Artificial Intelligence for Retail Customer Experience Training Course

Participants can build and rank an AI customer experience use-case portfolio.

• Use-case canvases defining users, decisions, data, outputs and owners
• Value-feasibility-risk matrix for comparing retail AI opportunities consistently
• RICE scoring adapted for customer experience product prioritisation
• Dependencies assessed across data, integration, process and workforce readiness
• Quick wins separated from foundational capabilities and long-horizon investments
• Business cases linking expected outcomes to cost, risk and adoption

Participants can assess customer data readiness for AI personalisation and service applications.

• First-party data sources: transactions, behaviour, service and loyalty records
• Customer identity resolution using deterministic and probabilistic matching methods
• Customer data platforms compared with CRM, warehouse and point solutions
• Data quality dimensions: completeness, accuracy, timeliness, consistency and uniqueness
• Consent records and preference centres governing permissible data activation
• GDPR purpose limitation, data minimisation and retention requirements
• Feature definitions documented for repeatable customer analytics and model development

Participants can specify and evaluate AI-driven personalisation for retail discovery and conversion.

• Segmentation methods: RFM, behavioural clustering and lifecycle classification
• Recommendation approaches: collaborative filtering, content-based and hybrid models
• Next-best-action rules combined with predictive customer propensity scores
• Retail search ranking using query intent, product attributes and availability
• Cold-start strategies for new customers, products and seasonal assortments
• Personalisation guardrails preventing repetition, inappropriate offers and filter bubbles
• Offline model metrics contrasted with online conversion and retention measures

Participants can design conversational AI services with reliable human support pathways.

• Intent taxonomy built from contact reasons, journeys and knowledge gaps
• Retrieval-augmented generation grounded in approved policy and product content
• Prompt patterns specifying role, task, constraints, examples and output format
• Conversation design for authentication, clarification, confirmation and closure
• Human handoff rules triggered by complexity, vulnerability and low confidence
• Agent-assist workflows summarising contacts, suggesting actions and retrieving knowledge
• Quality assurance sampling for factuality, tone, resolution and escalation accuracy

Participants can assess AI applications that improve in-store service and associate effectiveness.

• Clienteling tools combining customer preferences, inventory and associate recommendations
• Computer vision use cases assessed for queues, availability and loss prevention
• Associate copilots supporting product knowledge, policies and task prioritisation
• Inventory visibility requirements for accurate cross-channel customer promises
• Store-service triggers derived from appointments, dwell signals and purchase history
• Accessibility requirements considered for voice, kiosks and assisted digital services

Participants can define measurement systems for AI customer experience outcomes and model quality.

• Customer metrics: NPS, CSAT, customer effort score and complaint rate
• Commercial metrics: conversion, average order value, repeat purchase and margin
• Operational metrics: containment, handle time, resolution and transfer rate
• North-star metric selection linked to a defined customer journey outcome
• A/B testing design with hypotheses, control groups and guardrail metrics
• Incrementality measurement separating AI effects from seasonality and promotions
• Model monitoring for drift, calibration, latency and prediction error

Participants can apply practical controls for privacy, fairness, transparency and AI accountability.

• NIST AI RMF functions: Govern, Map, Measure and Manage
• ISO/IEC 42001 management system roles, policies and documented controls
• Bias testing across customer segments, languages, locations and accessibility needs
• Explainability messages matched to automated decisions and customer expectations
• Privacy impact assessments for high-risk customer data processing
• Human oversight design for consequential decisions, complaints and exceptions
• Incident response procedures for harmful outputs, data exposure and model failures

Participants can evaluate delivery options and establish accountable AI customer experience operations.

• Build-buy-partner decisions assessed against differentiation, speed and control
• Vendor due diligence covering data access, security, performance and exit rights
• Reference architecture connecting channels, CRM, data platforms and AI services
• Product operating model roles across business, data, technology, legal and risk
• MLOps controls for versioning, deployment approval, monitoring and rollback
• Service-level agreements defining availability, response times and support responsibilities
• Change management plans addressing associate adoption, training and process redesign

Participants can produce a sequenced plan for deploying governed AI customer experience initiatives.

• Ninety-day roadmap sequenced by use cases, dependencies and decision gates
• Minimum viable product scope defined through customer and business assumptions
• Pilot selection criteria covering segment size, channel readiness and measurability
• Stage-gate governance reviews for launch, scale, pause or retirement decisions
• Benefits realisation plan assigning baselines, targets, owners and review cadence
• Stakeholder communication tailored for executives, frontline teams and customers
• Personal application plan documenting priorities, actions, sponsors and milestones