5.1 Customer Identification and Segmentation

ASQ Certified Six Sigma Black Belt CSSBB

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1 Chapter 1: Introduction & CSSBB Exam Strategy

1.1 CSSBB Exam Format, Scoring, and Testing Windows 1.2 Eligibility, Project Affidavits, Application, and Recertification 1.3 Open-Book Strategy, Reference Materials, and a 16-Week Study Plan

2 Chapter 2: Organization-Wide Planning and Deployment

2.1 Fundamentals of Six Sigma and Lean Methodologies 2.2 Selecting the Right Methodology: DMAIC, DFSS, and Lean 2.3 Business Systems, Processes, and Stakeholder Relationships 2.4 Strategic Planning and Deployment of Six Sigma Initiatives 2.5 Six Sigma Roles and Responsibilities 2.6 Organizational Barriers and Common Causes of Six Sigma Failure 2.7 Change Management: Stakeholder Analysis, Readiness, and Communication

3 Chapter 3: Organizational Process Management and Measures

3.1 Impact of Six Sigma Projects on Customers, Suppliers, and Stakeholders 3.2 Benchmarking Types and Performance Goal Setting 3.3 Performance Measures: Balanced Scorecard, KPIs, KBIs, OKRs, and Line of Sight 3.4 Leading and Lagging Indicators and Performance Dashboards 3.5 Financial Measures: Revenue Growth, Margin, NPV, ROI, and Cost-Benefit Analysis 3.6 Cost of Poor Quality and Hard versus Soft Savings

4 Chapter 4: Team Management

4.1 Team Types and Constraints 4.2 Team Roles, Responsibilities, and Member Selection 4.3 Team Success Factors and Ground Rules 4.4 Motivational Techniques and Overcoming Demotivators 4.5 Stages of Team Development and Conflict Resolution 4.6 Team Communication Plans 4.7 Team Leadership Models: Direct, Coach, Support, and Delegate 4.8 Group Behaviors, Conflict Resolution, and Meeting Management 4.9 Facilitation Techniques and Team Decision-Making Methods 4.10 Team Training: Needs Assessment, Delivery, and Evaluation

5 Chapter 5: Define Phase

5.1 Customer Identification and Segmentation 5.2 Customer Data Collection: Methods, Validity, and Reliability 5.3 Customer Requirements: CTX, CTQ Trees, and Operational Definitions 5.4 The Kano Model and Quality Function Deployment 5.5 SIPOC and Process Mapping 5.6 Business Case Justification 5.7 The Project Charter and the Problem Statement 5.8 Project Scope and Boundaries 5.9 SMART Goals and Project Performance Measurements 5.10 Project Charter Reviews and Toll-Gate Governance 5.11 Project Management Tools: Gantt Charts, Toll-Gate Reviews, WBS, and RACI 5.12 Analytical Tools Used Throughout the DMAIC Cycle

6 Chapter 6: Measure Phase

6.1 Process Flow Metrics and the Hidden Factory 6.2 Process Analysis Tools and Value Stream Mapping 6.3 Types of Data and Measurement Scales 6.4 Sampling Concepts, Methods, and Sample Size for Estimation 6.5 Data Collection Plans and Common Pitfalls 6.6 Gage R&R for Variable Measurement Systems 6.7 Attribute Agreement Analysis and Audit MSA 6.8 Measurement Systems Across the Organization, and Metrology 6.9 Population Parameters, Sample Statistics, and Valid Statistical Conclusions 6.10 Descriptive Statistics: Central Tendency, Dispersion, and Shape 6.11 Graphical Methods for Exploratory Data Analysis 6.12 Probability Concepts, Common Distributions, and the Central Limit Theorem 6.13 Additional Distributions: Hypergeometric, Exponential, Lognormal, Weibull, and Bivariate 6.14 Process Capability and Performance Indices 6.15 Capability Studies, Attributes Capability, Yield Metrics, and Long-Term Capability

7 Chapter 7: Analyze Phase

7.1 The Correlation Coefficient, Its Confidence Interval, and Causation 7.2 Linear Regression and Residual Analysis 7.3 Multivariate Tools: Factor Analysis, Discriminant Analysis, and MANOVA 7.4 Hypothesis Testing Terminology, Errors, and Single-Sample Tests 7.5 Statistical Versus Practical Significance 7.6 Sample Size for Hypothesis Tests 7.7 Point Estimates, Confidence, Prediction, and Tolerance Intervals 7.8 Tests for Means, Variances, and Proportions 7.9 Analysis of Variance 7.10 Chi-Square Goodness-of-Fit and Tests of Independence 7.11 Types of Risk and Failure Mode and Effects Analysis 7.12 Gap Analysis and Root Cause Analysis 7.13 Waste Analysis: The Seven Classic Wastes and Resource Under-Utilization

8 Chapter 8: Improve Phase

8.1 Design of Experiments: Objectives, Terminology, and Effects 8.2 DOE Design Principles: Randomization, Blocking, Replication, and Resolution 8.3 Planning and Evaluating Designed Experiments 8.4 One-Factor Experiments: Completely Randomized, Randomized Block, and Latin Square Designs 8.5 Full Factorial Experiments and Design Selection 8.6 Two-Level Fractional Factorial Experiments and Confounding 8.7 Response Surface Methodology and Optimization 8.8 Waste Elimination: Pull Systems, Kanban, 5S, and Poka-Yoke 8.9 Cycle-Time Reduction: Continuous Flow, SMED, and Heijunka 8.10 Kaizen, Kaizen Blitz, Theory of Constraints, and OEE 8.11 Implementation Planning, Piloting, and Solution Selection

9 Chapter 9: Control Phase

9.1 SPC Objectives, Variable Selection, and Rational Subgrouping 9.2 Control Charts for Variables Data 9.3 Control Charts for Attributes Data 9.4 Short-Run SPC and Moving Average Charts 9.5 Control Chart Analysis: Common Cause, Special Cause, and Run Rules 9.6 Total Productive Maintenance and Visual Controls 9.7 Measurement System Reanalysis 9.8 Control Plan Development and Process Owner Handoff 9.9 Lessons Learned, Documentation, and Training for Sustainment 9.10 Ongoing Evaluation of the Improved Process

10 Chapter 10: Design for Six Sigma (DFSS)

10.1 DFSS Frameworks: DMADV, DMADOV, and Design Transfer 10.2 Design for X (DFX) 10.3 Robust Design, Parameter Design, and Verification 10.4 Tolerance Design and Statistical Tolerancing

5.1 Customer Identification and Segmentation

Key Takeaways Explain This Topic AI

External customers receive the organization's output; internal customers are downstream processes that receive work from the process in scope.

Segmentation dimensions include demographic, geographic, behavioural, value, product or service line, and channel.

An averaged requirement across heterogeneous segments describes no real customer and hides bimodal demand.

The end user is not always the buyer, and neither is always the decision maker; all three may impose different requirements.

ASQ tags customer identification at the Apply level, so the exam expects you to select the segmentation that resolves a described conflict.

Last updated: August 2026

Who is the customer?

The Body of Knowledge asks Black Belts to identify and segment customers and to show how a project

will impact both internal and external customers. Three distinctions carry most of the exam weight.

External customers receive the organization's product or service and pay for it directly or
indirectly. Internal customers are the downstream processes, departments, and colleagues who
receive the output of the process in scope. Every process has at least one internal customer unless it

is the last step before delivery.

Buyer, user, and decision maker are frequently different people with different requirements. A
hospital purchasing department buys infusion pumps on total cost and service terms; nurses use them
and care about alarm behaviour and setup time; the biomedical engineering director approves them on
maintainability and integration. A project that optimizes only for the buyer produces a pump nobody

wants to use.

Beyond these, a full customer map for a Six Sigma project usually includes:

Category Who Typical requirement
End user The person who uses the output Fitness for purpose, ease of use, reliability
Purchaser The person who pays Price, total cost of ownership, terms
Specifier / influencer Engineer, clinician, architect, IT Conformance to a standard or specification
Internal downstream process The next operation Complete, correct, on-time input
Channel partner Distributor, retailer, installer Packaging, lead time, documentation
Regulator Agency, notified body, accreditor Compliance evidence, traceability
Why segmentation is not optional
A segment is a group of customers whose requirements are similar enough that one specification serves

them and different enough from other groups that a single specification would not.

The reason to segment is that averages hide structure . Suppose an insurer measures claim cycle
time and finds a mean of 11.4 days with a large standard deviation. Segmented, the picture is
different: simple auto claims average 3.1 days, complex bodily-injury claims average 34 days, and
there are almost none in between. The distribution is bimodal, the "average customer" does not exist,

and any target set on the aggregate mean is wrong for both groups.

The same logic drives the analytical practice of stratification , which is the Measure and Analyze
phase expression of segmentation. If you have not segmented customers in Define, you will not know

which stratification factors to collect in Measure.

Segmentation dimensions

Dimension Examples When it discriminates

Demographic / firmographic Age, income; industry, company size, sector Requirements differ by who the customer is

Geographic Region, country, urban versus rural, climate Regulation, logistics, or usage conditions differ

Behavioural Usage frequency, order size, order pattern, channel used The way the customer transacts drives the process load

Value Revenue, margin, lifetime value, strategic importance Resource allocation must be differentiated
Product or service line SKU family, service tier, contract type Different value streams serve them
Needs-based What the customer is trying to accomplish The most useful and the hardest to obtain

Two practical tests for whether a segmentation is useful:

Do the requirements actually differ across segments? If two segments want the same thing to the

same tolerance, the split adds cost without insight.

Can you tell which segment a transaction belongs to at the moment you process it? A segment you

cannot identify in the operational data is a marketing construct, not a process input.

Prioritizing segments

Not every segment gets equal weight in the project. Two common approaches:

Value ranking. Rank segments by revenue, margin, or customer lifetime value. Pareto behaviour is

normal: a minority of customers usually generates most of the value.

Strategic weighting. A small segment may be weighted heavily because it represents a target

market, carries reputational risk, or is a reference account.

Record the weighting decision in the charter. It determines whose requirement wins when requirements

conflict, and that argument is much easier before the data arrives than after.

Showing the impact on internal and external customers
The Body of Knowledge asks specifically that you show how a project will impact both. A short impact

table in the charter does this and prevents the classic sub-optimization failure:

Customer Internal or external Current experience Expected impact of the project Risk

Assembly cell 2 Internal Receives 4% of kits short Kits complete; pick sequence changes Operators must learn new kit layout

Field service Internal Handles rework of miswired units Fewer callouts Loses visibility of failure modes unless data is shared

Fleet customers External 6-day average lead time 3-day lead time Batch size reduction may raise unit price
Retail customers External Unaffected No change None
The row that matters most is usually the internal one with a risk entry, because internal customers

absorb the friction of a change without having asked for it.

A worked identification

A Black Belt is chartered to reduce errors in outpatient prescription dispensing.

External customers : the patient (accuracy, wait time, clear instructions), the prescribing

physician (formulary substitution notification), and the insurer (correct coding).

Internal customers : the pharmacy technician who receives the entered order, the billing team

that receives the claim, and the inventory function that receives the consumption signal.

Segments that matter : new prescriptions versus refills (completely different error profiles),
controlled substances versus routine (different verification requirements), and walk-in versus

mail-order (different time requirements).

Without that segmentation the team would compute one error rate over everything and lose the fact that
almost all serious errors occur in new prescriptions for controlled substances -- a segment that is

under 5% of volume.