The Case Of Dropped Mobile Calls

The Case Of Dropped Mobile Calls

The case involves analyzing customer complaints related to dropped mobile calls at Intergalactic Telephone Corp (ITC). The assignment requires identifying specific problems faced by two influential customers, Nancy Johnson and Barb Griesser, understanding how they were recognized as churn customers, critically evaluating how investigators Zoey Feliciano and Jake Retsa identified different issues within ITC, examining how business intelligence was applied to resolve customer churn issues, and explaining the methodology used by the investigators to address these problems.

Paper For Above instruction

Introduction

In the telecommunications industry, customer satisfaction is crucial for maintaining loyalty and reducing churn rates. The case of dropped mobile calls at Intergalactic Telephone Corp (ITC) exemplifies how technical and customer service issues can lead to customer attrition. The investigation into customer complaints, particularly those from influential customers such as Nancy Johnson and Barb Griesser, provides insight into both the operational problems and strategic responses needed to mitigate churn.

Identification of Customer Problems

Nancy Johnson and Barb Griesser, as influential customers, reported significant issues with dropped calls that impacted their experience with ITC. Nancy Johnson, a business executive, emphasized the disruption caused by unreliable connectivity during critical communications, which led to frustration and diminished trust in ITC’s service. Similarly, Barb Griesser, a high-profile individual, encountered frequent call drops that compromised her personal and professional interactions. These specific complaints were critical in identifying systemic problems with ITC’s network infrastructure and customer service responsiveness.

Recognition as Churn Customers

Both Nancy Johnson and Barb Griesser were recognized as potential churn customers through detailed analysis of their complaint patterns and account activities. Their high-value status made their dissatisfaction more conspicuous, prompting ITC's customer retention teams to prioritize their cases. The evaluation involved tracking their usage patterns, complaint frequency, and response to initial troubleshooting efforts. When their issues persisted despite remedial actions, they were officially categorized as churn risks, underscoring the need for strategic intervention.

Investigator Analysis and Issue Identification

Zoey Feliciano and Jake Retsa, as investigators, utilized a combination of technical diagnostics and customer feedback analysis to uncover various underlying issues at ITC. Their approach involved analyzing network performance data, call quality metrics, and system logs to identify potential hardware faults, coverage gaps, or software glitches contributing to call drops. Additionally, they examined customer service records to determine if dissatisfaction stemmed from communication delays or ineffective solutions. Their comprehensive evaluation revealed multiple issues such as infrastructure limitations, outdated equipment, and inadequate proactive maintenance processes.

Application of Business Intelligence (BI) in Resolving Churn

Applying Business Intelligence (BI) tools and techniques was pivotal in addressing the customer churn problem at ITC. BI systems enabled the aggregation and analysis of vast amounts of operational data, allowing investigators to identify patterns and correlations between network health and customer complaints. For example, BI dashboards visualized hotspots of call drops, frequencies of incidents, and temporal trends, which facilitated targeted interventions. Strategic solutions derived from BI insights included upgrading network infrastructure in identified weak zones, deploying predictive maintenance models to prevent failures, and customizing customer outreach based on churn risk profiles.

Solutions and Strategic Recommendations

The investigators recommended a multi-layered approach to mitigate churn. Short-term solutions involved rapid technical fixes such as enhancing signal strength, increasing coverage in critical areas, and improving customer support responsiveness. Long-term strategies focused on infrastructure modernization, adopting AI-driven predictive analytics, and customer engagement initiatives. These measures aimed to stabilize network performance, improve customer satisfaction, and foster customer loyalty. The integration of BI tools helped ITC prioritize investments and resource allocations effectively, ensuring sustainable growth and service reliability.

Methodology Employed by Investigators

The methodology adopted by Feliciano and Retsa encompassed several key phases. Initially, they gathered comprehensive customer feedback and operational data to understand the scope of the problem. They then employed diagnostic techniques such as network testing and system audits to identify faults. Their analysis involved the use of BI platforms to detect patterns and forecast potential failures. Based on these insights, they formulated targeted technical solutions and strategic plans, emphasizing continuous monitoring and iterative improvements. This data-driven, analytic approach was essential in transforming reactive troubleshooting into proactive network management.

Conclusion

The case of dropped mobile calls at ITC underscores the importance of integrated investigative strategies combining customer insights, technical diagnostics, and business intelligence. Recognizing influential customers' issues promptly and analyzing the root causes through data-driven methods enabled ITC to develop effective solutions. The application of BI tools for predictive maintenance and strategic planning not only addressed immediate call quality concerns but also laid a foundation for long-term service excellence. The methodology exemplifies best practices in telecom customer retention and operational resilience, ultimately fostering trust and loyalty among high-value customers.

References

  • Chen, H., Chiang, R. H., & Storey, V. C. (2012). Business Intelligence and Analytics: From Big Data to Big Impact. Mis Quarterly, 36(4), 1165-1188.
  • De Mauro, A., Greco, M., & Grimaldi, M. (2016). A Formal Definition of Big Data Based on Its Essential Features. Library Review, 65(3), 122-135.
  • Gubbi, J., Buyya, R., Marusic, S., & Palaniswami, M. (2013). Internet of Things (IoT): A Vision, Architectural Elements, and Future Directions. Future Generation Computer Systems, 29(7), 1645-1660.
  • Kim, S., & Park, M. (2014). Predictive Maintenance in Telecommunications Using Big Data Analytics. Journal of Network and Computer Applications, 45, 80-88.
  • Li, S., Li, X., & Li, J. (2017). Data-driven Approach for Customer Churn Prediction. Expert Systems with Applications, 85, 70-80.
  • Nguyen, T. T., & Sampalli, S. (2014). Business Intelligence-Driven Data Mining Based Customer Churn Prediction Model. Expert Systems with Applications, 41(4), 1874-1884.
  • Palatini, P., & Lanza, A. (2020). Infrastructure Upgrade Strategies in Telecom Networks Using Big Data Analytics. Telecommunications Policy, 44(2), 101877.
  • Sahoo, S., & Sahay, S. (2015). Impact of Network Quality on Customer Satisfaction and Retention in Telecom Sector. Journal of Business Analytics, 15(2), 123-134.
  • Shah, H., & Kumar, P. (2018). Application of Business Intelligence Tools for Customer Engagement and Retention in Telecom. International Journal of Information Management, 44, 394-404.
  • Watson, H. J. (2017). Business Intelligence. Practical Business Intelligence. Morgan Kaufmann.

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