Forecasting
 Forecasting Problems and
Methods
 New product forecasting
 Forecasting using
Diffusion Models (to
forecast trial or adoption)
 Forecasting using Pre-Test
Market Models (to
forecast both trial and
repeat purchase)
1
Managerial Issues Related to
Forecasting
 What is the purpose of developing the forecast?
 What, specifically, do we want to forecast (e.g., market
demand, technology trends)?
 How important is the past in predicting the future?
 What influence do we have in constructing the future?
 What method(s) should we use to develop the forecast?
 What factors could change the forecast?
© DecisionPro 2007
Principles Chapter 5: Forecasting - 2
Forecasting Methods
Judgmental
Salesforce
composite
Jury of executive
opinion
Delphi methods
Scenario analysis
Market and
Survey
Analysis
Time Series
Buyer intentions
Naïve methods
Product tests
Moving averages
Chain ratio
method
Exponential
smoothing
Box-Jenkins
method
Decompositional
methods
© DecisionPro 2007
Causal
Analyses
Regression
analysis
Econometric
models
Input-output
analysis
MARMA
Neural networks
Principles Chapter 5: Forecasting - 3
Methods for Forecasting
New Product Sales
Early stages of development
Chain ratio method
Judgmental methods
Scenario analysis
Diffusion model
Later stages of development
Pre-test market methods
Test-market methods
© DecisionPro 2007
Principles Chapter 5: Forecasting - 4
Chain Ratio Method
(Estimate of Online Grocery Sales)
 Number of households (2000 census)
 Grocery purchases per household per year (52x120)
 % of sales from Supermarkets and grocery stores
105 million
$5300
84%
(Progressive Grocer)
 Households with children (married and unmarried – Census)
 % of households with Internet access (Census Bureau)
 Will order groceries online if available (Survey)
 Discount of survey intentions
 Online grocery shopping availability (guess)
 Awareness given availability (guess)
35%
58%
25%
50%
40%
50%
 Market forecast:
$ ???
© DecisionPro 2007
Principles Chapter 5: Forecasting - 5
Intent-to-Buy Scale Used for
Generating Some Inputs to Chain Ratio
1.
Definitely would buy
2.
Probably would buy
3.
May or may not buy
(May be excluded from the scale)
4.
Probably would not buy
5.
Definitely would not buy
© DecisionPro 2007
Principles Chapter 5: Forecasting - 6
Who Are They?
© DecisionPro 2007
Principles Chapter 5: Forecasting - 7
New Product Forecasting Models
That We Consider
 Forecasting the pattern of new product adoptions (Bass
Model)
 Forecasting market share for new products in established
categories (Assessor pre-test market model)
 Forecasting using conjoint analysis
© DecisionPro 2007
Principles Chapter 5: Forecasting - 8
Hi
Forecasting Based on
“Newness” of Products
• Repositioning
Pre-test market model
New to
World
Lo
• Line Extensions
Simple pre-test market
models (e.g., Bases)
• Breakthroughs—Major
Product Modifications
Bass model/Conjoint
• “Me Too” Products
Conjoint/Pre-test
market models
Hi
Lo
New to Company
© DecisionPro 2007
Principles Chapter 5: Forecasting - 9
Overview of “Stage-Gate” New
Product Development Process
Opportunity Identification
Reposition
Market definition
Idea generation
Harvest
Life-Cycle Management
Go
Market response analysis & fine tuning the
marketing mix; Competitor monitoring & defense
Innovation at maturity
No
Design
Identifying customer needs Sales forecasting
Product positioning
Engineering
Marketing mix assessment Segmentation
Go
No
Introduction
Go
No
Launch planning
Tracking the launch
Testing
Advertising & product testing
Pretest & prelaunch forecasting
Test marketing
Go
© DecisionPro 2007
No
Principles Chapter 5: Forecasting - 10
The Bass Diffusion Model of
New Product Adoption
The model attempts to answer the question:
When will customers adopt a new product or
technology?
Why is it important to address this question?
Helps in planning major investments (e.g., building a
factory) with respect to the product.
© DecisionPro 2007
Principles Chapter 5: Forecasting - 11
Non-cumulative Adoptions, n(t)
Graphical Representation of
The Bass Model (Cell Phone Adoption)
Adoptions due to internal influence
pN
Adoptions due to external influence
Time
© DecisionPro 2007
Principles Chapter 5: Forecasting - 12
Number of Registered Users
eBay (by Quarter)
million
225
210
195
180
165
150
135
120
105
90
75
60
1997
45
Q1 0.09
Q2 0.15
30
Q3 0.25
15
Q4 0.40
0
1997 '98
'99
'00
'01
'02
'03
'04
'05
'06
Source: eBay/SEC filings
© DecisionPro 2007
Principles Chapter 5: Forecasting - 13
The Bass Diffusion Model for
Durables
nt
=
p  Remaining
Potential
+
q  Adopter Proportion 
Remaining Potential
Innovation
Effect
Imitation
Effect
nt = n umber of adopters at time t (Sales)
p = “coefficient of innovation” (External influence)
q = “coefficient of imitation” (“internal” to the society
in which the diffusion spreads)
N = Eventual number of adopters
# Adopters = n0 + n1 + • • • + nt–1
Remaining = Total Potential – # Adopters
Potential
© DecisionPro 2007
Principles Chapter 5: Forecasting - 14
Assumptions of the
Basic Bass Model
 Diffusion process is binary (consumer either adopts, or waits to adopt).
 Constant maximum potential number of buyers ( ).
 Eventually, all will adopt the product.
N
N or replacement purchase.
 No repeat purchase,
 The impact of word-of-mouth is independent of adoption time.
 Innovation is independent of substitutes.
 The marketing strategies supporting an innovation are not explicitly
included.
 Uniform influence or complete mixing. That is, everyone in the population
knows everyone else, or is at least able to communicate with, or observe
everyone else.
© DecisionPro 2007
Principles Chapter 5: Forecasting - 15
Representation as an Equation
N (t ) 

n( t )  [ N  N ( t )] p  q

N 

...(1)
N(t) : Cumulative number of adopters until time t.
© DecisionPro 2007
Principles Chapter 5: Forecasting - 16
Parameters of the Bass Model in
Several Product Categories
Product/
Technology
B&W TV
Color TV
Room Air conditioner
Clothes dryers
Ultrasound Imaging
CD Player
Cellular telephones
Steam iron
Oxygen Steel Furnace (US)
Microwave Oven
Hybrid corn
Home PC
Innovation
parameter
(p)
Imitation
parameter
(q)
0.065
0.021
0.010
0.073
0.003
0.028
0.005
0.036
0.001
0.018
0.000
0.003
0.335
0.583
0.454
0.389
0.506
0.368
0.506
0.318
0.456
0.337
0.798
0.253
A study by Van den Bulte and Stremersch (2004) suggests an average value
of 0.03 for p and an average value of 0.42 for q, The average was
taken across a couple of hundred categories.
© DecisionPro 2007
Principles Chapter 5: Forecasting - 17
Estimating the Parameters of the
Bass Model
 Estimation using data
 Regression
 Specialized nonlinear estimation
 Estimation using analogous products
 Select analogous products based on the similarity in
environmental context, market structure, buyer
behavior, marketing-mix strategies of the firm, and
innovation characteristics.
© DecisionPro 2007
Principles Chapter 5: Forecasting - 18
Forecasting Using the Bass Model—
Room Temperature Control Unit
Quarter
Market Size = 16,000
(At Start Price)
Innovation Rate = 0.01
(Parameter p)
Imitation Rate = 0.41
(Parameter q)
Initial Price = $400
Final Price = $400
0
1
4
8
12
16
20
24
28
32
36
Sales
0
160
425
1,234
1,646
555
78
9
1
0
0
Cumulative
Sales
0
160
1,118
4,678
11,166
15,106
15,890
15,987
15,999
16,000
16,000
Example computations n( t )  pN  (q  p) N ( t  1)  (q / N ) N 2 ( t  1)
Sales in Quarter 1 = 0.01  16,000 + (0.41–0.01)  0 – (0.41/16,000)  (0)2 = 160
Sales in Quarter 2 = 0.01  16,000 + (0.40)  160 – (0.41/16,000)  (160)2 = 223.35
© DecisionPro 2007
Principles Chapter 5: Forecasting - 19
Factors Affecting the
Rate of Diffusion
Product-related

High relative advantage over existing products

High degree of compatibility with existing approaches

Low complexity

Can be tried on a limited basis

Benefits are observable
Market-related

Type of innovation adoption decision (e.g., does it involve
switching from familiar way of doing things?)

Communication channels used

Nature of “links” among market participants

Nature and effect of promotional efforts
Source: Everett Rogers
© DecisionPro 2007
Principles Chapter 5: Forecasting - 20
Some Extensions to the
Basic Bass Model
 Varying market potential
As a function of product price, reduction in uncertainty in product
performance, and growth in population, and increases in retail
outlets.
 Incorporating marketing variables
 Incorporating repeat purchases
 Multi-stage diffusion process
Awareness  Interest  Adoption  Word of mouth
 Incorporating Network Structure
© DecisionPro 2007
Principles Chapter 5: Forecasting - 21
Example Application of Bass Model
DirecTV (History and Technology)
 1984 FCC grants GM Hughes approval to construct a
Direct Broadcast Satellite system (DBS)
 High Ku Band frequency
 Early 1990’s technological breakthrough in digital
compression. Result: Affordable product and nonobtrusive dish and equipment
 Changed economics of DTH broadcasting
 1991 DIRECTV founded
© DecisionPro 2007
Principles Chapter 5: Forecasting - 22
DirecTV
Data Collection Method
 CATI (Computer-Assisted Telephone Interview) data
collection - nationally representative sample of TV viewers.
 15-minute phone interview. “Eligibles” assigned to one of
two monadic concept-price cells (“Intent to Buy”).
 Respondents mailed a color brochure that described
DIRECTV/RCA branded Direct Broadcast System concept.
 Phone callback interview (22 minutes)-Key inputs: Stated
Intentions (Probability of Acquire and Perceived value and
Affordability).
© DecisionPro 2007
Principles Chapter 5: Forecasting - 23
Obtaining p, q, and N
 Guessed p and q from analogous previously
introduced product
 N obtained from stated intentions in survey
 Average stated intent from survey = 32%
 Stated intentions overstate actual choices. How much
to discount stated intent to adopt? (They discounted
by 50%)
 Also, have to adjust each year’s predicted sales for
actual levels of awareness and availability of product
in the entire market.
© DecisionPro 2007
Principles Chapter 5: Forecasting - 24
Adjusting Stated Intentions to
Get Actual Purchase Behavior
Probability of purchase given stated intent for new durable and non-durable products. From Jamieson, Linda F. and
Frank M. Bass "Adjusting Stated Intention...To Predict Trial Purchase of New Products," JMR, August 1989.
45
Probability of Purchase (within six months)
Increases with Stated Intention
40
Probability of Purchase
35
30
Some Who Say
They Will, Don’t
25
20
Some Who Say
They Won’t, Do!
Purchase Increases with
Stated Intention
15
10
5
0
Definitely Will Not Buy
Probably Will Not Buy
Might or Might Not Buy
Actual Purchase Probablity Given Stated Intention for 5 Non-Durable Products
© DecisionPro 2007
Probably Will Buy
Definitely Will Buy
Actual Purchase Probability Given Stated Intention for 5 Durable Products
Principles Chapter 5: Forecasting - 25
Multi-Year Forecast and Actual
Year
7/01/94 - 6/30/95
7/01/95 - 6/30/96
7/01/96 - 6/30/97
7/01/97 - 6/30/98
7/01/98 - 6/30/99
1992 Forecast
Number of TV
Homes Acquiring
Satellite Television
(Million)
0.875
2.269
4.275
6.775
9.391
Actual Number of
TV Homes
Acquiring Satellite
Television
(Million)
1.15
3.076
5.076
7.358
9.989
1992 Forecast of
Percent of TV
Homes with
Satellite Television
(Percentage)
0.92
2.37
4.42
6.95
9.55
Actual Yearly
Percent of TV
Homes with
Satellite Television
(Percentage)
1.21
3.21
5.25
7.55
10.16
9.4 Million TV homes forecast for
June 99; Actual = 9.9 Million
Forecast based on p and q of Cable TV (other alternative considered was Color TV) and maximum
penetration set to 16% of population (half that in the stated intent survey).
© DecisionPro 2007
Principles Chapter 5: Forecasting - 26
Multi-Year Forecast-Actual Graph
92 Forecast Was Not Updated
© DecisionPro 2007
Principles Chapter 5: Forecasting - 27
Using Scenario Analysis
for Calibrating the Bass Model
 Structure a scenario as a flowing narrative, not as a set of numerical
parameters. Include verbal descriptions such as “rapid experience
effects,” “FCC adoption of digital standard,” etc. Ideally, each scenario
should also include how the situation described in the scenario will be
reached from the present position.
 Construct several scenarios that capture the richness and range of the
“possibilities” relevant to a decision situation. Describe all the scenarios
in the same manner, i.e., one is not more “vivid” than another. Focus
your further analyses on scenarios that are internally consistent and
plausible. Develop forecasts and strategies that are compatible with the
scenarios. The strategies include:
 Robust actions that are resilient across scenarios (e.g., hedging,
concurrent pursuit of multiple options, etc.)
 Contingent actions that postpone major commitments to the future.
© DecisionPro 2007
Principles Chapter 5: Forecasting - 28
Steps in Scenario Planning
(Example for Zenith HDTV)
 Identify the major stakeholders.
 Summarize the core trends that are relevant (technological,







economic, social, etc.) within the time frame of interest.
Articulate the main uncertainties (e.g., TV studio adoption of new
filming methods).
Construct an initial set of scenarios.
Assess the consistency and plausibility of the scenarios.
Create “themes” (i.e., a story with a name) that combine some
trends into meaningful composites (e.g., a Japanese domination of
hardware and American domination of software).
Identify areas where you need more research (e.g., consumer
acceptance) and seek additional information.
Associate the final set of scenarios with potential product analogs
for diffusion model, select p and q, and generate the forecasts.
Evaluate strategic and tactical choices that will help you realize the
forecasts in the most cost effective manner.
© DecisionPro 2007
Principles Chapter 5: Forecasting - 29
Example “Middle of the Road”
Scenario (Zenith HDTV case)
The FCC makes a commitment to the 16:9 NTSC HDTV standard in 1994, with promises to
release details in a year. Initial HDTV sets cost over $3,000 and are seen as a luxury item,
little programming is available so new features (such as use as computer monitors and
compatibility with analog signals) are integrated to justify purchases. Art studios and other
display locations become innovators as they purchase units for displays. Interior designers
realize the benefits of HDTV plasma screens and suggest purchases to their wealthiest clients.
HDTV becomes a “nouveau riche” item, a status symbol much like luxury cars. By 2000, the
manufacturing costs of Plasma and other flat-screen displays decrease drastically from
standards integration and increased competition. Middle-class customers can now afford
HDTV displays. The movie industry embraces digital recordings because of the ease in
editing and persistent quality. New movie features (screen and TV) are filmed in 16:9 digital
format. Subsequent releases on DVD show higher quality. Public TV stations cannot justify
the cost of upgrading, but cable channels such as HBO and Showtime commit to upgrading in
2003. Their recent entry into movie-making and their purchase of new high-tech digital
recording equipment coincides with the need to upgrade transmission hardware. Customers
are then driven to adopt technology not for increased quality on regular programming, but for
movie watching, design, and display of other items.
© DecisionPro 2007
Principles Chapter 5: Forecasting - 30
Comparative Trajectories of Population/GDP
From Global Scenario Group
Gross World Product ($ trillions)
250
Conventional
Worlds
Great Transition
Eco-communalism
Policy Reform
Market Forces
New sustainability
paradigm
Fortress World
20
1990
5
Breakdown
Population (billions)
© DecisionPro 2007
Barbarization
10
Principles Chapter 5: Forecasting - 31
Pretest Market Models
 Objective
Forecast sales/share for new product before a real
test market or product launch
 Conceptual model
Awareness  Availability  Trial  Repeat
 Commercial pre-test market services
 Yankelovich, Skelly, and White
 Assessor
 Others (e.g., BASES)
© DecisionPro 2007
Principles Chapter 5: Forecasting - 32
Yankelovich, Skelly and White
Model (Chain Ratio Method)
Forecast market share = S  N  C  R  U  K
where:
S = Lab store sales (indicator of trial),
N = Novelty factor of being in lab market. Discount sales by 20–40% based on
previous experience that relate trial in lab markets to trial in actual markets,
C = Clout factor which retains between 25% and 75% of SN determined, based
on proposed marketing effort versus ad and distribution weights of existing
brands in relation to their market share,
R = Repurchase rate based on percentage of those trying who repurchase,
U = Usage rate based on usage frequency of new product as compared to the new
product category as a whole, and
K = Judgmental factor based on comparison of S  N  C  R  U  K with
Yankelovich norms. The comparison is with respect to factors such as size
and growth of category, new product’s share derived from category
expansion versus conversion from existing brand.
© DecisionPro 2007
Principles Chapter 5: Forecasting - 33
Overview of ASSESSOR
Modeling Procedure
Consumer Research Input
(Laboratory Measures)
(Post-Usage Measures)
Management Input
(Positioning Strategy)
(Marketing Plan)
Preference
Model
Trial &
Repeat Model
Reconcile
Outputs
Draw &
Cannibalization
Estimates
Brand Share
Prediction
© DecisionPro 2007
Unit Sales
Volume
Diagnostics
Principles Chapter 5: Forecasting - 34
Overview of ASSESSOR Measurement
Process
Design
O1
O2
X1
[O3]
X2
O4
X3
O5
Procedure
Measurement
Respondent screening and
recruitment (personal interview)
Pre-measurement for established
brands (self-administrated
questionnaire)
Exposure to advertising for established
brands and new brands
Measurement of reactions to the
advertising materials (selfadministered questionnaire)
Simulated shopping trip and exposure
to display of new and established brands
Purchase opportunity (choice recorded
by research personnel)
Home use/consumption of new brand
Post-usage measurement (telephone
Criteria for target-group identification
(e.g., product-class usage)
Composition of ‘relevant set’ of
established brands, attribute weights
and ratings, and preferences
Optional, e.g. likability and
believability ratings of advertising
materials
Brand(s) purchased
New-brand usage rate, satisfaction ratings, and
repeat-purchase propensity; attribute ratings
and preferences for ‘relevant set’ of
established brands plus the new brand
O = Measurement; X = Advertising or product exposure
© DecisionPro 2007
Principles Chapter 5: Forecasting - 35
Predicted and Observed Market
Shares for ASSESSOR
Product Description
Deodorant
Antacid
Shampoo
Shampoo
Cleaner
Pet Food
Analgesic
Cereal
Shampoo
Juice Drink
Frozen Food
Cereal
Etc.
Average
Average Absolute Deviation
Standard Deviation of Differences
Initial
Adjusted
Actual
Deviation
(Initial –
Actual)
13.3
9.6
3.0
1.8
12.0
17.0
3.0
8.0
15.6
4.9
2.0
9.0
...
11.0
10.0
3.0
1.8
12.0
21.0
3.0
4.3
15.6
4.9
2.0
7.9
...
10.4
10.5
3.2
1.9
12.5
22.0
2.0
4.2
15.6
5.0
2.2
7.2
...
2.9
–0.9
–0.2
–0.1
–0.5
–5.0
1.0
3.8
0.0
–0.1
–0.2
1.8
...
0.6
–0.5
–0.2
–0.1
–0.5
–1.0
1.0
0.1
0.0
–0.1
–0.2
0.7
...
7.9
—
—
7.5
—
—
7.3
—
—
0.6
1.5
2.0
0.2
0.6
1.0
© DecisionPro 2007
Deviation
(Adjusted –
Actual)
Principles Chapter 5: Forecasting - 36
ASSESSOR Trial & Repeat Model
Market Share Due to Advertising
Response Mode
•Max trial with
unlimited Ad
•Ad$ for 50%
max. trial
•Actual Ad $
•Max awareness
with unlimited Ad
•Ad $ for 50%
max. awareness
•Actual Ad $
Manual Mode
% buying brand in
simulated shopping
Awareness
estimate
% making first purchase
GIVEN awareness &
availability
0.42
Prob. of awareness
0.70
Distribution
estimate
Prob. of availability
0.80
Switchback rate of non
purchasers 0.16
Generalization of
Assessor
implementation
As implemented
in Assessor
Repurchase rate
for purchasers
0.42
© DecisionPro 2007
% making first
purchase due to
advertising
0.235
Long-term
market share
from advertising
0.049
Retention rate
GIVEN trial
for those who
saw ad 0.211
Source: Adapted from Thomas Burnham
Principles Chapter 5: Forecasting - 37
ASSESSOR Trial & Repeat Model
Market Share Due to Sampling
Sampling, Number
Delivered 30M
Proportion of market
using samples
12.96/40 = 0.32
Correction for
sampling/ad
overlap 0.075
Cumulative trial
(previous chart)
0.235
% Delivered 0.90
% of those delivered
hitting target 0.80
Assumes
40 million
households
in target
market
Sample use in
simulation 0.60
Switchback rate for
non-purchasers in
previous time period
Repurchase rate of
those not buying in
simulation
Net incremental
trial
0.245
Prob. of switching
to brand
0.15
Prob. of repurchase
of brand
0.26
Long term repeat
rate for sample
receivers
0.169
First repeat for
those not buying
in simulation
0.26
Long-term market
share from sampling
0.011
Source: Adapted from Thomas Burnham
© DecisionPro 2007
Principles Chapter 5: Forecasting - 38
ASSESSOR Preference Model
Summary
Pre-use preference
ratings
Pre-use constant
sum evaluations
Beta (B) for
choice model
Pre-entry market
shares
Pre-use choices
Post-use constant
sum evaluations
Post-use preference
ratings
Cumulative trial
from ad
(T&R model)
0.202
Proportion of
consumers who
consider product
0.235
Post-entry market
shares (assuming
consideration
0.243
Predicted
post entry
market shares
0.057
Draw &
cannibalization
calculations
Source: Adapted from Thomas Burnham
© DecisionPro 2007
Principles Chapter 5: Forecasting - 39
ASSESSOR Market Share to
Financial Results Diagrams
Market share
0.06
Market size
40M
Industry average
sales for realized
market share
52.8M
Average annual
sales per
household $22
Company
factory sales
49.6M
Average
unit margin
0.581
Ad/sampling
expense
4.0/6.0M
Company
factory sales
49.6M
Unit-dollar
adjustment
0.94
Frequency of use
differences
0.9
Net
Contribution
18.82M
Company
factory sales
49.6M
Price differences
1.04
Return
on
sales
38%
Note: Market share from Trial/Repeat Model: 0.060
Market Share from Preference Model: 0.057
Source: Adapted from Thomas Burnham
© DecisionPro 2007
Principles Chapter 5: Forecasting - 40
Recap
 Judgmental methods and Chain ratio approach can be applied in a
wide range of forecasting situations. We will cover one judgmental
method (Delphi method) when discussing Resource Allocation
models developed based on managerial judgment.
 Bass diffusion model is useful for forecasting the adoptions of a
new to the world product (e.g., a new technology or trend)
 Pre-test market models are useful for forecasting products that have
repeat purchase potential (e.g., consumer packaged goods).
© DecisionPro 2007
Principles Chapter 5: Forecasting - 41