Increase efficiency of contact centre through call volume prediction

Contact centers are crucial for managing customer interactions and ensuring satisfactory customer service. However, they are cost-intensive operations, making the optimal management of representatives essential for profitability.

Two key factors threaten the smooth operation of contact centers:

1. High turnover rates due to work pressure.
2. Surges in demand during peak periods.
3. Failure to maintain service levels of 80-20

These issues often result in longer wait times and lower service quality for customers. Additionally, organizations miss opportunities for cross-selling and revenue growth under these conditions.

Proactive planning and resource allocation can effectively address these challenges. This is where call volume prediction becomes vital.

This solution utilizes a time series model to accurately predict future call volumes. It accounts for both trends and seasonality in the data, adjusting predictions to reflect these changes. The output includes the projected number of overall calls and call sub-types for the next 6 to 12 months, depending on data availability.

This solution caters to organizations that:

1. Experience difficulties in customer servicing caused by variable call volumes.
2. Are looking to manage their workforce efficiently, ensuring they are neither overstretched nor underutilized at different times.
3. Intend to optimize contact center costs and budget scientifically for future planning.

Businesses can anticipate the following benefits:

1. Cost reduction: Effective workforce management can lead to reduced service costs over time.
2. Customer satisfaction: Improved preparedness in servicing customers can protect customer sentiment, fostering long-term loyalty and enhancing brand image.
3. Increased revenue: Proper planning of call volumes allows representatives more time to cross-sell additional products and services, boosting revenue.

The solution needs the following data:

1. A minimum of 18-24 months of call volume data
2. Marketing events calendar covering the same timeframe
3. Data on system outages and outliers, if any, within the same period

A series of techniques can be used depending on the complexity and richness of data:
1. Classical Auto regressive processes like AR, ARMA, ARIMA.
2. Newer ML techniques like XG boost, Random forest and Convoluted Neural Network(CNN)

1. On-prem ( on customer systems or on DeepQ-AI Environment)
2. On hosted cloud space ( Customer or DeepQ-AI Environment)
   ( deployment is subject to data availability in the same environment, or feasibility of seamless data transfers within secured environments)

1. The final outputs may consist of predictions at daily, weekly, and monthly intervals, as well as for specific products or business units.
2. Batch files, including Excel files or flat files.
3. Outputs can be delivered via API requests. Send an API request with inputs and receive a response. This can be integrated into an existing application or a separate web application can be developed.