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).

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Digitizing Nigeria with Okra And MongoDB

Think of how often you open a banking app on your phone. What a feeling it is to have the security of knowing exactly what is in your account at all times. Now think back to the days before your bank had that mobile app and how you had to drive to the bank to deposit a check, check your balance, or withdraw some cash. Luckily for us, there's no sign of going back to the way things were. However, that way of life is still a reality for some countries. That's why Fara Ashiru Jituboh started Okra in Nigeria. Okra gives the citizens of Nigeria a way to access their money through modern means, rather than physically driving to a bank. Okra is connected to all banks in Nigeria, allowing customers to connect their bank accounts directly to financial apps with their internet or mobile banking credentials, making onboarding a faster and safer experience. All with 99.9% guaranteed uptime. Okra's API empowers companies and developers in Nigeria to build products with seamless access to inclusive financial data and secure payments. Built with MongoDB spoke with co-founder, CEO Fara Ashiru Jituboh to discuss how Okra came to be, the excitement of building something so impactful, working with MongoDB, and much more. Built with MongoDB: What exactly does Okra do? Fara Ashiru Jituboh: Okra simply enables developers to build personalized digital financial services. We’re digitizing financial services for Africa. We’re doing this as the infrastructure powering the fast-growing consumer platforms. Built with MongoDB: What made you want to start your own company? Fara Ashiru Jituboh: I've been a builder for a long time and have built many projects. When I moved to Nigeria in 2014, I wanted to rent a place, but you have to pay for the whole year upfront to rent a house. Same thing if you're going to buy a car, you have to pay for the entire thing in cash, and a lot of these things were due to this lack of access and data. That’s why the Fourth Industrial Revolution is so important, it involves personalization and access to data and generally democratizing access. I wanted to manage my finances, pay for something in a monthly installment, and access that kind of credit foundation. Through that, there are a lot of significant use cases and different products that companies will build. Built with MongoDB: What was your initial vision for Okra? Fara Ashiru Jituboh: Initially, I was just trying to build a personal finance platform, similar to the ones I’d used in the U.S. like Mint. I ran into hurdles doing that, and the biggest hurdle was access to real-time financial data. My co-founder, David Peterside , already had a strong hypothesis on the Fourth Industrial Revolution and its impact so we further explored and doubled down on building the data infrastructure. We knew the infrastructure we were building was something every financial service provider absolutely needed to become/remain relevant. Financial service providers tried to build the infrastructure and weren’t very good at it. They found that it distracted them from their core business and were happy to have a company like us abstract all that complexity for them, so they could focus on their core business — which is delivering the best financial services experience for consumers. We felt like we could make more of an impact by solving the problem for the entire continent as opposed to just ourselves. Hence where our mission comes from — which is digitizing financial services for Africa. Built with MongoDB: What is a cool feature of Okra that you’ve built? Fara Ashiru Jituboh: For me, it's going to be our returning user experience, and our widget upgrade. It showed us how big we are as a platform and that we're a network. And it started to show us the immense potential, and the amount of crossover between customers. Built with MongoDB: What have you enjoyed the most about building Okra? Fara Ashiru Jituboh: It's two things. Waking up every day and working with people that are just as passionate about a product as you are. And on the flip side of that is having people so passionate about it and seeing people using the product. We're on our third version of our widget, and we're starting to think about scale and growth. That journey has just been crazy in the sense of real fun. Everyone is so collaborative and has a passion for solving the same problems. Built with MongoDB: What were you optimizing for when it came time to select a database platform? Fara Ashiru Jituboh: The number one thing with MongoDB is knowing that you don't see what you're building yet, and NoSQL is the best for that. Early on, we had to rethink the architecture of the database. Because we use MongoDB, it's so easy to do that, because we could easily migrate and not have to worry about issues with schemas. It allowed us to be very agile and move pretty fast and iterate quickly. Built with MongoDB: Is there any advice you’d give other aspiring founders when they’re building or growing their startup? Fara Ashiru Jituboh: As a startup founder, there are always a million fires burning, so you can't put out every fire. It's really about figuring out the most critical and how to put them out or stop them from growing. Sometimes you have to do that before you can do anything else, and it's not always just about scaling your business; it's also about scaling your technology. When you're building, especially an engineering-driven product, you are making sure that there's a perfect cohesion between the business you're building and the technology you're scaling. Interested in learning more about MongoDB for Startups? Learn more about us here .

March 9, 2022
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Unmasking Deception: Harnessing the Power of MongoDB Atlas and Amazon SageMaker Canvas for Fraud Detection

Financial services organizations face growing risks from cybercriminals. High-profile hacks and fraudulent transactions undermine faith in the industry. As technology evolves, so do the techniques employed by these perpetrators, making the battle against fraud a perpetual challenge. Existing fraud detection systems often grapple with a critical limitation: relying on stale data. In a fast-paced and ever-evolving landscape, relying solely on historical information is akin to driving by looking into the rearview mirror. Cybercriminals continuously adapt their tactics, forcing financial institutions to stay one step ahead. The newest tactics often can be seen in the data. That's where the power of operational data comes into play. By harnessing real-time data, fraud detection models can be trained on the most accurate and relevant clues available. MongoDB Atlas, a highly scalable and flexible developer data platform, coupled with Amazon SageMaker Canvas, an advanced machine learning tool, presents a groundbreaking opportunity to revolutionize fraud detection. By leveraging operational data, this synergy holds the key to proactively identifying and combating fraudulent activities, enabling financial institutions to safeguard their systems and protect their customers in an increasingly treacherous digital landscape. MongoDB Atlas MongoDB Atlas , the developer data platform is an integrated suite of data services centered around a cloud database designed to accelerate and simplify how developers build with data. MongoDB Atlas's document-oriented architecture is a game-changer for financial services organizations. Its ability to handle massive amounts of data in a flexible schema empowers financial institutions to effortlessly capture, store, and process high-volume transactional data in real-time. This means that every transaction, every interaction, and every piece of operational data can be seamlessly integrated into the fraud detection pipeline, ensuring that the models are continuously trained on the most current and relevant information available. With MongoDB Atlas, financial institutions gain an unrivaled advantage in their fight against fraud, unleashing the full potential of operational data to create a robust and proactive defense system. Amazon SageMaker Canvas Amazon SageMaker Canvas revolutionizes the way business analysts leverage AI/ML solutions by offering a powerful no-code platform. Traditionally, implementing AI/ML models required specialized technical expertise, making it inaccessible for many business analysts. However, SageMaker Canvas eliminates this barrier by providing a visual point-and-click interface to generate accurate ML predictions for classification, regression, forecasting, natural language processing (NLP), and computer vision (CV). SageMaker Canvas empowers business analysts to unlock valuable insights, make data-driven decisions, and harness the power of AI without being hindered by technical complexities. It boosts collaboration between business analysts and data scientists by sharing, reviewing, and updating ML models across tools. It brings the realm of AI/ML within reach, allowing analysts to explore new frontiers and drive innovation within their organizations. Reference Architecture The above reference architecture includes an end-to-end solution for detecting different types of fraud in the banking sector, including card fraud detection, identity theft detection, account takeover detection, money laundering detection, consumer fraud detection, insider fraud detection and mobile banking fraud detection to name a few. The architecture diagram shown here illustrates model training and near real-time inference. The operational data stored in MongoDB Atlas is written to the Amazon S3 bucket using the Triggers feature in Atlas Application Services. Thus stored, data is used to create and train the model in Amazon SageMaker Canvas. The SageMaker Canvas stores the metadata for the model in the S3 bucket and exposes the model endpoint for inference. For step-by-step instructions on how to build the fraud detections solution mentioned above with MongoDB Atlas and Amazon SageMaker Canvas, read our tutorial .

June 21, 2023