A Renowned Tech Giant Reduced the Time to Find Customer Service Hotspots from 4 Days to 1 Hour with a Hotspot Identification Platform






Hotspot Identification Platform

The explosion of content prompted by the dawn of the digital age has given rise to new issues for technology companies. A major challenge for businesses is calculating the impact of past and current performance using the data they have acquired so as to define new business models and implement optimization strategies in accordance with areas needing improvement. Hotspot analysis (HSA) is an emerging technique being used in an increasing number of different analytical fields to address this very concern.

HSA analyzes data systematically to identify significant relationships and correlations amongst many variables accurately, as well as generates profiles of the areas or sectors that require prioritization. Other statistical and online analytical processing tools cannot match the analytical capability this method offers. Using AI&ML technology, this multidimensional analytics technique executes all of the following simultaneously:

  • Segmenting collected data
  • Creating profiles for said segments
  • Analyzing data in accordance with requirements
  • Automatically choosing variables as per relevancy and reported correlations
  • Prioritizing hotspots based on severity and impact
  • Identifying drivers for hotspots to provide actionable recommendations

A technique such as this has the potential to be used in many different business operations management settings. Examples include conducting investment analyses to assess company share performance and performing loan fraud investigations based on past customer data.


Our client, a multinational tech corporation, was looking for an agile, automated, and scalable solution to help identify hotspots based on customer behavior and data for improving customer support. The business operations managers (BOMs) of the client company found it a challenge to track multiple customer support metrics due to the following reasons:

  • The large enterprise supports multiple commercial strategic business units (SBUs) spanning hundreds of products and services.
  • All these products and services are delivered to over 70 countries, involving thousands of customers who speak different languages and have differing requirements for each product/service.
  • The client’s customer support is handled by a combination of full-time equivalent (FTE) as well as affiliated and third-party vendors.
  • Additionally, billions of combinations are possible for all tracked customer performance metrics, such as customer satisfaction score (CSAT), wait time, and time to resolve (TTR).
  • Further, performance can be defined and measured using multiple metrics that vary across SBUs. Hence, there was no consensus on which areas of concern are to be focused on.

Therefore, analyzing related data and gleaning actionable insights that could be used to bring improvement to customer support is no small feat. The client wished to know which areas needed attention on priority and requested an automated tool that would increase visibility into performance metrics and customer support health.

From standardizing the definition of a hotspot to developing strong data visualization capabilities for the software, at every stage of the project, the end user was taken into account in order to guarantee that the design and display are simple, clear, and intuitive. This approach meant that TheMathCompany accomplished what the giant corporation has been attempting to do (internally as well as with several different vendors) for years, resulting in highly positive feedback from all levels of the organization. The scope of the solution was also iteratively expanded to cover more SBUs, products, and KPIs, in line with the client’s requests.

Figure 1: Stages of the solutioning journey