The MongoDB Guide To Personalized Retail
Why digital differentiation matters for retailers
The MongoDB Guide to *Personalized Retail*
Why digital differentiation matters
Customers Want Personalized Experiences
In store, online, and everywhere in between
Customers want personalized experiences
That means retailers – already under pressure from discounters, online upstarts, and having to reconfigure their businesses after the pandemic – are now also in a race to differentiate themselves versus their competition by collecting relevant data, mining it for insights, and using it to deliver delightful customer experiences.
What's more, personalization is no longer confined to the online experience. Customers are starting to expect personalized store greetings, location-based offers on nearby products, and the hyper-customization of products, services, and special offers. 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. And customers are expecting this omnichannel, personalized experience to happen in real time, too.
As retailers look toward a post-COVID landscape, and with the new urgency to connect and personalize every touchpoint in the customer journey, many are left asking whether their data infrastructure is ready for real-time, omnichannel personalization.
The day-to-day consumer shopping journey
How Retailers Can *Master Their Data*
Why privacy, real-time data, agility, and your developers matter
How retailers can master their data
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:
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.
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, while also making it easier to evolve an application during its life cycle.
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.
*Legacy Infrastructure* is Holding You Back
Tabular data structures aren’t a good fit for modern data demands
Legacy infrastructure is holding you back
To succeed, retailers must evolve their shopping experience at the speed their customers expect. However, they’re often constrained by the capabilities of their data infrastructure. Existing legacy relational database management systems (RDBMS) make data security, data harmonization, real-time data delivery, and agile development harder than they need to be.
In addition, tabular data’s rigid schema, which is complex to evolve (see Figure 1), discourages experimentation and iteration with applications and requires significant work to ensure scalability and recovery. Finally, legacy RDBMS make it harder to adopt agile development methodologies, like continuous integration/continuous delivery (CI/CD).
Why NoSql Alternatives Are Not the Answer
How your legacy database problem could get worse
Why NoSQL alternatives are not the answer
Even NoSQL databases by themselves don’t solve the legacy database problem. For starters, multiple NoSQL databases are often required for special purposes, including key-value stores for handling customer sessions, search engines for search, graph databases for recommendations, feature-limited document databases for product catalogs, and time series databases for in-store beacons or supply chain logistics. Combined, the number of separate data hops between so many NoSQL databases adds vital seconds to data access and can mean lost opportunities to interact with the customer.
And that’s before you add in the need for search and separate technologies for local data storage on mobile and edge devices, as well as for mobile apps, push components, and message queues. Data remains spread across silos, making it difficult to bring it together for analytics purposes, which themselves require additional movement to dedicated analytics systems.
All of this additional infrastructure requires valuable time to process data, moving you further away from real-time personalization. At the same time, developers and DevOps teams have to deal with the added complexity of working with, securing, and scaling so many databases, slowing the pace of your company’s innovation. We refer to this as the data and innovation recurring tax (DIRT), and it leads to several unwanted outcomes that retailers often fail to anticipate — all of which harm progress toward personalization. These include:
- Fragmented developer experience
- Reduced predictability given multiple operational and security models
- Significant data integration efforts
- Unnecessary data duplication
The following diagram shows the many specialist NoSql and relational databases, and the additional mobile data and analytics platforms that make up a typical retail tech stack. The result is siloed data, slow data processing, and added complexity.
MongoDB is Different
The foundation of your real-time, omnichannel personalization experience
To remove the DIRT and solve the data privacy, agility, real-time data, and developer empowerment requirements for personalization, retailers need a single, flexible developer data platform, like MongoDB Atlas.
MongoDB and MongoDB Atlas, MongoDB’s fully managed cloud database, use object data types, which map to how developers think and code.
Documents — the heart of the MongoDB data model — are a superset of other data models, so there’s no need for additional niche NoSQL databases. With MongoDB, you can use a single unified interface to work with any data generated by modern applications.
In addition, MongoDB helps you:
Unlock data silos. Online, in store, and everywhere in between, MongoDB’s document data model allows you to integrate data from multiple siloed systems into an intelligent data platform, allowing distinct types of data — geospatial, graph, key value, relational, document, time series, and search history — to be accessed together.
Have the best of all clouds. With multi-cloud on MongoDB Atlas, take your data to Google Cloud, AWS, Microsoft Azure, and 80+ cloud regions around the world, bringing scalability and elasticity to the application layer, while also avoiding vendor lock-in.
Personalize in real time. MongoDB brings the core components of real-time analytics into one platform. You can pull “cold” historical customer data from data lakes on cloud object stores and instantly combine it with real-time customer interaction data. What’s more, with MongoDB, retailers have a data platform that supports operational and analytical workloads within a single replica set, where data is replicated in real time and tagged for specific workloads, and queries against a secondary node are isolated from other nodes.
Secure your data. Don’t let data security and privacy become a separate, resource-draining project. With enterprise data security features built in, MongoDB and MongoDB Atlas give retailers the power to be intentional about how they address security and data privacy. Satisfy sophisticated privacy requirements without compromising.
- Authentication: LDAP integration, X.509, SCRAM
- Authorization: role based access control, field level redaction, read only views
- Auditing: fully copnfigureable, CRUD, security, cluster
- Encryption: TLS (in-flight), at rest (database + volume), field level, key management integrations
- Certified: ISO/IEC 27001 | PCI DSS | SOC 2 | HIPPA
Streamline and simplify mobile app development. MongoDB Realm stores data locally (such as on the device), while Realm Sync ensures data remains synchronized as devices move on- and offline.
How retailers are using MongoDB
Beacon technology/sensor tracking
Real-time data analysis and smart loyalty
Gamification & reward systems
Consumer support (i.e. chat bots)
Extended & augmented reality
Supply & logistics operationalized maintenance
MongoDB retail view
The diagram shows a typical retail infrastructure.
On the left are point of sale and other data sources. With a relational database and/or multiple specialized NoSql databases, getting the many different data types into the database(s) involves a long and complicated ETL chain.
With MongoDB and MongoDB Atlas' document data model, the data is seamlessly ingested whatever the format.
MongoDB and MongoDB Atlas: the foundation of personalized, real-time retail
*Retailers Trust* MongoDB:
Boxed, OTTO, and AO
Boxed – streamlining supply chain management
MongoDB Atlas and Google Cloud have given us confidence. We passed this test together. We’ll pass the next.
William Fong, CTO and co-founder, Boxed
Just days into the COVID-19 lockdown, Boxed saw sales rise by 30x
To create charts of real-time data and dashboards
Boxed, a leading wholesale club in the United States, sets itself apart from competitors by not charging membership fees — and delivering its products according to a strict schedule. To fulfill this goal, Boxed 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.
As the COVID-19 lockdowns spread across the nation, Boxed saw its traffic spike to 30 to 35 times normal levels, which impacted the company’s relational data warehouse (an external, third-party solution) by introducing significant latency in accessing key data. As a result, Boxed employees couldn’t leverage this data to coordinate its fast-moving business operations.
To restore availability, Boxed CTO William Fong turned to dashboards in MongoDB Charts to pull real-time data directly from Atlas and act on it in a timely manner. Within 20 minutes, Fong was able to use Charts to bridge the gap left by the failure of Boxed’s data warehouse and immediately surface relevant, actionable data — putting warehouse logistics back on track.
OTTO – building personalization engines
OTTO reinvents their e-commerce personalization for more than 2 million visitors per day
Slashes site catalog update time from 12 hours to 15 minutes
MongoDB also helps retailers maintain a competitive edge by addressing the personal preferences and desires of their visitors in real time. To execute this degree of customization, retailers need a capable data platform that can scale on short notice.
As the leading fashion and lifestyle retailer in Germany, OTTO clears more than 2 billion euros annually, and its website sees more than 2 million daily visitors. Given this sheer volume, OTTO’s applications have fewer than one or two seconds to access customer information (such as location and shopping history) and alter variables including pricing, availability, and other attributes.
OTTO turned to MongoDB to accomplish this difficult task. Whereas relational databases require massive migrations and possess an inflexible structure that is ill-suited to massive amounts of unstructured data, MongoDB can store as many as 10,000 events per second (including clicks, hovers, and other actions). Using MongoDB, OTTO can quickly recommend products and categories that fit individual tastes, dynamically price goods based on market conditions, and even reduce the time it takes to make product catalog updates from 12 hours to only 15 minutes.
MongoDB was the go-to preference for every team, even though the business problems varied.
AO – building a single view of customer behavior
Achieved by building a 360-degree single-view platform of all its customer data
Working to help reduce the average call handling time by up to 40%
The most effective retailers must be able to seamlessly standardize and centralize data from disparate origins — brick-and-mortar stores, mobile apps, IoT devices, and desktop websites — in order to effectively harness data and take their business to the next level of revenue and growth. AO is a leading online retailer in Germany and the United Kingdom, specializing in a wide variety of electrical products including laptops, home appliances, and more. To adapt to a changing retail landscape, the AO team wanted to leverage real-time data to build modern applications for everything from personalization to delivery tracking.
Before AO could do so, it had to modernize existing legacy systems, as well as collect a diverse array of data (from an equally diverse range of sources) in a single, centralized view. In particular, the AO team needed to capture events, which could include anything from location data for deliveries to customer browsing history to order confirmations, and store it for easy access by developers and applications.
Thanks to MongoDB, AO realized several improvements. First, MongoDB’s document model, which relies heavily on BSON documents to store unstructured data, was the ideal solution for AO, which had to handle many types of event data. In addition, the AO team could use MongoDB to examine their data in detail, filtering for (or notifying on) changes in collections or deployments, monitoring the effectiveness of models in production with rich visualizations from MongoDB Charts, and more.
As a result, AO has seen indicative increases in sales growth, reduced the delivery time for data models, and improved data visibility via MongoDB’s real-time monitoring features.
MongoDB for Personalized Retail
Looking to learn more?
MongoDB for personalized retail
Arm yourself with data to meet unprecedented challenges and constantly meet the demands of the omnipresent consumer.
MongoDB gives retailers the tools to easily leverage their data to personalize the customer experience. But MongoDB’s developer data platform isn’t limited to these operations: It serves as a strong foundation for any technology that uses the high-volume, multi-structured, time-sensitive data typical of today’s fast-moving retail operations, including chat bots, in-store beacons, artificial intelligence, RFID tags, and much more.
To learn more about how MongoDB can empower your teams and transform your retail organization, take a look at the following.
MongoDB for retail – our dedicated retail page
Take a look at our dedicated retail page where we highlight how MongoDB can help you analyze data in real time and develop applications at warp speed. You will find:
- Retail-specific solutions
- Our latest resources
- Customer stories
Omnichannel, personalization and more for retail webinar
In this on-demand webinar we will walk you through the fundamental technologies for the various real-time use cases and their criticality for success in the retail industry. We cover:
- Personalization and next best offers
- Supply chain management
- Fraud detection