All watched over by machines of loving grace

I recently wrote about concerns about the role of big data that arose from Facebook’s research effort where the researchers sought to determine if emotional state could be manipulated by what appeared in their Facebook activity streams (see The fear of big data is growing). But directed efforts to shape our emotional state or to control our behavior are not the only ways that application of big data analysis might prove to be questionable.

Peter Capelli has posted a piece at the Harvard Business Review that poses some important questions about the application of big data and predictive analysis about future behavior and performance of individuals based on what big data may reveal. At core is the question of what businesses might do when confronted with the possibility of peering into the statistical future, and choosing who to hire — for example — based on big data-based predictions.

Peter Cappelli, We Can’t Always Control What Makes Us Successful

Many of the attributes that predict good outcomes are not within our control.  Some are things we were born with, at least in part, like IQ and personality or where and how we were raised.  It is possible that those attributes prevent you from getting a job, of course, but may also prevent you from advancing in a company, put you in the front of the queue for layoffs, and shape a host of other outcomes.

So what, if those predictions are right?

First is the question of fairness. There is an interesting parallel with the court system where predictions of a defendant’s risk of committing a crime in the future are in many states used to shape the sentence they will be given. Many of the factors that determine that risk assessment, some of which include things like family background that are beyond the ability of the defendant to control. And there has been pushback: is it fair to use factors that individuals could not control in determining their punishment?

Likening the assessment of an employee’s fate in a corporate downsizing to the judicial review of criminals may seem farfetched, but the parallels are obvious. The power lies in the hands of the courts and management in the two cases, and the employees and the criminals are powerless. One attribute of that powerlessness is that judges and management have access to statistical information — and its analysis — while the criminals and employees do not, in general.

Capelli makes an argument that the psychologists — who have been grappling with the ethics of human assessment in the enterprise for decades — are now being pushed aside by data scientists and software companies that are providing new ways to read the crystal ball. Instead of personality or IQ tests, machines are crunching big data, mined from hundreds or thousands of companies, that reveal who is most likely to be a good call center worker.

Xerox is using software from Evolv that is based on the analysis of the testing and performance tracking of tens of thousands of call center workers

Joseph Walker, Meet the New Boss: Big Data

By putting applicants through a battery of tests and then tracking their job performance, Evolv has developed a model for the ideal call-center worker. The data say that person lives near the job, has reliable transportation and uses one or more social networks, but not more than four. He or she tends not to be overly inquisitive or empathetic, but is creative.

Applicants for the job take a 30-minute test that screens them for personality traits and puts them through scenarios they might encounter on the job. Then the program spits out a score: red for low potential, yellow for medium potential or green for high potential. Xerox accepts some yellows if it thinks it can train them, but mostly hires greens.

The terminology — reds, yellows, greens — sounds more like the caste system of a dystopic science fiction novel than a contemporary business analytic tool, but it’s not. This is what is going on in business, today. And the reasons are simple, despite the ethical questions that accompany them. One driver for the rise of algorithmic HR is that people are bad at making hiring decisions: we have too many cognitive biases, and our capacity for balancing many independent factors for a candidate’s suitability for a job is limited. So the logical decision — as we have seen at Xerox — is to hand over the decision of who to hire and train to the machines.

The result is that there will be less turnover at Xerox, saving the company money, and the customers benefit from better customer support. The only ones iced out are the ‘reds’: those individuals who might have desired a job at the call center, but who will now have to find a job where their curiosity is a plus not a black mark.

The counter to Capelli’s concern should be the government or the education sector, who — armed with big data and analytic tools of their own — could be guiding those ‘reds’, and everyone else — toward the jobs and careers that line up with their gifts and backgrounds.

And the broadest ethical questions — like what to do about those raised in single parent homes in a world that might rate them a higher risk for all jobs — are beyond the scope of this analysis, today, but as Capelli points out, those questions need to be raised and answered by someone.

In a time when our institutions are in retreat, and the social contract between the worker and the business has been attenuated, you have to wonder who that someone is.

What we can learn from call centers: Humanize.

Call centers might be the front line in the war between traditional command-and-control approaches to management and the third way of work.

Alex Pentland found that the best predictor of performance in a call center he was researching was the level of engagement of the teams outside of formal meetings. It was the informal socializing that led to the best behavior, and so he arranged the almost unthinkable: instead of organizing their work schedules to optimize greatest numbers of staff on the phone, he organized to increase the number of workers on breaks at the same time, to increase socializing.

The result? As Pentland said,

It worked: AHT [average handling time] fell by more than 20% among lower-performing teams and decreased by 8% overall at the call center. Now the manager is changing the break schedule at all 10 of the bank’s call centers (which employ a total of 25,000 people) and is forecasting $15 million a year in productivity increases. He has also seen employee satisfaction at call centers rise, sometimes by more than 10%.

So, dismantling the ‘cut costs’ mindset leads to new sources of performance arising spontaneously from the social interactions of the staff formerly being treated like numbers in a spreadsheet instead of living, breathing human beings.

This can go even further. Jim Bush took over the Amercan Express service operations in 2005, as described by Rob Markey in HBR, and inherited the call center operations for the company. That was being managed with the same ‘by the book’ approach, including requiring staff to follow highly scripted responses to customer requests. He decided to through the book away, and dropped the focus on call time. He stated that the support representatives would set their own pacing, and — within clear limits — were empowered to help clients with all the tools at their disposal. He professionalized the job, calling them customer care representatives and providing them with business cards and other symbols of status. He began hiring for people with experience in retail and hospitality business instead of call centers, people with the right personality profile for making customer’s happy. He started paying higher salaries, to hold onto the best people. He upped training, and made clear the company’s doctrine to provide the best customer service within the restrictions of company policies.

Bush switched to a single metric: Net Promoter score — the percentage of callers who, when asked, said they would recommend American Express to a friend. And the company moved to a very fast feedback system, so that staff could see what they were doing well and where improvement was needed. And numerous opportunities were created to allow more training, both formally and informally between reps.

As Markey summarized the results,

Call-handling time edged up slightly at the very beginning, then dropped and kept falling. Likelihood-to-recommend scores doubled, indicating far more enthusiastic advocacy of American Express on the part of customers. Employee attrition was cut in half. Within just three years, the company saw a consistent 10% annual improvement in what Bush calls “service margins.” The company began to win the J.D. Power customer service award in credit cards year after year.

The bottom line is this: every part of the business needs to be made as human as possible, in order to gain the inherent leverage of social connection. That requires breaking the barriers between people, whether those are business processes, schedules, or policies intended to theoretically optimize the wrong metrics — like the length of calls — while missing the important ones, namely customer satisfaction and workforce turnover.

Sprint cuts 800 jobs in wake of SoftBank deal, Nextel shutdown
Whenever a company is party to a big M&A deal, layoffs always follow. Sprint(s s) confirmed with Bloomberg on Tuesday that it is cutting 800 jobs, but claimed that this doesn’t have anything to do with redundancies produced by its massive three-way tie up with SoftBank and Clearwire. Sprint told Bloomeberg it’s trimming its customer service workforce because it’s getting fewer complaints into its call centers. A big reason for the drop off could be the shutdown of its Nextel iDEN network last quarter, which likely generated calls from millions of customers about to lose service.

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When T-Mobile USA laid off thousands of workers in March, it wasn’t quite done handing out the pink slips. On Tuesday T-Mobile said it would enter into its next phase of restructuring, which means more layoffs on top of 1900 cuts it has already made.