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How BPOs Can Use Predictive Analytics to Improve Employee Retention
Predictive analytics answers “what if” questions, as in, “If we do X, what’s most likely to happen?” It’s not the same as having a crystal ball that shows you the company’s future, but it’s the closest thing we have right now. Big data makes it possible.
Modern computing and cloud storage systems have enabled businesses to collect, retain, analyze, and use massive amounts of data. Data analysis, machine learning, and predictive analytics all use lots of data, and for different reasons.
Data analysis creates a current state picture of any operation, machine, department, or line of business. Dashboards can be created to visualize the data for stakeholders in real or near-real time.
Machine learning (ML), a subset of artificial intelligence (AI), uses data to improve digital system outcomes. For example, your email client uses AI to identify spam messages and put them in your junk folder. Some spam messages may still come through. As you move those messages to your junk folder, ML teaches your email client that it should automatically put these spam messages in your junk mail, so you don’t have to see them again.
Unlike ML, which works in the present state, predictive analytics points to future outcomes by studying past events, generating models of future events, and predicting the likelihood of each model to deliver desired outcomes.
People can manually perform predictive analytics, one model at a time. Computers, on the other hand, generate dozens of predictive models for any scenario and show the most likely result for each model in a tiny fraction of the time it would take a person.
Predictive analytics uses historical data—the more data, the better—to show businesses the opportunities and challenges they’re likely to face moving forward. The results present stakeholders with a view of which options are most likely to succeed, without having to invest the time and money to test each option in the real world.
The Link Between Predictive Analytics, Employee Experience, and Great CX
Predictive analytics offers myriad business benefits for predicting future trends and events. This, combined with lower costs of data collection and storage, has made predictive analytics an attractive option for more companies than ever. Markets and Markets forecasts the predictive analytics market will grow from $10.5 billion in 2021 to $28.1 billion in 2026.
According to a 2022 SkyQuest Technology survey, companies that use workforce analytics better understand their employees’ needs than those that don’t and enjoy higher employee retention rates (64% vs. 40%).
This is further supported by a recent Gallup survey that highlights the importance of regular coaching, support, and career development between managers and their employees, finding that employees who receive meaningful feedback on a weekly basis are 50% less likely to seek new employment. Predictive analytics can improve how companies manage talent and help managers target their conversations to further increase employee retention.
In this blog post, we explore how BPOs can use predictive analytics to manage frontline employees and improve the employee experience, resulting in better employee retention and excellent CX outcomes.
How BPOs Can Use Predictive Analytics to Improve Customer Service
BPOs can use predictive analytics in support of strategic outsourcing for their clients. In this blog post you’ll get an overview of six predictive analytics use cases BPOs can employ.
1. Improving Employee Retention and Productivity
Retaining frontline employees is a challenge in any sector. Replacing workers—including recruiting, hiring, onboarding, and training—comes with substantial cost. Experienced employees take with them extensive knowledge that takes time for new employees to replace.
A decline in productivity is one of the first signs a frontline employee is at risk of voluntarily separating from the company. The key to retaining the employee is to intervene before they consider leaving.
A decline in productivity is one of the first signs a frontline employee is at risk of voluntarily separating from the company. The key to retaining the employee is to intervene before they consider leaving.
Using productivity data and other signals of employee sentiment, predictive analytics can identify workers who are more likely to voluntarily separate from the company and suggest the reasons why. Supervisors can then reach out to their at-risk employees and help put them on course to regain their positive sentiment for the company and desire to be more productive.
Every retained at-risk employee contributes to sustained productivity.
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iQor SVP IT Joe Przybylowski, Data Scientist Andrew Reilly, and VP Operations Terri Robertson explain how iQor uses machine learning to help reduce employee turnover.
2. Developing More Efficient Operational Processes
Robotic process automation (RPA) automates repetitive processes formerly performed by workers. In its most basic form, RPA records all the actions performed by an individual in performing a task and then performs them with a software robot, or bot.
Beyond the basics, AI can be built into the RPA, making it a form of intelligent automation. With intelligent automation, RPA not only performs a task, but also collects all data generated throughout the process, making it available for ML and predictive analytics.
With that data, data scientists can use predictive analytics to develop models that predict how changes to the automation will improve its efficiency.
In cases where machinery is involved, predictive analytics enables businesses to perform maintenance before it becomes an emergency (predictive maintenance). Plus, they can schedule a time when performing that maintenance will have the least effect on other processes.
3. Analyzing Customer Support Interactions
Customers receive net promoter score (NPS) surveys after they’ve interacted with customer support. They measure customer sentiment about the recent interaction to determine if that customer is a promoter, passive customer, or detractor of the brand. Many companies experience a low percentage of survey returns, which makes it difficult to rely on survey results.
Predictive NPS picks up where a small sample size leaves off, modeling a customer’s structured and unstructured data to build a view of their sentiment and determine how that customer can best be served.
Additional benefits include:
Immediate Benefits
- Increased customer feedback without increased customer effort.
- Increased sense of fairness and buy-in from agents.
- Identifying customers with high or low loyalty levels to receive special care.
Long-Term Benefits
- Drive more accurate insights.
- Identify trends related to brands, products, offerings, services, or branches.
- Increased revenue and reduced customer churn.
4. Predicting Revenue
Predictive analytics can generate models of a customer’s lifetime value (CLTV) and where they are in their customer lifecycle. That gives a sense of how much revenue they can expect to earn from each customer over the short and long term.
Predictive analytics can also help marketers determine which channels and offers would be most effective for different customers. It can also predict which campaign models would produce the most (and least) revenue with a given budget.
Predictive analytics can identify the best day, time, channel, and number of times to contact a customer to optimize revenue.
Predictive analytics can identify the best day, time, channel, and number of times to contact a customer to optimize revenue.
5. Measuring Employee Satisfaction
Satisfied employees engage with company initiatives, satisfy customers, and strive to be productive. Employee surveys are just one datapoint that measures employee satisfaction. Depending on the employee’s role, other datapoints might include customer feedback, employee productivity, attendance, and more. Analyzed together, these datapoints create employee satisfaction profiles.
When analyzing a group of employee satisfaction profiles, data scientists can segment the group into clusters of individuals with similar profiles. For each cluster, predictive analytics can generate models of other (and former) employees with similar satisfaction profiles. These profiles enable data scientists to predict how each cluster would likely perform in various scenarios.
Further analysis can help identify which employees are less engaged than others, why they are less engaged, and what steps to take to increase employee engagement. 
6. Monitoring Performance and Continuous Improvement
Predictive analytics can be an effective tool for helping employees reach their goals. When data indicate an employee is on track to hit their goals, supervisors can provide positive reinforcement to encourage them. Predictive analytics can also recommend recorded classes that will help the employee fill any knowledge gaps to keep them moving forward. Additionally, predictive analytics can recommend and push relevant content tailored to each employee’s needs to fill knowledge gaps and keep them moving forward.
Performance monitoring can also identify employees who could benefit from help staying on track. In those cases, predictive analytics can identify opportunities to improve their productivity and recommend specific coaching supervisors can provide, based on past coaching successes.
In some cases, predictive analytics might reveal that the employee’s supervisor has a history of workers with similar challenges. That’s often an indication that the supervisor needs coaching on how best to help their workers.
Is employee retention one of your priorities?
Have these six predictive analytics use cases piqued your interest?
Learn How Machine Learning Can Help You Reduce Employee Turnover
Check out our webinar featuring iQor SVP IT Joe Przybylowski, iQor Data Scientist Andrew Reilly, and iQor VP Operations Terri Roberts to discover how to harness technology to engage employees and reduce turnover.Get the details directly from the people responsible for the program.
Experience the Best in Data Analytics
iQor’s analytics as a service offering uses a combination of iQor’s proprietary speech analytics platform, cloud computing, machine learning, artificial intelligence, and data analysis to develop custom interventions for identified areas in need of improvement along the customer journey. The results produce targeted improvements for the employee, customer, and client.
iQor is a business process outsourcing company ideally suited to help brands create amazing customer experiences. iQor provides a comprehensive suite of full-service and self-service scalable offerings that are purpose-built to deliver enterprise-quality CX.
Our award-winning CX services include:
- A global presence with 40+ contact centers across 10 countries.
- A CX private cloud that maximizes performance and scales rapidly across multiple geographies on short notice.
- A partnership approach where we deploy agents and C-level executives to help maximize your ROI.
- The perfect blend of intelligent automation for scale and performance coupled with an irresistible culture comprised of people who love to delight your customers.
- Virtual and hybrid customer support options to connect with customers seamlessly, when and where they want.
- The ability to launch a customer support program quickly, even when you need thousands of agents ready to support your customers.
- A best-in-class workforce management team and supporting technology to create a centralized organization that can better serve your entire business.
iQor helps brands deliver the world’s most sought-after customer experiences. Interested in learning more about the iQor difference? If you’re ready to start a conversation with a customer experience expert, contact us to learn about how we can help you create more smiles.
Joe Przybylowski is SVP of IT at iQor. Connect with Joe on LinkedIn.
Andrew Reilly is a data scientist on the AI & Data Science Team at iQor. Connect with Andrew on LinkedIn.
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