Overview
This module captures and stores the sequence of product views per user session, serving as foundational data for downstream recommendation engines without directly influencing catalog operations.
Implementation Steps
Event Emitter Setup
Configure the catalog service to emit a 'product_viewed' event whenever a user interacts with a product card or detail page.
Session Context Management
Associate each event with a unique session ID and user identifier, ensuring data is scoped correctly per browsing context.
Data Persistence Layer
Write the view record to a dedicated time-series or relational table, including fields for product_id, user_id, timestamp, and session_id.
Retention Policy Configuration
Define automated cleanup rules to delete records older than the configured retention period (e.g., 30 days) to manage storage costs.
Evolution from basic logging to intelligent cross-device profiling.
Primary
The system records a timestamped entry for each unique product viewed by a logged-in or anonymous user within a defined session window (e.g., 24 hours).
Anonymous Tracking
Captures browsing data for unauthenticated users using session cookies or device IDs.
Session Timeout
Automatically clears view history when a user's inactivity exceeds the defined threshold.
Duplicate Filtering
Prevents multiple redundant entries for the same product within a single session window.
Unified intake pipeline
Consolidate all order sources into one governed OMS entry flow.
Schema normalization
Convert channel-specific payloads into a consistent operational model.
Variable based on traffic volume
Events Recorded per Day
Linear with retention period
Storage Growth Rate
< 5ms read/write overhead
Latency Impact
System Roadmap Architecture
The recent view feature begins as a simple digital bookmark, capturing user clicks to build immediate context. In the near term, we will enhance data latency and accuracy, ensuring real-time updates across all channels while integrating basic personalization algorithms that suggest related items based on viewing history alone. Moving into the mid-term, the roadmap shifts toward predictive analytics; we will employ machine learning models to anticipate future intent, dynamically reordering product displays before a user even searches for them. This phase also involves deep cross-channel synchronization, ensuring the experience remains seamless whether accessed via mobile, web, or in-store kiosks. By the long term, the function evolves into an autonomous discovery engine, leveraging behavioral patterns and external market trends to proactively surface high-value opportunities without explicit input. Ultimately, this strategic progression transforms a passive record into an active sales catalyst, driving conversion rates through hyper-relevant, timely recommendations that feel intuitively personalized to every individual shopper.
Connector resilience improvements
Strengthen retries, health checks, and dead-letter handling for source reliability.
Adaptive validation profiles
Tune validation by channel and account context to reduce false-positive rejects.
Exception intelligence
Prioritize high-impact intake failures for faster operational recovery.
Strategic Use Cases
Personalized Feed Generation
< 100ms latencyFeeds the recommendation engine with historical context to suggest products similar to those previously viewed.
Abandoned Cart Analysis
< 2 minCorrelates browsing sequences with cart abandonment events to identify high-intent users.
A/B Testing Attribution
15-40%Tracks which product variants were viewed most frequently during test periods.