Three Ways Retailers Use MongoDB for Their Mobile Strategy

Mobile experiences are a crucial aspect of a retail omnichannel strategy. Retailers strive to create a consistent customer experience as consumers switch between online, in-store, and mobile channels. This presents a complex data management challenge as views across customer and workforce mobile applications need real-time access to the same data sets, both on or offline.

Let’s dive into three ways retailers are tackling omnichannel data challenges with MongoDB mobile solutions.

Mobile solutions to omnichannel challenges

Achieving data centricity across channels

Before building any mobile omnichannel solution, you first have to solve the data-centricity problem. Established retailers tend to have fragmented and siloed data both in-store and online, which needs to be combined in real time to facilitate omnichannel experiences.

Consider Marks & Spencer’s loyalty program, which was part of a key strategic initiative to increase customer retention and drive multi channel sales. This required a data-centric solution to gain deep insight into customer behavior. As data size and traffic grew, the legacy solution couldn’t scale. The company addressed this problem by re-platforming the Sparks mobile application backend onto MongoDB Atlas, a high-performance data platform capable of expanding vertically and horizontally to deal with the heavy read/write throughput of a data-driven enterprise. Its Sparks customer mobile app caters to more than 8 million unique customers and is capable of calculating more than 15 million unique offers a day.

The flexibility of the document model allowed them to respond to trends in the market or new user behavior, and thus update its analytical framework. Taking advantage of the translytical data platform capabilities, business teams could classify and track customers, products, content, and promotions across any stage of the value chain, unlocking new revenue streams, all in real time. No matter the channel their customer is engaging with, which device they’re browsing on, or their geographical location, Marks & Spencer is able to cater their customers’ needs and use data to keep improving what their brand has to offer.

Delivering a cohesive omnichannel retail brand experience

It has become increasingly difficult for retailers to deliver a consistent experience across multiple channels. Think of the associated complexity of capturing and serving the right data at the right time, with extensive product catalogs, complex and changing categorization, regional nuances and language challenges for a global footprint, diverse seasonal sales, promotions, and more.

Because customers are engaging in “phygital” behavior, browsing the online product catalog while also walking through the store, enabling your workforce to respond to customer questions becomes crucial to deliver the expected brand experience. Retailers are creating Workforce Enablement Mobile Apps for complex store management operations or browsing global inventory that requires synced data to achieve a connected store.

For a company like 7-Eleven, whose value proposition is “Be the first choice for convenience. Anytime. Anywhere,” enabling its workforce became a critical issue for maintaining brand value.

Using an omnichannel approach, 7-Eleven deployed a custom mobile device using MongoDB Realm, MongoDB’s unified mobile platform, to manage its in-store inventory system. Leveraging the power of Atlas Device Sync, MongoDB’s mobile database service for syncing data across devices, users, and backends, 7-Eleven’s front-line staff can start using devices immediately, not having to wait minutes to download the data on initial startup, increasing data accuracy, especially around real-time stock management. Ease of access to correct and real-time product information boosts 7-Eleven’s convenience-centered brand offering and secures the cohesion of their brand experience with the brand both in the digital and brick-and-mortar stores.

Brand cohesion is dependent on efficient order management and visual merchandising. As customers are window shop, employees need to analyze real-time stock data from the retailer's supply chain while it passes from warehouse to in-store processes like stock delivery, visual merchandising, and product returns management, to optimize store operations and create seamless experiences.

Imagine the case of a fashion retailer with data gathered from RFID product tagging, scanned through mobile devices in the store. It can reduce total operating costs through optimized order processing, enabling logistics managers to discover pain points in the supply chain, forecast demand, and avoid stock breaks thanks to real-time triggers and alerts with auto-replenishment capabilities.

Retailers can also optimize in-store merchandising based on customer shopping behavior data gathered from garment SKUs scanned on the store racks or in fitting rooms with beacon-like RFID scanners. By viewing the movement of items and measuring things like how many times items are tried on related to how often they’re purchased, companies can understand how product assortment and movement affect purchasing intention, and relocate clothes based on that information.

Thanks to MongoDB's real-time architectures combined with Kafka managing the event streaming, and with MongoDB Realm providing a simple, fully integrated way to sync real-time inventory data to MongoDB Atlas, companies can achieve a deeper understanding of customer behavior as a competitive advantage and a reduction of total operational costs.

Data collection and its associated change events often occur in variable latency and low network availability scenarios, like warehouses, delivery trucks, or store buildings, creating the third challenge we will next address: Network Consistency.

Dealing with network consistency issues

Networks with variable latency across store floors, due to server distance, applications dealing with heavy content, or simply network congestion over sales periods, can generate the unwanted byproduct of data inconsistencies.

Apps can restart or shut down any time due to bugs, using too much memory, or other apps working in the background. To address these issues, businesses need an on-device database with offline synchronization, or an Offline-First approach.

The luxury market in particular expects perfection, where an item might range from $20,000 to more than $100,000. Customers expect more than just a purchase – they expect personalized experiences. One of our customers in luxury retail regularly holds pop-up events, often in destinations sometimes without network signal, which creates the need for a reliable mobile app with access to customer data, like purchase histories, to provide that personalized experience. For example, providing fast and easy check-in for customers at these events is critical to their experience-centric business. Thanks to MongoDB Realm, their event tablets always work, even when internet connections are poor.

Capitalizing on MongoDB Realm’s local data persistence, storing user data on their devices, and combining it with Atlas Device Sync, the retailer has a top-performing mobile web app with the ability to keep working offline storing user data to then sync it back to the database once connectivity is restored.

This approach allows the company to build an uninterrupted and unified 360-degree customer management platform spanning web and mobile touchpoints, with relevant data always up to date.

Conclusion

Mobile experiences are crucial in today's retail landscape, but integrating them can be challenging. By leveraging mobile channels, retailers can differentiate their brands, increase customer loyalty, and expand their reach in a fiercely competitive industry — staying ahead of the game and thriving in the omnichannel era.

Learn how to quickly launch and scale secure mobile apps on our MongoDB for Mobile site.