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The retail industry is MongoDB’s second largest customer base; we work with many of the big names in fashion, grocery, CPG (consumer packaged goods), and more.

As MongoDB is a general-purpose database, we are embedded in many different types of applications. One of the most common areas is e-commerce modernization, where retailers want to move from a legacy monolith on-premises system to a modern microservices architecture in the cloud. MongoDB is a great solution as it enables rapid development through an intuitive, flexible document model, and its cloud-native architecture gives the availability and resilience to provide a reliable 24/7 service.

In recent years, there have been more and more supply chain use cases: Optimize processes, give end-to-end visibility, or enable omnichannel experiences. Being able to unite disparate data sets and surface them for real-time consumption across organizations is vital for understanding the flow of stock and inventory.

Product catalogs are probably our most common use case in retail because our document model maps intuitively to the data set. The product on the shelf becomes an object in code and then becomes a document in MongoDB. This reduces complexity and improves performance compared to a relational structure.

MongoDB’s flexible document data model allows product data to change over time so new products can be brought to market quickly. Also, data of different shapes can easily co-exist (think of a retailer selling everything from lemons to mobile phones) and storing hierarchical data (e.g., product families), which is much less complex in a NoSQL structure. This makes MongoDB ideal for product use cases.

Customer expectations are constantly growing and evolving in this space. Digitally driven experiences like curbside pick-up and next-day delivery are now considered standard practices, but these are not easy to deliver if you’re an established retailer with decades of technical debt and siloed data stores.

Coupled with this, technology-forward retailers are taking market share by offering new experiences. For example, in-store staff gains the ability to access loyalty accounts at the register and offer customized offers or gifts in real time.

For any one of these experiences, retailers need to have a single view of stock, inventory, and the customer available in real time. Creating an operational data layer in MongoDB can be a great solution for this. By combining data in a performant and flexible manner, it becomes easy to create these new experiences.

There are two main groups of databases that we compete within the retail space.

One is traditional RDBMS, which includes Oracle, MySQL, Postgres, etc. Retailers will have established skill sets in these technologies but will often choose to modernize onto NoSQL technology. This is because the rigid structure of RDBMS inhibits change. The schema must be changed for every new product or attribute of a customer that will be added, which slows down the time to market. These technologies are also not built for the cloud; their active-passive architecture is designed for a data center, and it needs more resilience and availability for a 24/7, always-on service. For e-commerce, where downtime reduces revenue, this is unacceptable.

The second type is other NoSQL database services that are available in the cloud. The document model and always-on service is appealing, but many of these lack vital functionality. Some are merely key-value stores incapable of answering complex queries, and with no secondary indexing capabilities, they cannot serve multiple workloads adequately and do not handle relevant data types (e.g., Decimal128 for dynamic pricing). Retailers that begin on these services often move to MongoDB when they realize other databases are not meeting their requirements.

A great example of this is the ability in MongoDB to do in-app analytics. Our aggregation pipeline means that you can run simple analytics in the database in real time and perform complex transformations.

The ability to provide personalized experiences is vital to capture the attention of the customer and help them find what they want. MongoDB’s ability to do real-time analytics in the operational data layer means that retailers can build applications that react in real time to what the customer is doing. For example, personalized product recommendations, tailored offers, dynamic pricing, and more. Retailers are realizing that if they have to ETL data off to another system, then they are acting on data that is stale, and the customer has moved on.

See this use case at OTTO, where e-commerce modernization was reinvented on MongoDB. OTTO only takes one to two seconds to access customer profiles in real time and decide how to react.

The MACH Alliance is a non-profit organization fostering the adoption of composable architecture principles. It stands for Microservices, API-First, Cloud-Native SaaS, and Headless.

The MACH Alliance’s Manifesto is to future-proof enterprise technology and propel current and future digital experiences. The MACH Alliance and the creation of this set of principles originated in the retail industry. Several of the co-founders of the MACH Alliance are technology companies building for retail use cases: For example, commercetools is a composable commerce platform for retail (built completely on MongoDB).

MongoDB has been a member of the MACH Alliance since 2020 as an “enabler” member, meaning using our technology can enable the implementation of the MACH principles in application architectures. This is because a data layer built on MongoDB is ideal as the basis for a MACH architecture.