The origin of using data to improve workforce productivity can be found in Frederick Taylor’s famous book, “The Principles of Scientific Management.” Published in 1911, it was used to improve the efficiency and speed of Ford Motor Company’s production lines, and later became the foundation of workforce management practices in the post-war industrial era.
Today, BPOs collect all data about people, processes, customer interactions, and transactions. This data contains a treasure trove of information that can be analyzed and used to guide decisions, improve customer experience, and boost employee performance and engagement. The use of data-driven approaches to manage and optimize human resources and business performance is now well-proven and is often referred to as “people analytics.”
People analytics is about making employee decisions based on data points and increasing transparency, engagement, morale, and experience for better business outcomes. It’s no wonder that “more than 70% of organizations are investing in people analytics solutions to integrate data into their decision-making and leverage available data,” according to a 2019 Deloitte study report on the people analytics market landscape.
Artificial Intelligence (AI)) further enhances analytics in BPOs by enabling greater sophistication, such as adding predictive capabilities. AI can process large volumes of data more efficiently and accurately, revealing hidden patterns and trends that are not immediately noticeable.
AI can perform individual employee analysis to identify strengths, weaknesses, and areas for development. Personalized insights and recommendations enable customized coaching and training programs that address each individual’s specific skill gaps. This targeted approach not only improves individual performance, but also increases overall business results and employee satisfaction.
Let’s look at other areas where analytics can play a crucial role in BPO success.
Evolving from traditional methods, Automated Quality Assurance (QA) represents such an area. Quality assurance relied heavily on manual assessments, which often resulted in inconsistent assessments and carried the risk of bias and errors in quality assurance processes, which could lead to an inaccurate representation of overall performance.
Automated quality assurance adds significant value to the evaluation process. Advanced AI algorithms can sift through a massive amount of data, which can be both structured and unstructured (such as call transcripts, audio recordings, and emails) to identify consistent patterns, accurately, and without bias. They can assess relevant interactions that significantly influence the overall process’s key performance indicators to provide a comprehensive and fair assessment of employee performance.
In addition, automated quality assurance systems continuously Monitor and analyze interactions by providing real-time feedback and insights. This allows for corrective action and immediate improvement, rather than waiting for periodic assessments. Employees receive timely feedback, which improves their skill development along the way.
Automated processes also reduce the manual workload of supervisors, allowing them to focus on activities such as coaching, providing feedback, and developing employee skills. Analyzing quality assurance data can thus ensure a consistent, fair, and efficient quality assurance process in BPOs.
Predictive analytics is another key lever for BPOs to achieve more proactive, efficient and customer-centric operations, ultimately leading to better business results and better workforce management.
Predictive analytics analyzes historical data to provide key insights into customer expectations, preferences, and pain points. It anticipates future needs and behaviors, allowing BPOs to deploy strategies in advance to improve positive experiences and elevate the customer experience. Actions based on foresight improve CSAT scores, which helps build customer loyalty and trust.
It also plays a crucial role in controlling churn and improving up-sell and cross-sell opportunities. By identifying customers who are at risk of churn, BPOs can implement targeted retention strategies such as a personalized offer or enhanced support. Additionally, with insights into customer preferences and purchase history, BPOs can make relevant and timely up-sell and cross-sell recommendations, leveraging revenue enhancement opportunities.
Applied to workforce management, predictive analytics can forecast future staffing needs with high accuracy, taking into account past call volumes, peaks, resolution time, etc. Proactive resource management improves operational efficiency, reduces waiting times, and controls overstaffing or overtime costs.
The analysis also finds very useful application in Control burnout and reduce agent turnover which is a major challenge for BPOs. High levels of stress and staff turnover not only impact employee well-being but also degrade the quality of customer service.
One simple way to control burnout is to better manage workload. Through data analytics, BPOs can monitor agent workload and identify patterns, such as agents consistently handling difficult calls or working harder than others on difficult shifts, which can lead to stress and fatigue. A proactive effort for fair distribution ensures that no agent is overwhelmed, thus maintaining a healthy work-life balance for all.
Analytics can provide unbiased insights into why many agents leave their jobs. Targeted retention strategies such as career development, recognition, or skills development training can address these issues and reduce churn.
Conclusion
The journey from Frederick Taylor’s scientific management principles to the sophisticated use of AI and analytics in today’s BPOs demonstrates the remarkable evolution of workforce management practices. This progression underscores the importance of data-driven decision-making and its profound impact on organizational effectiveness and employee satisfaction.
As BPOs continue to invest in and develop advanced analytics applications, they can expect to achieve greater operational efficiency, higher employee productivity and morale, reduced employee turnover, and a more engaged workforce – all leading to sustained success in a highly competitive marketplace.
