AI Product Design

The Anatomy of an AI-Powered Nudge Engine

Crafting Hyper-Personalized Notifications for a Social Dining App

The Challenge: Cutting Through the Noise

Standard notifications fail to engage users. The goal is to leverage AI to deliver social proof-driven, scarcity-based, and personalized nudges that adapt to each user's journey, transforming notifications from interruptions into valuable opportunities for connection.

+45%
Projected Uplift in RSVP Conversion

with Hyper-Personalized Nudges

Vectorization & Attributes: The AI's Ingredients

👤 User Attributes

These attributes form a deep understanding of the user's social and dining preferences, creating a rich vector for matching.

  • Interests & Cuisine Preferences
  • RSVP & Attendance History
  • Social Graph & Friend Activity
  • Engagement (clicks, views, shares)
  • Typical Time Availability

🗓️ Event Attributes

Event vectors capture the essence and social context, allowing for nuanced matching beyond simple tags.

  • Theme, Cuisine & Exclusivity
  • Popularity (RSVP velocity)
  • Host Credibility & Reviews
  • Social Proof (Friends Attending)
  • Scarcity (Limited Spots)

Adaptive Nudges Across the User Journey

1. Onboarding

Trigger: Sign-up

"Welcome! Here are 3 dinner parties happening this week your friends might like."

2. Engagement

Trigger: New Event

"Only 3 spots left at the 'Taco Tuesday' your friend John is attending!"

3. Post-Event

Trigger: Attendance

"How was the pizza night? Here's another event from the same host."

4. Re-engagement

Trigger: Dormancy

"We miss you! 5 of your friends joined events last week. See what's new."

Real-Time Personalization Architecture on AWS

1. Trigger Event

(e.g., User Login, New Event)

2. AWS Lambda

Initiates RAG Workflow

3. Retrieval-Augmented Generation (RAG) Process

Retrieve Context

Fetch user/event vectors from MongoDB Atlas

OpenAI API Call

LLM drafts message with retrieved context

LLM Pre-Send Review

Agent checks for tone, accuracy, and spam risk

4. AWS SQS/SNS

Queue & Distribute Message

5. AWS Pinpoint

Deliver via Push, SMS, Email

The Personalization Spectrum

As user data deepens, the system transitions from rule-based fallbacks to sophisticated, LLM-crafted messages, ensuring relevance at every stage of the user's engagement.

Measuring Success: Key Performance Indicators

Click-Through Rate

Measures immediate notification relevance and appeal.

RSVP Conversion

Tracks how effectively nudges drive core actions.

Profile Completion

Gauges success of gamified nudges to enrich user data.

User Retention

The ultimate measure of long-term platform value.