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Call Centre Forecasting

Predicting call centre volume by day and within 30min windows

Challenge
  • Energy client had a team producing 7-day and 21-day forecasts manually

  • Challenge was to automate and beat the forecasting accuracy

  • One constraint was that this needed to be a fail-proof stand-alone tool that a single person could run on a virtual machine

  • Collected the datasets and created data preparation and feature engineering (lags, interactions, etc.) pipelines

  • Automated feature selection using machine learning techniques (Boruta, RFE, etc.)

  • Automated model training, evaluation and selection for both 7-day (overall and within 30min windows) and 21-day, pitting multiple models and parameters and picking the best one every time

  • Created a simple-to-use command line prompt for the end-user to run forecasts themselves

Solution
Value
  • Reduced time spent preparing the forecast from 2 days down to 10 minutes

  • Simple end-user steps to produce a new forecast (append new data, version the data and run the tool on the updated dataset)

  • Trained the data science team on model details for any refinements or future work required

Roles

Data Scientist, Data Architect

Tools

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Sector

Energy & EVs

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