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Customer Measurement Process

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Title: Customer Measurement Process


1
Customer Measurement Process
Decision Requirements
Research Methodology
Sampling
Measurement Instruments
Data Analysis
Decision Models
2
Customer Measurement Process
  • The success of a proactive design strategy
    depends on determining a product strategy that
    will be attractive to customers.
  • Customer measurement is critical to the success
    of new product strategy.
  • Research all the critical aspects of product
    response and integrate the findings into the
    decision process.
  • Technical breakthroughs can be the basis of
    successful products if the innovators
    customer-need intuition is correct.

3
Research Methodology
  • Archival Data
  • Important early in the process
  • Internal sources
  • Sales, advertising records, complaint/compliment
    files, warranty records.
  • External Sources
  • Government records, census data, county business
    patterns, Current survey of Business, Export
    shipments, etc.
  • Other data services, IRI, Nielsen, Arbitron, etc.
  • Use with caution because it typically has been
    collected for different purposes than what you
    want to use it for.
  • Remember to examine the definitions of the
    categories. See if this fits with your
    definition.
  • Advantage is that it is cheap, quickly obtained,
    and can be used as an external validation of your
    findings.

4
Research Methodology
  • Qualitative Measurement
  • Its purpose is to raise issues by exploring the
    customers basic needs and desires.
  • It is important any lists be exhaustive, covering
    a wide array of potential benefits.
  • Focus groups or personal interviews.

5
Research Methodology
  • Quantitative Measurement
  • Provides the input to the analytical techniques
    used that identify specific strategies that make
    the greatest impact on improving the success of a
    new product design.
  • Used to measure attitudes and customer response.

6
Sampling
  • Is difficult with a new product. Traditional
    methods are not as useful but they do provide a
    guideline.
  • Judgment, norms, and experience are the best
    guide for sampling.
  • Purposive samples can be used if they are done
    carefully and steps are taken to correct any
    biases.
  • Attractive due to reduced cost
  • In forecasting, representativeness is important

7
Measurement Instruments
  • Questionnaire Design
  • Its part art and part science.
  • Respondents must be motivated to participate.
  • Better design leads to better data and ultimately
    better results
  • Respondents must have the ability, knowledge, and
    willingness to answer your questions.
  • Must check for bad respondents.

8
Pitfalls
  • Wrong Semantics
  • Dont use jargon
  • Products are unfamiliar to respondents
  • No pretesting
  • No preanalysis
  • Poor Sampling
  • Under budgeted Market Research

9
Attitude Scaling
  • Used primarily to measure the customer needs that
    are based on the construct that have been
    identified in qualitative research.
  • Also measure tradeoff consumers are willing to
    make, demographics, preferences etc.
  • Likert Scales
  • Typically 1-5 or 1-7 with strongly agree/strongly
    disagree endpoints.
  • Semantic Differential
  • Give a respondent bipolar adjectives and let the
    respondent chose.
  • Graphical
  • Itemized
  • Attribute description and a response for each
    item i.e., very poor to excellent
  • Pairs
  • Allocate points between pairs.

10
Market Segmentation
  • Market segmentation is the subdividing of a
    market into distinct subsets of customers.
  • Segments
  • Members are different between segments but
    similar within.

11
Segmentation Marketing
  • Definition
  • Differentiating your product and marketing
    efforts to meet the needs of different segments,
    that is, applying the marketing concept to market
    segmentation.

12
Primary Characteristicsof Segments
  • Basescharacteristics that tell us why segments
    differ (eg, needs, preferences, decision
    processes).
  • Descriptorscharacteristics that help us find and
    reach segments.
  • (Business markets) (Consumer markets)
  • Industry Age/Income Size Education Locati
    on Profession Organizational Life styles
    structure Media habits

13
A Two-Stage Approachin Business Markets
  • Macro-Segments
  • First stage/rough cut
  • Industry/application
  • Firm size
  • Micro-Segments
  • Second-stage/fine cut
  • Different customer needs, wants, values within
    macro-segment

14
Relevant Segmentation Descriptor
Variable A Climatic Region 1. Snow Belt
2. Moderate Belt 3. Sun Belt
Fraction of Customers
Segment 1
Segment 2
Segment 3
0
100
Likelihood of Purchasing Solar Water Heater (a)
15
Irrelevant Segmentation Descriptor
Variable B Education 1. Low Education
2. Moderate Education 3. High Education
Fraction of Customers
Segment 1
Segment 2
Segment 3
0
100
Likelihood of Purchasing Solar Water Heater (b)
16
Variables to Segmentand Describe Markets
17
Segmentation in Action
  • A Marriott Hotel used to be a Marriott Hotel An
    upscale hotel catering to business people,
    pleasure seekers and international and group
    travelers. Today, besides the Marriott Hotels
    (the companys major business), there are
    Marriott Suites, Residence Inns, Courtyards by
    Marriott and Fairfield Innseach serving a
    smaller, targeted segment of the market.
  • Lenneman and Stanton, Mining forNiches,
    Business Horizons.

18
Segmentation in Action
  • We segment our customers by letter volume, by
    postage volume, by the type of equipment they
    use. Then we segment on whether they buy or
    lease equipment.
  • Based on this knowledge, we target our marketing
    messages, fine tune our sales tactics, learn
    which benefits appeal to which customers and zero
    in on key decision makers at a company.
  • Kathleen Synnot, VP, Worldwide Marketing
    Mailing Systems Division, Pitney Bowes, Inc.
  • quoted in Marketing Masters (Walden and Lawler)

19
Customers Diverse Needs Require Diverse Channels
  • Kodak increases customer contact and support with
    a three tiered distribution system.
  • . . . Business Imaging Division created three
    avenues for marketing microfilm, supplies and
    imaging systems and software
  • direct sales reps (for more complex systems)
  • brokers and distributors (for film sales and
    delivery)
  • Components Marketing Division (to sell to system
    integrators and VARs).
  • Business Marketing

20
Ad in London Newspapers, 1900
  • Men wanted for hazardous journey. Small wages,
    bitter cold, long months of complete darkness,
    constant danger, safe return doubtful. Honor and
    recognition in case of success.
  • Ernest Shakleton, Arctic Explorer
  • Did it work?

21
Segmentation
  • If youre not thinking segments, youre not
    thinking. To think segments means you have to
    think about what drives customers, customer
    groups, and the choices that are or might be
    available to them.
  • Levitt, Marketing Imagination

22
Segmentation Marketing Implies a Market
  • A market consists of all the potential customers
    sharing a particular need or want who might be
    willing and able to engage in exchange to satisfy
    that need or want.
  • Kotler, Marketing Management

23
Market Definition
Customer-Need Set 1 (Market 1)
Product 1
Technology A
Customer-Need Set 2 (Market 2)
Technology B
  • ð Common customer needs define a market not a
    product.

24
Implications
  • 1. Segmentation defines common customer needs.
  • 2. Those common needs may be satisfied by
    similar or dissimilar technologies or have
    different solutions.
  • Ex Customer dissatisfaction at long delays at
    supermarket checkout.
  • Solution 1 Faster UPC scanner systems.
  • Solution 2 Entertainment/Sales systems on
    checkout lines.
  • Note Total solution defines (competitive)
    market, not product or technology.

25
Market Definition Approaches
  • Customer-Behavior
  • Demand cross elasticity
  • Brand/product switching
  • Perception/Judgment
  • Engineering/technological substitution
  • Customer judgments/perceptual mapping

26
Why is Market Definition Important?
  • Strategy(What to focus on).
  • Resource allocation(How much/where/when?).
  • Feedback/performance measurement(How well are we
    doing? How can we learn from our actions?).

27
Electric Typewriter Market
  • 1980 1981 1982 1983 1984 1985
  • Shipments
  • A (Us) 403,027 495,192 548,905 550,351 541,388 515
    ,000B 369,916 388,520 349,396 323,005 342,197 297
    ,000Other 367,057 324,010 343,885 370,374 202,495
    129,070Total 1,140,000 1,207,722 1,242,186 1,243
    ,730 1,086,080 941,070
  • Market Shares ()
  • A (Us) 35.4 41.0 44.2 44.2 49.8 54.7B 32.4 32.2 2
    8.1 26.0 31.5 31.6Other 32.2 26.8 27.7 29.8 18.6
    13.7

28
Word Processor Market
1980 1981 1982 1983 1984 1985
Shipments A (Us) 403,027 495,192 548,905 550,
351 541,388 515,000B 369,916 388,520 349,396 323,
005 342,197 297,000Other Electric 367,057 324,010
343,885 370,374 202,495 129,070 Electronic
WordProcessors 60,040 112,220 209,800 392,352 733
,699 1,372,016 Total 1,200,040 1,319,942 1,451,986
1,636,082 1,819,778 2,313,086 Market Shares ()
A (Us) 33.6 37.5 37.8 33.6 29.8 22.3B 30.8 29.
4 24.1 19.7 18.8 12.8Other Electric
30.6 24.5 23.7 22.6 11.1 5.6 Electronic
WordProcessors 5.0 8.5 14.4 24.0 40.3 59.3
29
Market Definitionby Switching Behavior
  • Current
    Purchase Occasion
  • Diet Diet Diet Coke Coke Pepsi Pepsi Spr
    ite Sprite Total
  • Coke 53 9 27 4 5 2 100Last Diet
    Coke 12 61 4 15 2 5 100Purchase Pepsi 24
    3 58 9 5 1 100Occasion Diet
    Pepsi 4 14 11 63 2 6 100 Sprite 21 2 17
    3 52 6 100 Diet Sprite 2 15 2 12 7 61
    100

30
STP as Business Strategy
  • Segmentation
  • Identify segmentation bases and segment the
    market.
  • Develop profiles of resulting segments.
  • Targeting
  • Evaluate attractiveness of each segment.
  • Select target segments.
  • Positioning
  • Identify possible positioning concepts for each
    target segment.
  • Select, develop, and communicate the chosen
    concept.
  • to create and claim value

31
STPing the Market for Eggs
  • Segments Disinterested consumers Casual Egg
    UsersHealth conscious consumers Enthusiastic
    users
  • Profiles BeliefsAttitudesLifestylesHealth/Nutr
    ition consciousnessMedia habitsConsumption
    habitsDemographics
  • Frank and Phillips,Agribusiness, July 1990

32
Targeting and Positioning
33
Overview of Marketing Engineering Methods for STP
  • Clustering and discriminantanalysis (PDA
    exercise)
  • Choice-based segmentation(ABB Electric)
  • Perceptual mapping(G20 exercise)

34
Segmentation (for Carpet Fibers)
Perceptions/Ratings for one respondent Customer
Values
..
..
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D
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A
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Strength (Importance)
..
..
Distance between segments C and D
.
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.
B
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C
.
.
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.
.
A,B,C,D Location of segment
centers. Typical members A schools B light
commercial C indoor/outdoorcarpeting
D health clubs
.
.
.
.
Water Resistance (Importance)
35
Targeting
Segment(s) to serve
.
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Strength(Importance)
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Water Resistance (Importance)
36
Positioning
Product Positioning
.
.
Us
.
Comp 1
Comp 2
Strength(Importance)
Water Resistance (Importance)
37
A Note on Positioning
  • Positioning involves designing an offering so
    that the target segment members perceive it in a
    distinct and valued way relative to competitors.
  • Three ways to position an offering
  • 1. Unique (Only product/service with XXX)
  • 2. Difference (More than twice the feature
    vs. competitor)
  • 3. Similarities (Same functionality as
    competitor lower price)
  • What are you telling your targeted segments?

38
Steps in a Segmentation Study
  • Articulate a strategic rationale for segmentation
    (ie, why are we segmenting this market?).
  • Select a set of needs-based segmentation
    variables most useful for achieving the strategic
    goals.
  • Select a cluster analysis procedure for
    aggregating (or disaggregating customers) into
    segments.
  • Group customers into a defined number of
    different segments.
  • Choose the segments that will best serve the
    firms strategy, given its capabilities and the
    likely reactions of competitors.

39
Total Customer Value
  • Functional Value
  • (What does this product do for me?)
  • Supplier/Service Value
  • What does the product mean to me?
  • (What is the insurance? service? psychological?
    value of the product or supplier?)

Price/Performance
Advertising Selling Service Efforts
40
Customer Value Assessment Procedures
  • Attitude-Based Behaviour-Based Inferential/Value
    Based Choice models Internal
    engineering assessment Neural networks
    Indirect survey questions Discriminant
    analysis Field value-in-use assessment
  • Indirect/(Decompositional Methods) Direct
    Questions Conjoint analysis Preference
    Regression
  • Unconstrainted Constrained/Compositional
    Methods Focus groups Multiattribute value
    analysis Direct survey questions
    Benchmarking Importance and attitude ratings
    Rule-based system/AI/expert systems

41
Segmentation Methods Overview
  • Factor analysis (to reduce data before cluster
    analysis).
  • Cluster analysis to form segments.
  • Discriminant analysis to describe segments.

42
Cluster Analysis forSegmenting Markets
  • Define a measure to assess the similarity of
    customers on the basis of their needs.
  • Group customers with similar needs. The software
    uses the Wards minimum variance criterion and,
    as an option, the K-Means algorithm for doing
    this.
  • Select the number of segments using numeric and
    strategic criteria, and your judgment.
  • Profile the needs of the selected segments (e.g.,
    using cluster means).

43
Cluster Analysis Issues
  • Defining a measure of similarity (or distance)
    between segments.
  • Identifying outliers.
  • Selecting a clustering procedure
  • Hierarchical clustering (e.g., Single linkage,
    average linkage, and minimum variance methods)
  • Partitioning methods (e.g., K-Means)
  • Cluster profiling
  • Univariate analysis
  • Multiple discriminant analysis

44
Doing Cluster Analysis
a distance from member to cluster
center b distance from I to III
45
Single Linkage Cluster Example
  • Distance Matrix
  • Co1 Co2 Co3 Co4 Co5
  • Company 1 0.00Company 2 1.49 0.00Company
    3 3.42 2.29 0.00Company 4 1.81 1.99 1.48 0.00C
    ompany 5 5.05 4.82 4.94 4.83 0.00

ResultingDendogram
1
2
3
Company
4
5
1
2
3
4
5
Distance
46
Wards Minimum Variance Agglomerative Clustering
Procedure
  • First Stage A 2 B 5 C 9 D 10 E 15
  • Second Stage AB 4.5 BD 12.5
  • AC 24.5 BE 50.0
  • AD 32.0 CD 0.5
  • AE 84.5 CE 18.0
  • BC 8.0 DE 12.5
  • Third Stage CDA 38.0 CDB 14.0 CDE 20.66 AB
    5.0
  • AE 85.0 BE 50.5
  • Fourth Stage ABCD 41.0 ABE 93.17 CDE
    25.18
  • Fifth Stage ABCDE 98.8

47
Wards Minimum Variance Agglomerative Clustering
Procedure
98.80
25.18
5.00
0.50
A
B
C
D
E
48
Interpreting Cluster Analysis Results
  • Select the appropriate number of clusters
  • Are the bases variables highly correlated?
    (Should we reduce the data through factor
    analysis before clustering?)
  • Are the clusters separated well from each other?
  • Should we combine or separate the clusters?
  • Can you come up with descriptive names for each
    cluster (eg, professionals, techno-savvy, etc.)?
  • Segment the market independently of your ability
    to reach the segments (ie, separately evaluate
    segmentation and discriminant analysis results).

49
Profiling Clusters
Two Cluster Solution for PC Data Need-Based
Variables
1
Design
Means of Variables
0
Business
1
size
power
office use
LAN
storage needs
color
periph.
wide connect.
budget
50
Which Segments to Serve?Segment Attractiveness
Criteria
51
Selecting Segments to Serve
E
Strong
Firms Competitive Position
B
Medium
D
A
C
Weak
Low
Average
High
Segment Attractiveness
52
Discriminant Analysis forDescribing Market
Segments
  • Identify a set of observable variables that
    helps you to understand how to reach and serve
    the needs of selected clusters.
  • Use discriminant analysis to identify underlying
    dimensions (axes) that maximally differentiate
    between the selected clusters.

53
Two-Group Discriminant Analysis
XXOXOOO XXXOXXOOOO
XXXXOOOXOOO XXOXXOXOOOO XXOXOOOOOOO
Price Sensitivity
X-segment
Need for Data Storage
O-segment
x high propensity to buy o low propensity
to buy
54
Interpreting Discriminant Analysis Results
  • What proportion of the total variance in the
    descriptor data is explained by the statistically
    significant discriminant axes?
  • Does the model have good predictability (hit
    rate) in each cluster?
  • Can you identify good descriptors to find
    differences between clusters? (Examine
    correlations between discriminant axes and each
    descriptor variable).
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