When faced with a major investment decision, how many organizations would bet their success on a gut feeling? How many organizations would rely mainly on intuition when taking a new product to market? Not many. Yet, when it comes to the workforce — one of a company’s most expensive and valuable assets — too many executives rely on hunches, making decisions without making use of relevant data.
Skeptics like to claim that the value of employees cannot be measured or predicted. They say that workforce analytics is a way of treating people like widgets. To some, those of us who use analytics are no different than the strict headmasters and factory owners that populate the novels of Charles Dickens. Take Thomas Gradgrind in Hard Times. He’s “[a] man of facts and calculations. A man who is… ready to weigh and measure any parcel of human nature, and tell you what it comes to.” The truth is that Gradgrind would make a terrible manager. Turning humans into numbers isn’t what analytics is about; at least not good analytics.
Thankfully, in the course of researching our new book, my coauthors and I found several companies that don’t fall victim to the Gradgrind effect. These companies — IBM, Qantas, Luxottica, Sprint — see workforce analytics as a human endeavor. The goal is simple: put the right people with the right skills in the right work. When successful, these companies improve the productivity and capability of their workers — and that’s a good thing.
In my experience, organizations that use workforce analytics have the most engaged workforces and they thrive in tough conditions. Most importantly, they do fewer headcount reductions because they have lean and efficient workforces to begin with. Of course, not every organization thinks it can use analytics. But if you work for an organization that can, the first step is to focus your time and resources on exploring these four, key areas:
What is our organization’s strategy? What type of work needs to be done and how can we become more productive and competitive in accomplishing it?
How do we put the right number of people in the right roles without reducing headcount to reduce costs?
Are employees fully engaged and motivated? What do we expect from them and how can we help them meet those expectations?
How can we detect the need for change? How can we share insights and innovations that allow our employees to be more productive?
Asking those questions is the first step. The next: to collect the right data and determine the best plan of attack. Take Sprint. Once the shining example of call-center customer service, they dropped to nearly last in the industry in customer satisfaction in 2007. But by following a simple, six-step process, they were able to make a turnaround.
Step 1: Frame the central problem
Sprint’s call volume rose to twice as high as their competitors. The urgency of the issue was clear: Sprint had to improve their customer service — fast.
Step 2: Apply a conceptual model to guide the analysis
Sprint quickly discovered the core of the issue. Each month, managers had been focusing on a new performance metric — first-call resolution, average call-time — so the phone staff was confused as a result. Sprint also discovered that its existing metrics were rewarding the wrong behaviors. No wonder customer satisfaction plummeted.
Step 3: Capture relevant data
Sprint faced a common problem: they were awash in data. But like many companies, they didn’t know how to act on it. So early on, Finance and HR collaborated to simplify the process, reducing the number of data metrics. From there, they were able to isolate the most relevant data across the key business units.
Step 4: Apply analytical methods
I could go into basic frequency distributions and simple cross-tabulations, but I’ll save that for another day. Essentially, Sprint simplified their methods in order to find out what separated top performers from low performers.
Step 5: Present statistical findings to stakeholders
The analytics team focused on three questions. First, how can we improve the performance of our agents and teams? Second, how can we simplify our processes? Third, how can we improve call-volume? To answer these questions, the analytics team created focus groups of senior Sprint executives and managers, who reviewed the feedback and made informed decisions based on the data.
Step 6: Define action steps to implement the solution
Sprint reduced the number of metrics from eighty to twenty. They also eliminated the the “whack-a-mole” changes to product offers. Finally, they put a plan in place to improve the skills of supervisors and to provide coaching for low and average performers. This improved customer satisfaction by 43 percent. It also improved first-call resolution 42 percent after one year — a success.
Sprint’s analytics team didn’t just crunch numbers and treat people like widgets. They saw a problem and used data to help solve it — and they never lost focus of the human elements that drive performance. As result, they increased the productivity of their agents — and that’s a good thing.