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Activating Analytics with Call Center Volume Forecasting


Activating analytics in an organization requires more than buying a tool, acquiring data, or hiring a handful of data scientists. Great analytics takes place at the intersection of business understanding, executive buy-in, data science know-how, and the ability to iterate. Once unlocked, the power of analytics to drive transformation in a business creates new opportunities for growth and efficiency while reducing exposure to risk.

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To jumpstart analytics across their organization using a single platform for increased collaboration, a large US energy company partnered with Clarkston to conduct a quick Proof-of-Value (POV) project. The POV chosen focused on using advanced analytics to forecast incoming call volumes to their customer service center using the RapidMiner data science platform.

In a 6-week project, Clarkston increased the accuracy of the volumes using a machine learning approach. These forecasts were integrated with Qlik for reporting and distribution to weekly operations meetings and used to make staffing decisions and maintain customer service levels. Clarkston provided data science project management guidance to take the models through to the production environment and helped formalize a repeatable process for data science across the organization. This project not only supported greater forecasting, planning, and cost reduction relative to call center operations – it served as a catalyst for the use of advanced analytics across the broader business and created a foundation for a value-driven approach to analytics.

Primary Objectives:

Clarkston partnered with the client with aims to:

  • Increase adoption of the RapidMiner analytics platform for greater analyst collaboration across the organization,
  • Execute a data science project through to the production environment, and
  • Improve upon current forecasting for the incoming call volume to a customer service center.

Download the Full Case Study Here

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Tags: Advanced Analytics, Analytics, Case Study, Data & Analytics, Data Analytics & Insights