Predictive Analytics helps organizations leapfrog their competitors. Well-informed prediction models, to inform decision making, are essential during times of change.
Assessments and analytics are increasingly used to predict employee behavior, engagement, performance, and risks of attrition. In this article, Eric Shepherd explains what predictive analytics is and how this will revolutionize our thinking.
We see examples of predictive analytics in our day to day lives. Credit scores, product recommendations, dating apps, and supply-chain management systems all use predictive analytics. Credit scores predict how likely we are to repay a debt. Finely tuned algorithms can predict products that we're like to purchase. Dating apps use personal data to connect people that could be a match. And supply-chain management systems can pre-order and pre-position products that are likely to be consumed. Unlike the 2002 movie Minority Report that uses "precogs" to predict crimes, we use machines to crunch data in making predictions.
The social media giant Facebook uses predictive analytics to predict which user is likely to buy a product and place ads, based on the data collected from their profiles and the posts. It is a technology that uses historical data and predicts future events or outcomes.
The question arises, how does it work? Predictive analytics are data-driven and use statistical techniques to forecast and analyze the outcome. A mathematical model or algorithm is created by analyzing the data collected to project potential futures. For organization’s leaders and HR teams, this enables them to build predictive models to understand the possible engagement and effectiveness of its workforce.
Managing a golf club provides a useful example. Playing golf is most enjoyable with perfect weather conditions. If the manager tracks reservations, course usage, weather predictions, and actual conditions, over time, there is a possibility of predicting bookings and usage. Armed with this information, a manager could prepare staff and equipment for the busy times and reduce the facility's capacity based on the data collected.
Does the manager have enough data to predict course usage reasonably? The answer is yes and using a decision tree predictions can be more useful.
A decision tree is a tree-like model of decisions and consequences; it is a powerful tool used in analytics, where each leaf, or node, denotes an attribute, and each branch indicates the potential effects.
In the case of the probability of golf course usage, let's think about two predictors to keep the model simple, i.e., sunny day vs. rainy day. The likelihood of playing golf diminishes if it rains. Whereas the possibility of playing golf increases when the day is sunny. In short, weather forecast data can be used to predict the likelihood of golfers playing golf. Weather forecasts are not entirely accurate and so keeping an eye on the weather is another useful predictor. The leaf is the data, such as its raining, and the brand is the consequence, we need less staff and less equipment. This simple case of managing a golf course helps us understand the principles. Now imagine that we have hundreds of data inputs, and we are trying to predict if someone will enjoy a specific movie. And now imagine that we're trying to provide that service for millions of individuals. The problem becomes exponentially more complicated. Machine learning, which is a technique for computers to produce their own statistical models, can help here by digesting numerous data points and making sense of the patterns to come up with predictions. When organizations apply it to their workforce, it can provide useful insights on engagement, risk of loss, and productivity.
Data stored by organizations in their Human Resource Information System (HRIS) can be used to develop predictive models for employee behaviors. HR predictive analytics can help formulate policies for employee well-being, engagement, and efficiency, and to predict the performance the organization is likely to achieve.
Here are some examples:
All organizations have a level of regretted attrition, that is losing employees that an organization truly regrets losing. An employee's tenure will depend upon their circumstances and the nature of the work. Employee turnover may be benchmarked against similar job roles and industry sectors. Regretted attrition has many negative consequences which include loss in revenue, added cost of hiring, unwanted distractions, and reduced productivity.
Employees that enjoy their work and fit in with the culture and have good managers are likely to stay with organizations. Calculating a "Flight Risk Score" using mathematical models can help to predict the possibility of a worker resigning. Over time these models will improve to help the organization understand additional predictors for attrition.
There are privacy-related issues with accessing flight risk scores. That means access to this data must be restricted on a need to know basis. The power of the flight risk score is to help a manager intervene to avoid a regretted resignation and give them a game-plan in cases where an employee's departure is inevitable.
Statistics play an important role in predicting hiring success; many large organizations have accepted this. When recruiting, there are times of feast and famine. When there are more jobs than people looking, assessments can promote interest by using clickbait on social media to engage a qualified person who isn't yet looking. When more people are looking than jobs available, assessments can screen candidates into, or out of, the process.
Assessments, and the data collected, can streamline the engagement and interview process to find the right candidate. Using data helps determine cultural fit, i.e., does the candidate's values, motives, and preferences, match the organizational culture. Assessments can also predict job fit by testing to see if the person has the abilities required for the job. Assessments can promote better hiring practices but do not guarantee success.
Just as an organization wants to learn about the candidate to determine cultural and role fit, the candidate is making judgments about their potential employer. Using data to drive an active engagement process means an organization has a better chance of winning the heart and mind of the candidate. Predictive analytics can use data from multiple sources to guide the recruitment process and ensure that first-class candidates are properly engaged.
Studies reveal that there will be a decrease in morale and productivity should toxic employees be recruited or retained. Behaviors indicating disrespect, drug use, alcohol abuse, and sexual harassment need to be investigated before they fester. Predictive analytics can help spot the signs of toxic employees to promote an early intervention to discover the root cause of their behaviors.
Studies have uncovered that an increase in employee engagement leads to higher revenues. More engaged workers are more creative, less tardy, and work harder to achieve their goals resulting in greater productivity with higher levels of quality.
Measuring employee engagement and taking actions to create an extraordinary situation for individuals at work will positively impact an organization's performance. Using pulse surveys (a short, frequent survey with simple questions to give a quick insight into an organizations health) and other data sources, organizations can use predictive analytics to improve the workplace, engagement, retention, and performance.
An increasing and vital role for HR is to be an advisor to managers. HR, using data and predictive analytics, can help managers better understand themselves, others, and how to intervene to encourage behaviors required for success. Assessments and analytics are transforming the way HR is helping managers and employees and their work. Predictive analytics is also helping them forecast and optimize policies for employees and organizational growth.
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$We see examples of predictive analytics in our day to day lives. Credit scores, product recommendations, dating apps, and supply-chain management systems all use predictive analytics. Credit scores predict how likely we are to repay a debt.$
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