Forecasting

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)

Managerial Issues Related toForecasting

• 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?

Forecasting Methods

Judgmental

Market and Survey Analysis Time Series

Causal Analyses

Sales force compositeJury of executive opinionDelphi methodsScenario analysis

Buyer intentionsProduct testsChain ratio method

Naïve methodsMoving averagesExponential smoothingBox-Jenkins methodDecompositional methods

Regression analysisEconometric modelsInput-output analysisMARMANeural networks

Methods for ForecastingNew Product Sales

Early stages of developmentChain ratio methodJudgmental methodsScenario analysisDiffusion model

Later stages of developmentPre-test market methodsTest-market methods

Chain Ratio Method(Estimate of Online Grocery Sales)

• Number of households (2000 census) 105 million

• Grocery purchases per household per year (52x120) $5300

• % of sales from Supermarkets and grocery stores 84%

(Progressive Grocer)

• Households with children (married and unmarried – Census) 35%

• % of households with Internet access (Census Bureau) 58%

• Will order groceries online if available (Survey) 25%

• Discount of survey intentions 50%

• Online grocery shopping availability (guess) 40%

• Awareness given availability (guess) 50%

Market forecast: $ ???

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

New Product Forecasting ModelsThat 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

Forecasting Based on “Newness” of Products

New to World

LoH

i

Lo Hi

New to Company

• RepositioningPre-test market model

• Line ExtensionsSimple pre-test market models (e.g., Bases)

• Breakthroughs—Major Product ModificationsBass model/Conjoint

• “Me Too” ProductsConjoint/Pre-test market models

Overview of “Stage-Gate” New Product Development Process

Opportunity IdentificationMarket definitionIdea generation

Go No

DesignIdentifying customer needs Sales forecasting

Product positioning EngineeringMarketing mix assessment Segmentation

Go No

TestingAdvertising & product testingPretest & prelaunch forecastingTest marketing

IntroductionLaunch planningTracking the launch

Go No

Go No

Life-Cycle ManagementMarket response analysis & fine tuning the marketing mix; Competitor monitoring & defenseInnovation at maturity

RepositionHarvest

The Bass Diffusion Model ofNew 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.

Graphical Representation of The Bass Model (Cell Phone Adoption)

Adoptions due to internal influence

Adoptions due to external influence

Non

-cum

ulat

ive

Ado

ptio

ns, n

(t)

Time

0153045607590

105120135150165180195210225

1997 '98 '99 '00 '01 '02 '03 '04 '05 '06

Q1 0.09 Q2 0.15 Q3 0.25 Q4 0.40

Number of Registered Users eBay (by Quarter)

million

The Bass Diffusion Model for Durables

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)= Eventual number of adopters

# Adopters = n0 + n1 + • • • + nt–1Remaining = Total Potential – # Adopters

Potential

nt = p ´ Remaining + q ´ Adopter Proportion ´

Potential Remaining Potential

Innovation Imitation

Effect Effect

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.• No repeat purchase, or replacement 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.

)1...()()]([)(

NtN

qptNNtn

N(t) : Cumulative number of adopters until time t.

Representation as an Equation

Parameters of the Bass Model in Several Product Categories

Innovation ImitationProduct/ parameter parameter

Technology (p) (q)

B&W TV 0.065 0.335Color TV 0.021 0.583Room Air conditioner 0.010 0.454Clothes dryers 0.073 0.389Ultrasound Imaging 0.003 0.506CD Player 0.028 0.368Cellular telephones 0.005 0.506Steam iron 0.036 0.318Oxygen Steel Furnace (US) 0.001 0.456Microwave Oven 0.018 0.337Hybrid corn 0.000 0.798Home PC 0.003 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 wastaken across a couple of hundred categories.

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.

Cumulative Quarter Sales Sales

Market Size = 16,000(At Start Price) 0 0 0

1 160 160Innovation Rate = 0.01 4 425 1,118

(Parameter p) 8 1,234 4,678 12 1,646 11,166

Imitation Rate = 0.41 16 555 15,106(Parameter q) 20 78 15,890

24 9 15,987Initial Price = $400 28 1 15,999

32 0 16,000Final Price = $400 36 0 16,000

Example computationsSales in Quarter 1 = 0.01 ´ 16,000 + (0.41–0.01) ´ 0 – (0.41/16,000) ´ (0)2 = 160Sales in Quarter 2 = 0.01 ´ 16,000 + (0.40) ´ 160 – (0.41/16,000) ´ (160)2 = 223.35

Forecasting Using the Bass Model—Room Temperature Control Unit

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

Some Extensions to the Basic Bass Model

• Varying market potentialAs 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 processAwareness Interest Adoption Word of mouth

• Incorporating Network Structure

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 non-obtrusive dish and equipment

• Changed economics of DTH broadcasting• 1991 DIRECTV founded

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).

Obtaining p, q, and N

• Guessed p and q from analogous previously introduced product

• 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.

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.

0

5

10

15

20

25

30

35

40

45

Definitely Will Not Buy Probably Will Not Buy Might or Might Not Buy Probably Will Buy Definitely Will Buy

Prob

abili

ty o

f Pur

chas

e

Actual Purchase Probablity Given Stated Intention for 5 Non-Durable Products Actual Purchase Probability Given Stated Intention for 5 Durable Products

Purchase Increases withStated Intention

Adjusting Stated Intentions to Get Actual Purchase Behavior

Multi-Year Forecast and Actual

Year

1992 Forecast Number of TV Homes Acquiring Satellite Television (Million)

Actual Number of TV Homes Acquiring Satellite Television (Million)

1992 Forecast of Percent of TV Homes with Satellite Television (Percentage)

Actual Yearly Percent of TV Homes with Satellite Television (Percentage)

7/01/94 - 6/30/95 0.875 1.15 0.92 1.217/01/95 - 6/30/96 2.269 3.076 2.37 3.217/01/96 - 6/30/97 4.275 5.076 4.42 5.257/01/97 - 6/30/98 6.775 7.358 6.95 7.557/01/98 - 6/30/99 9.391 9.989 9.55 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).

Multi-Year Forecast-Actual Graph

0

2

4

6

8

10

12

94-95 95-96 96-97 97-98 98-99

Year

1992 Forecast Number of TV Homes Acquiring Satellite Television (Millions)

Actual Number of TV Homes Acquiring Satellite Television (Millions)

Figure 1: The 1992 Forecast of Homes with Satellite TV tracks the Actual quite well.

Forecast 1999=9.4Million

Actual 1999= 10 Million

Using Scenario Analysisfor 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.

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.

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.

Comparative Trajectories of Population/GDP From Global Scenario Group

Conventional Worlds

Policy ReformEco-communalism

Great Transition

Breakdown

New sustainabilityparadigm

Population (billions)5 10

Gro

ss W

orld

Pro

duct

($ tr

illio

ns)

20

250

Fortress World

Market Forces

Conventional Worlds envision the global system of the twenty-first century evolving without major surprises, sharp discontinuities or fundamental transformations in the basis for human civilization. Dominant values and institutions shape the future, the world economy grows rapidly and developing countries gradually converge toward the norms set by highly industrial countries. Conventional Worlds scenarios are the subject of Bending the Curve.

This variant incorporates mid-range population and development projections, and typical technological change assumptions. The problem of resolving the social and environmental stress arising from global population and economic growth is left to the self-correcting logic of competitive markets.

Policy Reform adds strong, comprehensive and coordinated government action, as called for in many policy-oriented discussions of sustainability, to achieve greater social equity and environmental protection. The political will evolves for strengthening management systems and rapidly diffusing environmentally-friendly technology, in the context of proactive pursuit of sustainability as a strategic priority.

http://www.gtinitiative.org/gsg/gsgpub.html

BarbarizationThese scenarios envision the grim possibility that the social, economic and moral underpinnings of civilization deteriorate, as emerging problems overwhelm the coping capacity of both markets and policy reforms. Barbarization scenarios are described in detail in Branch Points.

Breakdown

In this variant, crises combine and spin out of control, leading to unbridled conflict, institutional disintegration and economic collapse.

Fortress World features an authoritarian response to the threat of breakdown. Ensconced in protected enclaves, elites safeguard their privilege by controlling an impoverished majority and managing critical natural resources, while outside the fortress there is repression, environmental destruction and misery

http://www.gtinitiative.org/gsg/gsgpub.html

Pretest Market Models• Objective

Forecast sales/share for new product before a real test market or product launch

• Conceptual modelAwareness Availability Trial Repeat

• Commercial pre-test market services– Yankelovich, Skelly, and White – Assessor– Others (e.g., BASES)

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.

Overview of ASSESSOR Modeling Procedure

Management Input(Positioning Strategy)(Marketing Plan)

Consumer Research Input(Laboratory Measures)(Post-Usage Measures)

Preference Model

Trial &Repeat Model

ReconcileOutputs

Draw &CannibalizationEstimates Brand Share

PredictionUnit SalesVolume Diagnostics

Overview of ASSESSOR Measurement Process

Design Procedure Measurement

O1 Respondent screening and Criteria for target-group identification recruitment (personal interview) (e.g., product-class usage)

O2 Pre-measurement for established Composition of ‘relevant set’ of brands (self-administrated established brands, attribute weights questionnaire) and ratings, and preferences

X1 Exposure to advertising for established brands and new brands

[O3] Measurement of reactions to the Optional, e.g. likability and advertising materials (self- believability ratings of advertising administered questionnaire) materials

X2 Simulated shopping trip and exposure to display of new and established brands

O4 Purchase opportunity (choice recorded Brand(s) purchased by research personnel)

X3 Home use/consumption of new brand

O5 Post-usage measurement (telephone 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

Predicted and Observed Market Shares for ASSESSOR

Deviation Deviation Product Description Initial Adjusted Actual (Initial – (Adjusted – Actual)

Actual)

Deodorant 13.3 11.0 10.4 2.9 0.6

Antacid 9.6 10.0 10.5–0.9 –0.5

Shampoo 3.0 3.0 3.2–0.2 –0.2

Shampoo 1.8 1.8 1.9–0.1 –0.1

Cleaner 12.0 12.0 12.5–0.5 –0.5

Pet Food 17.0 21.0 22.0–5.0 –1.0

Analgesic 3.0 3.0 2.0 1.0 1.0

Cereal 8.0 4.3 4.2 3.8 0.1

Shampoo 15.6 15.6 15.6 0.0 0.0

Juice Drink 4.9 4.9 5.0–0.1 –0.1

Frozen Food 2.0 2.0 2.2–0.2 –0.2

Cereal 9.0 7.9 7.2 1.8 0.7

Etc. ... ... ... ... ...

Average 7.9 7.5 7.3 0.6 0.2

Average Absolute Deviation — — — 1.5 0.6

Standard Deviation of Differences — — — 2.0 1.0

ASSESSOR Trial & Repeat Model Market Share Due to Advertising

•Max trial with unlimited Ad•Ad$ for 50% max. trial•Actual Ad $

•Max awareness with unlimited Ad•Ad $ for 50% max. awareness•Actual Ad $

Generalization of Assessorimplementation

% buying brand in simulated shopping

Awarenessestimate

Distributionestimate

% making first purchaseGIVEN awareness &availability0.42

Prob. of awareness0.70

Prob. of availability0.80

Switchback rate of nonpurchasers 0.16

Repurchase ratefor purchasers0.42

% making first purchase due to advertising0.235

Retention rateGIVEN trialfor those who saw ad 0.211

•Max trial with unlimited Ad•Ad$ for 50% max. trial•Actual Ad $

•Max awareness with unlimited Ad•Ad $ for 50% max. awareness•Actual Ad $

% buying brand in simulated shopping

Awarenessestimate

Distributionestimate

% making first purchaseGIVEN awareness &availability0.42

Prob. of awareness0.70

Prob. of availability0.80

Switchback rate of nonpurchasers 0.16

Repurchase ratefor purchasers0.42

% making first purchase due to advertising0.235

Retention rateGIVEN trialfor those who saw ad 0.211

Response Mode Manual Mode

Long-term market share from advertising0.049

As implemented in Assessor

Generalization of Assessorimplementation

ASSESSOR Trial & Repeat ModelMarket Share Due to Sampling

Sampling, NumberDelivered 30M

% Delivered 0.90

% of those deliveredhitting target 0.80

Sample use in simulation 0.60

Switchback rate for non-purchasers inprevious time period

Repurchase rate ofthose not buying insimulation

Proportion of market using samples12.96/40 = 0.32

Prob. of switchingto brand0.15

Prob. of repurchaseof brand0.26

Long term repeat rate for sample receivers0.169

Long-term market share from sampling0.011

First repeat for those not buying in simulation0.26

Cumulative trial(previous chart)0.235

Correction for sampling/adoverlap 0.075

Net incrementaltrial0.245

Assumes40 million householdsin targetmarket

Sampling, NumberDelivered 30M

% Delivered 0.90

% of those deliveredhitting target 0.80

Sample use in simulation 0.60

Switchback rate for non-purchasers inprevious time period

Repurchase rate ofthose not buying insimulation

Prob. of switchingto brand0.15

Prob. of repurchaseof brand0.26

Long-term market share from sampling0.011

Cumulative trial(previous chart)0.235

Long term repeat rate for sample receivers0.169

Correction for sampling/adoverlap 0.075Proportion of market

using samples12.96/40 = 0.32

Net incrementaltrial0.245

Assumes40 million householdsin targetmarket

First repeat for those not buying in simulation0.26

ASSESSOR Preference Model Summary

Pre-use constantsum evaluations

Post-use constantsum evaluations

Cumulative trialfrom ad(T&R model)0.202

Pre-use preferenceratings

Pre-use choices

Post-use preferenceratings

Proportion of consumers whoconsider product 0.235

Beta (B) forchoice model

Pre-entry market shares

Post-entry marketshares (assumingconsideration0.243

Predictedpost entrymarket shares0.057

Draw &cannibalization calculations

Pre-use constantsum evaluations

Post-use constantsum evaluations

Cumulative trialfrom ad(T&R model)0.202

Beta (B) forchoice model

Pre-entry market shares

Post-entry marketshares (assumingconsideration0.243

Predictedpost entrymarket shares0.057

Pre-use preferenceratings

Pre-use choices

Post-use preferenceratings

Proportion of consumers whoconsider product 0.235 Draw &

cannibalization calculations

ASSESSOR Market Share to Financial Results Diagrams

Market share0.06

Market size40M

Average annual sales per household $22

Company factory sales49.6M

Average unit margin0.581

Ad/samplingexpense4.0/6.0M

Net Contribution18.82M

Companyfactory sales49.6M

Industry averagesales for realized market share 52.8M

Frequency of usedifferences0.9

Unit-dollar adjustment0.94

Price differences1.04

Returnon sales38%


Note: Market share from Trial/Repeat Model: 0.060Market Share from Preference Model: 0.057


• 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).

Recap

AMF, Inc.A New-Product-Trial Case

• Jane Mosbey sat at her desk wondering what he should say at the department meeting tomorrow. She had just received the market test concerning a new product the firm had recently purchased - Suregrip. The market research group would be making a thorough study of the results, but that wouldn't be ready for weeks. Jane knew everyone would be anxious to hear her preliminary evaluation of the new product.


• The ProductJulio Tancredi was a retired chemist who once worked in 3M company's research and development department. His youngest son Mario was a gifted athlete who excelled at football as a wide receiver. Julio took great interest in his son's football career and began working on a substance that could be rubbed on the hands to increase gripping ability. This substance would increase the hand’s friction against leather considerably without being sticky and messy. Mario had used this substance throughout his college football career as well as many of his teammates. Mario commented that he would often make one handed catches that "just weren't possible without Dad's stuff!"


• AMFAMF was a fully integrated manufacturer and distributor of high quality athletic equipment. AMF enjoyed a reputation for having the best and most modern athletic equipment available and achieved distribution directly to school and professional teams as well as high quality sports specialty shops. The salesperson assigned to Mario Tancredi's college team developed an interest in this "Suregrip" product, and soon AMF offered Julio Tancredi an offer he couldn't refuse for the exclusive rights to produce and market Suregrip.


• Market TestAs good as the product seemed, AMF didn't want to go national with the product without some testing to help it determine what share of the potential market this product could obtain. AMF was also unsure if it should price the product with traditional stickum substances or should charge a premium for the stick-only-to-leather quality.

• A test was conducted for AMF using salespeople to give the product presentation to a number of their teams. The product was then offered at various prices to different teams and sales were recorded. Free samples were offered to other teams. Later the teams given the free samples, as well as those that purchased it, were asked how they liked the product, as well as if they would purchase it again. See Table 1


• The Department MeetingJane needed a way to analyze this raw data in just a couple of hours so she could give some preliminary results to the department. She remembered something about the Parfitt & Collins model from a discussion with one of the newly hired MBA's and dug out the diskette that the new-hire had given her.

Table 1Suregrip Market Test

Sales demonstration $5.00 $3.50 $2.00

Trial 13 18 20

Intent to repurchase 10 15 18

Base # of teams 20 20 20

Sampling based share

Samples delivered 15 15 15

Samples used 13 12 14

Intent to repurchase 13 10 13

Base # of samples 15 15 15

DAIRY RESEARCH INCORPORATED: MOOSODAA Trial-Repurchase Case

• Anthony Luksas, president of Dairy Research, Inc. (DRI), was looking over data he had just received from the first market test of MooSoda. In wondering how well the new product would sell, Luksas commented, "If we could just capture 2 or 3 percent of the carbonated beverage market, we could wipe out the dairy surplus we have each year."

• DRI was a research group formed by the Dairy Farmers of America to increase dairy product consumption by developing new uses for dairy products, thus expanding the dairy product market. The idea for MooSoda, a carbonated milk drink, was stimulated by two factors:


• The dairy industry had been suffering from a surplus in milk production over the past several years, due primarily to a decrease in per capita milk consumption. Recently, the dairy industry reported an excess of 10-20 billion pounds per year.

• -There was an enormous increase in the consumption of carbonated beverages. Currently, carbonated beverages constituted 53% of the total U.S. beverage consumption, with wholesale sales totaling $15.6 billion.


• In October, a test market was conducted as the next step in MooSoda's product development. Luksas' job now was to determine the optimal strategy for the introduction of MooSoda. Specific questions that Luksas needed to answer included:

• 1. Will MooSoda gain sufficient market share to be profitable and to reduce the milk surplus in America?

• 2. Will positioning MooSoda as a "nutritious carbonated beverage," be effective? (DRI had adopted the strategy of positioning MooSoda as a carbonated beverage rather than as a milk substitute in order to reduce cannibalization of other dairy products.)

• 3. Is the advertising copy effective in generating awareness and trial?• 4. What will be the reaction of other carbonated beverage producers

to MooSoda?


• Test MarketDRI conducted market tests in Saint Louis. A television ad campaign was developed, featuring MooSoda as a nutritious, refreshing alternative to soda drinks. The campaign was released simultaneously with the introduction of MooSoda on the shelves of major grocery chains in each city. After three weeks, 300 people were contacted by telephone, using a random digit dialing system that generated phone numbers to call. The same sample of 300 was contacted every month for three months thereafter. Each person was asked the following:

• 1. Have you heard of MooSoda? (awareness)• 2. Have you purchased MooSoda? (trial)• 3. If you have, how many times have you purchased it? (repeat and

index)


• AnalysisAs Luksas reviewed the results of the test market, he realized that he needed to summarize the results of the tests. He had recently attended a conference presented by Parfitt and Collins at the New York Hilton, that introduced him to their market share prediction model. Rummaging through his desk, he found his conference notes, and proceeded to analyze the data with the Parfitt and Collins model.

• Using the model, Luksas hoped to predict fourth-period MooSoda share of the $5 billion retail sales carbonated beverage market, and to be able to recommend a positioning strategy for the product. He realized he needed about 3 percent of the market to make much of a dent in the mild surplus. The DRI market test did not reveal data on the cannibalization of milk products.

TABLE 1DRI TEST MARKET RESULTS

PeriodCumulativePenetration

RepeatPurchase

Buying RateIndex

1 111 33 .5

% 37 11

2 84 51 .5

% 28 17

3 63 57 .6

% 21 19

4 57 69 .65

% 19 23

Periods 1 & 2: Average number of purchases per week for repeat users = 2.5Periods 3 & 4: Average number of purchases per week for repeat users = 3Industry average purchases per week for repeat users = 5

Nike: Air JordanA New Product Case

• Background• Michael Jordan played college basketball for the University of

North Carolina, leading them to the NCAA championship in 1983. After leaving UNC he continued with basketball by playing professionally for the Chicago Bulls, and received nationwide attention and respect from both fans and players. NIKE took advantage of his new fame and developed the Air-Jordan basketball shoe.

• The competition for the new shoe consisted of a variety of firms that also solicit the services of professional athletes. One such firm is Converse, which employed Larry Bird and Earvin "Magic" Johnson for promotion of a line of basketball shoes


• In deciding upon an introductory strategy for its new shoe line (or even whether it should be introduced at all), Nike decided to use the Fourt and Woodlock model of product penetration.

• Using this model required them to project the cumulative penetration percentage, considering seasonal demand. They kept in mind that periods one, three, and five include the months of September through February -- peak times for the sale of basketball shoes. They also reminded themselves that periods two and four, representing March through August, indicate times where interests are on other sports, not basketball. However, basketball shoes are still being sold.


• Market ForecastUsing the Fourt and Woodlock model, Nike decided to use the diffusion model for Air-Jordan basketball shoes to forecast a for a market segment that includes a population of 60,000. Nike projected the percentage market penetration would be 9,000, or 15% of the population.

• Realizing that Michael Jordan is a role model for many junior high and high school basketball players, Nike further predicted that the 16% cumulative percentage penetration would exceed the 15% forecast. Moreover, they recognized that this would increase projected sales to 9,600 units, 600 over the initial projection. Because of "off-season" demand the second and fourth period projections would drop off. See Figure 1


• Nike's third period projections indicate a drop in unit sales. They reasoned that consumers would have reasons for not buying Air-Jordan again. These include dissatisfaction with the shoe, price being out of purchasing range, and even competitors signing new talent to promote their own shoes.

• Nike's fifth period projections go up compared to the third period because they assumed that the Chicago Bulls will be a dominant force in the NBA, with Michael Jordan leading the way. This will increase recognition of the Air-Jordan, and increase the market share of the new shoe.


• Nike's third period projections indicate a drop in unit sales. They reasoned that consumers would have reasons for not buying Air-Jordan again. These include dissatisfaction with the shoe, price being out of purchasing range, and even competitors signing new talent to promote their own shoes.

• Nike's fifth period projections go up compared to the third period because they assumed that the Chicago Bulls will be a dominant force in the NBA, with Michael Jordan leading the way. This will increase recognition of the Air-Jordan, and increase the market share of the new shoe.


If Nike's projections failed to meet expectations, then their forecast would need to be recalculated to determine the market share for the Air-Jordan.

FOURT AND WOODLOCK

MODEL

Model Inputs# OF RPTS

Rpt purchase ratios

Average price /unit $50 1 16%2 14.50%

Market size in units 60000 3 15.30%4 14.9

Maximum Mkt share% 15% 5 16%

% rate of market penetration of untapped potential 10%

Period First purchase Rpt Purchase Cumulative Sales Forecast by period

Unit sales Unit sales1 900 0 900 45000

2 810 144 1854 47700

3 729 150 2733 43950

4 656 139 3528 39736

5 590 125 4243 35786

6 531

QUITE WRITE, INC.A Product Portfolio Case

• Quite Write, Inc., was established as a manufacturer of mechanical pencils in Kingsport, Tennessee, in 1923. Since then, the company had grown and diversified into many fields, including pens and refillable cigarette lighters, as well as camera lenses. In the mid 1970s, international operations were set up in Europe, Brazil, and Canada, and Quite Write began to be very profitable.

• However, competition began to be increasingly fierce, and several major companies, especially Bic, Pentel, and Gillette, challenged Quite Write's position in its key markets, writing instruments and cigarette lighters. By early 1999, Quite Write was no longer a major competitor, and the company lacked a standout product. Quite Write had a full line of pens, pencils, and lighters, but no one product was an exceptional money maker, and the international markets were losing money.


• Last April, a new president, Helen Timms, was appointed. She took over operations after the company had recorded losses for three years in a row, and the board of directors wanted her to define a new strategy. Timms had worked for an eastern consulting firm for the past five years, and came from a marketing background. Part of his job would be to consolidate existing operations and eliminate unprofitable items from Quite Write's product lines.

• After some preliminary research, Timms determined that the market for a disposable pipe/cigar lighter had not yet been tapped, and she thought this would be a good product to develop. But Quite Write would have to enter the market quickly, since both Bic and Gillette were developing these heavy duty disposable lighters. Timms remembered how Bic had virtually created the market for disposable stick pens and was now the market leader. In fact, Bic was largely responsible for Quite Write's poor performance in the writing instruments market.


• If Quite Write wanted to get a jump on Bic and Gillette, it would have to develop a lighter and enter the market in the near future. However, Quite Write was in financial trouble, and didn't have the funds to finance a new product line. If Quite Write could enter the market quickly, Timms figured they could easily capture a 30-40% share of a market she estimated to be $50,000,000 a year and growing. If the disposable lighter was a success, she believed the market would grow 20% a year for the first few years.

• With approval of the board of directors, Quite Write's European operations and camera lens production facilities were sold to pay off existing debts and to provide some funds for entry into the disposable cigarette lighter market. However, Timms still needed to eliminate the unprofitable items from Quite Write's product lines, and determine Quite Write's relative product strengths.


• Quite Write's main products in the writing instruments market included retractable and nonretractable ball-point pens, and mechanical pencils. The ball point pen market had been very successfully exploited by Bic in the early sixties with the introduction of the $.49 nonretractable stick pen. The stick pen successfully competed with pencils and captured a large portion of the market belonging to pencils. Pentel had also introduced a line of retractable and nonretractable roller-ball pens that competed with Quite Write. Growth in this market had slowed to 6-8 percent a year, and total annual sales were estimated to be $120,000,000 in 1999. Table shows the comparative market shares for Bic, Quite Write, and Pentel.


• Quite Write's mechanical pencil was doing slightly better than its line of pens. In 1999, Quite Write held a 14% share of the mechanical pencil market, compared with 22% share for Bic and an 11% share for Pentel. Total 1999 estimated sales of mechanical pencils was $70,000,000, and the market was growing at a 10% annual rate.

• Quite Write had established itself in the refillable lighter market in the late seventies, and competed on the higher priced models. Ronson, Scripto, and Zippo were its main competitors. Fuel and accessory sales also counted for a portion of Quite Write's sales. Table 6-5 shows the 1999 refillable lighter retail sales. Lighter and accessory sales had been good money makers for Quite Write in the past. However with the litigation troubles in the tobacco industry, and the expected decrease in cigar and pipe sales, Timms wasn't sure, that this market would continue to grow at as it had in earlier years.


• If the company were to survive, Timms had to decide what to do with the product lines. What were Quite Write's competitive advantages? Since only limited financial resources were available, Timms had to decide which products to emphasize and which products to drop. Since Quite Write couldn't support all the existing products and introduce a disposable lighter, Timms needed to weed out the "dogs" from the product portfolio. She also had to adapt a long run strategic policy that would allow Quite Write not only to survive, but emphasize a select few products that would make money.

TABLE 6-4 1999 ESTIMATED PEN MARKET SHARES

Pentel Bic Quite Write

$.49 Roller-ball pens:

Price: $.49 $.49 $.49

Market Share (%) 5 31 1

All Pens:

Price: $.49-1.98 $.49-1.98 $.49-1.98

Market Share (%) 15 66 2

TABLE 6-5 1999 REGULAR REFILLABLE LIGHTER

RETAIL SALES

Estimated Lighter Sales

Estimated Fuel and Accessories Sales

Ronson $16,672 $26,676

Quite Write 18,339 4,335

Zippo 31,678 1,667

Scripto 16,888 3,355

Total Market $109,000 $38,250

Documents

Forecasting