Laurie Guest’s picture

By: Laurie Guest

Everyone’s heard of it by now: “Quiet quitting” is the freshly coined phrase to describe the age-old behavior of not quite leaving one’s job entirely but rather opting to no longer go above and beyond. It’s service fatigue to the extreme, risking not just customer satisfaction but also staff loyalty and your business’s bottom line, too.

While the idea of quiet quitting may still be new to many, those of us who study customer service have spoken for decades about the root causes and possible solutions for this kind of disengagement and underperformance. The issues may not be new, but these innovative solutions offer fresh ways to reinvigorate your team.

To bust out of service fatigue and prevent quiet quitting, leadership must take bold action, making changes that aren’t always easy. But then again, if it were easy everyone would be doing it, and we wouldn’t be facing an epidemic of workplace ambivalence.

There are three foundational business elements that affect team engagement: rules, beliefs, and praise. When leadership actively turns its attention to these governing principles, the desire to untether from one’s career shrinks.

Adriana Lynch’s picture

By: Adriana Lynch

The decade of the 2020s has given businesses—and the consumers who count on them—quite a ride. Neither tea leaves nor the best efforts of analysts could have predicted the impact that the one-two punch of a pandemic and roller-coaster economy would have on both markets and marketplaces.

Consumers have found their pockets filled and emptied. Employers have had trouble staying open and then difficulty hiring enough staff for swelling demand. Moreover, companies have struggled to get the raw materials and transportation network needed to deliver their products to a wanting public.

In this decade, consumers, already emboldened and savvier due to a digital revolution, have continued to evolve in buying habits and expectations. However, even considering recent events, these “evolved” consumers didn’t emerge overnight. Instead, they have been evolving for the last two decades, maybe even more.

William A. Levinson’s picture

By: William A. Levinson

‘You see, but you do not observe,” Sherlock Holmes told Dr. Watson in Sir Arthur Conan Doyle’s (1891) A Scandal in Bohemia. Taiichi Ohno, who developed Henry Ford’s lean production system into the Toyota Production System, told managers to stand in a circle on the shop floor and observe everything that happened there. If people who try this exercise see but do not observe, neither they nor their organizations will gain from it. History shows that countless people, possibly tens of millions, have overlooked breakthrough improvement opportunities that are obvious to anybody who knows what to look for.

Gleb Tsipursky’s picture

By: Gleb Tsipursky

Do bosses trust employees to be productive when working out of the office? Microsoft released a new study in which it found that 85 percent of leaders say the “shift to hybrid work has made it challenging to have confidence that employees are being productive.” More concretely, 49 percent of managers of hybrid workers “struggle to trust their employees to do their best work.”

This lack of trust in worker productivity has led to what Microsoft researchers termed productivity paranoia, “where leaders fear that lost productivity is due to employees not working, even though hours worked, number of meetings, and other activity metrics have increased.”

Tom Taormina’s picture

By: Tom Taormina

We live in a rural area, and many of our nonconsumables are purchased online. We’re deceivingly spoiled living near an Amazon fulfillment center because we can order an item on Saturday, and it arrives on Sunday. To me, this is a masterful logistical feat of filling an order, getting it into the delivery system, and into our community mailbox the next day, especially on a Sunday.

Unfortunately, the glaring downfall in creating a satisfied customer comes at the final leg of the journey: getting the product into our hands. Amazon’s logistical prowess loses its patina when a desktop computer is shipped via the U.S. Postal Service, and we must make a 20-mile round trip and stand in line at the post office. If the package won’t fit in a rural parcel locker, UPS or FedEx are the logical choices. The question is who makes those choices—or any one of the many choices that affect customer satisfaction.

Tom Taormina’s picture

By: Tom Taormina

It’s a conundrum that faces everyone who operates a manufacturing or service business: Most are unaware of the dire consequences of a defect reaching a customer until a process server hands them a lawsuit. By then it’s too late. Regardless of the outcome, the people and businesses will be permanently scarred and damaged.

Most of the more than 700 companies I’ve worked with during the last 50 years have robust customer service and warranty processes that never address the question: What might be the dire consequences of a defect reaching one of our customers?

During the last two decades, I’ve expanded my quality management consulting work to include consulting and testifying in products liability and organizational negligence cases. In more than 40 lawsuits, I’ve gathered empirical evidence which concludes that our fundamental tenets of quality management are seriously flawed. Our charter has been to minimize process variability and reduce defects to an acceptable level. Both canards are folly.

Patrick Hardy’s picture

By: Patrick Hardy

Responding to disasters is one of the most important activities that employees can be asked to grapple with. From natural disasters like hurricanes and earthquakes to technological situations such as power outages, chemical spills, and transportation accidents, as well as security emergencies like acts of terrorism and mass shootings, a property should be prepared for any of these events.

The metric for success in a disaster response isn’t the detail of the plans or the usefulness of the equipment; it’s the level of employee empowerment that makes all the difference. Employees must go beyond being just bystanders who are told what to do. They must be transformed into emergency responders capable of activating themselves and leading in the instant a disaster strikes.

Leon Chao’s picture

By: Leon Chao

I am 100 percent a millennial (lol), which—according to the Merriam-Webster dictionarymeans I’m a “person born in the 1980s or 1990s.” To me, being a millennial means belonging to a cultlike group within a large population of present-day wannabe-adults for whom seemingly arbitrary words like Rugrats, Dunkaroos, Charizard, Spice Girls, and It’s Morphin’ Time!!! can spark extreme nostalgia and reboot our brains back to the golden days of real pop culture.

Like many other ’90s kids, I grew up raising my Tamagotchis, scraping my knees because I refused to wear lame knee pads when rollerblading, transforming my Transformers, and terrorizing my sister with Ninja Turtles action figures.

Donald J. Wheeler’s picture

By: Donald J. Wheeler

Last month we found that capability and performance indexes have no inherent preference for one probability model over another. However, whenever we seek to convert these indexes into fractions of nonconforming product, we have to make use of some probability model. Here, we’ll look at the role played by probability models when making these conversions.

Many people have been taught that the first step in the statistical inquisition of their data is to fit some probability model to the histogram. Here we’ll begin with the 250 values shown in figure 1. These values have an average of 7.4 and a standard deviation statistic of 5.2.

Figure 1: 250 observations from one process

Bruce Hamilton’s picture

By: Bruce Hamilton

I responded recently to a LinkedIn post regarding AI-assisted robotic recycling. The sorting speed is so fast, we almost miss each sort in the blink of an eye. Having observed this same activity attempted by humans—and overlooking the upstream potential to avoid this kind of recycling mess at the source (the wasteful consumer)—I’m all for the potential to pass off these kinds of tasks to machines. 

Humans doing this work must operate at a much slower pace, risk injury, and aren’t as precise as the AI robot. And, of course, humans must also deal with the stench of garbage; these robots, at least, have no sense of smell to distract them from their work. 

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