讀了整家公司的電郵後你的老闆可能了解什麼
What Your Boss Could Learn by Reading the Whole Company’s Emails
What Your Boss Could Learn by Reading the Whole Company’s Emails
AUGUST 7, 2018
員工電郵含有對公司士氣的寶貴見解 - 及甚至可能作為發現瀆職行為的早期預警系統。
Employee emails contain valuable insights into company morale—and might even serve as an early-warning system for uncovering malfeasance.
When Andrew Fastow, the former chief financial officer of Enron, finishes a public-speaking gig these days, a dozen or so people from the audience are typically waiting to talk to him. Some ask about his role in the scandal that brought down the energy company. Others want to know about his six years in prison. After a 2016 event in Amsterdam, as the crowd was thinning out, Fastow spotted two men standing in a corner. Once everyone else had left, they walked up to him and handed him a laminated chart.
The men were there on behalf of KeenCorp, a data-analytics firm. Companies hire KeenCorp to analyze their employees’ emails. KeenCorp doesn’t read the emails, exactly—its software focuses on word patterns and their context. The software then assigns the body of messages a numerical index that purports to measure the level of employee “engagement.” When workers are feeling positive and engaged, the number is high; when they are disengaged or expressing negative emotions like tension, the number is low.
The two men in Amsterdam told Fastow that they had tested the software using several years’ worth of emails sent by Enron’s top 150 executives, which had become publicly available after the company’s demise. They were checking to see how key moments in the company’s tumultuous collapse would register on the KeenCorp index. But something appeared to have gone wrong.
The software had returned the lowest index score at the end of 2001, when Enron filed for bankruptcy. That made sense: Enron executives would have been growing more agitated as the company neared insolvency. But the index had also plummeted more than two years earlier. The two men had scoured various books and reports on Enron’s downfall, but it wasn’t clear what made this earlier date important. Pointing to the sudden dip on the left side of the laminated chart, they told Fastow they had one question: “Do you remember anything unusual happening at Enron on June 28, 1999?”
The so-called text-analytics industry is booming. The technology has been around for a while—it powers, among other things, the spam filter you rely on to keep your inbox manageable—but as the tools have grown in sophistication, so have their uses. Many brands, for instance, rely on text-analytics firms to monitor their reputation on social media, in online reviews, and elsewhere on the web.
Text analytics has become especially popular in finance. Investment banks and hedge funds scour public filings, corporate press releases, and statements by executives to find slight changes in language that might indicate whether a company’s stock price is likely to go up or down; Goldman Sachs calls this kind of natural-language processing “a critical tool for tomorrow’s investors.” Specialty-research firms use artificial-intelligence algorithms to derive insights from earnings-call transcripts, broker research, and news stories.
Does text analytics work? In a recent paper, researchers at Harvard Business School and the University of Illinois at Chicago found that a company’s stock price declines significantly in the months after the company subtly changes descriptions of certain risks. Computer algorithms can spot such changes quickly, even in lengthy filings, a feat that is beyond the capacity of most human investors. The researchers cited as an example NetApp, a data-management firm in Silicon Valley. NetApp’s 2010 annual report stated: “The failure to comply with U.S. government regulatory requirements could subject us to fines and other penalties.” Addressing the same concern in the 2011 report, the company clarified that “failure to comply” applied to “us or our reseller partners.” Even a savvy human stock analyst might have missed that phrase, but the researchers’ algorithms set off an alarm.
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