This solution delivers a comprehensive customer journey analytics platform that transforms how organizations understand and optimize customer behavior. By integrating path analysis, attribution modeling, behavioral segmentation, and predictive analytics within Snowflake, it enables analysts and marketers to uncover actionable insights from complex event sequences in seconds.

The solution demonstrates how organizations across industries can modernize their customer analytics by centralizing all behavioral data, ML-powered attribution, and AI-driven insights on a unified platform.

Path Analysis & Visualization: Explore customer journeys through interactive Sankey diagrams, hierarchical tree views, force-directed graphs, and sunburst charts. Compare behavioral patterns across segments using TF-IDF ranking and side-by-side visualizations to identify what differentiates high-value customers from churners.

Multi-Touch Attribution Modeling: Quantify the contribution of each touchpoint to conversions using 7 attribution methodologies. Rule-based models (First Touch, Last Touch, Linear, U-Shaped, Time Decay) provide quick insights, while ML-driven Markov Chain and Shapley Value models deliver statistically rigorous, data-driven attribution that accounts for complex interaction effects.

Behavioral Segmentation: Transform event sequences into meaningful customer segments using Event2Vec embeddings and 5 clustering algorithms (K-Means, DBSCAN, Gaussian Mixture, Hierarchical, LDA). Visualize segment distributions with PCA and t-SNE dimensionality reduction to understand behavioral archetypes.

Association Analysis: Discover hidden patterns and relationships between events using market basket analysis techniques. Metrics including support, confidence, lift, conviction, and Z-score reveal which actions frequently co-occur, enabling cross-sell recommendations and journey optimization.

Pattern Mining: Uncover frequent sequential patterns and behavioral signatures through advanced pattern discovery. Identify common subsequences, temporal dependencies, and recurring behaviors that define customer journeys across your entire population.

Predictive Modeling: Build and deploy ML models to predict customer outcomes using Snowpark ML integration. Train Random Forest and Naive Bayes classifiers on journey data, evaluate performance with comprehensive metrics, and register models for production scoring.

This solution leverages Snowflake's native compute and AI capabilities to create a seamless, end-to-end customer analytics platform without external infrastructure.

Data Foundation: Customer events, timestamps, attributes, and conversion flags are stored as structured data in Snowflake tables. The setup script generates 5 industry-specific datasets (Retail, Financial Services, Hospitality, Gaming, Food Delivery) with realistic event sequences representing ~100K customer journeys each.

Snowpark-Optimized Processing: A dedicated Snowpark-optimized warehouse handles computationally intensive operations including Markov Chain transition matrix calculations, Shapley Value permutation analysis, Event2Vec embedding training, and ML model fitting—all executed server-side within Snowflake.

Server-Side Attribution: Stored procedures implement Markov Chain removal effect calculations and Shapley Value cooperative game theory algorithms directly in Snowflake SQL, eliminating data movement and ensuring enterprise-scale performance on millions of customer journeys.

Snowflake Cortex Integration: AI-powered interpretation is available across all modules through Snowflake Cortex LLM functions. Users can ask natural language questions about discovered patterns, attribution results, and segmentation outputs to receive actionable recommendations and strategic insights.

Interactive Visualization: Six Streamlit applications provide intuitive interfaces for each analytics capability, enabling business users to explore complex behavioral data through drag-and-drop configuration, real-time computation, and interactive charts.

This solution helps organizations across industries transition from fragmented, manual customer analysis to an integrated, AI-driven behavioral intelligence platform, resulting in:

Enhanced Customer Understanding: Analysts gain complete visibility into how customers navigate touchpoints, which interactions drive conversions, and what behavioral patterns predict outcomes—enabling truly data-driven customer strategies.

Optimized Marketing Spend: Attribution modeling quantifies the true contribution of each channel and touchpoint, enabling marketers to reallocate budgets from underperforming channels to high-impact interactions with measurable ROI improvement.

Reduced Churn: Predictive modeling identifies at-risk customers before they leave, while path analysis reveals the warning signs and intervention points where proactive engagement can retain valuable relationships.

Personalized Experiences: Behavioral segmentation creates actionable customer groups based on actual journey patterns rather than demographics, enabling hyper-personalized messaging, offers, and experiences that resonate with each segment's preferences.

Accelerated Insights: What previously required weeks of data engineering, custom analytics, and manual interpretation now happens in minutes through intuitive interfaces—democratizing advanced analytics across the organization.

Operational Simplification: Eliminates the need for multiple external analytics tools, ETL pipelines, and data platforms. All sensitive customer data remains securely within Snowflake's governed environment with enterprise-grade security and compliance.

This solution serves as a comprehensive foundation for customer analytics across multiple industries:

E-Commerce Conversion Optimization: Analyze the paths customers take from landing page to purchase, identify abandonment points, and discover which product browsing sequences lead to higher cart values. Attribution modeling reveals whether email campaigns, social ads, or organic search drive the most valuable conversions.

Financial Services Customer Acquisition: Track the journey from initial awareness through account opening, understanding which touchpoints (branch visits, digital interactions, advisor consultations) most influence application completion. Predict which prospects are likely to convert and prioritize outreach accordingly.

Subscription Churn Prevention: Identify behavioral patterns that precede cancellation—reduced engagement, support contacts, feature abandonment—and build predictive models to flag at-risk subscribers for proactive retention campaigns.

Gaming Player Engagement: Understand player progression paths, identify where players get stuck or frustrated, and discover which in-game events correlate with long-term retention and monetization. Segment players by behavioral archetypes for targeted content and offers.

Hospitality Guest Experience: Map the guest journey from booking through post-stay feedback, identifying touchpoints that drive loyalty and repeat bookings. Association analysis reveals which amenity combinations and service interactions create the most satisfied guests.

Food Delivery Order Optimization: Analyze ordering patterns to understand what drives repeat orders, which promotions are most effective, and how delivery experience impacts customer lifetime value. Predict which customers are likely to become regular users.

Healthcare Patient Engagement: Track patient interactions across appointment scheduling, portal usage, and care touchpoints to identify patterns associated with treatment adherence and positive outcomes. Segment patients for personalized communication strategies.

Manufacturing Process Analytics: Apply journey analytics to machine events and IoT data to understand equipment behavior patterns, predict maintenance needs, and optimize production workflows based on sequential event analysis.

Zero Data Movement Architecture: All analytics—from Markov Chain transition matrices to Shapley Value permutations—execute entirely within Snowflake. No data extraction, no external compute, no security exposure.

Server-Side ML at Scale: Snowpark-optimized warehouses handle Event2Vec embedding training, Random Forest/Naive Bayes model fitting, and clustering algorithms on millions of journeys without client-side memory constraints.

Real-Time Interactive Analysis: Sub-second response times for path visualizations and attribution calculations through intelligent caching and stored procedure optimization.

AI-Augmented Insights: Snowflake Cortex LLM integration transforms complex analytical outputs into natural language explanations, strategic recommendations, and actionable next steps.

Ready to transform your customer analytics? Deploy this solution in minutes:

1. Github Repository — Get the complete source code and setup scripts
2. Run setup.sql — Creates the database, sample datasets, and stored procedures
3. Run deploy_streamlit.sql — Deploys the Sequent application to your Snowflake account
4. Start Exploring — Open Sequent from Snowsight and start analyzing customer journeys