The origin of the use of data to drive workforce productivity can be found in Fredrick Taylor’s eminent book “The Principals of Scientific Management”. Published in 1911, it was famously used to improve efficiency and speed at the production lines of Ford Motor Company, and later became the bedrock of workforce management practices in the post-war industrial era.
Fast-forwarding to modern times, BPOs collect every bit of data about people, processes, client interactions and transactions. This data contains a treasure of insights that can be analyzed and used for driving decisions, elevating customer experience and enhancing employee performance and engagement. The use of data-driven approaches to manage and optimize human resources and business performance is well-proven now and is often referred to as ‘people analytics’.
People analytics is about taking decisions about employees based on data points and boosting transparency, engagement, morale and experience for improved operational outcomes. Little wonder then that “over 70 percent of organizations are making investments in people analytics solutions to integrate data into their decision-making, and derive value from available data” according to a 2019 Deloitte report on the market landscape of people analytics.
Artificial Intelligence (AI) further augments analytics in BPOs by enabling more 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 conduct individual employee analysis to identify strengths, weaknesses and areas for development. Personalized insights and recommendations help in customized coaching and training programs that address specific skill gaps of each individual. This focused approach not only enhances individual performance but also augments overall operational results and employee satisfaction.
Let us look at some other areas where analytics can play a crucial role for BPO’s success.
Evolving from traditional methods, Automated Quality Assurance (QA) represents one such domain. QA relied heavily on manual evaluations , often resulting in inconsistent assessments and loaded with a potential for biases and errors in QA processes which may lead to inaccurate representation of overall performance.
Automated QA adds significant value to the assessment process. Advanced AI algorithms can sift through a huge amount of data, which can be both structured or unstructured—such as call transcripts, audio recordings and emails—to identify consistent patterns, precisely and without any bias. It can evaluate relevant interactions that significantly influence overall process KPIs to provide a comprehensive and fair assessment of employee performance.
Additionally, automated QA systems continuously monitor and analyze interactions providing real-time feedback and insights. This allows for immediate corrective actions and improvement, rather than waiting for periodic reviews. Employees receive timely feedback, enhancing their skill development on-the-go.
Automated processes also reduce the manual workload of supervisors, freeing up their time to focus on activities such as coaching, providing feedback and developing employee skills. QA data analytics can thus ensure a consistent, fair and effective quality assurance process in BPOs.
Predictive Analytics is another vital leverage for BPOs to achieve a more proactive, efficient, and customer-centric operations, ultimately leading to better business outcomes and workforce management.
Predictive analytics parses historical data to provide key insights into customer expectations, preferences and issues. Importantly, it anticipates future needs and behavior, which allow BPOs to roll out strategies in advance to enhance positive experiences and elevate CX. Actions based on foresight improve CSAT scores, winning customer loyalty and trust.
This also plays a crucial role in controlling churn and improving upsell and cross-sell opportunities. By identifying customers who are at risk of churning, BPOs can implement targeted retention strategies like a personalized offer or enhanced support. Further, armed with insight on customer preferences and purchase history, BPOs can make relevant and timely upsell and cross-sell recommendations, cashing on the opportunities to improve revenue.
When applied to workforce management, predictive analytics can forecast future staffing requirements with high accuracy considering past call volumes, peaks, resolution time etc. Proactive resource management improves operational efficiency, reduces wait times and controls overstaffing or overtime costs.
Analytics also find a highly useful application in controlling burnout and reducing agent churn which is a critical challenge for BPOs. High levels of stress and turnover not only impact employee well-being but also degrade the quality of customer service.
A basic way to control burnout is through better workload management. Using data analytics, BPOs can monitor agent workloads and identify patterns, like agent who consistently handle difficult calls or work challenging shifts more than others, that can lead to stress and fatigue. A proactive effort for equitable distribution ensures no single agent is overwhelmed, maintaining a healthy work-life balance for all.
Analytics can provide unbiased insights into why many agents leave. Targeted retention strategies like career development, recognition or competency development training address these issues and reduce churn.
Conclusion
The journey from Fredrick Taylor’s principles of scientific management to the sophisticated use of AI and analytics in BPOs today demonstrates the remarkable evolution of workforce management practices. This progression underscores the importance of data-driven decision-making and its profound impact on organizational efficiency and employee satisfaction.
As BPOs continue to invest and develop advanced analytics applications they can expect to achieve greater operational efficiency, higher employee productivity and morale, reduced churn and a more engaged workforce – all leading to sustained success in a highly competitive marketplace.
