Retail Insights With MongoDB: Shoptalk Fall
The retail industry has continued to evolve into an omnichannel marketplace since the 2020 pandemic sparked a surge of online shipping. Now, nearly five years later, the line between in-person shopping and e-commerce has grown thinner, thanks to technological advancements and shifting consumer expectations.
The advent of AI and a focus on generative AI (gen AI) has made these shifts especially prominent.
Shoptalk Fall 2024
focused on how to apply these technologies to consumer behavior, merchandising, supply chain optimization, and the like. As a retail principal in MongoDB’s industry solutions team, I manned our booth and walked the exhibit floor, answering this question: How can
MongoDB Atlas
—a flexible, cloud-enabled modern database—solve many data challenges that retail enterprises face?
Let’s explore some of the key themes that emerged at Shoptalk Fall 2024, including unified commerce, AI-driven innovation, and operational efficiency.
1. Unified commerce: Seamless integration across channels
Unified commerce is often touted as a transformative concept, yet it represents a long-standing challenge for retailers—disparate data sources and siloed systems. It’s less of a revolutionary concept and more of a necessary shift to make long-standing problems more manageable. In a sense, it’s “old wine in a new bottle,” unifying fragmented data ecosystems to serve an omnichannel experience. In essence, unified commerce is the integration of physical and digital retail channels, and it is essential for delivering a frictionless customer experience. However, the complexity of managing data silos and diverse technology sprawling across diverse platforms is a major challenge for a wide variety of enterprises.
We’re working with retailers globally to simplify cross-channel data unification into an
operational data layer
that enables easy synchronization across e-commerce, social and mobile platforms, and physical stores. This data platform approach with built-in
text search
and
vector search
, enables retailers to facilitate a superior customer experience with powerful search and gen AI capabilities on their e-commerce or mobile portals. A great example is
CarGurus
, which manages vast amounts of real-time data across its platform and supports seamless, personalized user experiences online and in person.
Figure 1.
Reference architecture of an operational data layer built on MongoDB Atlas, capable of serving multiple types of customer requests across engagement channels.
2. AI and data-driven innovation: Personalization at scale
Across several major retailers, changes indicate that AI is reshaping retail, enabling hyperpersonalized experiences and data-driven decisions. However, the success of AI models hinges on the quality, accessibility, and scalability of data. Without a flexible, powerful data platform, scaling AI initiatives across a retailer’s data landscape becomes daunting. AI adoption requires vast amounts of structured and unstructured data. The reliance on aging infrastructure and legacy data estates significantly hinders retailers’ ability to adopt transformative innovations like gen AI, as doing so demands substantial upgrades to their underlying data architecture. Fragmented technology ecosystems—with disparate AI and machine learning (ML) systems and siloed data estates lacking integrated frameworks—further complicate this modernization journey.
Retailers that we work with use MongoDB’s efficient handling of unstructured data combined with vector search to build AI-enabled applications. The
aggregation framework
enables powerful real-time data processing, and we have a broad
ecosystem of integrations with AI platforms
to trigger algorithms in real-time. These can fuel data-driven personalization engines to deliver tailored product recommendations and targeted marketing campaigns.
Figure 2.
Operational data, analytical insights, and unstructured data combine to form a data layer for AI-enabled applications.
3. Supply chain optimization: Operational efficiency
Operational efficiency was a key focus at Shoptalk, particularly in improving supply chain management and inventory optimization in real-time. Retailers struggle with legacy systems that are not equipped to handle modern data processing needs. Traditional database systems often lack the real-time data processing ability necessary for today’s fast-paced environment, which can lead to costly delays. To drive operational efficiency by building real-time data processing capabilities (critical for supply chain optimization and many other use cases), a retail organization needs a single view of data entities. It also needs to be able to track inventory levels, forecast demand, and optimize logistics using live data streams from Internet of Things devices, sensors, and external partners. Delivering real-time or near real-time insights on inventory, stock locations, and other critical information empowers the workforce, enhancing team efficiency and development across the organization.
To consolidate inventory data from different regions into a centralized view, MongoDB’s flexible data model can handle disparate data. At the same time,
real-time triggers
and
change streams
update applications instantly when inventory levels fluctuate. With these capabilities, MongoDB provides a robust platform for building a resilient, responsive supply chain capable of handling global expansion and complex logistics requirements, ultimately reducing stockouts, optimizing fulfillment, and improving the customer experience. For example,
Lidl
built an automatic stock reordering application for its branches and warehouses to increase efficiency along the supply chain when placing orders. In doing so, it addressed the challenge of complex data structures and an enormous volume of data to be processed.
Figure 3.
Reference architecture showing how MongoDB becomes the central part of the solution for supply chain optimization.
4. Product innovation and assortment: Agile data management
At Shoptalk, speakers also highlighted product innovation as a key driver for retail success. Retailers are moving toward rapid product development cycles and diverse product assortments. Product innovation and assortment management are vital as retailers work to capture consumer interest and meet evolving demands. Retailers often need a flexible system that can support rapid product iteration and the addition of new attributes, without delays. Agile and quick product-catalogs management requires a data platform that can deploy rapid updates and support complex product catalogs with ease. MongoDB’s flexible document-oriented model enables retailers to store and manage diverse product attributes without predefined schemas or evolving schemas as needed, making it easy to integrate data from different catalog systems while retaining flexibility for rapid updates and new product attributes. This consolidated view helps streamline catalog management and enables retail teams to easily track product availability, pricing, and specifications across channels.
When combining this view with sales data in MongoDB Atlas, retailers gain powerful real-time insights into consumer preferences, demand patterns, and emerging trends. With MongoDB’s aggregation framework and real-time analytics capabilities, retailers can quickly analyze sales trends against product data to identify high-performing products, seasonal trends, and gaps in the market. For instance, by evaluating purchase patterns, retailers can identify attributes or combinations (e.g., color, style, or size) that drive higher sales, informing future product development and marketing strategies. MongoDB Atlas’s data integration capabilities enable retailers to incorporate additional data sources, such as customer feedback or social media insights, to enrich product and sales data. This comprehensive, multifaceted analysis enables data-driven decisions that can refine product assortments and inform new product launches, maximizing the chance of success in the market.
5. Customer loyalty and trust
Customer loyalty programs have evolved dramatically in recent years. Consumers are expecting personalized interactions and rewards without any delay in retailers understanding their behavior. However, effectively managing and utilizing customer data for loyalty initiatives requires advanced data management capabilities. Customer loyalty programs are increasingly personalized, with retailers leveraging data to build trust and deliver consistent value. Retailers need to build sophisticated loyalty programs by understanding real-time customer data. The biggest challenge that retailers encounter is consolidating all customer data, such as transactions, loyalty profiles, and shopping behavior, stored across several operational systems.
As discussed earlier, MongoDB Atlas makes it easy to bring diverse datasets into a single database, enabling data access as required by any consumer of that data. Once the data is consolidated and established using real-time data feeds, retailers can use
MongoDB Atlas Charts
to visualize customer engagement trends and respond proactively with personalized offers and rewards. The
end-to-end encryption
and compliance features built into MongoDB Atlas help make sure that customer data is secure, fostering trust and supporting adherence to data privacy regulations.
Learn how
L’Oréal
created several apps and improved customer experiences by championing personalized, inclusive, and responsible beauty at scale.
6. Growth opportunities: Agile scalability
Enterprises today often aim to expand their digital reach and scale their operations globally. As retailers expand their footprints into new markets, they encounter different requirements in terms of languages, product assortments, and customer expectations. Managing data across multiple geographies and ensuring fast access is a considerable challenge that is difficult to achieve with traditional databases. As retailers reach new markets, scalability becomes a pressing concern.
Figure 4.
Modern retailers distribute their data globally to provide customers with low-latency access.
For multinational retailers looking to expand geographically, MongoDB helps them build
distributed architectures
(sometimes even
multi-cloud
) to deliver fast, low-latency access for customers worldwide. MongoDB Atlas offers built-in scalability features, including horizontal scaling, that provide fast performance at any scale. With its
workload isolation capabilities
, real-time operations can continue seamlessly because the analytics workloads can be segregated to eliminate resource contention.
Learn how Commercetools modernized its
composable commerce platform
using MongoDB Atlas and MACH architecture and achieved amazing throughput for
Black Friday 2023
.
Enabling the future of retail with MongoDB Atlas
As the key themes of Shoptalk Fall 2024, unified commerce, AI-driven innovation, and operational efficiency all highlight the critical need for a flexible and scalable data platform. MongoDB Atlas answers these challenges with its robust, cloud-native architecture, offering retailers the tools they need to thrive in an evolving landscape. From real-time data processing and global scalability to advanced AI integrations, MongoDB Atlas empowers retailers to stay competitive and deliver exceptional customer experiences.
By adopting MongoDB Atlas, retailers can unlock the full potential of their data, streamline operations, and future-proof their businesses in an increasingly complex retail environment.
Want to learn more about MongoDB in the retail industry? Read our
Essential Elements to Ecommerce Modernization E-book
on our retail page today.
January 13, 2025