Proof of Concept Journeys
UC1: Abandoned Cart
Use Case Requirements
Business Goal
Re-engage customers who have added products to their cart but have not completed a purchase, using contextual triggers and inventory-aware personalization to drive in-store or online conversions.
Use Case Description
Triggered when a customer adds a product to their cart and abandons.
- Journey progresses only if the item remains unpurchased after a defined period.
- Once abandoned, the system validates customer consent and monitors for physical proximity (POI radius).
- When the customer enters a defined radius, the platform queries the inventory system in real time to check local product availability.
- If available, a personalized push notification with product details is sent instantly.
- If there’s no engagement within 24 hours, an SMS or email fallback is triggered.
Target Audience
Customers who have added one or more products to their cart but have not completed checkout within a defined timeframe.
Data Requirements
- Event: Add to Cart, Purchase, Location Entry (POI)
- Attributes: Customer ID, Product ID, Store ID, Timestamp,
- Real-time integration with inventory system for product availability lookup.
Segmentation
- Real-time streaming segmentation using cart activity and location event data.
- Batch data from Amperity or internal systems for customer identity, and preference enrichment.
Personalization
- Product(s) in cart
- Nearest store location
- Inventory availability
- Channel preference
External APIs Required:
- Inventory API (real-time product availability)
- Consent Management (built into Adobe/tools for this is acceptable)
UC2: Loyalty Re-Engagement
Use Case Requirements
Business Goal
Re-engage dormant loyalty members by personalizing outreach based on tier, engagement behavior, and product affinity to increase retention and incremental spend.
Use Case Description
Triggered by inactivity and loyalty tier segmentation.
- Identify profiles in Silver or Gold tier with no purchase, login, or message engagement in the last 90 days (consent required).
- Touch 1: Send “We miss you” message via preferred channel (Email or Push), personalized by product affinity.
- Wait 3–5 days for engagement.
- Touch 2: Conditional split—engaged customers move into “Loyalty Booster” journey; non-engaged receive stronger incentive (e.g., higher discount).
- Loyalty Booster Journey:
- Triggered in real time when a dormant customer re-engages.
- Touch 1: Personalized “Welcome Back” offer (e.g., 15% off).
- Wait X days, evaluate purchase behavior.
- Touch 2: For reactivated but non-purchasing customers, send an alternate incentive or content.
- Journey ends upon purchase or after 10 days with no engagement.
Target Audience
Dormant loyalty members in Enthusist tiers tiers with 90+ days of inactivity
Data Requirements
- Event data: Purchase, Login, Message Engagement, Opt-in/Consent
- Loyalty data: Tier, Points balance, Enrollment date, Last activity timestamp
- Behavioral data: Product category affinity
Segmentation
- Batch segmentation from Amperity (90-day inactivity, loyalty tier)
- Streaming segmentation for real-time engagement events (login, click, open)
Personalization
- Channel preference (Email vs Push)
- Content personalized by affinity, tier, and offer type
- Dynamic incentive adjustment based on engagement response
External APIs Required:
- Loyalty API (Tier, Status, Points)
- Amperity Segment API (Audience file sync)
(AJO Introductory LabLab)
Use Case Requirements
Business Goal
Reduce customer attrition by identifying and targeting customers with a high likelihood of churn using predictive model outputs or simulated risk scores.
Use Case Description
Triggered by updates to churn risk scores (batch or simulated).
- The system uses decisioning logic to tailor incentives based on risk level:
- High Risk: Aggressive offer
- Medium Risk: Moderate offer
- Low Risk: Nurture message (no offer)
- Journey is triggered when a customer’s risk score exceeds a defined threshold.
- Touch 1: Deliver message via preferred channel with dynamic content and offer details.
Target Audience
Customers identified through predictive modeling or simulated scoring as being at risk of churn.
Data Requirements
- Churn risk score or simulated model output (numeric or categorical)
- Customer ID, Channel preference, Offer eligibility data
- Event triggers for score updates
Segmentation
- Batch segmentation for model outputs (daily or weekly updates)
- Streaming trigger when new scores exceed defined threshold
Personalization
- Dynamic offer and content based on:
- Churn risk score tier
- Channel preference
- Historical engagement and purchase behavior
External APIs Required:
- Predictive Model API or Scoring Feed
- Offer Decisioning API (selects appropriate incentive)
- Channel API (Email/SMS/Push delivery)