Boards and investors should be challenging retailers about how they are planning to use advanced technologies to increase their workforce productivity without compromising on customer service. Trying to increase labor productivity with the traditional straightforward mindset “doing more with less” is not a viable answer anymore (it if ever was).
We’ve all heard the news – with changing consumer behaviors and e-commerce gaining a greater share of the overall market, several traditional brick-and-mortar stores are struggling to stay relevant. The Retail apocalypse has been ripping through the industry, putting several traditional players into years-long death spirals of diminishing returns and budget cuts.
Big bets are placed on artificial intelligence (AI) and machine learning (ML) to leverage insights from vast amounts of data to level-out the playing field against digital-native players like Amazon. According to recent IDC report on worldwide spending on cognitive and AI solutions, retail is expected to overtake banking in 2019 as top industry to invest in AI use cases to enhance their customer experience and optimize operations.
“No matter how you slice it in the retail business, payroll is one of the most important parts of overhead, and overhead is one of the most crucial things you have to fight to maintain your profit margins. That was true then, and it is still true today.” - Sam Walton, founder of Walmart
Achieving profitability in retail is more important than ever and thus creating an urgent need for investigating and innovating means to improve operations. During the pressures from diminishing margins and available opportunities from automation and application of digitization and artificial intelligence, many retailers struggle to see other options than continue seeking means to minimizing their labor costs relating to operating physical stores.
At the same time, many traditional physical store operators risk losing their main advantage over online shopping: having real people available for human to human customer service experience.
The future of retail– will intelligent machines take over human labor?
With the current excitement and hype surrounding AI and ML – everything that is made possible with these technologies doesn’t necessarily make sense in real life. In other words, not everything that works brilliantly in online setting will necessarily make sense in physical stores.
Customer service and expert advice makes a prime example for this confusion.
Online retailers have a huge disadvantage over physical stores: how to make shoppers buy and continue browsing their endless product offering by merely looking at pictures and reading product reviews written by other shoppers?
This obvious disadvantage has forced online retailers to use their biggest advantage: being able to collect vast amounts of detailed data from shoppers’ interactions with their websites. By striving to understand every single detail of customers’ shopping habit, what products they are looking at and what they are likely to be interested in next, online retailers can use this information to provide a more personalized shopping experience and potentially relevant product recommendations without traditional human-to-human interaction.
Physical stores, however, seem to have forgotten about what their main advantages over the online retailers are: ability to provide quality in-store assistance from human workers relevant to customer needs.
While everyone is excited about the opportunities of automating and enhancing physical store customer experience, most retailers are missing out on the opportunities regarding their workforce development and getting people, processes and technology aligned in a way that delivers real value for customers.
In the survival fight of physical retailers, unfortunately many are adopting the age-old strategies of cutting workforce budgets, personnel training and development efforts.
Ever gone into a store just to realize there is no one to assist you?
Instead of providing either good or bad customer service, some retailers simply have chosen to provide almost no service at all to their customers. Other physical store operators seem to be working hard to digitize their in-store shopping service by introducing self-service checkouts and in-store shopping apps – or put in other way, hoping to make customers do tasks that once used to done by paid labor.
As in many other settings, also here the narrow-minded discussion on “how AI can be used to replace humans” in various tasks misses the point. A much more valuable approach would be to look at the setting from customers’ points of view and then innovate around how we should use AI and ML to improve customer service accessibility and quality.
What happens when a digitally savvy retailer introduces physical stores?
I believe many of you have likely already heard of the futuristic Amazon GO -store concept, where customers are able to purchase products without being checked out by a cashier or using self-checkout station. This is made possible by applying computer vision, sensor fusion and deep learning. These technologies make it possible to detect what products are taken from or returned to shelves and keeps track of them in a virtual shopping cart – as it happens in the online version. After finished shopping, customers can simply leave the store and soon after they will get the receipt and their Amazon account is charged accordingly.
Store associates are empowered to create better value for customers by forgoing certain less value-adding tasks.
The most surprising thing about Amazon GO? The fact that a lot of REAL PEOPLE work there greeting customers, restocking shelves, handling returns, preparing ready-to-eat food and providing product recommendations. In the Amazon GO stores, the sales associates are actively interacting with customers rather than just waiting at the checkout. I can’t help thinking how ironic it is that an online retailer is apparently nailing the human-to-human customer service aspect, an area that physical retail SHOULD have a clear advantage in, but is seemingly moving away from.
Rather than eliminating the retail workforce, the Amazon GO concept is setting an example for other retailers by changing the roles and tasks of store associates for the sake of enriching customer experience and optimizing work between humans and smart automation. Store associates are empowered to create better value for customers by forgoing certain less value-adding tasks.
What value can advanced analytics and optimization provide to the staffing decisions?
"It is unclear why retailers who invest millions of dollars to drive traffic into the stores through marketing activities would not invest sufficiently in labor planning to ensure that the incoming traffic is converted to sales." – Kesavan and Mani (2015)
The key question for many retailers is to figure out the optimal staffing levels and training necessary to run effective store operations. Research suggests however, that retailers may be thinking too simplistically about the costs and potential value of their workforce.
The end-to-end workforce management process is typically handled through a robust software solution, whose vendors often base their standard logic and algorithms in operations research that is well suited for operating machines in factories - not human workers. Lean scheduling or just-in-time scheduling seeks to squeeze profits by staffing stores with the very bare minimum number of employees.
Retailers should staff their stores to maximize long term profitability, not to simply minimize short term labor costs.
For a number-savvy business executive who is trying to achieve quarterly profitability goals it is easy to overemphasize those measures that are easy to assess and focus too little on those that are more complex to quantify.
To satisfy pressures to achieve results, a store manager may be inclined to make some quick savings on staffing costs. The problem with this approach is that it fails to consider the fact that store personnel drive sales results - by keeping shelves stocked, helping customers find products and providing advisory.
The money retailer saved in labor costs, it may be loosing on the other end from lost sales and dissatisfied customers who leave empty handed because they couldn’t find available & knowledgeable assistance from an associate. Over time this can create a death spiral of under-investing in staff training, under-staffing shifts, causing disappointing customer experiences, leading to declining revenues and resulting in further cuts in labor budget.
Traditional retail is in turmoil and we can expect to see many more store closures and chains failing in the coming years. Getting out of the downward spiral requires a shift in the mindset of executives from seeing employees simply as costs to be minimized to seeing them as assets that should be optimized to create value for the customers and profit for the organization. Applying some analytics and modelling can help make a strong business case here.
However, simply paying workers more, treating them better or increasing staffing budgets alone won’t guarantee results.
Store operations need to be designed in a way that allows workers to be productive and store associates to be empowered and well equipped to deliver quality customer service. By redesigning and elevating traditional store associate roles and making their work more meaningful, retailers can expect to attract a larger and more qualified talent pool with better retention and engagement towards their work and customer service.
By now, everyone knows there is potential in data. The key to getting store staffing right is to create improved understanding of the relationship between store sales potential, store staffing and revenues. By combining predicted store sales potential and shopper behavior with the abilities of store personnel to convert that potential into actual sales, we are able to make informed decisions about retail store staffing and achieve better business performance.
The thinking in retail that AI and ML technologies should be used to replace humans is not a healthy goal, nor it likely makes good business either. Retailers realize that they must adapt to this new environment before its too late. To achieve a productive relationship between AI & Retail, decision makers need to be thinking about the means to enrich customer experience and optimizing the work and tasks between humans and automation.