By Mr Vikas Wahee, Head of Solutions, BPM & ITES, Intellicus Technologies
Machine learning has continuously pushed boundaries and influenced various industries by establishing new standards including BPOs. With more and more organizations outsourcing their contact centers and business processes, the BPM industry is undergoing rapid growth and the resultant high competition. Managing workforce efficiency in this competitive landscape presents them with unique challenges. Strict service level agreements (SLAs) and ever-growing client expectations strain managers to provide consistent, high-quality service to clients. They are also responsible for fostering a motivated and productive workforce that requires balancing results with employees’ needs like competitive salaries and benefits, flexible work schedules, continuous skills development opportunities and talent management to attract and retain talent.
While spreadsheets have served the purpose for many years, they fall short in today’s dynamic BPM landscape. Therefore, machine learning-powered workforce management tools have arisen as game changers in this domain. BPMs can obliterate guesswork and transform workforce management by using ML to analyze historical patterns for identifying trends and correlations that are difficult to identify manually. This capability unlocks the potential for complex forecasting techniques including bi-variate and multi-variate forecasts. As ML algorithms process more and more data, forecasts become more reliable and less prone to errors through varied methods of Forecasting. These accurate forecasts with reduced variance allow managers to make better decisions about staffing requirements for meeting SLAs. Let’s have a look at how ML can help BPOs:
ML Improves Overall Performance
BPMs manage huge volumes such as historical volume, customer interactions, customer feedback etc which can be effectively analyzed and used by Intelligent WFM systems through AI/ML algorithms to identify hidden patterns and enhance future workflow predictions. These tools and techniques helps managers to proactively anticipate any leakages, effectively achieve resource allocation and enhance the overall efficiency of the BPO operations.
By analyzing data from customer interactions, agent performance metrics and customer feedback, ML algorithms help in identifying areas of improvement. ML-enhanced quality assurance tools can unlock actionable insights from data, enabling feedback loop between agents, supervisors and management. With this knowledge, BPOs can continually refine their personalization strategies.
Optimize Workforce Planning
The various techniques of evaluating future workloads such as Bi-Variate and Multi-variate helps BPMs to be well prepared for the future. ML-based workforce management platforms can detect these surges early and prescribe resource reallocations. These algorithms help plan staffing needs more effectively by considering internal/external factors like patterns, trends, seasonality, holidays, and external events, providing valuable insights to BPMs and allowing them to minimize customer wait times and enhance customer satisfaction levels.
ML algorithms can also aid in automating the process of matching each incoming call or inquiry with the best-suited agent based on their skills, experience and performance. Optimized skill-based allocation can boost first-call resolution rates, while maximizing workforce utilization.
In addition, ML-based workforce management platforms facilitate adaptive scheduling by considering agent preferences such as desired work hours or days off to ensure their availability during peak workload times to avoid service disruptions. These platforms also handle unforeseen incidents, by identifying alternate staffing options in case of unexpected absentees or last-minute roster changes. Modern ML-powered platforms also allow employees to bid for preferred shifts and manage their weekly offs to improve their work-life balance. It also enables BPMs to effectively manage their bottom quartile employees by providing them enough green time for self-learning, coaching, training etc. and control the employee churn.
Significant Cost Savings
ML-powered tools can help in cost management by automating repetitive and manual tasks. For example, data-driven automated scheduling frees supervisors from administrative responsibilities and allows them to focus on high-value initiatives. Machine learning can also help BPOs in mitigating risks. For instance, these algorithms can analyze external factors or events data of past to predict potential growth in volume and alert BPOs to implement proactive strategies to control them.
Apart from this, insights into future resource requirements using ML-based forecasting can enable BPO management to make informed investment decisions and avoid unnecessary last-minute expenditures.
Machine Learning Powers the Future of BPOs
Today, ML is revolutionizing how BPOs manage their workforce using self-learning algorithms trained on vast datasets. Unlike traditional statistical models, these algorithms forecast future workloads with greater precision, enhance rostering, improve overall cost management and increase operational efficiency. Gone are the days when ML was considered a “good to have” feature. It is now an essential element of any modern BPO workforce management software.
