Estudios: 6 ways AI is driving retail employee productivity, engagement
Author: Tom Besselman
Source: Retail Customer Experience
It may come as a surprise, but the majority of retail sales still take place in physical stores. According to Forbes, 91% of all retail sales in 2017 were completed in a brick-and-mortar location. Even with the incredible growth of online shopping, by the year 2025 more than 80% of retail sales will continue to be in-store transactions.
It’s no wonder that there’s been such an increased focus on customer experience in retail. From the customer journey and brand touch points, through the overall omnichannel experience, retailers have been hard at work discovering what works and what doesn’t work in getting customers to walk through their doors, make purchases, and come back.
Fortunately, modern digital technology and the “always-on” consumer have given retailers access to more data than ever before to help guide their CX decision-making. Thanks to advances in machine learning and artificial intelligence (AI) we now have the ability to make predictions based on this data faster, better, and cheaper than ever before.
Retail AI provides insights into the buying habits of shoppers that were only dreamed of by retailers a decade ago. But with all the talk surrounding retail AI, what’s often missing from the equation is the customer’s main point of in-store contact — the in-store retail employee.
From CX to EX
Reinventing retail requires intense digital focus for hyper connected shoppers. Customers expect a frictionless experience with instant results, which can be intimidating to an in-store employee.
Taking this one step further, brand leadership expert Denise Lee Yohn states that «since CX is the sum of all interactions a customer has with a company, then: EX (employee experience) is the sum of everything an employee experiences throughout his or her connection to the organization.» Yohn continues that with organizational focus and resources already firmly rooted in CX, «the next competitive frontier is EX.»
But what does that transition look like? Well-implemented EX creates engaged and efficient in-store employees, empowered with the knowledge and tools necessary to interact with today’s informed consumer. This transition also leads to the ability for brick and mortar shops to leverage AI to automatically detect and remediate tech issues that would otherwise:
1. Bog down the retail employee by having them troubleshooting technology or interrupt other staff.
2. Frustrate the customer with a slow, outdated experience.
3. And sabotage the smooth transition between the online and digital environments that retailers work so hard to create.
Here’s a more detailed look at how AI is being used to make retail employees better and the customer experience smoother.
AI: The retail prediction engine
According to Joshua Gans, co-author of Prediction Machines and Professor of Strategic Management at Rotman School of Management, «AI is essentially the ability we have to make use of the torrents of big data now flowing into many businesses to use complex arithmetic to crunch the data and make predictions from the patterns that emerge from that.»
In the quest to improve the customer experience, AI’s predictive analytics capabilities provide actionable insights that improve EX from the hiring process through the customer interaction and final sale, empowering and engaging retail employees.
1. The hiring process
In-store associates are often the difference between a positive or negative customer experience. AI can be employed to improve associate quality by quickly isolating key characteristics of the best associates and efficiently identifying best-fit candidates, making sure retailers have the right people on their front line.
AI and automation do the number crunching that helps find the perfect job candidate. Companies like Pymetrics provide AI and automation that scans résumés to help keep unconscious bias out of the hiring process. And according to CIO.com, “Some forward-thinking recruiters and hiring managers are using AI and machine learning to reverse-engineer candidate ‘fit,’ and to predict a potential candidate’s performance in the role.”
Once hired, retailers are outfitting their employees with mobile and wearable technologies that improve CX and provide them data-driven insights and training through AI enabled apps and services. Predictive analytics directs the right training to the right employee at the right time.
Employees armed with these insights, in-depth product knowledge, and training can utilize data from customer purchase histories and preferences to create a more intuitive in-store experience. AI can then guide employee recommendations in order to increase up-selling and cross-selling opportunities.
3. Improving the supply chain
Of course, employees only succeed when there are products on the shelves to sell. Artificial intelligence and machine learning create efficiencies in complex systems which involve multiple, often compartmentalized processes like the supply chain. Forecasting, stock location and replenishment, order fulfillment, and returns aided by AI and automation, help in-store associates get customers the products they want, when they want them.
Additionally, AI applied to order management helps associates stay on top of product availability and order fulfillment whether in-store or online for in-store delivery. AI also improves the efficiency of reverse logistics and the complexities of the return process. For example, AI can be used to help predict returns of a particular item based on the past behavior of your current customers.
4. IoT and in-store design
According to Forbes, “Today’s Internet of Things industry means that everything is connected and capable of collecting and sharing data on how it is operating. This means that everything can be measured.” The data from cameras, sensors, wearables, and other IoT devices provide in-store associates powerful and actionable insights into customer behavior, stock levels, and more.
This data helps inform refinements in store design and product placement. AI prescriptive analytics «takes raw data and automatically identifies opportunities to improve processes and customer service,» like detecting where customers linger or when they put items back on shelves. Depending on the AI employed, associates can be informed in near real-time of issues as they arise, in order to better serve their customers.
5. Self-healing technology
In the end, the analysis and insights don’t really matter if an associate’s technology isn’t working, or working properly. When any end-user device fails, it has a significant impact on the sales process, employee productivity, EX, and CX.
Advances such as self-healing technology provide AI-enabled tech support that uses automation to monitor and repair associate devices and technology. When a loss of connectivity is detected, it instantly diagnoses the problem and takes action to fix it — without employee intervention.
Associates no longer have to worry about embarrassing slow-downs or tech failures that derail the sale or interfere with CX. AI has their back and is working hard behind the scenes to make them better employees.
6. Empowering and engaging the end user
While AI is sometimes pictured as a technology that spells the end for humans in the workplace, currently its ability to make sense of consumer and business data is driving innovations that make employees better. AI helps employees get hired for the jobs that fit them, train on the latest technologies, and it augments the tools designed to make them successful.
Organizations leading with AI and data-driven insights are learning more about their employees and their employee experience. By applying the same technologies used to improve customer experience, not only are retail employees getting better at their jobs but they are becoming more empowered and engaged along the way.