We’ve seen it countless times: corporate teams get wrapped up in an idea of how a tech solution should work, even if it interrupts the flow of work for their essential frontline employees (essential industry workers such as sales floor, warehouse, back room, etc.). Without insights being surfaced naturally during the point of need, employees simply avoid adopting these solutions, or create their own workarounds.
Here's an example:
During our work with a client seeking to build a tool for pharmacists, the executives in the room were debating what to change about the existing piece of software, getting into the weeds of what worked and what didn’t. As the debate dragged on, someone leaned over to a pharmacist in the room and asked what she found useful about the tool. She replied that no one really used the software as intended because it was only functional on a computer, and their jobs required them to move around to serve customers. In actual use, the pharmacists were using a notepad, then updating the system at the end of their shift or in between customers.
When data exists to help improve frontline employees’ day-to-day jobs but the data isn’t understandable, easy to consume, or accessible when and where people need it, it’s impossible for them to make meaningful decisions in the moments that matter.
An effective frontline tool will go beyond simply providing employees with access to data. It should feel like a helpful, natural part of their workflow. The best data tools may not even feel like a data tool, but instead an informative or process management tool that makes employees’ jobs easier. By bringing your end users alongside you through the design process you will create the solution they want and need — versus what leaders at corporate think people will use. You’ll ensure you’re addressing users’ pain points, reducing manual processes, and ultimately, lowering barriers to adoption.
Let's jump into a few real-world examples of tools and best practices to surface insights to your frontline employees.
Drive Up, Target’s top-rated service that brings guests’ online orders to their cars for free, is able to put together orders in as fast as 21 minutes. This is due in no small part to trained frontline staff using an intuitive tool so they can handle orders as soon as they come in. To complete an order, customers must hit the “I’m on my way” button in the app, after which Drive Up staff can track pending orders and customers’ ETA to the store from their own handheld devices. When a customer signals their arrival in a Drive Up parking spot, team members are notified on their devices with a familiar “honk” sound — and a notification of who the guest is. In a busy retail setting, the sound notification ensures alerts aren’t missed among other in-store sounds.
A leading transportation and logistics company operating in the continental U.S., Canada and Mexico launched a market platform that connects carriers to shippers. Wanting to create a market differentiator, they set out to create a load recommendation system that would help with carrier adoption of the tool and retention on the platform. To help with this process, we built a machine learning model that recommends loads to carriers based on their historical interest in specific loads. Now, carriers no longer have to sort through hundreds of potential loads to find their ideal match because the best options are intuitively surfaced to them
A bank started gaining buy-in by working with a small number of account managers and clients to identify developments that would improve the business. Through their work, they developed a system that would send account managers real-time alerts about potential opportunities. When oil prices dropped, the system alerted managers to call their top five clients whose businesses would be impacted. Over multiple 90-day cycles, the company was able to test new alerts to see which ones worked best, and they continued to refine the tool as it was rolled out to more managers. Today, nearly all of the account managers get business insights from the tool and spend about 20% more time with their clients.
Prior to changes rolled out onto the sales floor of a retail client, frontline workers were reluctant to ask questions about things that went beyond the initial training they received. But when an AI-powered virtual assistant chatbot sitting atop a vast employee knowledge base was created for them, it turned out these frontline workers were willing to ask almost any question in relation to their work – over 10 million to date. Employees were familiar with this kind of technology outside of work, so they quickly adopted the new functionality. The chatbot made it easier and less intimidating to ask for the data or information they needed to do their jobs compared to traditional training. Eventually, common, repetitive tasks like item lookup saw a time reduction of 40% with this technology — and employees’ valuable time on the floor with customers increased.
Marks & Spencer, one of Britain’s best known and widely respected retail brands had a vision to integrate machine learning, computer vision, and AI both in its stores and behind the scenes. With the help of Microsoft, every surface, screen and scanner in their stores is able to create data and, more importantly, enable employees to act upon it. Every Marks & Spencer store worldwide is being outfitted to track, manage, and replenish stock levels in real time, so their frontline can use insights for action.
Matt Hart launched his startup, Soter Analytics, when he found that musculoskeletal injuries were the biggest injury cost in the mining industry. He came up with a wearable sensor and app that records individual movement data in order to improve worker safety. The wearable device clips to the user’s garment at the back of the neck and monitors movement in real time and beeps and vibrates if the wearer engages in a high-risk motion. After wearing a device for two weeks, the amount of high-risk movement users performed fell by 30% to 50%, and their behaviors changed in response to the device feedback.
You cannot approach building data solutions for your frontline in the same way you’d build for an internal analyst. It’s time for organizations to think creatively about the best way to pare down and surface the most important data — and it’s imperative to bring frontline employees along with you in this process so you build a tool they want, need and use.