New Product Management

New Product Management

•COMPONENTS OF DESIGN

–Specify:

•"WHO"1. Target Consumers

•"WHAT"2. Core Benefit Proposition (CBP)

•"WHY"3. New Product Position

vs.

Competition Product Position

•"FEATURES" 4. Physical Characteristics of Product ---->CBP

•"MARKETING

MIX"5. Initial price, Advertising & Distribution survey

THE DESIGN PROCESS

OpportunityConsumer Measurement

Identification1. Qualitative

2. Quantitative

Refinement

* MKTG.MODELS OF CONSUMERSSURVEYS

* R & D* Perception

* ENGR.

* PRODCTN.* PreferenceFOCUS

EvaluationGROUPS

Go / On / No Go * Segmentation

IN-DEPTH

GO No Go* ChoiceINTERVIEWS

T D

e r

s o

t pPREDICT MARKET BEHAVIOR

i

n

g

BEST CBP ====> 4 P's

THE DESIGN PROCESS

SURVEYS FOCUS GROUPS IN-DEPTH

•To identify important attributes & predict how a new product concept will be perceived.

•Identify ideal vector & predict how new product will be compared to existing products.

•Determine best strategy to serve target customers.

•Determine what to control to ensure purchase & predict probability that consumer chooses product.

–Aggregate individuals perceptions, etc. yielded by above models.

–Measure awareness & availability.

PRODUCT PERCEPTUAL MAPPING & POSITIONING

Perceptual Mapping & Positioning: Measurement techniques to reveal how consumers mentally compare products or brands.

1. Managerial Requirements

a. Abstract & label underlying dimensions.

b. Position existing products on these dimensions.

c. Identify consumer preferences on these dimensions.

d. Identify new product opportunities on perceptual map.

e. Determine the physical features which correspond to the perceptual position.

f. Design (or modify) own product to fit in the BEST position.

PRODUCT PERCEPTUAL MAPPING & POSITIONING

Perceptual Mapping & Positioning:

Methods: Producing positioning graphs

–* Perceptual mapping techniques

–* Joint space techniques:

adding preference vectors or ideal points

–Perceptual Space - Multidimensional

•Axes - General Properties/ Benefits of Brands

•Brand Similarity - Inversely Proportional to Distance between Brands.

DESIGN PROCESS

Joint Space Analysis (JSA)

A. Perceptual Space Construction Issues

1. Methods: Composition vs. Decomposition

2. Choosing Brands & Attributes (Evoked set?)

3. Alternative method: Discriminant Analysis

4. Alternative method: Correspondence Analysis

B. Joint Space Construction Issues

Adding Preferences: Two Methods

1. Ideal Points

2. Preference Vectors

DESIGN PROCESS

Joint Space Analysis (JSA)

C. Operational Techniques

1. Package Programs (SAS, SPSS, etc.)

2. Specialized Packages (e.g., Adaptive Perceptual Mapping; Marketing Engineering)

D. Interpretation

1. Perceptual Space

2. Joint Spaces

3. Benefit segments

CONSTRUCTING PERCEPTUAL SPACES (In General)

DecompositionComposition

- Similarity Scaling- Rate Brands on Attributes

( 5 to 10 pt. scale)

–e.g., MDS- Space Reduction:

Factor Analysis

Discriminant Analysis

A * useful for exploratory work

speedier==> Option: Contingency Data --

A * attributes do not need

to be defined Dichotomous scale --

* Possess attribute or not.

D * Respondent must be familiar with

large number brands Use correspondence analysis.

D * Interpretation of axes difficult

-- manager judgment

PERCEPTUAL MAPPING TECHNIQUES

MDS FACTOR ANALYSIS

(Decomposition)(Composition)

A. Data Objective:

1. Similarities1. Attribute rating

2. Recovering ranking2. Weighted summary of ratings

B. Input level:

Individual ====> Aggregated 1. Individual ====> Aggregated

PERCEPTUAL MAPPING TECHNIQUES

MDS FACTOR ANALYSIS

(Decomposition) (Composition)

C. Advantages:

1. Attribute set not required1. Easier to name dimensions - - use attribute factor loadings.

2. Indirect measure 2. Statistical analysis readily available.

===> More honest answer

3. Perceived "Product" Similarity

D. Disadvantages:

1. Requires special program 1. Require complete set of product

to create similarity matrix attributes.

2. Can't use for less than 8 "brands" 2. May ignore important attributes.

3. Comparing N(N-1)/2 pairs is3. Halo-effect

exhausting.

4. Respondent must be familiar with

large number brands

PERCEPTUAL MAPPING TECHNIQUES

Choosing Brands & Attributes: Salient?

Sources:1. ManagersEvoked set

2. ConsumersConsideration set

Brands- Number ?

(Recall) - Which ?

-- w/in 1 product class ??

Attributes - Cognitive Benefits

(importance - Affective vs.

in decision) Features

Use Principle Components Analysis to reduce set size

( not to collapse into factors -- see which are related).

COMPETITIVE SET CONSTRUCTION

Joint Space Construction

-- adding some measure of preference

(ideal points or preference vectors)

to perceptual map

Determine:

* Most preferred attribute/ benefit combination.

* Segments based on ideal points/ preference vectors.

(Benefit segmentation)

Preference Regression

-- Application --

where:p = preference rating

w = est. importance weight

x = individual's perception of producer

i = individual or person

j = product or brand (eg. Toyota)

k = dimension (eg. performance)

Xijk obtained from:

1 MDS - similarity dimension

or

2 factor scores

pij obtained from transformed preference rankings = J - rij

where:J = number of brands

rij = rank preference

wk is obtained from regression analysis

Preference Regression

Compare:

product / brand

forecast preference

to

actual preference

Should = 40 to 80% accuracy

Market Share: % who prefer each brand j.

COMPETITIVE SET CONSTRUCTION

Joint Space Interpretation

Brand Projection:

* perpendicular link : brand onto preference vector

* Longitudinal:

During Product Development

During Product Life Cycle

* Map segments onto Geodemographic/socioeconomic profiles.

* Improving Brand Performance

* What combo of marketing - mix for each?

1. - change brand perceptions

2. - change ideal points

3. - change attribute importance weights

4. - add new attributes to achieve (3)

PERCEPTION PREFERENCE FEATURES

PreferenceSelf-reportConjointLogit

RegressionImportanceAnalysisAnalysis

Data:

Preference ratingsAttribute PreferenceChoice importance Ratings

Object:

Explain ratingsWeight relative RevealExplain

importanceTrade-offChoice

Method:

RegressionRegressionMonAnovaMaximum Likelihood

Input level:

Individual/IndividualIndividualAggreg.

aggregated

PreferenceSelf-report ConjointLogit

RegressionImportanceAnalysisAnalysis

Advantages:

Conceptually EasyUse hypotheticalPredict

simple Direct profilesMarket

.: can predict Share

Easy to analyze future

opportunities

Force rankingExplain

preferencemany

.:can Attributes

distinguish

order ofMore

importanceAccurate

PreferenceSelf-reportConjointLogit

RegressionImportanceAnalysisAnalysis

Disadvantages:

Average weights SubjectiveDifficult Complex

may mislead.: unstable to use

.: not good for if too

Heterogeneous many

Populationsattributes

ADAPTIVE PERCEPTUAL MAPPING - SAWTOOTH SOFTWARE

Hybrid Approach

Individual DataAggregate Data

Method

Discriminant X

Prin ComponentsX

Brand Familiar XX

Attribute Importance XX

Preference f (Indiv.

Perception)X

f ( Avg. Perception) X

Preference

Ideal Point

Self-RatedXX

Estimated

Pref. Vector