Today, few companies can deny that data analytics is an important piece of the puzzle in gaining competitive edge....
Data analytics enables companies to access data about customers, products and even processes to reduce costs, gain efficiencies and more. But companies are increasingly turning to analytics to gain insight about other internal factors, such as employees. They have found that they can use analytics to learn more about employee satisfaction, motivation and retention strategies.
Before we explore this trend, let's point out that there are different kinds of analytics that companies can make use of -- for example, descriptive, predictive and prescriptive. Michael Wu of Lithium Technologies Inc. says that of these descriptive analytics is considered the most basic, but it may also be the most valuable to executives because it summarizes what happened. Wu estimates that 80% of business analytics is descriptive in nature and data for descriptive analysis is the easiest to accumulate.
Most of the time, descriptive analytics is used to fine-tune the performance of the enterprise with respect to customers: Which events facilitate sales? What is the customer's historical or recent behavior? Have customers received satisfactory attention and support? At what rate is the enterprise gaining or losing customers? Most organizations have a great deal of data to support analysis that answers such questions. The analytical know-how to ask and answer them is not daunting.
Customers, however, are not the only ones who can be studied with this technique. The workforce can be analyzed with the same methods, and the outcome of such analysis can be just as useful and important to the enterprise.
Employees give enterprises their most valuable capital -- human talent and effort -- along with the lion's share of their most precious commodity -- their time. Although they are in turn compensated with a salary, most employees also value nonmonetary rewards such as challenge, opportunity, security and a sense of belonging. Today, those things are not as plentiful as they once were, and employee loyalty has diminished in proportion to job security.
Employee retention strategies are, in fact, becoming increasingly difficult to craft for organizations generally, and even more in certain sectors. The cost of replacing a departing employee isn't simply a matter of finding someone new with equivalent skills (which is costly enough); departing employees also leave in their wake costly gaps in institutional knowledge. Seeing employees as customers highlights the reality that employees can trade with the enterprise or not (for example, stay with the company, and in a productive manner) and that this decision depends largely on employee satisfaction. Descriptive analytics can provide insight into securing that satisfaction.
The questions to be answered
Descriptive analytics answer the questions, "What happened?" and "To whom did it happen?" In the case of customers, analytics describes product shipped, actual sales, product returned, who bought what and when they bought it, and what each customer paid. There's an ocean of knowledge in that data, and it can be studied to determine optimal timing on offerings, optimal target audience, optimal pricing (and under what circumstances), what went wrong in selling product or reaching customers and how to do it better next time.
All of these factors and the questions that precede them apply as much to employees as they do to customers. And while the body of data from which to draw might not be quite as voluminous or detailed as sales records, there's still going to be a lot of it, and it can be analyzed and acted upon to address the challenge of employee satisfaction.
'Why are they here? Why did they leave? What makes them stay?'
There are many aspects to employee satisfaction beyond compensation, a vast array of offerings that encourage them to stay and grow with the enterprise rather than seek satisfaction elsewhere. Advancement and creative opportunities, subsidized training and a fruitful social environment are just a few traits of a workplace that can affect an employee's loyalty. And all of these may be rendered and studied as descriptive analytics.
The three kinds of analytics
As the field of analytics matures, it's important to recognize the differences among different kinds of data analytics and the insight they can bring to the business.
Descriptive analytics. This kind of data analytics summarizes what happened. This is the most basic type of analytics and the easiest to gather. Michael Wu of Lithium Technologies estimates that 80% of business analytics is descriptive in nature. Descriptive analytics may also be the analytics that is most central to the mission of executives.
Predictive analytics. This category uses modeling and other techniques to review current data and historical information to enable predictions about the future. But "the purpose of predictive analytics is not to tell you what will happen in the future," Wu noted. "It cannot do that. In fact, no analytics can do that. Predictive analytics can only forecast what might happen in the future, because all predictive analytics are probabilistic in nature."
Prescriptive analytics. This kind of data recommends a course of action -- and shows the likely outcome of each decision.
The process begins with a summary of data that describes where the enterprise is currently, with respect to its workforce. What is the rate of attrition? How has it changed in recent years? What has changed in the market with company leadership or within the workforce during that period that might have affected that rate of change?
Beyond these global questions, a great deal of data can be amassed to describe individual defections: What are the most common reasons cited for departure? Is there any pattern of complaint against management policy or behavior? Are there market conditions that seem to signal impending company instability to employees?
The beauty of this level of analytical study is that one need not have all the questions in hand before attacking the problem: Mining HR's data will inevitably produce useful questions that haven't been thought of.
What are we doing right?
Similarly, employee retention strategies can be bolstered by studying that portion of the workforce that is content and not considering a new job. The supporting data is easy to come by, from the same place, and yields the same analytical inquiries: What happened, and who did it happen to?
Translated, that can produce a great many useful questions to be answered through analysis. How rapidly are employees advancing? Does promotion boost employee satisfaction? Who is availing themselves of training programs, and which programs have yielded the most promotions or reassignments? Which benefits programs are most popular? Which have made the biggest difference in employee wellness and stability?
The method can go further still. If a company can measure which management policies (and managers) are causing employees to jump ship, it can also determine which ones inspire loyalty and security, and act accordingly. And just as members of the workforce can be optimized in their training and placement by analytics, members of management can be made more effective and placed more strategically.
Descriptive and predictive
Finally, it should be noted that there's a degree to which descriptive analytics can be made predictive, at least in a general sense. Just as it is possible (and desirable) to gather and study descriptive data about the enterprise environment and workforce in the here-and-now in order to optimize it, so is it possible to do the same with yesterday's data. Descriptive analysis of past times of high turnovers, workforce downturns and attrition problems can identify causal trends that haven't yet recurred but may do so. By studying these trends closely, companies can detect and act upon the information in a timely manner before more in-house talent jumps ship.
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