Customer Profiling and Segmentation Training Course

Duration 5 days 10 modules · 33 contact hours
Level Intermediate Certificate on completion
Who attends Marketing Managers, Customer Insight Analysts and others
Fee from USD 1,100 KSh 95,000 for Kenyan organisations
Where 52 cities + online See dates by city
Next start 9 Nov 2026 Course code SD-M-032

Before you join: Participants should understand basic marketing terms such as target audience, campaign, product offer and customer journey.

Dates & prices

Two start dates a month in every city, and live online on weekdays or weekends. Fees are per participant; classroom fees include tuition, materials, lunch and refreshments, and the certificate.

Suggest a date or city

Showing the next six months. Later dates, other cities and private deliveries for your team are available on request.

Course overview

Customer databases often contain transaction records, channel interactions, service contacts and demographic fields, yet marketing teams still rely on broad audiences and generic campaign messages. Poorly defined segments waste media spend, obscure high-value customer groups and create inconsistent treatment across sales, marketing and service channels. Effective customer profiling turns available evidence into clear segment definitions, prioritised audiences and actions that teams can execute.

This course teaches the procedures used to profile customers, assess data quality, build meaningful segments and select target groups. Participants use the STP model, behavioural segmentation, RFM analysis, customer lifetime value and needs-based profiling to distinguish customers by value, behaviour, needs and decision context. They learn how to write usable segment definitions, build profile cards, size opportunities, test segment usefulness and translate findings into positioning, offers, channel choices and retention actions.

Instruction combines concise briefings with guided analysis of realistic banking, retail, telecommunications, public-service and B2B account cases. Participants interpret sample customer data, compare segmentation approaches, make targeting decisions and critique profiles for bias, evidence quality and commercial relevance. They leave with a customer segmentation workbook containing a data audit, segment decision criteria, segment profiles, prioritisation matrix, targeting recommendations and a 90-day activation plan.

The course suits professionals who already work with customer, sales, marketing or service information and need a disciplined method for converting it into audience decisions. It is particularly relevant where several teams serve the same customers but use different classifications, campaign rules or value assumptions.

What you will be able to do

  • Audit customer data fields for profiling suitability, completeness, consent and analytical limitations
  • Apply the STP model to define markets, construct segments and select target audiences
  • Build demographic, behavioural, value-based and needs-based customer profiles from available evidence
  • Calculate RFM scores and interpret recency, frequency and monetary-value patterns
  • Estimate customer lifetime value using contribution margin, retention and acquisition-cost assumptions
  • Evaluate segments against measurability, accessibility, substantiality, differentiation and actionability criteria
  • Produce segment profile cards with needs, behaviours, triggers, barriers and channel preferences
  • Create a targeted activation plan linking priority segments to offers, messages, channels and measures

Benefits of attending

For you

  • Make audience recommendations using stated evidence rather than intuition alone
  • Build segment profiles that colleagues can use for campaigns and service design
  • Interpret RFM and lifetime-value measures without relying solely on analysts
  • Challenge vague personas and unsupported customer assumptions constructively
  • Present targeting choices with clear commercial and customer-service rationale

For your organisation

  • Reduces generic campaign activity directed at low-response or low-value audiences
  • Creates shared customer classifications across marketing, sales and service teams
  • Improves offer, message and channel choices for priority customer groups
  • Identifies retention and cross-sell opportunities from behavioural and value patterns
  • Provides documented criteria for fair, explainable and commercially useful segmentation

Course modules

10 modules over 5 days

Participants can frame a customer profiling assignment around a defined commercial decision.

  • Customer profiling distinguishes observable attributes, inferred needs and predicted behaviours
  • Segmentation decisions linked to acquisition, retention, cross-sell and service priorities
  • STP model links market definition, segmentation choices and positioning decisions
  • Business questions converted into profiling objectives, hypotheses and required evidence
  • Unit of analysis selected: individual, household, account, site or buying centre
  • Stakeholder requirements mapped across marketing, sales, service, finance and compliance

Participants can assess whether available customer data supports a proposed profiling method.

  • First-party, second-party and third-party data sources compared for profiling use
  • Data dictionary records field definitions, ownership, refresh rates and permitted uses
  • Completeness, accuracy, consistency, timeliness and uniqueness assessed through data-quality checks
  • Customer identity resolution addresses duplicates, householding and account-contact relationships
  • Missing values, stale records and selection bias documented before analysis
  • GDPR principles applied to purpose limitation, minimisation, consent and retention
  • Sensitive attributes handled with fairness, explainability and approval requirements

Participants can select segmentation variables that fit the decision, data and customer context.

  • Geographic variables define service areas, territories, access constraints and local demand
  • Demographic measures include age, income band, life stage and household composition
  • Firmographic criteria classify B2B accounts by industry, size, location and ownership
  • Behavioural variables capture purchases, usage, interactions, loyalty and channel response
  • Psychographic and attitudinal evidence identifies motivations, preferences, beliefs and risk tolerance
  • Needs-based segmentation separates desired outcomes, pain points and decision criteria
  • Variable selection avoids proxy discrimination, duplication and weak decision relevance

Participants can derive actionable behavioural profiles from transaction and interaction data.

  • Customer event histories organised by purchase, service, digital and contact events
  • RFM analysis scores recency, frequency and monetary value from transaction records
  • Cohort analysis compares retention patterns by acquisition month or campaign source
  • Purchase cadence identifies replenishment cycles, lapsed customers and intervention windows
  • Channel preference measured through response, conversion, contact and service-resolution patterns
  • Basket analysis identifies product affinities using support, confidence and lift
  • Lifecycle stage classified from prospect, new customer, active, at-risk and lapsed signals

Participants can distinguish customer revenue, contribution, potential and lifetime value.

  • Revenue differs from margin, contribution, cost-to-serve and customer profitability
  • Customer lifetime value estimates future contribution using retention and margin assumptions
  • Acquisition cost allocated by source, campaign, sales effort and onboarding activity
  • Pareto analysis identifies concentrated revenue, profit and service-demand patterns
  • Potential value combines current spend, category need, eligibility and growth indicators
  • Value tiers designed with transparent thresholds, review periods and exception rules
  • Negative-value segments assessed before changing service levels or commercial treatment

Participants can incorporate qualitative evidence into needs-based customer profiles.

  • Jobs to Be Done identifies functional, emotional and social customer progress
  • Interview guides separate stated preferences from underlying needs and constraints
  • Decision-making units map users, buyers, influencers, gatekeepers and approvers
  • Buying triggers include renewal, life events, incidents, expansion and policy changes
  • Barriers captured through risk perceptions, switching costs, trust and effort
  • Customer journey evidence links needs to moments, channels and service failures
  • Voice-of-customer themes coded into recurring needs, language and proof requirements

Participants can create evidence-based profiles that guide commercial and service actions.

  • Segment profile cards combine defining variables, needs, behaviours and value
  • Persona narratives anchored in evidence rather than fictional biographical detail
  • Representative quotes labelled by source, sample limitations and supporting evidence
  • Segment size estimated using population counts, incidence rates and eligibility rules
  • Confidence levels distinguish measured findings, informed assumptions and unknowns
  • Naming conventions create memorable segments without stereotyping or misleading labels
  • Profile review tests whether different teams classify customers consistently

Participants can rank segments using explicit commercial, operational and customer criteria.

  • Segment attractiveness assessed through size, growth, value, fit and competitive intensity
  • Measurability, accessibility, substantiality, differentiation and actionability test segment viability
  • Prioritisation matrices weight commercial opportunity, strategic fit and delivery feasibility
  • Targeting strategies compare undifferentiated, differentiated, concentrated and micro-marketing approaches
  • Scenario analysis tests priorities under budget, capacity and regulatory constraints
  • Cannibalisation risks examined when offers overlap across adjacent customer segments
  • Decision records document assumptions, exclusions, approvals and review triggers

Participants can translate priority segments into differentiated commercial and service treatments.

  • Value propositions match segment jobs, pains, gains and competitive alternatives
  • Positioning statements specify target, frame of reference, benefit and reason-to-believe
  • Offer architecture varies product bundles, pricing, incentives and eligibility criteria
  • Message matrices align segment needs, proof points, objections and calls-to-action
  • Channel strategy assigns digital, branch, field, partner and contact-centre roles
  • Contact policies set frequency, suppression rules, escalation paths and service standards
  • Test-and-learn plans define cells, hypotheses, control groups and success measures

Participants can plan how a segmentation model will be governed, activated and reviewed.

  • CRM fields and business rules translate segments into usable customer labels
  • Segment assignment logic specifies inputs, thresholds, refresh schedules and exceptions
  • Data governance assigns ownership for definitions, access, changes and quality controls
  • Dashboard measures track segment migration, response, conversion, retention and value
  • Model monitoring detects drift, stale assumptions and unexpected treatment outcomes
  • Privacy and fairness reviews assess exclusions, disparate effects and complaint signals
  • Ninety-day action plans sequence data fixes, pilots, decisions and stakeholder reviews

Exercises and examples are adapted to each cohort's sectors and roles.

What you take back to work

  1. Customer data audit checklist
  2. Segmentation variable selection matrix
  3. RFM scoring worksheet
  4. Customer lifetime value model
  5. Evidence-based segment profile card
  6. Ninety-day segment activation plan

Who should attend

  • Marketing Managers — need defensible audience priorities and campaign targeting decisions
  • Customer Insight Analysts — convert customer data into useful segment definitions
  • CRM Managers — tailor lifecycle communications using behaviour and value signals
  • Product Managers — identify priority customer needs for proposition development
  • Sales Managers — focus account coverage and offers on profitable customer groups
  • Customer Experience Managers — align service treatments with distinct customer needs

Skills and tools covered

Customer data auditingMarket segmentationRFM analysisCustomer value analysisTarget audience selectionSegment activation planning

Tools and standards

STP framework, RFM analysis, Customer Lifetime Value (CLV), Jobs to Be Done

How the course runs

Training methodology

The instructor uses short briefings to introduce profiling procedures, followed by case-based analysis of customer records, survey evidence and campaign results. Participants work in groups to classify variables, compare segmentation options, calculate RFM scores and assess segment attractiveness. Simulated stakeholder discussions test how segment recommendations hold up against sales, service, finance and compliance questions. Individual and group application planning connects the methods to participants' own customer portfolios, data sources and campaign decisions.

Requirements and prerequisites

Participants should understand basic marketing terms such as target audience, campaign, product offer and customer journey. Experience using customer lists, CRM reports, sales data, survey findings or web analytics is helpful. Participants should be comfortable reading simple tables, percentages and averages, although advanced statistics are not required. No prior use of specialist analytics software, SQL, Python or data visualisation tools is assumed. The course explains the required calculations and provides structured data cases; participants do not need to bring their organisation's customer data.

A typical training day

08:30 – 10:30First session
10:30 – 10:45Refreshment break
10:45 – 12:30Second session
12:30 – 13:30Lunch and networking
13:30 – 15:00Third session
15:00 – 15:15Refreshment break
15:15 – 16:30Workshop and daily review

Live online deliveries follow the same structure in East Africa Time, with shorter screen blocks and longer breaks.

What the fee includes

  • Instruction by a practitioner facilitator
  • Full course workbook and materials
  • Exercise files, templates and case studies
  • Refreshments and lunch on classroom deliveries
  • Certificate of completion
  • Post-course application plan

Frequently asked questions

Participants need familiarity with basic marketing or customer-management concepts and should be able to read tables and percentages. Advanced statistics, SQL, Python and specialist analytics software are not required.

A laptop is useful for working with course materials and templates. Participants may bring non-sensitive examples of customer fields, campaign reports or segment definitions, but no organisational data is required.

It is designed for marketing, CRM, insight, product, sales and customer-experience professionals who make or support customer targeting decisions. It also suits managers responsible for aligning teams around shared customer groups.

Market research courses focus on collecting and interpreting research evidence, while analytics courses often focus on technical data methods. This course concentrates on the management procedure for turning customer evidence into profiles, segments, targeting choices and activation rules.

Participants can use the templates to audit available data, define a segmentation question and build a first set of profile cards. The activation plan helps them agree owners, pilot actions, measures and review points with colleagues.

Each day runs from 08:30 to 16:30 and includes approximately 6.5 contact hours. Time is divided across ten modules covering data, profiling methods, segment selection, positioning, activation and review planning.

Questions about this course?

Ask about dates, fees, group bookings or running it for your team. Our training team replies by email.

Ask on WhatsApp info@skillsetdevelopment.com

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