From Data Overload to Insight: Transforming Workforce Management

Have you ever encountered a situation where standard reporting tools in contact center platforms or Workforce Management (WFM) software fall short of your specific needs? This often leads analysts to manually extract and process data, using tools like Google Sheets or Excel to create reports that align with management's precise specifications. Once management is satisfied and requests these reports regularly, the team's workload inevitably increases. 

Alternatively, you might have a data engineering and analytics team dedicated to supporting your data needs. Yet, these teams often juggle multiple priorities and backlogged requests, delaying their response to your needs. This can leave your business and team in the dark for extended periods, hindering your understanding of business performance.

As customer service departments diversify communication channels, segment clients, and realign agent skills to better match customer needs, these challenges will only grow. Leaders must find solutions to avoid overburdening their teams or operating without crucial business insights.

A solution I increasingly advocate for is upskilling team members in data analysis and engineering. This might have been daunting a decade ago, due to the complexity of programming languages and ETL tools. However, today's landscape offers a plethora of user-friendly ETL and data visualization tools. The learning curve is much more manageable, especially with Language Learning Models (LLMs) that can generate basic code templates and simplify API documentation, significantly speeding up the training process.

In the past year alone, my team faced numerous instances where necessary data was either unavailable in standard reports or not properly formatted in databases. Without addressing this, our operations would have operated blindly. Fortunately, some team members proactively improved their skills, allowing us to overcome software limitations and other teams' priorities. We achieved significant milestones like analysing ticket volume drivers by integrating various data sources, automating WFM report generation, and using WFM data to enhance operational efficiencies.

If you decide to develop a micro data analytics team within your WFM department, maintain a strong connection with the central analytics team. Share your source code, KPI definitions, and analysis rationale openly. This ensures consistency and clarity for stakeholders when they encounter different analyses of similar data sets, facilitating smoother communication and alignment.