Transform retail customer experiences
Omnichannel product catalog and inventory management in real time, in the same data store with informed recommendations.
Sync what your customers see—whether on the web or in the store—across channels and with your back-end systems to create seamless, endless aisle shopping.
Easily build and use rich customer profiles by leveraging new types of data that are too complex for relational databases to handle, such as “social footprints.”
Develop a holistic view of your businesses so you can support omnichannel initiatives, aggregate inventory updates, and act on trends in real time.
Respond to changing customer and merchant expectations with the agility enabled both by MongoDB’s JSON document model and flexible schemas.
The retail industry is MongoDB’s second largest customer base; we work with all 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 ecommerce 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 gives the availability and resilience to provide a reliable 24/7 service.
In recent years, there are more and more supply chain use cases: To optimize processes, give end-to-end visibility, or to 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 that then becomes a document in MongoDB. This reduces complexity and improves performance compared to a relational structure.
MongoDB’s flexible easily allows for 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) 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 gain 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 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 with in the retail space.
One is traditional ; Oracle, or even the likes of 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 time to market. These technologies are also not built for the cloud, their architecture of active-passive is designed for a data center and does not provide the resilience and availability that is required for a 24/7 always-on service. For ecommerce where downtime costs revenue, this is unacceptable.
The second 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 incapable of answering complex queries, and with no secondary indexing capabilities they cannot serve multiple workloads performantly, or do not have the necessary data types (e.g. Decimal128 for dynamic pricing). We may see retailers begin on these services and then move to MongoDB when they realize their requirements are not being met by other databases. A great example of this is the ability in MongoDB to do . Our means that you can run simple analytics in the database in real time and perform complex transformations.
Almost all retailers run and develop several mobile applications: customer facing ones (e.g., ecommerce, loyalty) and internal workforce apps (e.g., inventory management, delivery/routing). These applications often need to sync to the backend data or have an offline mode (e.g., delivery drivers in remote areas or the fridge area of a warehouse).
Building this sync or offline mode is incredibly complex for retailers, and it’s not value-add work. It’s standard functionality. For this reason, MongoDB Realm is a great solution. Our flexible sync offering has out-of-the-box offline mode and data sync built in. We are now a common choice for functionality like in-app grocery shopping lists or inventory management mobile applications.
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 five 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 use of 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.