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The Four Data Platform Must Haves for Personalized Retail

Even before the events of 2020, consumers were increasingly embracing a hybrid shopping experience that mixed the digital and the physical. Rather than making a binary choice between “bricks” or “clicks”, the customer journey can start online, before moving to a physical store for an in person demo, and then back online for the final purchase. As if that wasn’t hard enough for retailers to handle, consumers are also expecting the same personalized treatment they’re used to online to follow them in store too. Personalized store greetings, location-based offers on nearby products, and the hyper-customization of products, services, and special offers are quickly becoming table stakes experiences. With in-store beacons, electronic shelf labels, smart mirrors, AI, and a host of other connected technologies, the next frontier of personalization is the seamless connection, and blurring, of the digital and physical retail experience. The future of retail, therefore, belongs to those able to differentiate themselves with a better omnichannel, personalized experience than the competition. Talk data to me For personalized retail to work, in real time and on every channel, retailers need to master the collection, analysis, and deployment of data. Specifically, retailers need a data platform built for the following: Data privacy: The more data you collect, and the more personalized you get, the greater your responsibility to be a good steward of that data. Every aspect of data privacy, from the latest in authentication and authorization to auditing, encryption, and compliance, should be at the heart of your data strategy and a core capability of your data platform. In addition, customers expect more than just complete data privacy; they also demand that retailers allow them to take control of their data when requested. Agility: Retailing today means ingesting many different types of data from many different sources. Whereas yesterday’s retailers built their infrastructure using relational databases, with a rigid schema defined by tables, tomorrow’s retail leaders will choose a data platform based on a flexible data model, such as the document model. This flexibility can be particularly helpful for modeling data where structures can change between each record, such as polymorphic data. It also makes it easier to evolve an application during its life cycle, such as by adding new fields or enriching application-generated data with third-party data sources to provide an even fuller picture of the customer. Real-time data: While many retailers use operational analytics and intelligence in decision making, the data they’re working from is often days old, at best. The need now is for real-time data to influence real-time, real-world customer behavior. To get there, retailers must have a data platform that is capable of concurrently supporting both operational and analytical workloads without sacrificing performance. Developer and DevOps enablement: The speed at which retailers can bring new applications and services to market has never been more important. A modern data platform enabled through a database-as-a-service capability, like MongoDB Atlas, gives developers the freedom and flexibility to work seamlessly with data wherever their applications and users need it. Understand More with the MongoDB Guide to Personalized Retail The digital differentiation With the database foundations in place, retailers can begin to differentiate themselves digitally by using technology in unexpected ways. Think loyalty programs activated by in-store facial recognition sensors, beacon technology connecting with apps to guide customers through stores, chat bots to help with customer questions, and augmented reality apps to help shoppers try out products at home. Strategically and successfully implementing and combining these digital capabilities will drive more customers to your products, be it online or in-store. Take a look how OTTO reinvented their ecommerce personalization for more than 2 million users per day . They were able to slash catalog update times from 12 hours to 15 minutes. Retailers can also build omnichannel experiences that blur the boundaries between the online and in-store spheres. This could take the form of scanning QR codes on in-store items for deals or information, ordering out-of-stock items to be sent to a specific store for pickup, returning online purchases to physical outlets, or redeeming loyalty points on an app for in-person purchases. Building a comprehensive picture of consumers is vital for understanding how you can create this experience. Are your consumers using smart speakers to order products? Are they active on your mobile app instead of going in-store? Can you gather data on in-store behavior — and provide online recommendations for shoppers to continue their journeys via other channels? By having this view, you can create the seamless, personalized experience your consumers desire — through any channel. Find out how AO.com turned to MongoDB to build a single view to leverage real-time data to build modern applications for everything from personalization to delivery tracking. Lastly, retailers must build a resilient supply chain to ensure a consistent flow of inventory from factory to warehouse to stores and customer homes in both directions — for purchases and returns (reverse logistics). Organizations can utilize RFID tags, GPS tracking, IoT devices, and sensors to locate, mobilize, and manage inventory across large areas and different branches and warehouses, oversee returns and exchanges, and even forecast demand based on historical trends. By unifying data from disparate sources, retailers can streamline and strengthen the supply chain, improving inventory management and enhancing customer relationships. Now, shoppers are much less likely to be blindsided by product shortages or shipping delays. Further, when these events do occur, retailers can communicate with buyers in a timely and open manner. Boxed, a leading wholesale club in the United States, who needs to deliver its products according to a strict schedule built its entire digital environment from scratch and on MongoDB Atlas, including supply chain management infrastructure, enterprise resource planning systems, warehouse management, robotics, and more. Learn how they managed to cope with a traffic spike of 30 to 35 times normal levels . Understand More with the MongoDB Guide to Personalized Retail

June 24, 2021

Data in 2021: Four Predictions For an Uncertain Future

What a year it’s been. A global pandemic, a recession, and a U.S. presidential election unlike any in living memory made 2020 a tragic and tumultuous 12 months many want to forget, but can’t. Despite the uncertainty, looking back we can be sure of at least one thing: we’ve seen several years of digital disruption in a matter of months. The race to digitize as fast as possible, our “next normal”, has cut across all industries, accelerating several ancillary trends like cloud adoption, AI, and IoT. Ironically, one of the lasting effects of 2020’s profound unpredictability is just how certain we now are of the growing centrality of digitization, and therefore data, as the primary driver of business success, consumer demand, and even societal change in 2021 and beyond. As such, we asked several of MongoDB’s brightest minds to look ahead to the coming year and share their insights into how these trends in data management may play out. Petabyte-Scale Goes Mainstream The idea of “big data” isn’t new, and many firms have been working with petabyte, and even exabyte, sized data sets for some time. 2021, however, may just be the year that data finally goes “big” for everyone else. For many organizations, particularly those mid-sized and smaller, data management has until now been confined to the realm of terabytes. However, trends like the explosion of connected devices, the roll out of 5G, and the continuation of 2020’s headlong rush to digitize every aspect of business mean petabyte-scale data management is likely to become a reality for many more. And to paraphrase a famous saying: “Mo data, mo problems.” Keeping petabytes of data accessible and safe, while at the same time using it to meaningfully enrich a business, is an order of magnitude more difficult and complex than what many mid-sized enterprises are used to. Petabyte-scale data management demands stricter tolerances for uptime, scalability, and performance. In addition, the data is likely to be more distributed — on prem, in the cloud, and even across different clouds. Real-time analytics becomes a business necessity, as does taking advantage of features like automated tiering. The security and data privacy implications of holding that much data, and making it accessible to more people and connected “things,” mean petabyte-scale data management is also a business opportunity tinged with considerable financial and reputational risk. Data Privacy Continues to Be a Hot Button The coming year will further define the relationship between consumers and their data. In November, California voters approved the California Privacy Rights Act (CPRA). Along with enhancements to the already enacted CCPA (the California Consumer Privacy Act), the CPRA establishes an independent watchdog, the California Privacy Protection Agency, to enforce the CCPA now, and the CPRA when it comes into effect on January 1, 2023. There’s growing expectation that 2021 will also be the year the U.S. Federal government begins drafting a nationwide privacy law. With more states likely to follow California and enact their own CCPA-inspired privacy laws, and a new administration headed to Pennsylvania Avenue on January 20th, a national answer to the patchwork of state-based data privacy laws might finally see the light of day. An online ad for one of Apple's latest releases, a credit card Elsewhere, China and Canada are just two of several major world economies set to introduce new data privacy statutes, or overhaul existing laws over the coming 12 months. For businesses, 2021 is also set to be a landmark year for the emergence of data privacy as a competitive advantage. The latest indicator of this trend came in the closing weeks of 2020. In December, simmering tension between two of the largest and most influential companies on the planet spilled into open conflict when Facebook took out full-page advertisements in the New York Times, Wall Street Journal, and Washington Post declaring, “We’re Standing Up To Apple For Small Businesses Everywhere.” A full page ad Facebook took out in several national publications The ads were a response to changes in Apple’s iOS 14, which will prompt users to grant apps permission to gather data and track them as they move across other apps on their iPhone or iPad. That move will “break” parts of Facebook’s ad targeting system, among other things. Apple CEO Tim Cook has staked the company’s brand on becoming known as the big tech company that respects user privacy, in direct contrast to Facebook and other companies that rely heavily on customer data for their advertising-based business models. “You are not our product,” Cook said in an interview with ABC’s Diane Sawyer in 2019 . “Our products are iPhones and iPads. We treasure your data. We want to help you keep it private and keep it safe.” Make no mistake, Facebook vs. Apple is just one battle in a much larger conflict over data privacy and brand equity. No longer just a compliance challenge, the sanctity of customer data is now a business and brand burnishing advantage too. Real-time Analytics Becomes a Differentiator It’s one thing to ingest a lot of data, and quite another to put that data to use. As 2020’s digitization stampede continues, the next frontier for enterprises is to mine the information they collect for insights that drive personalized customer experiences—at scale and in real time. And to achieve this level of near-instantaneous insight and response, 2021 will be the year businesses focus their attention on moving to converged data platforms. Unlike the siloed databases of yesteryear, converged data platforms (otherwise known as translytical data platforms, like MongoDB !), combine transactional (System of Record), operational (System of Engagement), and analytical (System of Insight) workloads onto a single, unified data platform. A converged data platform allows businesses to exploit their mountains of data at the speed and efficiency consumers now demand, and all with lower complexity and risk. As business leaders seek an edge over their competition, those that prioritize real-time analytics, and move to a converged data platform, will pull further away from their peers. Not Every Cloud Has a Silver Lining From retail to recreation, hospitality to healthcare, moving data and operations to the cloud was already a right of passage on the way to digital transformation. The COVID-19 pandemic simply accelerated this move. But with speed, comes even greater risk , and embracing the cloud on an accelerated timeline is fraught with danger. Do it without proper planning—as in a simple “lift and shift” of your existing setup—and you may find the on-premise issues that currently hamper developer velocity and business agility simply follow you to the cloud. The COVID-19 pandemic has heightened the need for companies to adopt digital business models—and only cloud platforms can provide the agility, scalability, and innovation required for this transition. McKinsey, The Next-Normal Recovery Will be Digital Additionally, all the advantages the cloud affords, such as the ease of scaling your infrastructure, can quickly lead to more architectural silos and technical complexity if handled incorrectly. Our warning is that, with so many companies rushing their move to the cloud in 2021, many will fail to seize on its transformational benefits, and spend 2022 (and beyond) undoing bad architectural decisions.

December 31, 2020