CRM Strategies: Analyze, Design, and Implement Course

Lesson 1 • What CRM Means for Business

Defines CRM as a business philosophy and technology ecosystem. Connects customer-centric thinking to measurable organisational outcomes.

Lesson 2 • History and Evolution of CRM

Traces CRM from contact management tools to AI-driven platforms. Provides context for understanding current capabilities and future directions.

Lesson 3 • CRM Maturity Models

Introduces frameworks for assessing an organisation's CRM readiness. Helps learners benchmark current state and plan improvement roadmaps.

Lesson 4 • Business Case for CRM Investment

Quantifies the financial and strategic value of CRM adoption. Prepares learners to justify CRM projects to executive stakeholders.

Lesson 1 • Customer Journey Mapping

Guides learners through creating visual maps of customer touchpoints and emotions. Reveals friction points that CRM interventions can resolve.

Lesson 2 • Customer Lifetime Value Calculation

Teaches quantitative methods for measuring long-term customer worth. Enables prioritisation of CRM resources toward high-value segments.

Lesson 3 • Customer Lifecycle Stages Defined

Outlines acquisition, onboarding, growth, retention, and win-back stages. Anchors all subsequent CRM tactics to a unified lifecycle model.

Lesson 4 • Loyalty and Advocacy Development

Explores how satisfied customers become brand advocates. Connects loyalty programme design to CRM data and automation capabilities.

Lesson 1 • Privacy, Consent, and Compliance

Covers consent management, opt-in practices, and data subject rights. Prepares learners to operate CRM programmes within privacy regulations.

Lesson 2 • Types of Customer Data

Categorises first-, second-, and third-party data and their CRM applications. Establishes a shared data vocabulary for cross-functional teams.

Lesson 3 • Data Quality Management

Addresses deduplication, standardisation, and enrichment of CRM records. Poor data quality is the leading cause of CRM failure; this section prevents it.

Lesson 4 • Data Governance Frameworks

Establishes policies, roles, and accountability structures for CRM data. Ensures consistent data stewardship across departments and systems.

Lesson 1 • Behavioural and Predictive Segmentation

Uses purchase history, engagement patterns, and predictive scores to form segments. Enables proactive CRM actions before customers signal intent.

Lesson 2 • Segmentation Principles and Criteria

Defines what makes a segment viable: measurable, accessible, substantial, and actionable. Prevents over-segmentation and under-segmentation errors.

Lesson 3 • RFM Analysis for CRM

Teaches recency, frequency, and monetary scoring to rank customer value. Directly applicable to prioritising outreach and resource allocation in CRM.

Lesson 4 • Targeting Strategy and Prioritisation

Matches segments to CRM channels and investment levels based on strategic value. Ensures budget and effort align with highest-return customer groups.

Lesson 1 • CRM Configuration and Customisation

Distinguishes configuration from custom development and guides appropriate choices. Minimises technical risk while meeting unique business requirements.

Lesson 2 • CRM Architecture and Data Modelling

Covers object relationships, field design, and record hierarchy within a CRM. Proper architecture prevents technical debt and supports future scalability.

Lesson 3 • Integration Architecture Planning

Maps CRM connections to ERP, marketing, and support systems. Ensures data flows seamlessly across the customer technology stack.

Lesson 4 • CRM Platform Evaluation Framework

Provides a structured scorecard for comparing CRM vendors across key dimensions. Removes bias from vendor selection and supports defensible decisions.

Lesson 1 • Go-Live and Hypercare Management

Manages the critical launch window with support structures and rapid issue resolution. Hypercare practices protect user confidence during the highest-risk period.

Lesson 2 • Change Management for CRM Adoption

Applies structured change frameworks to overcome resistance and build buy-in. Addresses the human side of CRM, which is the most common failure point.

Lesson 3 • Data Migration Strategy

Covers extraction, transformation, and loading of legacy data into the new CRM. Clean migration is critical to user trust and system credibility at launch.

Lesson 4 • Implementation Planning and Governance

Establishes project scope, timeline, and steering committee structure. A strong governance model prevents scope creep and keeps implementations on track.

Lesson 1 • Revenue Forecasting with CRM Data

Uses pipeline data, win rates, and deal velocity to produce reliable revenue forecasts. Connects CRM discipline to executive planning and financial accuracy.

Lesson 2 • Marketing Automation Integration

Connects marketing automation platforms to CRM for seamless campaign execution. Ensures marketing activity is visible to sales and tied to revenue outcomes.

Lesson 3 • Sales and Marketing SLA Design

Formalises agreements between sales and marketing on lead definitions and response times. SLAs enforced through CRM automation reduce inter-team friction.

Lesson 4 • Lead Management and Scoring

Builds systematic processes for capturing, qualifying, and routing leads within CRM. Reduces lead leakage and accelerates pipeline velocity.

Lesson 1 • CRM Audit and Health Assessment

Provides a systematic process for evaluating CRM data quality, adoption, and process adherence. Regular audits prevent gradual system degradation.

Lesson 2 • Long-Term CRM Roadmap Planning

Translates business strategy into a multi-year CRM evolution roadmap. Ensures CRM investments remain aligned with changing organisational priorities.

Lesson 3 • CRM KPI Framework Design

Defines the right metrics across acquisition, retention, and service dimensions. A balanced KPI framework prevents optimising one area at the expense of another.

Lesson 4 • A/B Testing and Campaign Optimisation

Applies controlled experimentation to CRM marketing campaigns and workflows. Builds a culture of evidence-based optimisation rather than assumption-driven decisions.