Content Discovery: How to Win the Battle for Attention
Think of the last app you used today. For me, it was searching for the latest episode of Sesame Street on HBOMax for my toddler. For someone else, it was finding a YouTube video on how to bake a cake. Or listening to a song recommended by Spotify. All of these instances, steps we barely put any thought into, are examples of content discovery , the bidirectional process by which users and applications interact, ensuring users’ known and unknown content consumption needs are fulfilled. As content is being generated at a nearly unfathomable and exponential pace ( think 500 hours of videos uploaded to YouTube every single minute ), catching and holding consumers’ attention with content is only going to become more difficult. Delivering great content discovery experiences that meet evolving customer expectations will be the only way to keep up. Content discovery happens in two ways, resembling push and pull forces: Push (recommendation engines): Content is suggested to the user. This can look like personalized landing pages or content recommendations. Pull (search): The customer searches for content, typically via a search bar. The user leads the action, and a new opportunity for suggesting relevant content is created. Consider how you consume content. Maybe you’re searching for a show you want to watch, or once that show is completed, the app you’re using recommends another similar show you might like. If media providers can master both of these processes – accurate search and intuitive recommendations – you can expect to fuel user engagement and decrease churn. Simple enough, right? Unfortunately, developing and deploying cutting-edge search and recommendation engines is easier said than done. A few major challenges stand in the way, like integrating data from multiple sources with excruciating extract, transform, load (ETL) pipelines, adding and maintaining a separate search engine solution, reduction in both time-to-value and developer productivity, and more. Having a unified application data platform that can handle analytics at scale and search natively is a massive advantage for effective content discovery. Let’s look at how an advanced application data platform like MongoDB Atlas makes the push and pull of content discovery possible. The push: Real-time, relevant recommendations Hitting users with the content they want when they want it (whether they know it's the content they want or not) is the aim of any recommendation engine. It’s particularly important in the media content consumption game, since there are so many competing platforms vying for user attention. As the volume and variety of user data increases by the second — generated from what they’re watching, what they stopped watching, what devices they’re using to interact with content — recommendations engines need to move beyond simple if-then-else statement based on historical data to advanced machine learning model that learns with data captured in real time, such as a causal inference models that predict what people might want to watch based on what other users with similar profiles and viewing habits are currently watching. MongoDB integrates natively with machine learning and artificial intelligence engines, using change streams to update the ML models to provide recommendations. The consumer profile is updated and saved in MongoDB, acting as the persistence layer and effectively becoming the single-view consumer data platform, a critical component in the pursuit of real-time analytics informing recommendations. Developers now have a single view of data, and machine learning models use that unsoiled data to make lightning-fast, accurate recommendations to keep users engaged with content. MongoDB acts as the catalyst for real-time recommendations informed by customer behavior triggers. The pull: Solving the search bar Virtually every application today has a search function — but it is also challenging to get right. Unlike database queries, where the user knows exactly what they are querying for, search has to give fast and relevant results to open ended, natural language inputs, tolerating typos and partial search terms, and essentially inferring users intent. Ultimately, consumers expect a Google-level search experience, and if they don’t get it, they’ll move to the next content platform. Building your own search engine, that will meet user expectations even as those expectations evolve, is costly in terms of time and resources spent developing and maintaining the engine. Many more database indexes need to be added to support search queries, and the search workload will start to contend for system resources with the core data persistence and processing demands of the application. To avoid resource contention between these two workloads, the database needs to be carefully sized and closely monitored and scaled, driving up operational overhead and cost. Also developing a database search solution won’t offer you any advantage over the competition, since there are dedicated search engines in the market that can do that heavy lifting for you. This reality has led companies to bolt-on a specialized search engine to their database – not that this is a simple solution either. Bolting on a search cluster to your database requires adding a new query language to integrate your application with the search engine, which increases the operational and architectural complexity of your current environment. This results in an elongated time for market for what could be a suboptimal search engine. Atlas Search solves the architecture and operational challenges of adding a separate search engine, since it’s fully integrated with the MongoDB Atlas Data Platform. Powered by the market-leading search engine Apache Lucene, it provides advanced search capabilities, while reducing architectural sprawl. Customers have reported improved development velocities of 30% to 50% after adopting Atlas Search. Atlas manages the required search infrastructure and automatically keeps the search indexes in sync with data mastered in the MongoDB database. Developers interact with search using the same universal interface that they are accustomed to using when interacting with other data in the platform, which means no new solutions to learn or decrease in developer productivity. Maintaining two separate systems adds complexity and lowers productivity, compared to the unified platform offered by MongoDB Atlas. With MongoDB Atlas, you can deliver the right recommendations at the most opportune time, and provide a best-in-class search experience to keep users engaged. No secondary solutions. No months of wasted development. Just a single, simplified process for game-changing content discovery. Take a deeper look into content discovery powered by MongoDB in our recent guide, Simplifying Content Discovery .
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) .