Customer Satisfaction and Experience Analytics Training Course

Participants can select, calculate and interpret appropriate customer experience metrics.

• Net Promoter Score calculation, detractor analysis and reporting limitations
• Customer Satisfaction Score designs for interactions, channels and overall relationships
• Customer Effort Score applied to problem resolution and self-service journeys
• Top-box, mean-score and distribution reporting compared for management decisions
• Metric targets set using baselines, benchmarks, confidence intervals and strategic priorities
• Score movements separated from meaningful change using statistical significance tests

Participants can create a sampling and fieldwork plan that supports valid interpretation.

• Target populations specified using customers, transactions, journeys and contact permissions
• Sampling frames assessed for coverage gaps, duplicates and outdated contact details
• Probability and non-probability sampling compared for customer feedback programmes
• Sample sizes estimated using confidence levels, margins of error and subgroup needs
• Response-rate calculations distinguish invitations, completes, partials and ineligible contacts
• Fieldwork timing aligns invitations with the customer interaction being measured
• Non-response bias assessed through respondent profiles, timing and channel comparisons

Participants can build concise customer questionnaires that produce useful and reliable data.

• Questionnaire flow follows customer recall, experience evaluation and respondent effort
• Closed questions use mutually exclusive categories and balanced response scales
• Likert, numeric and verbal rating scales selected for measurement objectives
• Leading, double-barrelled and ambiguous questions identified and rewritten
• Open-text prompts capture explanation, recovery needs and improvement suggestions
• Survey length managed through priority questions, routing logic and completion burden
• Cognitive testing checks comprehension, recall periods and interpretation consistency

Participants can prepare customer feedback data for credible analysis and reporting.

• Data dictionaries document variables, codes, derived fields and calculation rules
• Duplicate responses detected through identifiers, timestamps and response patterns
• Missing-data treatment distinguishes skipped questions, refusals and system failures
• Outliers reviewed using distributions, completion times and inconsistent answer patterns
• Weighting adjusts known sample imbalances by segment, channel or customer value
• Data quality checks reconcile survey records with CRM and transaction sources
• Privacy controls address consent, retention, access rights and identifiable comments

Participants can identify the service attributes most strongly associated with satisfaction and loyalty.

• Cross-tabulation compares satisfaction results across segments, channels and journey stages
• Confidence intervals distinguish stable differences from sampling variation
• Correlation analysis tests associations between service attributes and outcome measures
• Multiple regression estimates relative driver importance while controlling related attributes
• Importance-performance matrices identify priority improvements and possible overinvestment
• Root-cause analysis connects low ratings to process failures and operational evidence
• Driver findings translated into hypotheses for service redesign and testing

Participants can combine customer comments and complaints with quantitative satisfaction evidence.

• Verbatim coding frameworks classify themes, sentiment, causes and requested improvements
• Thematic analysis groups recurring customer issues without losing contextual detail
• Complaint taxonomies distinguish product, process, people, policy and communication failures
• Sentiment analysis outputs validated against manually coded customer comments
• Frequency and severity matrices prioritise recurring issues with material customer impact
• Representative quotations selected without exposing personal or sensitive information
• Mixed-method findings connect comments, scores, complaints and operational metrics

Participants can locate experience gaps by journey stage, customer segment and service channel.

• Journey-level scorecards compare acquisition, onboarding, service, renewal and exit experiences
• Customer segmentation combines needs, value, behaviour and service expectations
• Channel-switching analysis identifies friction between digital, branch, phone and field service
• Moments of truth ranked by emotional impact, dissatisfaction risk and business value
• Journey maps incorporate feedback scores, operational handoffs and failure points
• Equity checks test whether satisfaction gaps affect vulnerable customer groups
• Cohort analysis tracks experience changes among customers with shared start periods

Participants can establish response processes that turn customer feedback into accountable action.

• Inner-loop recovery contacts dissatisfied customers within defined service-level targets
• Outer-loop governance addresses systemic causes revealed by recurring feedback patterns
• Service recovery protocols define contact ownership, remedy authority and documentation
• Root-cause workshops use five whys, fishbone diagrams and process evidence
• Action registers record improvement owners, milestones, benefits and verification measures
• PDCA cycles test changes before broader implementation and score monitoring
• Escalation rules distinguish urgent detractor cases, vulnerable customers and regulatory complaints

Participants can present customer experience evidence and create a practical measurement improvement plan.

• Executive dashboards combine headline scores, drivers, segments, trends and action status
• Control charts separate normal score variation from special-cause performance changes
• Narrative reporting explains implications, decisions required and accountable next actions
• Metric hierarchies connect journey indicators with customer outcomes and operational measures
• Data visualisation choices match comparison, trend, distribution and diagnostic questions
• Reporting calendars align feedback review with operational governance and planning cycles
• Ninety-day measurement action plans specify priorities, owners, dependencies and milestones