Genevieve Broadhead

8 results

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 .

March 29, 2024

Powering Vector Search Maturity in Retail with Pureinsights

In a competitive retail market, with customer demands higher than ever, retailers are on a constant journey toward search maturity. With the recent announcement of MongoDB’s Vector Search offering , retailers are implementing smarter search solutions to provide customers and staff with delightful experiences. Here we’ll explore how partners like Pureinsights are helping retailers to understand what true search maturity entails, and how to start their vector search journey on MongoDB Atlas. Check out our AI resource page to learn more about building AI-powered apps with MongoDB. How MongoDB Partners Like Pureinsights Can Help Search and AI application specialists like Pureinsights can shorten the planning and development cycle, bring applications to production faster, and accelerate time to value for the customer. The Architecture of Vector Search Applications Virtually every Vector Search application will follow the basic logical flow illustrated below. A Client creates a complex query, which is then submitted to an encoder. The encoder turns the query into a Vector and submits it to the Vector Search Engine. The Vector Search engine searches the Vector Database and returns results, which are then formulated and returned to the Client for presentation. A complete Vector Search application includes all of the elements in this diagram, but not all of them are currently provided in the MongoDB Atlas platform. Everything to the left of the Vector Search Engine has to be developed by someone. MongoDB provides the vector store and a means to search it, but someone has to build the client and logic for the complete application. Why Involve Pureinsights to build your Vector Search applications? Pureinsights is a MongoDB BSI partner and has extensive knowledge and expertise in helping customers accelerate time-to-production of premier search applications. Pureinsights specializes in search applications and provides services to build end-to-end vector search solutions, including solutions to create and populate MongoDB Vector Search and UI/Client to search MongoDB Atlas using Atlas Search and Atlas Vector Search. Customers can focus on their core business while we do the development. Pureinsights Search Maturity Matrix – A Roadmap for Better Search, including Vector Search All of the use cases we discussed – e-commerce search, AI-powered search for support, and product information/reviews are advanced search features for Retail. But it’s always best to walk before you run, so before implementing Vector Search, a good strategy is to make sure your current applications have been optimized. Pureinsights methodology for search applications includes analyzing the state of current applications using a Search Maturity Matrix. Pureinsights - Design, Build, and Manage After mapping out their journey to build out advanced search capabilities for their retail applications, Pureinsights can help customers build the applications on the MongoDB Atlas Platform from design, to build, to operations. Application Design and Architecture: A well-defined plan is the key to efficient application development. Pureinsights with their immense experience can help with complex design decisions, such as choosing the right AI models and creating the best architecture for performance and security. Application Build: With over 20 years of experience in search, Pureinsights can help you build and deploy your Atlas Search application quickly and efficiently. Pureinsights has developed methodologies and frameworks like the Pureinsights Discovery Platform, which work with AI technologies (e.g., ChatGPT) and integrate with the Atlas platform to reduce development time and accelerate time to production. Managed services: Pureinsights can even run your search application for you with our SearchOps and maintain it for optimum performance with their fully managed service so you can focus on your core business. Conclusion Pureinsights can help customers overcome the challenges of building vector search applications and accelerate the time to production. With their expertise in application design, build, and managed services, Pureinsights can help customers build and deploy next-generation vector search applications that deliver real business value. Is your e-commerce store ready for AI? And are your products as easy to find as your competitors? Modern consumer expect flawless search experiences in mobile and online e-commerce search. Join MongoDB and Pureinsights on Tuesday, January 23, at 1pm ET for an insightful new webinar hosted by Digital Commerce 360 to learn: What is the search Maturity Matrix, and which capabilities are your organization missing to achieve better results How retailers are building smarter search applications with AI What's possible with MongoDB's new Vector Search offering Related resources: Modernize E-commerce Customer Experiences with MongoDB | MongoDB Atlas Vector Search | MongoDB MongoDB Atlas for Retail: Driving Innovation from Supply Chain to Checkout | MongoDB MongoDB Atlas Search for Retail: Go Beyond the E-commerce Store | MongoDB

December 14, 2023

Best of Breed: commercetools on Building Composable Commerce on MongoDB

What’s behind the power of a modern, data-centric, composable commerce platform that solves all of the consumer demands in an increasingly AI-driven e-commerce landscape? Just ask Michael Scholz, the VP of Product and Customer Marketing at commercetools . commercetools Composable Commerce is an industry-leading commerce platform used by leading household brands like Express Inc., Danone, Ulta Beauty , Salling Group , John Lewis and partners , and KMart , all of which are building best-in-class omnichannel shopping experiences. Modern, data-centric composable commerce Mr. Scholz took to the stage at MongoDB.local NYC to talk about how MongoDB is powering commercetools' Composable Commerce platform, and how together MongoDB and commercetools are addressing the challenge of growth in the retail industry. “Global retail sales are about to grow 56% to just a little over 8 trillion. The real question here is: Are the retailers ready for what’s ahead of them?” he asked. It’s a difficult question for many of those in the retail industry to answer, whether they’re retailers building e-commerce stacks in-house, or software companies trying to build a packaged e-commerce solution. Here’s a deep insight into how commercetools have succeeded and why they chose MongoDB as their trusted advisor. commercetools started building on MongoDB from day one, as they saw the database and the Atlas fully managed service as a best-of-breed option. They’re not in the business of managing data, they want to focus on value add features for their product and company: “We don’t want to be the custodians of data, we want to focus on what is important to us, which is commerce,” Mr. Scholz said in New York. MongoDB Atlas allows commercetools to do that by offering a fully managed database as a service that is cloud-native SaaS, reducing the operational effort for commercetools of managing thousands of databases and providing a highly available and scalable service. Elastic scale is incredibly important in retail with peak events like Black Friday, Christmas, and unplanned traffic surges, for example, should an influencer spark demand for a product unexpectedly. The shared ability of MongoDB Atlas and commercetools’ ability to grow or shrink automatically in response to demand is key, making the system highly performant at scale and also cost-effective during low traffic. commercetools are considered thought leaders in the software industry due to their thinking and sharing of architectural best practices. commercetools CEO Dirk Hoerig coined the term Headless , and commercetools are the co-founders of the MACH Alliance , which champions microservice, API-first, cloud-native SaaS, and headless architecture practices. MongoDB is an enabler member of the MACH Alliance; its global multi-cloud database enables the building of a MACH compliance architecture and promotes a lean and agile development environment. “APIs can and will forever be able to be consumed by any consumer device, front-end, or other application,” according to Dirk Hoerig, the CEO and co-founder of commercetools. In this setup, it’s vital that the backend is fast and dependable. With MongoDB Atlas’ high availability architecture, commercetools was capable of offering the unbelievable SLA of 100% uptime for three years in a row in Europe! “Nobody is going to believe that, but if we’re looking at that and looking at our GCP instance, we have 100% uptime for GCP and for MongoDB. In the U.S., we had 99.99%, and it’s really just a rounding error,” Mr. Scholz said at MongoDB.local NYC. “It’s all about high performance and low latency.” Dive further into the talk to learn about composable commerce, and why MongoDB is a match made in heaven for commercetools to unlock more growth possibilities and deliver outstanding shopping experiences while innovating fast to be ready for what’s next. What makes commercetools & MongoDB the perfect match MongoDB powers commercetools to deliver innovation at speed. Through this partnership and with MongoDB's robust technology, commercetools has built truly composable technology for businesses that require unlimited flexibility and infinite scale at lower costs. “We’ve realized the only constant is change,” Mr. Scholz said. “We don’t really know what’s about to happen. It’s all about how we can future-proof our software, and how we accomplish that with MongoDB.” Mr. Scholz illustrates why MongoDB is the perfect match for modern data-centric commerce in four key areas: Figure 1: commercetools chose MongoDB because it helps them iterate quickly, gives them unlimited scale, can run anywhere and to build better apps faster. Embracing the future: Integrating AI into retail Looking forward to the future of retail tech, the challenge of integrating AI into applications is fast approaching. Mr. Scholz highlighted how the ability to clean, migrate, and enrich data through the use of MongoDB’s flexible document model helps them to build out customized AI experiences for customers. These are the building blocks from which we can begin to talk about AI-powered analytics, supply chain, personalization, and more. Figure 2: Retail reference architecture with commercetools and MongoDB commercetools have been using machine learning for a long time, one of the key use cases is to help retailers easily create categories and product types automatically when they import product data sets into MongoDB. With GenAI top of mind, commercetools are looking for the first set of use cases like speeding up promotion creation- leveraging models on top of their data in MongoDB to auto-generate content for brand portfolios that matches their tone and audience. A perfect partnership This modern, data-centric, composable commerce platform is the basis of huge success for commercetools and its customers. Through innovative architecture and quick iteration of new features, commercetools has become the leading technology in its field. Their customer’s results include inspiring numbers such as: + 35% average order value, + 2X Sales Order Increase, + 40% Increase in Cross-Selling, and + 100ms response time. For more reading on how MongoDB enables software companies and retailers to build architectures that align to MACH Principals: MACH Aligned for Retail (Microservices, API-First, Cloud Native SaaS, Headless) | MongoDB

August 10, 2023

Three Ways Retailers Use Search Beyond the Ecommerce Store

When consumers think of retail search, the first thing that comes to mind is typically the search bar of an ecommerce website. This is for a good reason: a Salesforce commerce study shows that 87% of shoppers begin their shopping journey in the search bar, and Forrester has found that as many as 68% of shoppers would not return to a site that provided a poor experience. But retailers that exclusively focus on search capabilities in the context of ecommerce are missing out on huge benefits in customer experience and workforce efficiency. To drive fast application experiences, the querying and indexing of data sets is vitally important, and can be a game changer for easy performance optimization. Let’s explore some of the innovative ways that retailers are using search indexes to super-power their application experiences. Why search is important across retail organizations Large retail data sets, like product and customer data, are used by both customer-facing ecommerce or loyalty applications and internal use cases: inventory management, stock management, customer care, purchasing, supplier and vendor management, marketing and more. Customers using ecommerce search bars typically have an excellent “Google-like” experience with auto-complete, faceting, fuzzy matching, etc., but the retail workforce and back office staff often aren't given the same luxury. These internal teams are trying to work efficiently, but they are stuck using front ends powered by a traditional operational database with no search indexing capabilities. These teams are missing out on a search engine that is optimized for unknown or unpredictable workloads. Search indexing will speed up queries where the input is user-defined or might be searching across multiple fields. Let’s look at a comparison: FIgure 1: Database Query vs. Search Query For these under-served and often overlooked use cases, retailers need a quick and cost-effective solution to adding search, like MongoDB Atlas Search. Adding a search index to an application can be done in minutes, without creating operational complexity. MongoDB manages the spin up and management of the backend Apache Lucene search engine, and the complex data and index synchronization activity. Figure 2: MongoDB Atlas: Integrated database and search The three most common use cases for retail The easy addition of search can optimize application performance and usability in the retail industry in three important areas: In-store workforce applications Back office inventory and assortment Customer servicing Figure 3: Example Search use cases in three retail industry areas In-store workforce applications Speed is important in workforce applications, because these interactions happen in real time. Think of an in-store customer spelling out a name in full at checkout for a grocery purchase to be added to a loyalty account. This could add five minutes to a checkout experience, disincentivizing the customer to engage with the loyalty program. Now imagine that the same checkout attendant can identify the customer by any number of data points, not only loyalty number, but also name, first line of address, email, etc., with faster lookup through auto-complete and fuzzy matching. A retailer that MongoDB works with does this auto-lookup in store with Atlas Search in 200-300 milliseconds for optimum customer satisfaction. Customers and staff also can have difficulty remembering or correctly identifying products. A DIY amateur or a new employee can’t be expected to know the exact name or product ID. This is a great use case for search indexing as we do not know the field in the document or the product attribute that we are querying against. MongoDB has customers that stock more than 150 million products. Strong typo tolerance makes life easier for everyone. Back-office inventory and assortment Flawless purchasing and stock management ensures brick and mortar and online stores get the right inventory at the right time to maximize sales and reduce wastage or deadstock. An operator responsible for distributing products into categories will define in which store shelf a product needs to be and adjust this depending on customer behavior and contractual changes with the supplier. Inventory applications will be used on a daily basis by every operator. These are small internal applications that can have a huge impact on the overall business, but are often overlooked by large IT programs for budget or have a smaller IT team. These teams are adopting Atlas Search because they can get it up and running in as few as three weeks and fully integrated into their application without taking on more operational overhead. Customer servicing Long call center or chat conversations wait times have high operational costs and cause customer churn. It is vital to identify the customer as quickly as possible by the data they provide: order or customer ID, phone number, store address, etc. Retailers who have created a “Customer 360” across their customer relationship management and loyalty systems have created a large complex pool of data. The ability to run a single query to search across all available attributes makes it much faster to identify a customer. Search can also be used to optimize speed and accuracy of results for chat applications and chat bots who have to answer a large volume and variety of questions. This is a perfect use case for search with unpredictable user inputs. If answers to the questions can be searched across the entire knowledge base, speed and relevancy can be improved. MongoDB has retailer customers building chatbot applications for internal use cases like an IT team answering common questions, and external ones. For example, on the ecommerce homepage, a chatbot needs search functionality to be able to quickly do product lookup, customer identification, or make a suggestion. Quick and easy search implementation will add to the customer experience and reduce staff operations. Where your company could add search functionality It’s time to think beyond the ecommerce search bar. What are the search workloads within your company’s retail estate? Are there internal applications that have your frontline or back-office staff frustrated with inefficient lookups? Is the reason you’re not implementing search today the fact that it's a heavy lift to add an additional technical component to your architecture? These are the types of conversations that are driving adoption of Atlas Search across the retail industry, as businesses persevere in a tough macro-economic climate to do more with less. Adding vital functionality to applications without adding complexity is a win for the retailer, the workforce and the consumer. Want to learn about how MongoDB has integrated Search into the Atlas Developer Data Platform? Head to the Search solution page to explore more technical and in-depth resources.

March 29, 2023

MACH Aligned for Retail (Microservices, API-First, Cloud Native SaaS, Headless)

Across the Retail industry, MACH principles and the Mach Alliance are becoming increasingly common. What is MACH and why is it being embraced for Retail? The MACH Alliance is a non-profit organization fostering the adoption of composable architecture principles. It stands for Microservices, API-First, Cloud-Native SaaS and Headless. The MACH Alliance’s Manifesto is to: “Future proof enterprise technology and propel current and future digital experiences" The MACH Alliance and the creation of this set of principles originated in the Retail Industry. Several of the 5 co-founders of the MACH Alliance are technology companies building for retail use cases: for example commercetools is a composable commerce platform for retail (built completely on MongoDB). MongoDB has been a member of the MACH Alliance since 2020, as an “enabler” member, meaning use of our technology can enable the implementation of the MACH principles in application architectures. This is because a data layer built on MongoDB is ideal as the basis for a MACH architecture. Members of our Industry Solutions team sit on the MACH technology, growth and marketing councils, and actively are involved with furthering the adoption of MACH across the Retail Industry. What is MACH, why is it important for retail? The retail industry has long been a fast adopter of technology and a forerunner in technology trends. This is because of the competitive nature of the business leading a drive towards innovation- its vital that retails are able to react quickly to new technologies (e.g. NFTs, VR, AI) to capture market share and stay ahead of the competitors. Retailers have realized that to be able to deliver new and value-add experiences to their customers, they have to cut back on operational overhead that leads to increased cost and build standard functionality that can either be bought or re-used. This is where the benefits of MACH comes in- it's all about increasing the ability to deliver innovation quickly while lowering operational costs & risk. Microservices: 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 means the failure or outage of one microservice will not affect another and teams can develop in parallel, increasing efficiency. API-First: A style of development where the sharing and use of the data via API (application programming interface) is considered first and foremost in the development process. This means that services are designed to aid the easy sharing of information across the organization and simple interconnectivity of systems. Cloud-Native SaaS: Cloud-native SaaS solutions are vendor-managed applications developed in and for the cloud, and leveraging all the capabilities the cloud has to offer, such as fully managed hosting, built-in security, auto-scaling, cross-regional deployment and automatic updates. These are a good fit for a MACH architecture as adopting them can reduce operational costs and frees up developers for value-add work like new unique customer experiences. Headless: Decoupling the front end from the back-end so that front ends (or “heads”) can be created or iterated on with no dependencies on the back end. The fact that the layers are loosely coupled decreases time to market for new front ends, and encourages the re-use back-end services for multiple purposes. It also de-risks change in the long term as services can function independently. Where does MongoDB come in? MongoDB is an enabler for MACH, meaning that using MongoDB as your data layer helps retailers and retail software companies. achieve MACH compliance. Our data model, architecture and functionality empower IT organizations to build in line with these architecture principles. During a digital transformation, where a retailer is modernizing a monolith into a microservices based architecture, they're looking for a data layer which will enable speed of development & change. MongoDB is the "most wanted" database 4 years running on Stack Overflow's developer survey- this is because our document model maps to the way developers are thinking & coding, and the flexibility allows for iterative change of the data layer. When looking at API based communication, the standard format for APIs is JSON, which again maps to MongoDB's document model. The idea with API-first development is to develop with the API in mind- why not store the data the way you're going to serve it by API. This reduces complexity and increases performance. Cloud Native and SaaS products have become the norm as retailers wish to reduce maintenance and management work. MongoDB Atlas, provides a database-as-a-service, guaranteeing 99.995% uptime, automatic failover and self-healing and allowing DevOps engineers to spin up databases in minutes or by API/ script. Many retail software companies are also built on MongoDB Atlas- for example commercetools, which provides an ecommerce solution as a SaaS product. Headless architectures require a data layer that is able to adapt and change for new workloads. The ability to change the schema at runtime, with no downtime, makes MongoDB's document model ideal for this. Performance and the ability to scale for new "heads" is also important. MongoDB is known as a high performance database and can scale vertically automatically or scale out horizontally seamlessly. So MongoDB becomes a great choice for retailers choosing to adopt a MACH architecture (see figure 1 below). As a general purpose database with high performance, a rich expressive query language and secondary indexing, MongoDB is a really good fit as a data layer as it is capable of handling operational and analytical needs of the application. FIgure 1: Example of a MACH architecture Want to know more? Are you interested in a transition to MACH? Dive into our four part blog series exploring each topic in detail and how MongoDB supports each of these principles: Microservices API-First Cloud-Native SaaS Headless

February 1, 2023

Break Down Silos with a Data Mesh Approach to Omnichannel Retail

Omnichannel experiences are increasingly important for customers, yet still hard for many retailers to deliver. In this article, we’ll cover an approach to unlock data from legacy silos and make it easy to operate across the enterprise — perfect for implementing an omnichannel strategy. Establishing an omnichannel retail strategy An omnichannel strategy connects multiple, siloed sales channels (web, app, store, phone, etc.) into one cohesive and consistent experience. This strategy allows customers to purchase through multiple channels with a consistent experience (Figure 1). Most established retailers started with a single point of sale or “channel” — the first store — then moved to multiple stores and introduced new channels like ecommerce, mobile, and B2B. Omnichannel is the next wave in this journey, offering customers the ability to start a journey on one channel and end it on another. Figure 1: Omnichannel experience examples. Why are retailers taking this approach? In a super-competitive industry, an omnichannel approach lets retailers maximize great customer experience, with a subsequent effect on spend and retention. Looking at recent stats , Omnisend found that purchase frequency is 250% higher on omnichannel, and Harvard Business Review’s research saw omnichannel customers spend 10% more online and 4% more in-store. Omnichannel: What's the challenge? So, if all retailers want to provide these capabilities to their customers, why aren’t they? The answer lies in the complex, siloed data architectures that underpin their application architecture. Established retailers who have built up their business over time traditionally incorporated multiple off-the-shelf products (e.g., ERP, PIMS, CMS, etc.) running on legacy data technologies into their stack (mainframe, RDBMS, file-based). With this approach, each category of data is stored in a different technology, platform, and rigid format — making it impossible to combine this data to serve omnichannel use cases (e.g., in-store stock + ecommerce to offer same-day click and collect). See Figure 2. Figure 2: Data sources for omnichannel. The next challenge is the separation of operational and historical data — older data is moved to archives, data lakes, or warehouses. Perhaps you can see today’s stock in real time, but you can’t compare it to stock on the same day last year because that is held in a different system. Any business comparison occurs after the fact. To meet the varied volume and variety of requests, retailers must extract, transform, and load (ETL) data into different databases, creating a complex disjointed web of duplicated data. Figure 3 shows a typical retailer architecture: A document database for key-value lookup, cache added for speed, wide column storage for analytics, graph databases to look up three degrees of separation, time series to track changes over time, etc. Figure 3: An example of a typical data architecture sprawl in modern retailers. The problem is that ETL’d data becomes stale as it moves between technologies, lagging behind real-time and losing context. This sprawl of technology is complex to manage and difficult to develop against — inhibiting retailers from moving quickly and adapting to new requirements. If retailers want to create experiences that can be used by consumers in real-time — operational or analytical — this architecture does not give them what they need. Additionally, if they want to use AI or machine learning models, they need access to current behavior for accuracy. Thus, the obstacle to delivering omnichannel experiences is a data problem that requires a data solution. Let's look at a smart approach to fixing it. Modern retailers are taking a data mesh approach Retail architectures have gone through many iterations, starting from vendor solutions per use case, moving toward a microservices approach, and landing into domain-driven design (Figure 4). Vendor Applications Microservices Domain-Driven Design * Each vendor decides the framework and governance of the data layer. The enterprise has no control over app or data * Microservices pull data from the API layer * Microservices and core datasets are combined into bounded contexts by business function * Data is not interoperable between components * DevOps teams control their microservices, but data is managed by a centralized enterprise team * DevOps teams control microservices AND data Figure 4: Architecture evolution. Domain-driven design has emerged through an understanding that the team with domain expertise should have control over the application layer and its associated data — this is the “bounded context” for their business function. This means they can change the data to innovate quickly, without reliance on another team. Of course, if data remains in its bounded context only, we end up with the same situation as the commercial off-the-shelf (COTS) and legacy architecture model. Where we see value is when the data in each domain can be used as a product throughout the organization. Data as a product is a core data mesh concept — it includes data, metadata, and the code and infrastructure to use it. Data as a product is expected to be discoverable (searchable), addressable, self-identifying, and interoperable (Figure 5). In a retail example, the product, customer, and store can be thought of as bounded contexts. The product bounded context contains the product data and the microservices/applications that are built for product use cases. But, for a cross-domain use case like personalized product recommendations, the data from both customer and product domains must be available “as a product.” Figure 5: Bounded contexts and data as a product. What we’re creating here is a data mesh — an enterprise data architecture that combines intentionally distributed data across distinctly defined, bounded contexts. It is a business domain-oriented, decentralized data ownership and architecture, where each makes its data available as an interoperable “data product.” The key is that the data layer must serve all real-time workloads that are required of the business — both operational and real-time analytical (Figure 6). Figure 6: Data mesh. Why use MongoDB for omnichannel data mesh Let’s look at data layer requirements needed for a data mesh move to be successful and how MongoDB can meet those requirements. Capable of handling all operational workloads: Expressive query language, including joining data, ACID transactions, and IoT collections make it great for multiple workloads. MongoDB is known for its performance and speed. The ability to use secondary indexes means that several workloads can run performantly. Search is key for retail applications — MongoDB Atlas has Lucene search engine built-in for full-text search with no data movement. Omnichannel experiences often involve mobile interaction. MongoDB Realm and Flexible Device Sync can seamlessly ensure consistency between mobile and backend. Capable of handling analytical workloads: MongoDB’s distributed architecture means analytical workloads can run on a real-time data set, without ETL or additional technology and without disturbing operational workloads. For real-time analytical use cases, the aggregation framework can be used to perform powerful data transformations and run ad hoc exploratory queries. For business intelligence or reporting workloads, data can be queried by Atlas SQL or piped through the BI Connector to other data tools (e.g., Tableau and PowerBI). Capable of serving data as a product: When serving data as a product, it is often by API: MongoDB’s BSON-based document model maps well to JSON-based API payloads for speed and ease. MongoDB Atlas provides a fully hosted HTTPS Endpoints service. Depending on the performance needed, direct access may also be required. MongoDB has drivers for all common programming languages, meaning that other teams using different languages can easily interact with it. Rules for access of course must be defined, and one option is to use MongoDB App Services . Real-time data can also be published to Apache Kafka topics using the MongoDB Kafka Connector , which can act as a sync and a source for data. For example, one bounded context could publish data in real-time to a named Kafka topic, allowing another context to consume this and store it locally to serve latency-sensitive use cases. The tunable schema allows for flexibility in non-product fields, while schema validation capabilities enforce specific fields and data types in a collection to provide consistent datasets. Resilient, secure, and scalable: MongoDB Atlas has a 99.995% uptime guarantee and provides auto-healing capability, with multi-region and multi-cloud resiliency options. MongoDB provides the ability to scale up or down to meet your application requirements — vertically and horizontally. MongoDB follows a best-in-class security protocol. Choose the flexible data mesh approach Providing customers with omnichannel experiences isn’t easy, especially with legacy siloed data architectures. Omnichannel requires a way of making your data work easily across the organization in real-time, giving access to data to those who need it while also giving the power to innovate to the domain experts in each field. A data mesh approach provides the capability and flexibility to continuously innovate. Thank you to Ainhoa Múgica and Karolina Ruiz Rogelj for their contributions to this post. Ready to build deeper business insights with in-app analytics and real-time business visibility? Read our new white paper: Application-Driven Analytics: In-App and Real-Time Insights for Retailers .

January 10, 2023

MACH Aligned for Retail: Headless

The MACH Alliance is a non-profit organization fostering the adoption of composable architecture principles, namely Microservices , API-First , Cloud-Native SaaS , and Headless. MongoDB, among many other technology companies, is a member of this Alliance, enabling developers to adopt these principles in their applications. In this article, we’ll focus on the fourth principle championed by the MACH Alliance: Headless. Let’s dive in. What is headless? A headless architecture is one where the layers or components of the architecture are decoupled. The “heads” (i.e., frontends) operate independently from the backend logic or “core body” microservices and share data via API. This concept is key to a successful shift toward microservices — without decoupling the architectural layers, you’re running on a modern monolith. Looser coupling also leads to an increase in frontend change and flexibility, reusability of core features, less downtime because there’s no single point of failure, and promotes reusability of key features. Headless applied to retail Retail was one of the first industries to embrace headless architectures, with the term coined in 2012 by Dirk Hoerig, founder of commercetools . These concepts were originally applied to building modern ecommerce solutions and are now being expanded to any application in the IT stack. In this model, the head can be an ecommerce web frontend, or mobile app, or an internal frontend system for stock management. The core body components support the heads (Figure 1). They can be a payment system, a checkout solution, a product catalog, or a warehouse management application. Figure 1:   The “head” and “core body” components, sharing data as part of APIs. Customers and their experiences are at the heart of retail. Adopting headless principles can greatly help companies meet rapidly changing customer requirements and stand out from the competition. Customers require a seamless journey between mobile, web applications, and in-store with data and logic consistent across channels. New channels might also need to be added such as integration with social media, to reach a younger customer base. Retailers might need to be able to sell in multiple regions or across product lines, requiring them to adopt multiple frontends to serve different customer groups without having to rewrite or duplicate the whole IT stack. New features might need to be added quickly to reflect competitors’ moves without tracing changes back through every component of the stack or experiencing downtime. Internal workforce systems can follow similar principles. The common denominators of these example use cases include speed of change and frontend flexibility, avoiding downtime, and reusability of the backend components. Headless solutions enable developers to avoid duplicating efforts by reusing the core capabilities of applications and adapting them to various target systems and use cases. Those principles save developers’ time and can be leveraged to provide a seamless experience to customers, as the underlying data layer and workflows are shared across multiple services offering similar functionalities. Headless architectures also come with the following advantages. Bring new features to market faster New features and MVPs can be introduced with minimal impact on other application components. Release cycles can be managed efficiently via a microservice architecture relying on different squads, and new releases can be pushed to production when ready, independently of the work of other squads. For example, a retailer can expand into a new country quickly by developing a country-specific frontend that reuses existing core components and requires no backend downtime. Scale to meet seasonal demand Companies can independently scale application components where and when required. For example, increased user traffic might require more resources to support frontend components, leaving the backend untouched and vice versa. In an ecommerce scenario, this can take the form of expected deviations from a seasonality standpoint (e.g., end-of-month transactions following salary distribution, holiday shopping) or unplanned variations (e.g., influencer marketing). Thus, this model can result in: Cost savings: Achieve cost reductions as a headless architecture running on the cloud enables to further decouple its pay-as-you-go model, by only paying for the infrastructure required by each front/backend component. Improved customer experience: Develop highly available and responsive applications so that customer experience is not affected by computing resources. Leverage best-of-breed technologies Headless architectures can help companies gain greater flexibility in deploying and managing the IT stack, allowing them to: Focus on value-add development: A composable headless architecture enables companies to choose to build or buy individual components in the stack. As the components are decoupled, it becomes easier to unpick than if the stack is fully integrated — as the APIs can be redirected to the new solution more easily. This approach lets companies put their development activity into value-added functionality should a best-of-breed vendor solution arrive on the market delivering core functionality. Avoid vendor lock-in: This also allows for more seamless technology switches should companies decide to bring development back in-house or switch vendors. Improve talent acquisition and retention: Deploying in a flexible and composable manner lets development teams choose the programming languages and tools they feel best match the requirements, allowing companies to attract and retain top talent. Less downtime with faster troubleshooting A headless architecture also makes it easier to pinpoint which single layer/component is the root cause of issues, as opposed to troubleshooting in monolithic applications where dependencies can be difficult to map. Fewer dependencies mean less downtime; when a change or failure occurs to one component, it doesn't affect the whole stack. For ecommerce retailers, any downtime can have a direct impact on revenue, so an architecture that supports a move towards 24/7 uptime is ideal. Removing data silos and sharing data across multiple journeys also enables companies to implement truly omnichannel experiences and leverage the datasets for other downstream processes, such as user personalization and analytics. Learn how Boots is using MongoDB Atlas to standardize their infrastructure via an API and microservice-driven approach . How can MongoDB help? Headless architectures require a strong data layer to reap all the above-mentioned benefits. MongoDB includes several key features that enable developers to speed up the pace of delivery of new features and bug fixes, scale with minimal effort, and leverage APIs to share data with the different components of the stack. Deliver faster with no downtime MongoDB provides a flexible document model that easily adapts to the needs of different microservices and supports adding new features and data fields without having to rethink the underlying data schema or experience downtime. Let’s consider a product catalog microservice that uses a particular API to read data from certain fields. A second microservice can be developed requiring the same set of fields as the first along with a few new ones connecting via a new API. MongoDB allows the change to be made with no downtime of the product catalog microservice and related API. Scale effortlessly Adding new features and services will likely require scaling the data layer to cater to higher storage and workload. MongoDB, through its sharding capabilities , enables a distributed architecture by horizontally scaling the data layer and by distributing data across multiple servers. This approach can provide better efficiency than a single high-speed, high-capacity server (vertical scaling), to build highly responsive retail solutions. Support composable architectures MongoDB also possesses strong API capabilities to support a microservice-based backend architecture and make data accessible and shareable across components (Figure 2). These capabilities include APIs and drivers supporting a dozen programming languages on the market, such as C, Python, Node.js, and Scala. The MongoDB Unified Query API allows working with data of any type, including time series, arrays, and geospatial. MongoDB Atlas, MongoDB’s Developer Data Platform, comes with the Atlas Data API allowing to programmatically create, read, update, and delete data stored on Atlas clusters as part of standard HTTPS requests. Figure 2:   MongoDB supports a headless architecture via APIs. Data availability and resiliency should also be considered when adopting headless architectures. MongoDB Atlas clusters are highly available and backed by an industry-leading uptime SLA of 99.995% across all cloud providers. If a primary node becomes unavailable, MongoDB Atlas will automatically failover in seconds. Clusters can be also deployed across multiple cloud regions to weather the unlikely event of a total region outage, or in multiple cloud platforms together. Summary Adopting a headless architecture is paramount for retailers wanting to enhance customer experience and build more resilient applications. MongoDB, with its leading database offering, API layer, and high availability is strongly suited to meet the requirements of modern applications. Read our previous blog posts in the MACH series covering Microservices , API-First , and Cloud-Native SaaS .

November 30, 2022

MACH Aligned for Retail: Cloud-Native SaaS

MongoDB is an active member of the MACH Alliance , a non-profit cooperation of technology companies fostering the adoption of composable architecture principles promoting agility and innovation. Each letter in the MACH acronym corresponds to a different concept that should be leveraged when modernizing heritage solutions and creating brand-new experiences. MACH stands for Microservices, API-first, Cloud-native SaaS, and Headless. In previous articles in this series, we explored the importance of Microservices and the API-first approach. Here, we will focus on the third principle championed by the alliance: Cloud-native SaaS. Let’s dive in. What is cloud-native SaaS? Cloud-native SaaS solutions are vendor-managed applications developed in and for the cloud, and leveraging all the capabilities the cloud has to offer, such as fully managed hosting, built-in security, auto-scaling, cross-regional deployment, automatic updates, built-in analytics, and more. Why is cloud-native SaaS important for retail? Retailers are pressed to transform their digital offerings to meet rapidly shifting consumer needs and remain competitive. Traditionally, this means establishing areas of improvement for your systems and instructing your development teams to refactor components to introduce new capabilities (e.g., analytics engines for personalization or mobile app support) or to streamline architectures to make them easier to maintain (e.g., moving from monolith to microservices). These approaches can yield good results but require a substantial investment in time, budget, and internal technical knowledge to implement. Now, retailers have an alternative tool at their disposal: Cloud-native SaaS applications. These solutions are readily available off-the-shelf and require minimal configuration and development effort. Adopting them as part of your technology stack can accelerate the transformation and time to market of new features, while not requiring specific in-house technical expertise. Many cloud-native SaaS solutions focused on retail use cases are available (see Figure 1), including Vue Storefront , which provides a front-end presentation layer for ecommerce, and Amplience , which enables retailers to customize their digital experiences. Figure 1: Some MACH Alliance members providing retail solutions. At the same time, in-house development should not be totally discarded, and you should aim to strike the right balance between the two options based on your objectives. Figure 2 shows pros and cons of the two approaches: Figure 2: Pros and cons of cloud-native SaaS and in-house approaches. MongoDB is a great fit for cloud-native SaaS applications MongoDB’s product suite is cloud-native by design and is a great fit if your organization is adopting this principle, whether you prefer to run your database on-premises, leveraging MongoDB Community and Enterprise Advanced , or as SaaS with MongoDB Atlas . MongoDB Atlas, our developer data platform, is particularly suitable in this context. It supports the three major cloud providers (AWS, GCP, Azure) and leverages the cloud platforms’ features to achieve cloud-native principles and design: Auto-deployment & auto-healing: DB clusters are provisioned, set up, and healed automatically, reducing operational and DBA efforts. Automatically scalable: Built-in auto-scaling capabilities enable the database RAM, CPU, and storage to scale up or down depending on traffic and data volume. A MongoDB Serverless instance allows abstracting the infrastructure even further, by paying only for the resources you need. Globally distributed: The global nature of the retail industry requires data to be efficiently distributed to ensure high availability and compliance with data privacy regulations, such as GDPR , while implementing strict privacy controls. MongoDB Atlas leverages the flexibility of the cloud with its replica set architecture and multi-cloud support, meaning that data can be easily distributed to meet complex requirements Secure from the start: Network isolation, encryption, and granular auditing capabilities ensure data is only accessible to authorized individuals, thereby maintaining confidentiality. Always up to date: Security patches and minor upgrades are performed automatically with no intervention required from your team. Major releases can be integrated effortlessly, without modifying the underlying OS or working with package files. Monitorable and reliable: MongoDB Atlas distributes a set of utilities that provides real-time reporting of database activities to monitor and improve slow queries, visualize data traffic, and more. Backups are also fully managed, ensuring data integrity. Independent Software Vendors (ISVs) increasingly rely on capabilities like these to build cloud-native SaaS applications addressing retail use cases. For example, Commercetools offers a fully managed ecommerce platform underpinned by MongoDB Atlas (see Figure 3). Their end-to-end solution provides retailers with the tools to transform their ecommerce capabilities in a matter of days, instead of building a solution in-house. Commercetools is also a MACH Alliance member, fully embracing composable architecture paradigms explored in this series. Adopting Commercetools as your ecommerce platform of choice lets you automatically scale your ecommerce as traffic increases, and it integrates with many third-party systems, ranging from payment platforms to front-end solutions. Additionally, its headless nature and strong API layer allow your front-end to be adapted based on your brands, currencies, and geographies. Commercetools runs on and natively ingests data from MongoDB. Leveraging MongoDB for your other home-grown applications means that you can standardize your data estate, while taking advantage of the many capabilities that the MongoDB data platform has to offer. The same principles can be applied to other SaaS solutions running on MongoDB. Figure 3: MongoDB Atlas and Commercetools capabilities. Find out more about the MongoDB partnership with Commercetools . Learn how Commercetools enabled Audi to integrate its in-car commerce solution and adapt it to 26 countries . MongoDB supports your home-grown applications MongoDB offers a powerful developer data platform, providing the tools to leverage composable architecture patterns and build differentiating experiences in-house. The same benefits of MongoDB’s cloud-native architecture explored earlier are also applicable in this context and are leveraged by many retailers globally, such as Conrad Electronics, running their B2B ecommerce platform on MongoDB Atlas . Summary Cloud-native principles are an essential component of modern systems and applications. They support ISVs in developing powerful SaaS applications and can be leveraged to build proprietary systems in-house. In both scenarios, MongoDB is strongly positioned to deliver on the cloud-native capabilities that should be expected from a modern data platform. Thank you to Ainhoa Múgica and Karolina Ruiz Rogelj for their contributions to this post. Stay tuned for our final blog of this series on Headless and check out our previous blogs on Microservices and API-first .

September 22, 2022