
Cannibalized
Incremental
Page 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 28
Forecasting Fiction
Page 29
Forecasting Fiction
“Homeruns capture 10% market share”
Parent
Child
In most categories 3 to 5% is more realistic…fragmented categories more like .5 to 1%
Line extensions typically garner 10 to 30% SOM of parent – cannibalizes parent at 2 to
3 times “fair share.”
X
Page 30
Forecasting Fiction
“A restage can drive 25% growth”
Assessor: An OverviewRestaging
“Real” Gain
2009 2010 2011
SOM
~ 10% is the most yr1 growth expected~ Primary objective should be to hold SOM or re‐capture lost share
X
Page 31
Assessor: An Overview
Forecasting Fiction
“Not to worry – they will learn to like it!”
~ Most trial occurs within first 6 months – typically peaking at month 4
2 4 6 8 10 12
10
20
30
10
20
40
60
80
100
Monthly
Trial (%)
CumulativeTrial (%)75%
X
Page 32
“This product will be everywhere!”
Forecasting Fiction
~ 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
X
Page 33
Competitive Context and Differentiation incorporated
Marketing spend fairly represented
Source of volume considered
Flexible enough to accommodate complex launches
Account for multiple layers of influence, cross‐channel buying
Ability to profile identified triers (targeting, penalty analysis)
So, what is important in choosing a Forecasting Methodology?
Page 34
M/A/R/C® ResearchStrong brands start with
strong research
Randy WahlExecutive Vice President
1660 North Westridge Circle Irving, TX 75038-2424
tel: 972-983-0469 fax:972-983-0444 [email protected]
www.MARCresearch.com
M/A/R/C® Research Strong brands start with
strong research
Scott WallerVice President
1660 North Westridge Circle Irving, TX 75038-2424
tel: 972-983-0412 fax:972-983-0444 [email protected]
www.MARCresearch.com
M/A/R/C® Research Strong brands start with
strong research
Amy BarrentineExecutive Vice President,
General Manager
1660 North Westridge Circle Irving, TX 75038-2424
tel: 972-983-0476 fax:972-983-0444 [email protected]
www.MARCresearch.com
