- Campaign Speed: 3X faster campaign deployment and go-live times using prompt-based generative AI.
- Click-Through Rates: More than doubled average in-app CTRs, jumping from 10% to 21%.
- Campaign Scale: Streamlined operational workflows to easily manage over 6,000+ campaigns per month.
- Setup Efficiency: Reduced sophisticated journey configuration time from hours down to a few minutes.
RETAIL & E-COMMERCE | AI
From Scheduling Campaigns to Managing Agents: How Swiggy Scales 6,000+ Campaigns with MoEngage + Aampe
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3X
Faster Campaign Go-Lives
2X
Lift in In-App CTRs
6000+
Monthly Campaigns Managed
Understanding The Swiggy Multiverse
Swiggy operates an immense digital “Multiverse” that engages over 25.2 million Monthly Transacting Users across core offerings like Food Delivery, Instamart Groceries, and DineOut. Moreover, decentralized teams spread across 11 distinct Business Units like the Food Marketplace, Crew, and Toing increase the platform’s operational complexity.
The Bottlenecks Of Swiggy’s Hyper-Scale
This scale of operation introduces three massive pain points:
• User Fatigue: 11 BUs managing 6,000+ monthly campaigns across 5 apps
• Slow Execution: 3-5 days to get campaigns live due to heavy engineering dependencies
• Fragmented Messaging: Uncoordinated, competing offers bombarding the same shared user base, strained user attention spans
With MoEngage coming in, I think we broadly drew different pillars on how we want to build with AI. The first one being unification of data across channels. We're able to leverage all of that from a single platform, setting the right context for the AI system to build on.
Parth Shah
Growth Lead
Earlier as marketers, we would schedule campaigns, and now we are sort of managing agents to run these campaigns. That's how our job has moved on.
Parth Shah
Growth Lead
Solving it Agentically with MoEngage & Aampe
To overcome these hyper-scale challenges, Swiggy deployed the unified MoEngage and Aampe agentic marketing framework. By moving past rigid segments and static user journeys, this combined architecture restructures growth around true 1:1 personalization. The framework relies on three interconnected pillars:
• Data Consolidation: Consolidating cross-channel data for real-time context
• Agentic Workflows: Leveraging MoEngage’s Merlin AI to accelerate operations through prompt-based asset creation and journey orchestration
• Per-User Agentic Decisioning: Deploying a dedicated, autonomous Aampe AI agent for every individual customer to calculate the next best action in real time
Consolidating Data & Channels
MoEngage unified Swiggy's user data across all touchpoints while bringing channels like push, in-app, email, RCS, SMS, and WhatsApp onto a single platform. This allows marketers to launch real-time, cross-channel journeys from a centralized hub without switching tools.
With all data and lifecycle campaigns in one place, the AI engine sees every customer action and gains the context to continuously optimize engagement.
Faster Workflows with Prompt-Based AI Agents
Swiggy removed engineering bottlenecks by leveraging MoEngage’s prompt-based AI agents to create campaigns in a fraction of the time.
Multi-Channel Journeys
Merlin AI Flow Assist helped Swiggy build complex, multi-channel journeys with natural language prompts. Marketers describe the logic, channels, delays, and exit triggers, and the agent generates the complete workflow, cutting setup from hours to minutes.
In-App Template Builder
Merlin AI In-App Template Builder generates polished in-app layouts such as surge pay alerts, multi-step surveys, and payment breakdowns, directly from text prompts, eliminating engineering dependencies. By streamlining these creative and operational workflows, Swiggy launches campaigns 3x faster while doubling in-app Click-Through Rates (CTRs) from 10% to 21%.
Swiggy’s Campaign Personalization Challenges
Swiggy ran 6,000+ monthly campaigns but struggled with user-level unknowns: how many notifications to send, which business units to prioritize, when to target them, and what content to use. Manually scheduling this across fragmented business units made true personalization impossible.
Transitioning to Aampe's 1:1 Agentic Decisioning
To resolve this, Swiggy replaced static scheduling with Aampe's 1:1 agentic decisioning engine via a streamlined, three-step automated workflow:
• Goal Setting: Marketers define overarching business objectives rather than building fixed workflows.
• Reward Signal Configuration: User behaviors are weighted to balance positive rewards (conversions) against negative feedback (unsubscribes).
• Autonomous Optimization: Aampe's per-user agents process real-time signals and guardrails to automatically deliver optimized 1:1 journeys on the fly.
Products Used
Agentic Decisioning
Drive personalized experiences with Decisioning Agents that translate business goals and real-time user signals into the perfect message, channel, and timing.
Merlin AI Flow Assist
Complex, multi-channel user journeys from plain-language prompts.
Merlin AI In-App Template Builder
Polished in-app layouts like surge alerts and surveys directly from text.
Results
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