Make receipts a growth engine with the document model and AI insights powered by MongoDB.
Use cases: Modernization, Personalization, Single View
Industries: Retail
Products: MongoDB Atlas, MongoDB Atlas Triggers, MongoDB Atlas Vector Search, MongoDB Change Streams, Voyage AI
Partners: Microsoft Azure
Solution Overview
We live in an era where paper processes are quickly being replaced by automated, personalized solutions like digital receipts. These elements are becoming the industry standard and are delivering tailored customer experiences.
Retailers who embrace this transformation gain a competitive advantage by unlocking the value of their data. Digital receipts provide rich, actionable insights that can drive personalized product recommendations and help businesses stand out from their competition.
This document explores how retailers can use MongoDB to store and manage digital receipt data to deliver personalized product recommendations and enhance the post shopping experience. You'll discover the common challenges of managing receipt data and how MongoDB simplifies access and activation for real-time, customer-centric experiences.
Transform Transaction Data into Actionable Value
When a customer shops in a physical store, digital receipts become a strategic touchpoint. Retailers can use these items to provide tailored suggestions and as a link to continue online shopping, all within the same digital receipt.
When moving to the online experience, whether through a mobile app or a browser, customers receive personalized product recommendations based on their complete purchase history. They also keep their loyalty benefits across all channels, enjoying a personalized experience powered by a unified 360° customer journey.
Figure 1. With MongoDB, digital receipts connect in-store and online journeys with real-time AI-powered recommendations, enabling a 360° customer experience
Digital receipts provide businesses access to multiple strategic advantages, including:
Enhancing customer engagement through personalized experiences: With transaction data, retailers can tailor marketing efforts such as delivering smart product recommendations, sharing personalized offers, and running targeted ad campaigns.
Unlocking valuable customer insights: By analyzing behavior and preferences at scale.
Reducing operational costs: By eliminating the need to print, store, and manage paper receipts.
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Customer Success Story
Discover how Albert Heijn, the largest supermarket chain in the Netherlands, uses MongoDB to support customer-facing applications.
Data Management Challenges and How MongoDB Can Solve Them
Managing receipt data can be difficult. In this section, you will explore the key obstacles and learn how MongoDB can help overcome them.
Siloed Data and No Single Source of Truth
Retailers often store multiple copies of sales data across disconnected third-party tools, leading to limited visibility and fragmented information that's hard to analyze, or activate. MongoDB addresses this challenge by acting as a centralized, flexible data platform that consolidates data from multiple systems. It simplifies integration, breaks down silos and creates a unified operational data layer that supports real-time data analysis for actionable insights.
Sales Receipt Data Is Complex
Sales data often comes from legacy systems and is stored in complex, high-volume formats like XML, which are deeply nested and inconsistent. In RDBMS, architects must spread the data in multiple tables to handle that complexity. Instead, MongoDB uses its flexible document model to adapt to the data requirements, not the other way around. This capability enables faster development and easy schema changes, such as adding fields for personalized recommendations, at any time with no downtime.
Rapid Data Scaling
Retailers generate massive volumes of receipt data every day and systems must keep up. For example, Albert Heijn, the largest supermarket chain in the Netherlands, uses MongoDB to process over one million daily transactions across 50 services. MongoDB scales automatically, offers sharding and replica sets for high availability, and allows you to separate workloads. This means real-time AI, reporting, or analytics can run without disrupting your main application.
Customer Privacy
Receipts often contain personal customer data making security and compliance essential. MongoDB supports secure and compliant data handling with robust, built-in enterprise security features and data privacy. Including full lifecycle data encryption, RBAC, and auditing. In addition, zone sharding allows you to store data in your chosen region, supporting compliance and performance across regions.
Unlock Real-time Insights from Receipt Data
Turn receipt data into real-time value with this proof of concept using MongoDB Atlas as the central data store. The demo showcases a backend microservice for invoice creation in a mock e-commerce system, adaptable to physical stores. MongoDB unifies operational and AI data, providing full control over your data while keeping it accessible for downstream use.
Reference Architectures
Build a Foundation for Real-time Personalization
Personalized customer experiences start with connected, accessible, and actionable data. A unified view of operational data is key to unlock the full potential of modern technologies.
This demo shows how MongoDB's flexible document model and real-time integration capabilities can turn operational data into intelligent, responsive customer journeys. The diagram below shows a high-level view of the solution:
E-commerce and in-store transactions flow into the MongoDB invoices collection, creating a complete and unified purchase history for each customer.
Operational data is stored using the MongoDB document model as a single source of truth.
Microservices and external systems seamlessly integrate with MongoDB capabilities to deliver real-time and AI-powered recommendations, triggered by invoice data.
Figure 2. This diagram illustrates the core components of the demo solution
To build this solution, you deploy two backend microservices - invoice and recommendation - on Azure App Services, and use Azure Functions to simulate external integrations, such as tax logic or loyalty programs. Rendered receipts are stored as PDFs in Azure Blob Storage, tightly linked to their structured metadata in MongoDB.
Model Invoices and Catalog Data for an AI-powered Use Case
The document model stores all invoice-related data in a single collection, from tax details to a list of personalized product recommendations based on a specific purchase. This flexibility ensures that the schema can evolve alongside the business, enabling seamless updates without friction or downtime.
To enable personalization, you can store semi-structured data in MongoDB. The product catalog contains core details—like price, description, and categories—plus semantic data generated by AI embedding models. With Voyage AI voyage-3-large text embedding model, you can enrich product data with vector embeddings for smarter, AI-driven catalog retrieval.
Figure 3. MongoDB combines traditional product details with vector embeddings from Voyage AI, enabling semantic search and AI features directly in the product catalog.
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Embedding Models with Voyage AI
Voyage AI offers multiple text embedding models tailored to different domains, including legal, code, and finance. To explore the available models and find the best fit for your use case, visit the Text Embeddings Documentation.
Furthermore, you can implement product recommendations with Vector Search to identify products similar to those previously purchased by the customer. In this use case, a recommendation microservice generates suggestions for each customer. This microservice can be further improved by incorporating an AI model trained on previous purchase data.
Orchestrate Real-time Data Flows with an Event-driven Design
Real-time customer experiences rely on systems that respond instantly to events. Event-driven architecture makes this possible by enabling services to communicate through events, signaling that something important has happened. This pattern is widely adopted in modern retail systems because it allows services to stay decoupled, scalable, and highly responsive.
You can apply Change Streams and Atlas Triggers to detect database changes and coordinate microservices through a choreography pattern. This pattern is explained in the invoice and recommendation microservices.
Figure 4. Event-driven choreography enables real-time recommendations based on a customer's most recent purchase, improving their overall experience
Invoice microservice: When a customer places an order (step 1), the invoice microservice listens to the orders collection via Change Streams. It automatically detects the new order and creates the invoice document inserting it into MongoDB Atlas (step 2). Then, the transaction data can continue its flow to external providers like ERP platforms (step 3).
Recommendation microservice: As soon as a new invoice is added to the collection, the recommendation microservice reacts and creates AI-powered product suggestions based on the purchase (step 4). These recommendations are embedded in the invoice and the user profile documents using Atlas Triggers (step 5). This update personalizes the invoice and enriches the user profile with the latest embedded recommendations. The frontend can then quickly retrieve this data with a simple query and display it on the homepage (step 6), keeping the systems updated and the user experience responsive.
Reliable Event Handling at Scale
You can integrate MongoDB into an event-driven architecture using an event broker to route and distribute events across multiple services. This enables systems to react in real time, ensuring decoupling, scalability, and fault tolerance.
Figure 5. Azure cloud integrations in action
In the demo, the microservice serves as a Change Stream listener that reacts to database events. These events could then be forwarded to Azure Event Grid or Azure Service Bus to trigger downstream workflows, or integrate with external systems. Alternatively, an Azure Function can act as a serverless bridge to capture Change Stream events from the database and push them to the event broker. For high-throughput scenarios, such as real-time analytics or AI training, you can stream events using the Kafka Connector to Azure Event Hubs.
Build the Solution
This solution shows how retailers can centralize invoice data and deliver real-time personalization with a clean, event-driven architecture powered by MongoDB Atlas.
The backend repository implements two microservices: one for invoice generation and one for recommendations, along with supporting components such as Atlas Triggers, Azure Functions, and Azure Blob Storage. These services simulate how systems react to customer purchases in real time.
The frontend repository contains a mock e-commerce site, Leafy Popup Store, for handling orders and users. To get started, follow the pre-requisites and setup instructions, then use the UI to simulate purchases and see the demo in action.
Set up instructions
The project is organized as a multi-service backend system, with each microservice in its own folder and a shared Makefile and Docker Compose setup at the root level. To run the full solution or work on individual services, you'll find detailed setup steps in each README.
Here’s a quick summary of what each part contains:
Main Project README: Overview of the architecture, system components, and how to run all services together using make commands and Docker Compose.
services/invoice-ms: Setup instructions for the invoice microservice. Covers how to run it locally or in Docker, configure Azure Blob Storage and Azure Functions, and connect to MongoDB.
services/recommendations-ms:. Setup for the recommendation engine, which listens to new invoices and performs a vector search in MongoDB Atlas. It includes the environment setup, connection to the vector index, and how to trigger and observe recommendations. Additionally, you will also find:
A /docs/adr/ folder with ADRs explaining modeling and design choices.
An /external/ folder containing sample Azure Functions and Atlas Triggers used in the demo, each with its own corresponding README.
Note: Deploy the frontend and order system from the Leafy Pop-Up Store demo before using this solution.
Key Learnings
Lets recap some of the key takeaways from the solution:
AI-Powered personalization is crucial to stay competitive and on top of the game: It can boost customer engagement and strengthen loyalty.
Digital receipts offer more value than just a transactional record: Their insight can be used to personalize a 360 shoppers journey.
MongoDB is built for modern use cases: It offers flexibility at scale, built-in security, and removes friction for seamless integration with real-time architectures and AI features.
Ready to turn your receipt data into personalized customer moments? Start building with MongoDB today or join the MongoDB Developer Community to connect with other tech lovers.
Authors
Florencia Arin, MongoDB
Angie Guemes, MongoDB