Prashant Juttukonda

11 results

AI-Powered Retail With Together AI and MongoDB

Generative AI (gen AI) is changing retail in fascinating ways. It’s providing new avenues for IT leaders at retailers to enhance customer experiences, streamline operations, and grow revenue in a fast-paced environment. Recently, we’ve been working closely with a fascinating organization in this space—Together AI. In this blog, we’ll explore how Together AI and MongoDB Atlas tremendously accelerated the adoption of gen AI by combining the capabilities of both platforms to bring high-impact retail use cases to life. Check out our AI resource page to learn more about building AI-powered apps with MongoDB. Introduction to Together AI and MongoDB Atlas From the first look, it’s impressive how well Together AI is designed for gen AI projects. It’s a powerful platform that lets developers train, fine-tune, and deploy open-source AI models with just a few lines of code. This is a critical component for retrieval-augmented generation (RAG) . With RAG, AI can pull real-time business-specific data from MongoDB Atlas , which means retailers get more reliable and relevant outputs. That’s crucial when dealing with data as dynamic as customer behavior or inventory movement from online and physical stores. With its flexible data model, MongoDB Atlas is an ideal database engine for handling diverse data needs. It’s fully managed, multi-cloud, and exceptional at managing different data types, including the vector embeddings that power AI applications. One important feature is MongoDB Atlas Vector Search , a smart library that stores and indexes vector embeddings, making it simple to integrate with Together AI. This lets retailers generate timely, personalized responses to customer queries, creating a better experience all around. Identifying retail use cases With Together AI and MongoDB Atlas working together, the possibilities for retail are huge. Here are some of the use cases we’ve been exploring and testing with clients, each bringing measurable value to the table: Product description generation Product onboarding to a retail e-commerce portal is a time-consuming effort for many retailers. They need to ensure they’ve created a product description that matches the image, then deploy it to their e-commerce portal. For multilingual portals and multiple operating geographies, this challenge of accuracy increases. With Together AI’s support for multimodal models (e.g. Llama 3.2) and MongoDB Atlas’s vector embeddings, we can create accurate product descriptions in multiple languages. Check out a demo app to see it in action. Figure 1. Demo application for generating product descriptions. Personalized product recommendations Imagine being able to offer each customer exactly what they’re looking for, without them even asking. With Together AI’s retrieval and inference endpoints and MongoDB Atlas Vector Search, we can create highly personalized product recommendations. Analyzing individual preferences, browsing history, and past purchases becomes seamless, giving customers exactly what they need, possibly exactly when they need it. Conversational AI-powered tools (a.k.a. chatbots) We’re also deploying intelligent conversational tools that can understand complex questions, offer personalized assistance, and drive conversions. Together AI, paired with MongoDB Atlas, makes these bots responsive and relevant so customers feel like they’re talking to a knowledgeable adviser rather than a chatbot. When real-time data informs the responses, customer experience is enhanced. Dynamic pricing and promotions Pricing in retail is often a moving target, and AI-driven insights help us optimize our approach. We’ve used Together AI and MongoDB Atlas to analyze market trends, competitor pricing, and customer demand to keep our pricing competitive and adjust promotions in real-time. It’s incredible how much more strategic we can be with AI’s help. Inventory management and forecasting This might be one of the most impactful use cases I’ve worked on—using AI to predict demand and optimize stock levels. With Together AI and MongoDB Atlas, it’s easier to balance inventory, reduce waste, and ensure the products customers want are always in stock. This leads to better efficiency and fewer out-of-stock scenarios. Implementing retail use cases with Together AI and MongoDB Atlas Let me share a concrete example that really brings these concepts to life. Case study: Building a multilingual product-description-generation system We recently worked on a solution to create a product-description-generation system for an e-commerce platform. The goal was to provide highly descriptive product information based on the images of the products from the product catalog. This use case really demonstrated the value of storing the data in MongoDB and using the multilanguage capabilities of Together AI’s inference engine. Embeddings and inference with Together AI: Together AI generated product descriptions based on images retrieved from the product catalog using Llama 3.2 vision models. This way, each product’s unique characteristics were considered, then generated in multiple languages. These descriptions could then be embedded into the MongoDB Atlas Vector Search database via a simple API. Indexed embeddings with MongoDB Atlas Vector Search: Using MongoDB Atlas Vector Search capabilities, we created embeddings, and then indexed them to be used to retrieve relevant data based on other matched product queries. This step made sure the product descriptions were not just accurate but also relevant to the images. Real-time data processing: By connecting this setup to a real-time product dataset, we ensured that product descriptions in multiple languages were always updated automatically. So when a marketplace vendor or retailer uploads new images with distinct characteristics, they get up-to-date product descriptions in the catalog. This project showcased how Together AI and MongoDB Atlas could work together to deliver a solution that was reliable, highly efficient, and scalable. The feedback from users was overwhelmingly positive. They especially appreciated how intuitive and helpful the product descriptions were and how simple the whole product onboarding process could become for multilingual businesses spread across multiple geographical regions. Figure 2. An example of a query and response flow for a RAG architecture using MongoDB and Together AI. Looking at the business impacts For a retail organization, implementing Together AI and MongoDB Atlas can streamline the approach to gen AI, creating an effective and immediate positive impact to business in several ways: Reduced product onboarding time and costs: Retailers can onboard products faster and quickly make them available on their sales channels because of the ready-to-use tools and prebuilt integrations. This cuts down on the need for custom code and significantly lowers development costs. Increased flexibility and customization: MongoDB’s flexible document model and Together AI’s inference engine enables retailers to mold their applications to fit specific needs, such as back-office data processing, demand forecasting, and pricing as well as customer-facing conversational AI. Seamless integration with existing systems: MongoDB Atlas, in particular, integrates seamlessly with other frameworks we’re already using, like LangChain and LlamaIndex. This has made it easier to bring AI capabilities to adopt across various business units. Added support and expertise: The MongoDB AI Applications Program (MAAP) is especially helpful in beginning the journey into AI adoption across enterprises. It offers not just architectural guidance but also hands-on support, so enterprises can implement AI projects with confidence and a well-defined road map. Combining Together AI and MongoDB Atlas for a powerful approach to retail Together AI and MongoDB Atlas are a powerful combination for anyone in the retail industry looking to make the most of gen AI. It is evident how they help unlock valuable use cases, from personalized customer experiences to real-time operational improvements. By adopting MongoDB Atlas with Together AI, retailers can innovate, create richer customer interactions, and ultimately gain a competitive edge. If you’re exploring gen AI for retail, you’ll find that this combination has a quick, measurable, and transformative impact. Learn more about Together AI by visiting www.together.ai . For additional information, check out Together AI: Advancing the Frontier of AI With Open Source Embeddings, Inference, and MongoDB Atlas . Want to learn more about why MongoDB is the best choice for supporting modern AI applications? Check out our on-demand webinar, “ Comparing PostgreSQL vs. MongoDB: Which is Better for AI Workloads ? ” presented by MongoDB Field CTO, Rick Houlihan.

January 13, 2025

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 developer data platform—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

Using Agentic RAG to Transform Retail With MongoDB

In the competitive world of retail and ecommerce, it’s more important than ever for brands to connect with customers in meaningful, personalized ways. Shoppers today expect relevant recommendations, instant support, and unique experiences that feel tailored just for them. Enter retrieval-augmented generation (RAG) : a powerful approach that leverages generative AI and advanced search capabilities to deliver precise insights on demand. For IT decision-makers, the key challenge lies in integrating operational data with unstructured information—which can span object stores (like Amazon S3 and SharePoint), internal wikis, PDFs, Microsoft Word documents, and more. Enterprises must unlock value from curated, reliable internal data sources that often hold critical yet hard-to-access information. By combining RAG’s capabilities with these data assets, retailers can find contextually accurate information. For example, they can seamlessly surface needed information like return policies, refund processes, shipment details, and product recalls, driving operational efficiency and enhancing customer experiences. To provide the most relevant context to a large language model (LLM) , traditional RAG (which has typically relied on vector search) needs to be combined with real-time data in an operational database, the last conversation captured in a customer relationship management API call to a REST endpoint, or both. RAG has evolved to become agentic—that is, it’s capable of understanding a user inquiry and translating it to determine which path to use and which repositories to access to answer the question. Check out our AI resource page to learn more about building AI-powered apps with MongoDB. MongoDB Atlas and Dataworkz provide an agentic RAG as a service solution that enables retailers to combine operational data with relevant unstructured data to create transformational experiences for their customers. MongoDB Atlas stores and unifies diverse data formats—such as customer purchases, inventory levels, and product descriptions—making them easily accessible. Dataworkz then transforms this data into vector embeddings, enabling a multistep agentic RAG pipeline to retrieve and create personalized, context-aware responses in real time. This is especially powerful in the context of customer support, product recommendations, and inventory management. When customers interact with retailers, Dataworkz dynamically retrieves real-time data from MongoDB Atlas, and, where needed, combines it with unstructured information to generate personalized AI responses, enhancing the customer experience. This architecture improves engagement, optimizes inventory, and provides scalable, adaptable AI capabilities, ultimately driving a more efficient and competitive retail operation. Reasons for using MongoDB Atlas and Dataworkz MongoDB Atlas and Dataworkz work together to deliver agentic RAG as a service for a smarter, more responsive customer experience. Here’s a quick breakdown of how: Vector embeddings and smart search: The Dataworkz RAG builder enables anyone to build sophisticated retrieval mechanisms that turn words, phrases, or even customer behaviors into vector embeddings—essentially, numbers that capture their meaning in a way that’s easy for AI to understand—and store them in MongoDB Atlas. This makes it possible to search for content based on meaning rather than exact wording, so search results are more accurate and relevant. Scalable, reliable performance: MongoDB Atlas’s cloud-based, distributed setup is built to handle high-traffic retail environments, minimizing disruptions during peak shopping times. Deep context with Dataworkz’s agentic RAG as a service: Retailers can build agentic workflows powered by RAG pipelines that combine lexical and semantic search with knowledge graphs to fetch the most relevant data from unstructured operational and analytical data sources before generating AI responses. This combination gives ecommerce brands the power to personalize experiences at a vastly larger scale. Figure 1: Reference architecture for customer support chatbots with Dataworkz and MongoDB Atlas Retail e-commerce use cases So how does this all work in practice? Here are some real-world examples of how MongoDB Atlas and Dataworkz are helping ecommerce brands create standout experiences. Building smarter customer-support chatbots Today’s shoppers want quick, accurate answers, and RAG makes this possible. When a customer asks a chatbot, “Where’s my order?” RAG enables the bot to pull the latest order and shipping details stored in MongoDB Atlas. Even if the question is phrased differently—say, “I need my order status”—the RAG-powered vector search can interpret the intent and fetch the correct response. As a result, the customer gets the help they need without waiting on hold or navigating complex menus. Personalizing product recommendations Imagine a customer who’s shown interest in eco-friendly products. With MongoDB Atlas’s vector embeddings, a RAG-powered system can identify this preference and adjust recommendations accordingly. So when the customer returns, they see suggestions that match their style—like organic cotton clothing or sustainably sourced kitchenware. This kind of recommendation feels relevant and thoughtful, making the shopping experience more enjoyable and increasing the chances of a purchase. Creating dynamic marketing content Marketing thrives on fresh, relevant content. With MongoDB Atlas managing product data and Dataworkz generating personalized messages, brands can send out dynamic promotions that truly resonate. For example, a customer who browsed outdoor gear might receive a curated email with top-rated hiking boots or seasonal discounts on camping equipment. This kind of targeted messaging feels personal, not pushy, building stronger customer loyalty. Enhancing site search experiences Traditional e-commerce searches often rely on exact keyword matches, which can lead to frustrating dead ends. But with MongoDB Atlas Vector Search and Dataworkz’s agentic RAG, search can be much smarter. For example, if a customer searches for “lightweight travel shoes,” the system understands that they’re looking for comfortable, portable footwear for travel, even if none of the product listings contain those exact words. This makes shopping smoother and more intuitive and less of a guessing game. Understanding trends in customer sentiment For e-commerce brands, understanding how customers feel can drive meaningful improvements. With RAG, brands can analyze reviews, social media comments, and support interactions to capture sentiment trends in MongoDB Atlas. Imagine a brand noticing a spike in mentions of “too small” in product reviews for a new shoe release—this insight lets them quickly adjust sizing info on the product page or update their stock. It’s a proactive approach that shows customers they’re being heard. Interactions that meet customers where they are In essence, MongoDB Atlas and Dataworkz’s RAG models enable retailers to make e-commerce personalization and responsiveness smarter, more efficient, and easier to scale. Together, they help retailers deliver exactly what customers are looking for—whether it’s a personalized recommendation, a quick answer from a chatbot, or just a better search experience. In the end, it’s about meeting customers where they are, with the information and recommendations they need. With MongoDB and Dataworkz, e-commerce brands can create that kind of connection—making shopping easier, more enjoyable, and ultimately more memorable. Learn more about Dataworkz on MongoDB by visiting dataworkz.com . The Dataworkz free tier is powered by MongoDB Atlas Vector Search . Want to learn more about why MongoDB is the best choice for supporting modern AI applications? Check out our on-demand webinar, “ Comparing PostgreSQL vs. MongoDB: Which is Better for AI Workloads ? ” presented by MongoDB Field CTO, Rick Houlihan.

December 23, 2024

Revolutionizing Retail with RFID and MongoDB Atlas

In today's fast-paced retail environment, keeping pace with both customer expectations and competition, maintaining accurate, real-time inventory is more critical than ever. Radio Frequency Identification (RFID) technology has emerged as a transformative solution , enabling retailers to track inventory with unprecedented precision and efficiency. However, the potential of RFID can only be fully realized when paired with a robust, scalable data platform capable of handling large volumes of data and providing actionable insights. MongoDB Atlas, with edge technologies, is an ideal solution for leveraging RFID data in retail. The role of RFID technology in retail RFID technology uses electromagnetic fields to automatically identify and track tags attached to objects. This technology offers significant advantages over traditional barcode systems, including the ability to read multiple tags simultaneously and from a distance, and provide real-time updates on inventory levels. For retailers, RFID technology translates into enhanced accuracy in stock management, reduced labor costs, and improved customer satisfaction through better product availability. However, the implementation of RFID technology generates vast amounts of data that need to be efficiently captured, processed, and analyzed. This is where MongoDB Atlas , a fully managed cloud database, and associated edge computing capabilities can make this adoption easy. Figure 1: E2E Supply Chain RFID Tracking Architecture Managing RFID data with MongoDB Atlas and Edge technologies Retail enterprises face significant challenges when integrating RFID data into their existing systems to provide real-time visibility and actionable insights. While RFID technology offers substantial benefits, the sheer volume of data generated by RFID tags and readers can be overwhelming without an efficient, scalable database solution. Below are key challenges retailers encounter and how MongoDB Atlas, with edge technologies, can address these issues: Real-time data synchronization: Ensuring that RFID data from multiple locations is synchronized in real-time is critical for maintaining accurate inventory levels and providing timely insights to store associates and management. Data integration and flexibility: Retailers often have legacy systems that need to integrate seamlessly with new RFID data streams. A flexible database schema is required to accommodate different data types and structures. Data volume and velocity: RFID systems can generate millions of data points daily. Retailers need a database solution that can handle this high volume and velocity of data without compromising performance. Data security and compliance: Protecting sensitive inventory and customer data is paramount. Retailers must ensure that their database solution complies with industry standards and regulations. Research-driven solutions with MongoDB Atlas Recent studies have shown that retailers who integrate RFID technology with a robust database platform experience significant operational improvements. According to a report by GS1 US , retailers implementing RFID saw a 25% improvement in inventory accuracy and a 30% reduction in out-of-stock incidents. Additionally, a study by Auburn University RFID Lab found that RFID technology can increase inventory accuracy from an industry average of 65% to more than 95%. MongoDB Atlas enhances these benefits by offering a fully managed, cloud-based database solution that simplifies the process of capturing and analyzing RFID data. Key features of MongoDB Atlas that support RFID integration include: Scalability: MongoDB Atlas can handle the vast amounts of data generated by RFID systems, ensuring that retailers can scale their operations without worrying about database performance. Real-time data processing: With edge technologies, data from RFID readers can be synchronized in real-time, providing instant visibility into inventory levels across all locations. Flexibility: The flexible schema of MongoDB allows retailers to store various types of data, including complex inventory information and transactional data. Security: MongoDB Atlas offers robust security features, including end-to-end encryption, to protect sensitive inventory data. Use cases for MongoDB Atlas and RFID in retail The integration of MongoDB Atlas and RFID technology can revolutionize various aspects of retail operations. Here are some key use cases: Effective inventory management involves several key strategies to ensure seamless operations and customer satisfaction. Real-time inventory tracking plays a crucial role by automatically updating stock levels whenever products are moved, sold, or restocked, providing up-to-date and accurate information. This allows businesses to maintain an accurate view of their inventory at all times. Additionally, automated stock replenishment systems predict stock shortages and trigger reorder requests based on current inventory levels, helping to avoid stockouts and overstock situations. Proper stock management is another essential component. Implementing loss prevention measures helps to identify discrepancies between recorded and actual inventory levels, enabling the detection of theft or loss. Detailed stock insights , such as analyzing inventory turnover rates and product performance, further enhance inventory management by optimizing stock levels and guiding strategic decisions on product placement. In-store associates also benefit from real-time inventory data , which empowers them to provide better customer service. For example, associates can quickly check stock availability and assist customers in locating items. Moreover, efficient stocking is made possible as associates receive guidance on which products need replenishment, ensuring shelves are always stocked with high-demand items. For businesses engaged in omnichannel retailing, integrating online and in-store operations is critical. A seamless connection between these channels enables the efficient fulfillment of Buy Online, Pickup In-Store (BOPIS) orders by leveraging accurate inventory data across both platforms. Additionally, returns management is streamlined by updating inventory levels in real-time as returned items are processed and restocked, ensuring that stock information remains consistent and up-to-date across all channels. Below you can see a simple representation of how to connect your hardware devices—in this case, a Zebra RFD8500—to interact with your MongoDB Atlas clusters, effectively acting as the perfect data capturing/retrieval tool to elevate the previously commented use cases. Figure 2: RFID Product Tracking Architecture Embracing MongoDB Atlas for RFID solutions in retail For IT decision-makers, adopting MongoDB Atlas can unlock the full potential of RFID technology in retail. By providing a scalable, flexible, and secure platform, MongoDB Atlas ensures that retailers can capture and analyze RFID data in real-time, driving operational efficiencies and enhancing customer satisfaction. What’s more, by integrating MongoDB Atlas with RFID technology, retailers can achieve unprecedented levels of inventory accuracy, streamline their operations, and provide a seamless shopping experience for their customers. It's time for Retail IT to leverage the power of MongoDB Atlas to transform their retail operations and stay ahead in the competitive market. Adopting MongoDB Atlas along with appropriate edge technology can revolutionize your retail operations by harnessing the power of RFID technology. As a decision-maker, investing in this key platform will provide enterprises with the tools needed to enhance inventory management, optimize stock levels, and deliver exceptional customer service. Visit our solutions page to learn more about MongoDB for retail innovation.

August 27, 2024

Enhancing Retail with Retrieval-Augmented Generation (RAG)

In the rapidly evolving retail landscape, tech innovations are reshaping how businesses operate and interact with customers. Generative AI could add up to $275 billion of profit to the apparel, fashion, and luxury sectors’ by 2028, according to McKinsey analysis . One of the most promising developments in this realm is retrieval-augmented generation (RAG) , a powerful application of artificial intelligence (AI) that combines the strength of data retrieval with generative capabilities to supercharge retail enterprises. RAG offers compelling advantages specifically tailored for retailers looking to enhance their operations and customer engagement from personalization to enhanced efficiency. Let’s delve into how RAG is revolutionizing the retail sector. Check out our AI resource page to learn more about building AI-powered apps with MongoDB. Why RAG in retail Imagine a customer walks into your store, and based on their previous opt-in online interactions, your technology recognizes their preferences and seamlessly guides them through a personalized service—a feat made possible by RAG. Central to RAG’s effectiveness is its ability to integrate and analyze diverse data sources scattered across data warehouses. This integration enables retailers to gain comprehensive insights into their business performance, understand consumer behavior patterns, and make data-driven decisions swiftly. Below are some of the compelling advantages that RAG can offer: Personalization: RAG enables retailers to deliver highly personalized customer experiences by leveraging AI to understand and predict individual preferences based on past interactions. Operational efficiency: By integrating diverse data sources and optimizing processes like supply chain management, RAG helps retailers streamline operations, reduce costs, and improve overall efficiency. For example, RAG aids in tracking shipments and optimizing logistics—a traditional pain point in the industry. Data utilization: It allows retailers to harness the power of big data by integrating and analyzing disparate data sources, providing actionable insights for informed decision-making. Customer engagement: RAG facilitates proactive customer engagement strategies through features like autonomous recommendation engines and hyper-personalized marketing campaigns, thereby increasing customer satisfaction and loyalty. In essence, RAG empowers retailers to harness AI's full potential to deliver superior customer experiences, optimize operations, and maintain a competitive edge in the dynamic retail landscape. But without a clear roadmap, even the most sophisticated AI solutions can falter. By pinpointing specific challenges—such as optimizing inventory management or enhancing customer service—retailers can leverage RAG to tailor solutions that deliver measurable business outcomes. Despite its transformative potential, retailers must first be AI-ready and able to integrate it in a way that enhances operational efficiency without overwhelming existing systems. To achieve this, retailers need to address data silos, ensure data privacy, and establish robust ethical guidelines for AI use. According to a Workday Global Survey , only 4% of respondents said their data is fully accessible, and 59% say their enterprise data is somewhat or completely siloed. Without a solid data foundation, retailers will struggle to achieve the benefits they are looking for from AI. Embracing the future of retail with RAG and MongoDB By harnessing the power of data integration, precise use case definition, and cutting-edge AI technologies like RAG, retail enterprises can not only streamline operations but also elevate customer experiences to unprecedented levels of personalization and efficiency. Building a gen AI operational data layer (ODL) enables retailers to make the most of their AI-enabled applications. A data layer is an architectural pattern that centrally integrates and organizes siloed enterprise data, making it available to consuming applications. As shown below in Figure 1, pulling data into a single database eliminates data silos, centralizes data management, and improves data integrity. Using MongoDB Atlas to unify structured and unstructured operational data offers a cohesive solution by centralizing all data management in a scalable, cloud-based platform. This unification simplifies data management, enhances data consistency, and improves the efficiency of AI and machine learning workflows by providing a single source of truth. With a flexible data schema, retailers can accommodate any data structure, format, or source—which is critical for the 80% of real-world data that is unstructured . Figure 1: Generative AI data layer As AI continues to evolve, the retail industry is poised to see rapid advancements, driven by the innovative use of technologies like RAG. The future of retail lies in seamlessly integrating data and AI to create smarter, more responsive business models. If you would like to learn more about RAG for Retail, visit the following resources: Presentation: Retrieval-Augmented Generation (RAG) to Supercharge Retail Enterprises White Paper: Enhancing Retail Operations with AI and Vector Search: The Business Case for Adoption The MongoDB Solutions Library is curated with tailored solutions to help developers kick-start their projects Add vector search to your arsenal for more accurate and cost-efficient RAG applications by enrolling in the MongoDB and DeepLearning.AI course " Prompt Compression and Query Optimization " for free today.

July 30, 2024

Enabling Commerce Innovation with the Power of MongoDB and Google Cloud

Across all industries, business leaders are grappling with economic uncertainty, cost concerns, disruption to supply chains, and pressure to embrace new technologies like generative AI. In this dynamic landscape, having a performant and future-proofed technology foundation is critical to your business’s success. Kin + Carta, a Premier Google Cloud Partner and MongoDB Systems Integrator Partner, recently launched the Integrated Commerce Network . The Integrated Commerce Network is an Accelerator that enables clients to modernize to a composable commerce platform and create value with their commerce data on Google Cloud with a pre-integrated solution in as little as six weeks. This article explains the concept of composable commerce and explores how MongoDB and Google Cloud form a powerful combination that enables innovation in commerce. Finally, it explains how Kin + Carta can help you navigate the complexity facing businesses today with their approach to digital decoupling. Unraveling the complexity: What is composable commerce? Why microservices and APIs? The evolution of commerce architecture Traditional monolithic architectures, once the cornerstone of commerce platforms, are facing challenges in meeting the demands of today's fast-paced digital environment. Microservices, a paradigm that breaks down applications into small, independent services, offer a solution to the limitations of monoliths. This architectural shift allows for improved agility, scalability, and maintainability. Defining composable commerce Composable commerce is a component-based, API-driven design approach that gives businesses the flexibility to build and run outstanding buying experiences free of constraints found in legacy platforms. To be truly composable, the platform must support key tenets: Support continuous delivery without downtime at the component level Have API as the contract of implementation between services, with open, industry-standard protocols providing the glue between components Be SaaS based, or portable to run on any modern public cloud environment Allow the open egress and ingress of data — no black-boxes of vendor data ownership Defining APIs and microservices APIs play a pivotal role in connecting microservices, enabling seamless communication and data exchange. This modular approach empowers businesses to adapt quickly to market changes, launch new features efficiently, and scale resources as needed. Enhanced scalability, resilience, and agility Taking a microservices approach provides businesses with options and now represents a mature and battle-tested approach with commoditized architectures, infrastructure-as-code, and open-source design patterns to enable robust, resilient, and scalable commerce workloads at lower cost and risk. Additionally, the decoupled nature of microservices facilitates faster development cycles. Development teams can work on isolated components, allowing for parallel development and quicker releases. This agility is a game-changer in the competitive e-commerce landscape, where rapid innovation is essential for staying ahead. Microservices and API-based commerce solutions (like commercetools, which is powered by MongoDB) have begun to dominate the market with their composable approach, and for good reason. These solutions remove the dead-end of legacy commerce suite software and enable a brand to pick and choose to enhance its environment on its own terms and schedule. MongoDB Atlas: The backbone of intelligent, generative AI-driven experiences As e-commerce has developed, customers are expecting more from their interactions — flat, unsophisticated experiences just don’t cut it anymore and brands need to deliver on the expectation of immediacy and contextual relevance. Taking a microservices approach enables richer and more granular data to be surfaced, analyzed, and fed back into the loop, perhaps leveraging generative AI to synthesize information that previously would have been difficult or impossible without huge computing capabilities. However, to do this well you need core data infrastructure that underpins the platform and provides the performance, resilience, and advanced features required. MongoDB Atlas on Google Cloud can play a pivotal role in this enablement. Flexible data models: Microservices often require diverse data models. MongoDB Atlas, a fully managed database service, accommodates these varying needs with its flexible schema design, which allows businesses to adapt their data structures without compromising performance. Horizontal scalability: Modern commerce moves a lot of data. MongoDB Atlas excels in distributing data across multiple nodes, ensuring that the database can handle increased loads effortlessly. Real-time data access: Delivering on expectations relies on real-time data access. MongoDB Atlas supports real-time, event-driven data updates, ensuring you are using the most up-to-date information about your customers. Serverless deployment: Rather than spend time and money managing complex database infrastructure, MongoDB Atlas can leverage serverless deployment, allowing developers to focus on building features that delight customers and impact the bottom line. Unleashing generative AI with MongoDB and Google Cloud Generative AI applications thrive on massive datasets and require robust data management. MongoDB effortlessly handles the complex and ever-evolving nature of gen AI data. This includes text, code, images, and more, allowing you to train your models on a richer data tapestry. MongoDB Atlas: Streamlined gen AI development on Google Cloud MongoDB Atlas, the cloud-based deployment option for MongoDB, integrates seamlessly with Google Cloud. Atlas offers scalability and manageability, letting you focus on building groundbreaking gen AI applications. Here's how this powerful duo functions together: Data ingestion and storage: Effortlessly ingest your training data, regardless of format, into MongoDB Atlas on Google Cloud. This data can include text for natural language processing, code for programming tasks, or images for creative generation. AI model training: Leverage Google Cloud's AI services like Vertex AI to train your gen AI models using the data stored in MongoDB Atlas. Vertex AI provides pre-built algorithms and tools to streamline model development. Operationalization and serving: Once trained, deploy your gen AI model seamlessly within your application. MongoDB Atlas ensures the smooth data flow to and from your model, enabling real-time generation. Vector search with MongoDB Atlas: MongoDB Atlas Vector Search allows for efficient retrieval of similar data points within your gen AI dataset. This is crucial for tasks like image generation or recommendation systems. Advantages of this open approach By leveraging a microservices architecture, APIs, and the scalability and flexibility of Atlas, businesses can build agile and adaptable composable platforms. Atlas seamlessly integrates with Google Cloud, providing a streamlined environment for developing and deploying generative AI models. This integrated approach offers several benefits: Simplified development: The combined power of MongoDB Atlas and Google Cloud streamlines the development process, allowing you to focus on core gen AI functionalities. Scalability and flexibility: Both MongoDB Atlas and Google Cloud offer on-demand scalability, ensuring your infrastructure adapts to your gen AI application's growing needs. Faster time to market: The ease of integration and development offered by this combination helps you get your gen AI applications to market quickly. Cost-effectiveness: Both MongoDB Atlas and Google Cloud offer flexible pricing models, allowing you to optimize costs based on your specific gen AI project requirements. Digital decoupling, a legacy modernization approach With so much digital disruption, technology leaders are constantly being challenged. Existing legacy architectures and infrastructure can be extremely rigid and hard to unravel. Over 94% of senior leaders reported experiencing tech anxiety . So how do you manage this noise, meet the needs of the business, stay relevant, and evolve your technology so that you can deliver the kinds of experiences audiences expect? Digital decoupling is a legacy modernization approach that enables large, often well-established organizations to present a unified online experience to their users, take full advantage of their data, innovate safely, and compete effectively with digital natives. Technology evolves rapidly, and an effective microservices solution should be designed with future scalability and adaptability in mind. Kin + Carta helps to ensure that your solution is not only robust for current requirements but also capable of evolving with emerging technologies and business needs. It all starts with a clear modernization strategy that allows you to iteratively untangle from legacy systems, while also meeting the needs of business stakeholders seeking innovation. Navigating commerce complexity with Kin + Carta on Google Cloud Commerce is undergoing a significant transformation, and businesses need a future-proof technology foundation to handle the demands of complex models and massive datasets. That’s why Kin + Carta launched their Integrated Commerce Network , the first commerce-related solution that’s part of Google’s Industry Value Network . With the right tools and partners, your business can be at the forefront of innovation with generative AI, through automating tasks in revolutionary new ways, creating entirely new content formats, and delivering more personalized customer experiences. The complexities of commerce transformation can be daunting. But you can master the art of digital decoupling and leverage the strengths of the Integrated Commerce Network to unlock limitless possibilities and gain an edge over your competition. Check out Kin + Carta’s guide: Flipping the script — A new vision of legacy modernization enabled by digital decoupling . Get started with MongoDB Atlas on Google Cloud today.

April 9, 2024

Transforming Industries with MongoDB and AI: Retail

This is the third in a six-part series focusing on critical AI use cases across several industries . The series covers the manufacturing and motion, financial services, retail, telecommunications and media, insurance, and healthcare industries. With generative AI, retailers can create new products and offerings, define and implement upsell strategies, generate marketing materials based on market conditions, and enhance customer experiences. One of the most creative uses of gen AI help retailers understand customer needs and choices that change continually with seasons, trends, and socio-economic shifts. By analyzing customer data and behavior, gen AI can also create personalized product recommendations, customized marketing materials, and unique shopping experiences that are tailored to individual preferences. AI plays a critical role in decision-making at retail enterprises; product decisions such as design, pricing, demand forecasting, and distribution strategies require a complex understanding of a vast array of information from across the organization. To ensure that the right products in the right quantities are in the right place at the right time, back-office teams leverage machine learning arithmetic algorithms. As technology has advanced and the barrier to adopting AI has lowered, retailers are moving towards data-driven decision-making where AI is leveraged in real-time. generative AI is used to consolidate information and provide dramatic insights that could be immediately utilized across the enterprise. AI-augmented search and vector search Modern retail is a customer-centric business, and customers have more choice than ever in where they purchase a product. To retain and grow their customer base, retailers are working to offer compelling, personalized experiences to customers. To do this, it is necessary to capture a large amount of data on the customers themselves—like their buying patterns, interests, and interactions—and to quickly use that data to make complex decisions. One of the key interactions in an ecommerce experience is search. With full-text search engines, customers can easily find items that match their search, and retailers can rank those results in a way that will give the customer the best option. In previous iterations of personalization, decisions on how to rank search results in a personalized way were made by segmentation of customers through data acquisition from various operational systems, moving it all into a data warehouse, and then running machine learning algorithms on the data. Typically, this would run every 24 hours or a few days, in batches, so that the next time a customer logged in, they’d have a personalized experience. This did not, however, capture the customer intent in real-time, as intent evolves as the customer gathers more information. These days, modern retailers augment search ranking with data from real-time responses and analytics from AI algorithms. It's also now possible to incorporate factors like the current shopping cart/basket and customer clickstream or trending purchases across shoppers. The first step in truly understanding the customer is to build a customer data platform that combines data from disparate systems and silos across an organization: support, ecommerce transactions, in-store interactions, wish lists, reviews, and more. MongoDB’s flexible document model allows for the easy combination of data of different types and formats with the ability to embed sub-documents to get a clear view of the customer in one place. As the retailer captures more data points about the customer, they can easily add fields without the need for downtime in schema change. Next, the capability to run analytics in real-time rather than retroactively in another separate system is built. MongoDB’s architecture allows for workload isolation, meaning the operational workload (the customer's actions on the ecommerce site) and the analytical or AI workload (calculating what the next best offer should be) can be run simultaneously without interrupting the other. Then using MognoDB’s aggregation framework for advanced analytical queries or triggering an AI model in real time to give an answer that can be embedded into the search ranking in real time. Then comes the ability to easily update the search indexing to incorporate your AI augmentation. As MongoDB has Search built in, this whole flow can be completed in one data platform- as your data is being augmented with AI results, the search indexing will sync to match. MongoDB Atlas Vector Search brings the next generation of search capability. By using LLMs to create vector embeddings for each product and then turning on a vector index, retailers can offer semantic search to their customers. AI will calculate the complex similarities between items in vector space and give the customer a unique set of results matched to their true desire. Figure 1: The architecture of an AI-enhanced search engine explaining the different MongoDB Atlas components and Databricks notebooks and workflows used for data cleaning and preparation, product scoring, dynamic pricing, and vector search Figure 2: The architecture of a vector search solution showcasing how the data flows through the different integrated components of MongoDB Atlas and Databricks Demand forecasting and predictive analytics Retailers either develop homegrown applications for demand prediction using traditional machine learning models or buy specialized products designed to provide these insights across the segments for demand prediction and forecasting. The homegrown systems require significant infrastructure for data and machine learning implementation and dedicated technical expertise to develop, manage, and maintain them. More often than not, these systems require constant care to ensure optimal performance and provide value to the businesses. Generative AI already delivers several solutions for demand prediction for retailers by enhancing the accuracy and granularity of forecasts. The application of retrieval augmented generation utilizing large language models (LLMs) enables retailers to generate specific product demand and dig deeper to go to product categories and individual store levels. This not only streamlines distribution but also contributes to a more tailored fulfillment at a store level. The integration of gen AI in demand forecasting not only optimizes inventory management but also fosters a more dynamic and customer-centric approach in the retail industry. Generative AI can be used to enhance supply chain efficiency by accurately predicting demand for products, optimizing/coordinating with production schedules, and ensuring adequate inventory levels in warehouses or distribution centers. Data requirements for such endeavors include historical sales data, customer orders, and current multichannel sales data and trends. This information can be integrated with external datasets, such as weather patterns and events that could impact demand. This data must be consolidated in an operational data layer that is cleansed for obvious reasons of avoiding wrong predictions. Subsequently, feature engineering to extract seasonality, promotions impact, and general economic indicators. A retrieval augmented generation model can be incorporated to improve demand forecasting predictions and avoid hallucinations. The same datasets could be utilized from historical data to train and fine-tune the model for improved accuracy. Such efforts lead to the following business benefits: Precision in demand forecasting Optimized product and supply planning Efficiency improvement Enhanced customer satisfaction Across the retail industry, AI has captured the imaginations of executives and consumers alike. Whether you’re a customer of a grocer, ecommerce site, or retail conglomerate, AI has and will continue to transform and enhance how you do business with corporations. For the retailers that matter most globally, AI has created opportunities to minimize risk and fraud, perfect user experiences, and save companies from wasting labor and resources. From creation to launch, MongoDB Atlas guarantees that AI applications are cemented in accurate operational data and that they deliver the scalability, security, and performance demanded by developers and consumers alike. Learn more about AI use cases for top industries in our new ebook, Enhancing Retail Operations with AI and Vector Search: The Business Case for Adoption . Head over to our quick-start guide to get started with Atlas Vector Search today.

March 29, 2024

MACH Aligned for Retail: Microservices

MACH is an approach to architecting modern applications through open tech ecosystems. It is an acronym representing Microservices, API-first, Cloud-native SaaS, and Headless. With the accelerating digitalization of retail experiences requiring new technology stacks that provide agility, flexibility, and performance at scale, MACH is especially relevant for retail and ecommerce , a far cry from current legacy, monolithic architectures. The MACH Alliance is an organization, of which MongoDB is a member, dedicated to educating and driving the adoption of the MACH framework and to “future proof enterprise technology and propel current and future digital experiences.” This is the first of a series of blog posts dedicated to MACH and how retail organizations are leveraging this framework to gain a competitive advantage. Let us begin with the first letter of MACH: microservices. Read the next post in this series, "MACH Aligned for Retail: API-First." What are microservices and why should I care? In simplest terms, microservices are an approach to building applications in which business functions are broken down into smaller, self-contained components called services. These services function autonomously and are usually developed and deployed independently. This independence means the failure or outage of one microservice will not affect another. Each service serves a particular business function or objective. The benefits of a microservices-based architecture are clear. The modular approach of microservices provides companies with quicker time to market and value, ultimately leading to a better customer experience. Development teams can work independently on different app functionalities, consequently shortening development cycles to get more features deployed in less time, which means the reaction to changing customer demands improves dramatically. Also, since services are deployed in independent environments, scalability concerns are managed in a much more convenient (and efficient) way, and resilience is strengthened significantly because there is no single point of failure, as there would be with monolithic applications. Microservices provide a modern architecture for app development, which ultimately delivers the best experience for customers. Learn how Boots modernized its stack with MongoDB and Microservices . Applying microservices for retail What does a microservice-based application look like in a real-world scenario? Let’s say an ecommerce application is being built. Microservices will greatly optimize the following challenges: Dynamic product catalog: An ecommerce app might involve a large number of products (maybe from different suppliers) with changing availability. With each supplier and/or product category as a part of a microservice, it becomes easier and more efficient to manage and provide an always up-to-date product catalog for users. Changing customer needs: A microservice-based architecture increases speed of development and testing, ultimately allowing new features to be deployed faster and enabling developers to quickly pivot to new customer needs. Different teams can work in parallel and independently, with little to no dependencies, rolling out or rolling back features as needed without risk. Scale flexibly: Independently scale app functionalities up during peaks or down for valleys with on-demand cloud-based microservices. The world before microservices Before microservices were an option, the typical data infrastructure would look like a data access layer on top of a database in order to get all the datasets containing information needed for running the application, as seen in Figure 1. There would be many databases to pull data from and various information silos, making for a painful process. Business logic had to be generated to transform these datasets to perform specific functions, namely a product catalog, cart, checkout, payments, and the like. Before building any application, the relational data objects would need to be mapped out to match an object-oriented programming paradigm. Figure 1: The monolithic application diagram before microservices This is not easily scalable or flexible for modern applications because every change in a dataset needs to be pushed upstream, and every new feature request for the app implies a data schema change downstream. This complicates life for developers and makes adaptation to customer needs a nightmare. For a deeper look into technical details about microservices, check out MongoDB’s specific guides dedicated to this topic. Decoupled app functionality with microservices With microservices, business functions are decoupled as much as possible in order to create a bounded context that is clearly independent of the others, meaning a failure or outage in one does not affect the others. This often means having a separate database per service, as seen in Figure 2. Figure 2: A first approach to microservices In this first approach to microservices, decoupled application functionalities can be developed, maintained, and scaled independently. However, having a separate database for each business functionality is not the ideal. It adds operational complexity, defeating the purpose of a microservices approach since maintaining and scaling a distributed system is not a simple task. But there is light in all of this: a middle ground between strong decoupling and operational efficiency can be found with MongoDB. MongoDB and microservices MongoDB is built under a number of fundamental technology principles that ensure companies can reap the advantages of microservices, specifically around a flexible data model, redundancy, automation, and scalability. MongoDB can be deployed in any environment (on-premises or cloud for example), but all industries are moving or have already moved toward the cloud, with its lower cost of ownership and flexibility. Retail is no exception. The cloud is again the natural next step for microservices. It provides dynamic scalability and high availability, freeing teams of the operational burden of maintaining a distributed system in-house. This is why MongoDB clients are choosing MongoDB Atlas as their cloud database-as-a-service to deploy applications based on microservices. As a step to modernization , MongoDB can be used as an operational data store, as seen in Figure 3. Legacy data silos are needfully connected via change data capture (CDC) and/or ETL processes in order to have an up-to-date copy of the data, stored as JSON documents. This is a first major advantage, since now applications can be developed against a data model that fits how developers think and code, therefore providing unprecedented agility to the development cycle. Figure 3: Microservices with MongoDB, acting as an operational data Store. Applications can be developed taking advantage of its flexible data model and scalability MongoDB Atlas allows for all of the benefits and flexibility of a fully managed, API-driven database. It allows for environment automation without dealing with every detail of database operation and scalability. This makes development teams more agile so that they can evolve applications at the pace customers expect and require today. Read the next post in this series, "MACH Aligned for Retail: API-First."

April 28, 2022

How to Build the Right App For Your Mobile Workforce

The average turnover rate in the retail industry is slightly above 60%. This high turnover rate translates into more than 230 million days of lost productivity and $19 billion in costs associated with recruiting, hiring, and training, according to Human Resources Today . When surveyed by Harvard Business Review , 86% of the organizations polled said frontline workers need better technology-enabled insights to be able to make good decisions in the moment. The survey also pointed out that leading retailers are starting to consider the impact tech can have on productivity. Combined, the data points to a growing chorus of evidence that suggests a mobile workforce — where employees are empowered with the digital tools needed to not only provide a great customer experience but also make their own jobs easier — is less likely to feel burnout and be dissatisfied with their jobs. What a mobile workforce can do for your organization With an intuitive, modern app, you can accomplish key business objectives. Improve the customer buying experience: Frontline staff equipped with mobile-first technologies can better match the fluency of the customers. It enables them to serve the customer better by providing accurate, real-time information, such as what items are in stock, or make suggestions based on customer buying history. Increase employee productivity: According to Deloitte , workers spend as much as three hours each week looking for the information they need. Imagine the impact regaining those hours could have on worker productivity! Track and improve performance, sales, and buying experience through data analysis: The potential of workforce enablement apps extends beyond just identifying what items are in stock at which stores. They can also gather valuable data that can reveal key patterns in everything from customer purchase habits and target peak shopping times to individual worker metrics such as number of successful sales. With those data insights, you can better allocate workers, assign workers based on strengths, stock items based on buying trends, and more. Challenges when building a retail worker app An always-connected and innovative retail workforce enablement app sounds great, but building this kind of intuitive app from the ground up presents a lot of challenges for already strained IT teams. Many retailers still rely heavily on relational databases that require additional support from a sprawl of supporting databases and technologies. As shown in this typical retail tech stack, legacy architectures are often made up of specialist NoSQL and relational databases, and additional mobile data and analytics platforms — all resulting in siloed data, slow data processing, and unnecessary complexity. This “spaghetti” architecture has several drawbacks when it comes to building a mobile app that truly empowers developers. The data from all these systems ends up siloed, requiring time-consuming ETL maneuvers to bring it together into a single view. Real-time access to data and insights, required to know what’s out of stock, who made a purchase for pickup, and more becomes harder to orchestrate. It’s hard to ensure data synchronization between a worker’s app and the backend database when they’re moving in and out of connectivity (when workers walk to the back of a warehouse or stockroom, for instance). It’s even harder with a sprawling data architecture to account for. The added complexity managing multiple databases, analytics suites, and the connections between them slows down your development teams, burdening them with additional complexity and maintenance issues to manage. As a result, IT teams will spend more time managing data silos and supporting old systems and applications than enabling mobile platforms to support new applications and empower frontline staff. To learn more about these issues — and overcome them — read our latest whitepaper, Why It’s So Hard for Retailers to Build a Workforce Enablement App (and How to Do It Right) .

March 10, 2022

Retail Tech in 2022: Predictions for What's on the Horizon

If 2020 and 2021 were all about adjusting to the Covid-19 pandemic, 2022 will be about finding a way to be successful in this “new normal”. So what should retailers expect in the upcoming year, and where should you consider making new retail technology investments? Omnichannel is still going strong Who would have anticipated the Covid-19 pandemic would still be disrupting lives after two years? For the retail industry this means more of the same - omnichannel shopping. Despite the hope many of us had for the end of the pandemic and the gradual increase of in-person shopping, retail workers can expect to continue accommodating all kinds of shopping experiences – online shopping, brick and mortar shopping, buy online and pick up in store, reserve online and pick up in store. Even beyond the pandemic, the face of shopping is likely forever changed. This means retailers need to start considering the long-term tech investments required to meet transforming customer expectations. Adopting solutions that offer a single view of the consumer gives you the unique opportunity to personalize offerings, products and loyalty programs to their demand. With a superior consumer experience, you can achieve repeat business and increased customer loyalty. While many retailers may have thought they could “get by” with their current solutions until the pandemic ends, it’s time to rethink that approach and start exploring more long-term solutions to improve omnichannel shopping experiences. Leaner tech stacks over many specialized solutions In 2022, you should explore solutions that allow your IT teams to do more with less. The typical retail tech stack looks something like the diagram below. Legacy, relational databases are supplemented by other specialist NoSQL and relational databases, and additional mobile data and analytics platforms. As a result, retailers looking to respond quickly to changing consumer preferences and improve the customer experience face an uphill battle against siloed data, slow data processing, and unnecessary complexity. Your development teams are so busy cobbling solutions together and maintaining different technologies at once that they fail to innovate to their full potential, so you’re never quite able to pull ahead of the competition. This is the data innovation recurring tax (or DIRT) . Think of this as the ongoing tax on innovation that spaghetti architectures, like the example above, legacy architecture costs your business. As technology grows more sophisticated and data grows more complex, companies are expected to react almost instantaneously to signals from their data. Legacy technologies, like relational databases, are rigid, inefficient, and hard to adapt, making it difficult to deliver true innovation to your customers and employees in a timely manner. Your development teams are so busy cobbling solutions together that they fail to innovate to their full potential, so you’re never quite able to pull ahead of the competition. It’s time to rethink your legacy systems, and adopt solutions that streamline operations and seamlessly share data to ensure you’re working with a single source of data truth. Many retailers recognize the need to upgrade legacy solutions and get away from multiple different database technologies, but you may not know where to start. Look for modern data applications that simplify data collection from disparate sources and include automated conflict resolution for added data reliability. Also, consider what you could do with fully managed data platforms, like MongoDB Atlas . With someone else doing the admin work, your developers are free to focus on critical work or turn their talents to innovation. Digital worker enablement will increase retention For employees, 2022 looks set to continue last year’s trend of the “ Great Resignation ”. To combat worker fatigue, and retain your workforce you need to prioritize worker engagement. One way to better engage your employees is through mobile workforce enablement. While many companies consider how to engage their customers with a more digital-friendly work environment, you shouldn’t forget about your workers in the process. Global companies like Walmart are starting to invest in mobile apps to enable their workforce. A modern, always-on retail workforce enablement app could transform the way your employees do their jobs. Features like real-time view of stock, cross-departmental collaboration, detailed product information, instant communication with other stores can simplify your workers’ experiences and help them to better serve your customers. Your workers need an always-on app that syncs with your single source of data truth, regardless of connectivity (which may be an issue as retail workers are constantly on the move). But building a mobile app with data sync capabilities can be a costly and time-intensive investment. MongoDB Realm Sync solves for this with an intuitive, object-oriented data model that is simple to use, and an out-of-the-box data synchronization service. When your mobile data seamlessly integrates with back-end systems, you can deliver a modern, distributed data platform to your workers. Huge investment in the supply chain From microchips to toilet paper, disruptions in the supply chain were a huge issue in 2020 and 2021, and the supply chain pain continues in 2022. And while there continue to be supply chain issues beyond the control of retailers, there are steps that can be taken to mitigate some of the pain and prepare for future disruptions. Warehouse tech is getting smarter, and you need to upgrade your solutions to keep up. For starters, consider adopting the right data platform to unify siloed data and gain a single view of operations . A single view of your data will allow for better management of store-level demand forecasts, distribution center-to-store network optimizations, vendor ordering, truck load optimizations, and much more. With a modern data platform, all this data feeds into one, single view application, giving retailers the insights to react to supply chain issues in real time. With disruption set to dominate 2022, as it did in 2020 and 2021, investing in proactive solution upgrades could help your business not only survive, but thrive. Want to learn more about gaining a competitive advantage in the retail industry? Get this free white paper on retail modernization .

January 13, 2022

What is MACH Architecture for ecommerce?

In the past, retailers faced the looming battle of brick and mortar vs. digital buying experiences. While most in the retail industry accepted the inevitability of needing some kind of digital experience, COVID-19 forced retailers to refocus efforts to digital-first, or at the very least, hybrid digital and in-person buying options. What customers expect (and why legacy systems don't hold up) Which leads us to one of the underlying problems for modern retailers: legacy architecture. The digital solutions many depend on aren’t able to meet consumers’ digital-first (or at the very least digital-friendly) ecommerce expectations. Today’s customers expect: Mobile-friendly architecture - People shop from their phones. If your ecommerce experience was designed with web-first in mind, only retrofitting a mobile component to meet buyer demand, you may need to rethink your mobile offering. Omnichannel experience - Beyond having a mobile-friendly buying experience, consumers want to carry their purchasing power from channel to channel and even into the physical store. Think buying online and picking up in store (BOPIS), or starting an order from your phone and completing it in store, or vice versa. Dynamic product catalogues - Consumers want ample choice and a smooth search experience. Can your systems hold up with thousands of products all displayed, searchable, managed, updated, and dynamically enriched with discounts, product offerings, and more? They also expect real-time stock availability, both in store and online. They want to know you really have an item in stock at their local store before venturing out to buy it. Personalization - Personalization is so ingrained in the online retail experience now that consumers have come to expect it. They want real-time recommendations for the items they’re interested in, with predictions based on past online purchases and searches, items in their cart, and in-person buying experiences. Why is it difficult to live up to these expectations? For many in ecommerce, they’re still running monolithic applications built as a single, autonomous unit. This means even the smallest changes, like altering a single line of code or adding a new feature, could require refactoring the entire software stack, leading to downtime and lost business. In addition, the long-term opportunity cost of having your development team waste time simply maintaining and patching such a brittle ecommerce system is a constant drain, or Innovation Tax , on your business. So retailers face a unique challenge. The thought of overhauling their current systems lead to fears like downtime, expensive investments in new solutions, and ultimately, massive loss of profit. But providing an e-commerce experience that lives up to consumer expectations isn’t optional anymore; it’s how your business thrives. That’s where the MACH Approach comes in. MACH Approach: ecommerce modernization with flexibility in mind So, what’s the MACH approach and, to put it bluntly, why should the retail industry care? The MACH approach, championed by the MACH Alliance , an industry body of which MongoDB is a member, is focused on facilitating the transition from monolithic, legacy ecommerce architectures to modern, streamlined e-commerce applications. Microservices - Microservices break down specific business functionalities into smaller, self-contained services. Instead of taking your whole application offline to add new shopping cart features, you update specific elements of your architecture without disrupting the entire application. This affords developers a level of flexibility that monolithic systems can’t compete with. Greater developer flexibility means minimal downtime, faster updates, an improved experience for consumers, and ultimately faster time to value for your business. API-first - APIs, the pieces of code allowing communication between separate applications or microservices, should be at the forefront of solution development, instead of an afterthought. An API-first approach to development is just that — APIs are built first and all other actions are developed to preserve the original API for greater consistency and reusability. This approach ensures planning revolves around the end product being consumed by different devices (like mobile) and APIs will be consumed by client applications. Cloud-native - At this point, to say “the cloud is the future of app development” is cliche; we’re already there. Building and running applications exclusively in the cloud, whether public or private, allows you to reap all the benefits of cloud development from the start. There are also some cost-cutting benefits to cloud-native environments. You avoid the investment that often comes with on-prem equipment. Most cloud SaaS options have pay-as-you-go cost structures, ensuring you only pay for what you use and leading to most predictable monthly expenses. Using managed cloud solutions, like MongoDB Atlas , also frees up your development team to focus their efforts on where they’re needed most — actually developing your application — instead of sinking valuable time into burdensome administrative tasks. Headless - If your application is down, even for a minute, you run the risk of the consumer simply moving on to another retail option. Downtime equates to lost profits, so to avoid the dreaded disruption to your revenue stream, take a headless approach to application development. With headless, changes to the front end (web store layout, UX, frameworks, design, etc.) can be made without interruption to back end (products, business logic, payments , etc.) operations and vice versa. What's the upside for ecommerce? The four elements of the MACH approach come together to help ecommerce businesses reframe operations, avoid downtime, preserve revenue, provide the best user experience possible, and ultimately ensure your solutions are able to develop and evolve. To maintain a competitive advantage in a growingly competitive commerce market, your application needs to keep up. The MACH approach to ecommerce could be the ideal way to set your application and your business apart. Want to learn more about the MACH Approach and the role cloud-native database solutions like MongoDB Atlas play in the evolving world of digital retail? Get your free copy of Ecommerce at MACH Speed with MongoDB and Commercetools today.

November 30, 2021