Day 1: Experience Questions and Measurement Architecture

Starting with the Decision Question

• Define decision the analysis should support
• Identify genuinely decision-changing information
• Separate useful evidence from data volume
• Write one bounded experience analytics question

Designing Measurement Structure

• Connect journey stages with experience measures
• Balance perception behavior and operating indicators
• Define baseline source and measurement frequency
• Identify gaps in existing measurement

Data Quality and Comparability

• Check definitions across channels and periods
• Identify nonresponse/selection bias risks
• Normalize comparisons where conditions differ
• Document limitations before interpretation

Day 2: Segmentation and Experience Pattern Analysis

Designing Meaningful Segmentation

• Segment by need, behavior, and channel
• Avoid demographic grouping without decision value
• Compare experience outcomes across segments
• Identify groups requiring deeper diagnosis

Trend and Variation Analysis

• Read trends, not isolated scores
• Compare locations teams and channels appropriately
• Identify unusual variation worth investigation
• Separate structural change from temporary fluctuation

Combining Experience and Operations

• Connect experience scores with operational outcomes
• Test operating-change alignment with experience shifts
• Identify contradictions between perception and performance
• Form hypotheses explaining the combined pattern

Day 3: Text Feedback and Causal Diagnosis

Interpreting Customer Feedback Themes

• Create themes reflecting service experience
• Separate stated issues from deeper expectations
• Compare themes across high/low ratings
• Select representative comments without cherry-picking

Building Cause Map

• Link journey breakdowns to process causes
• Separate symptoms from plausible drivers
• Identify dependencies creating several customer problems
• Build causal map around one pattern

Testing the Cause

• Seek evidence supporting/challenging hypotheses
• Compare cases without the problem
• Identify variables requiring additional data
• Decide whether confidence supports intervention

Day 4: Prioritizing and Testing Service Improvements

Improvement Opportunity Portfolio

• Translate causes into specific improvement opportunities
• Estimate customer value and operating effort
• Assess confidence in the diagnosis
• Rank opportunities by impact and evidence

Applied Intervention Package

• Select process communication or capability changes
• Define owner scope and implementation dependency
• Set leading indicators for each intervention
• Identify possible unintended effects

Applied Improvement Test

• Choose controlled service area or segment
• Define baseline and post-change evidence
• Run small treatment before broad rollout
• Capture learning and adjust the intervention

Day 5: Customer Experience Analytics Decision Lab

Building the Decision Story

• Start with business and customer issue
• Show only evidence changing interpretation
• Explain uncertainty and limitations transparently
• Connect the diagnosis with proposed action

Applied Experience Dashboard

• Select indicators answering recurring management questions
• Show segments and variation where useful
• Set thresholds triggering investigation
• Create a concise dashboard view

Analytics Review Board

• Present analysis to simulated review panel
• Defend cause and priority choices
• Respond to alternative interpretations
• Approve improvement decision and measurement plan

• Experience datasets combining survey feedback comments and service operating measures
• Segmentation and causal-analysis workshops using decision-focused questions
• Improvement portfolio exercises balancing impact evidence and implementation effort
• A decision lab requiring a concise evidence narrative and management recommendation