TopN AnalyticsAutomated Demand Forecasting for an Ecommerce Giant
A large e-commerce company needed to accurately plan customer support capacity across multiple teams and time periods, balancing fluctuating customer demand with the cost of maintaining excess staffing.
Where things stood
Support headcount planning relied heavily on historical trends, new sales, and mega-sale events. A lot of manual calculations were being done, making it difficult to anticipate short-term demand spikes during sales events, holidays and seasonal periods as well long-term seasonal demand cycles.
Overstaffing increased operating costs, while understaffing led to poor customer experience due to longer response times and increased pressure on support teams.
What we built
Historical ticket volumes, average handling times, staffing levels and calendar/event data were consolidated into a centralized analytical dataset.
A R-software-based forecasting pipeline generated demand forecasts at the required time and team levels, with outputs made available to planning teams through automated reports and dashboards.
How it works
A customized forecasting solution based on ARIMA, Random Forest and ensemble models, generated demand forecasts for short-term and long-term capacity requirements.
The forecasting model analyzed historical support demand, seasonality, trends and major business events to predict future workload.
Forecasting accuracy with various metrics such as MAPE, WAPE, and Bias were used as feedback into the model for refinement and increasing future accuracy.
Forecasted workload was converted into required staffing levels using historical productivity and average handling time, enabling managers to plan hiring, shifts and staffing buffers proactively.
What we delivered and learned
Headcount requirement calculations based on forecasted workload and team productivity.
Planning-ready reports showing forecast demand, required capacity and staffing gaps.
Incorporating business events and seasonality significantly improved forecast reliability.
What it's built on
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