Description

This curriculum spans the design and operationalization of customer satisfaction models across data integration, real-time scoring, and enterprise governance, comparable in scope to a multi-phase advisory engagement addressing analytics, infrastructure, and cross-functional alignment in large organisations.

Module 1: Defining Objectives and Scope for Customer Satisfaction Analysis

Select key performance indicators (KPIs) such as CSAT, NPS, or churn rate based on business unit priorities and data availability.
Negotiate access to cross-functional data sources including support tickets, survey responses, and transaction logs with data stewards.
Determine whether analysis will be reactive (post-interaction) or proactive (predictive of dissatisfaction).
Establish boundaries for customer segments to be analyzed, considering regional, product, or service-line differences.
Align analytical scope with compliance constraints, particularly when handling PII in global customer bases.
Define frequency of analysis cycles—real-time, daily, or monthly—based on operational response capacity.
Document assumptions about customer feedback representativeness, especially when response rates are low.
Map stakeholder requirements to analytical deliverables, distinguishing between executive dashboards and agent-level feedback.

Module 2: Data Integration and Preprocessing from Heterogeneous Sources

Design ETL pipelines to consolidate unstructured survey comments, structured ratings, and behavioral logs into a unified schema.
Resolve entity resolution issues when customers interact across multiple channels with inconsistent identifiers.
Apply sentiment-aware text cleaning techniques to social media inputs, preserving sarcasm and negation cues.
Impute missing satisfaction scores using behavioral proxies such as repeat purchase or session duration.
Normalize rating scales across different survey instruments (e.g., 5-point vs. 10-point) using statistical equating.
Flag and handle bot-generated or incentivized feedback that skews sentiment distribution.
Implement time-based partitioning to maintain temporal consistency between interaction events and feedback.
Validate data lineage and transformation logic with source system owners to ensure auditability.

Module 3: Feature Engineering for Satisfaction Modeling

Derive interaction complexity metrics from support ticket resolution paths, including escalation count and agent handoffs.
Construct lagged features that capture customer history, such as number of prior complaints within 90 days.
Generate text-based features using TF-IDF and n-grams from open-ended feedback, weighted by response urgency.
Encode categorical service attributes (e.g., support channel, agent tenure) using target encoding with smoothing.
Build composite indicators like service recovery effectiveness by combining initial dissatisfaction with resolution speed.
Apply time decay functions to historical satisfaction signals to prioritize recent behavior.
Test feature stability across customer cohorts to avoid spurious correlations in minority segments.
Document feature definitions in a shared catalog to ensure model reproducibility and regulatory compliance.

Module 4: Model Selection and Validation for Satisfaction Prediction

Compare logistic regression, random forest, and gradient boosting models on precision-recall trade-offs for high-risk customers.
Use stratified temporal splits for validation to prevent data leakage from future interactions.
Optimize thresholds for alerting systems based on operational capacity to intervene, not just model accuracy.
Assess model calibration using reliability diagrams, especially when outputs inform escalation workflows.
Conduct ablation studies to quantify the impact of text features versus behavioral features on prediction lift.
Validate model performance across demographic slices to detect unintended bias in satisfaction inference.
Implement shadow mode deployment to compare model predictions against actual agent assessments.
Define retraining triggers based on feature drift metrics such as PSI exceeding 0.2.

Module 5: Sentiment and Theme Extraction from Unstructured Feedback

Select between rule-based parsers and transformer models (e.g., BERT) based on domain specificity and compute constraints.
Customize sentiment lexicons to include industry-specific terms like “backorder” or “service level agreement.”
Apply topic modeling (e.g., LDA or NMF) with coherence score optimization to identify emerging dissatisfaction themes.
Link extracted themes to operational metrics, such as associating “delay” topics with shipping SLA breaches.
Use zero-shot classification to categorize feedback into predefined issue types without labeled training data.
Implement human-in-the-loop validation for topic coherence, especially after product or policy changes.
Track theme prevalence over time to identify systemic issues versus transient complaints.
Integrate negation handling in parsing logic to prevent misclassification of “not satisfied” as positive.

Module 6: Real-Time Scoring and Alerting Infrastructure

Deploy models via microservices with <500ms latency to support real-time agent desktop alerts.
Design streaming pipelines using Kafka or Kinesis to process customer interactions as they occur.
Implement circuit breakers to disable scoring during upstream data outages and prevent false positives.
Route high-risk predictions to CRM workflows with priority tagging and SLA-based follow-up rules.
Apply rate limiting to alert delivery to avoid overwhelming frontline staff with low-actionability cases.
Log prediction provenance including input features, model version, and confidence score for audit trails.
Use feature stores to synchronize real-time and batch feature values across environments.
Monitor inference drift using statistical tests on prediction distribution shifts week-over-week.

Module 7: Governance, Ethics, and Model Transparency

Conduct fairness assessments across protected attributes, even when not used as model inputs, via proxy detection.
Document model limitations in plain language for legal and compliance review prior to deployment.
Implement data retention policies that align with right-to-be-forgotten requests in satisfaction models.
Establish escalation paths for customers who dispute automated satisfaction classifications.
Register models in a central inventory with ownership, versioning, and dependency tracking.
Perform periodic model risk assessments in line with internal financial or regulatory standards.
Restrict access to sensitive model outputs (e.g., predicted churn risk) based on role-based permissions.
Design opt-out mechanisms for customers who decline participation in predictive monitoring.

Module 8: Operational Integration and Feedback Loops

Embed model outputs into agent performance dashboards without creating perverse incentives to manipulate scores.
Link satisfaction predictions to root cause analysis workflows in IT service management tools.
Measure closed-loop effectiveness by tracking resolution rates of model-flagged cases versus controls.
Design A/B tests to evaluate impact of model-driven interventions on long-term retention.
Integrate model insights into product backlog prioritization through quantified impact estimates.
Establish routines for retraining models using feedback from resolved cases as new labels.
Report model utility metrics such as percentage of high-risk cases correctly escalated.
Coordinate with change management teams to update workflows when model logic evolves.

Module 9: Scaling and Cross-Organizational Alignment

Standardize satisfaction metrics across business units to enable enterprise-level benchmarking.
Negotiate shared data contracts for customer identifiers and interaction events across siloed systems.
Develop a centralized analytics layer that supports both local customization and global consistency.
Align model KPIs with financial outcomes such as cost-to-serve or lifetime value in business cases.
Facilitate cross-functional workshops to resolve conflicts between operational efficiency and satisfaction goals.
Implement model monitoring dashboards accessible to non-technical stakeholders with drill-down capability.
Scale infrastructure using container orchestration to handle peak loads during post-campaign feedback surges.
Document interdependencies between satisfaction models and other enterprise AI systems to manage technical debt.