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Drowning in calls — inbound call center challenges

Imagine anticipating both the timing and content of your customers’ inquiries. Turns out machine learning can help you do that.

Efficiently addressing customer queries is vital for all businesses, with call centers handling most of these interactions. However, handling a high volume of calls can be expensive, prompting large companies to find more effective ways to meet their customers’ needs. To this end, integrating machine learning in call center operations optimizes efficiency and reduces costs.


Striving for a data-driven inbound call volume strategy

Our client, a prominent property and casualty insurer in the US, sought a data-driven approach to improve customer service. Their primary goal was to reduce the overwhelming volume of incoming calls.

Implementing chat support for cost savings

Recognizing the potential cost savings, our client aimed to implement chat support options. By doing so, they estimated they could save two-thirds of the expenses of running a call center. However, they needed an innovative approach to integrate these options successfully.

Utilizing customer data for self-serve optimization

Our client formulated a data-driven strategy to refine customer service operations by analyzing their customers’ online behaviors. The goal was to bolster self-service options, ultimately elevating client contentment and streamlining operational workflows.


Exceeding chatbots: data-driven insights and predictive intelligence

Our client needed machine learning and deep learning models that could harness their available datasets to anticipate call frequency and purposes.

The client could devise strategies to lower their call center expenses with these insights. Our Customer Genomics solution was the perfect fit for this project. It provides cutting-edge deep learning algorithms for data training and significantly reduces model development time.

Phase Action Result

Problem alignment

  • Aligned problem statement with critical stakeholders

  • Explored data sets to gather information

  • A clear understanding of the problem

  • Relevant data for integration

Data transformation

Built ETL pipelines for data transformation

Data integration into the Customer Genomics platform

Customer Genomics platform creation(using Spark for big data processing and Python for data exploration/analysis)

  • Created a 360° view of the digital activity of customers

  • Developed predictive models for customer calls

  • Handle data from various assets

  • Manage data in different formats

  • Utilize various data variable types effectively

  • Comprehensive analysis of customer behavior

  • Predictive modeling

  • APIs for faster model development


Real-time interventions, reduced costs, and deeper customer understanding

Immediate enhancements to customer service

By applying our clickstream-based model, we found significant value in predicting call likelihood and reasons based on customers’ digital activity. The client could then implement near real-time interventions for potential callers. The insights also empowered stakeholders with a deeper understanding of their digital channels.

Long-term efficiency enhancements

As customer interactions increasingly favor chatbots and live chat support, our client anticipates sustained enhancements in operational productivity. This pivot is poised to lower call traffic and cut expenses. Simultaneously, the data science crew will benefit from ongoing enhancements to our Customer Genomics solution.

Immediate Results
  • Near real-time interventions for callers
  • A deeper understanding of digital channels
Long Term Gains
  • Increased operational efficiency
  • The data science team’s integration of our Customer Genomics solution
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