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 as top industry to invest in AI use cases to enhance their customer experience and optimize operations.
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 digitalization 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: 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 digitalize 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 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.
How does workforce optimization play part in this equation?
By now, I hope I have made a fair point that a certain number of store associates still makes a lot of sense in order to support the future of brick-and-mortar retail operations and create compelling customer experiences.
The question is how do we determine the optimal number of store associates?
Now consider this: labor costs are typically among the largest controllable expense for most retailers and store managers are able to cut from their labor budgets simply by giving their part-time workforce fewer working hours. The simple metric typically being followed is sales per hour – and there is a tendency to think that the more sales the store creates with the fewer labor hours spent – the more efficiently the store is operating.
For a number-savvy business executive it is easy to overemphasize those measures that are easy to assess and focus too little on those that are more complex to quantify. In the case of workforce, their related costs are relatively straightforward to calculate, but quantifying the impact of store personnel on store sales and maximizing overall profitability is less straightforward.
Retailers should staff their stores to maximize profits, not to minimize costs.
To satisfy pressures to achieve results, a store manager may be inclined to make some quick savings on staffing costs- hoping those won’t impact customer service and sales negatively. This could cause lost sales and gross margin if customers leave empty handed because they couldn’t find available & knowledgeable assistance from an associate. Over time this can create a death spiral of underinvesting in staff training, understaffing shifts, causing disappointing customer experiences, leading to declining revenues and resulting in further labor budget costs.
For many, the short-term financial pressure may prove too difficult to escape from and make it challenging to convert traditional physical store operations into effective omni-channel environments.
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 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.
Author: Pauli Dahlbom
Peoplegeeks is striving to co-create a healthy future of work. We combine the theory and practice of modern people science, data science and agile expertise to create meaningful improvement.