Success Story

Redesigned Data Ops for a Telecom Major to Achieve Operational Excellence

About the client

Leading telecom provider

Challenges

    • The client lacked data-driven insights, hindering service operation optimization.
    • The existing data warehouse lacked capabilities for analyzing unstructured customer data.
    • Data needed normalization for analysis due to format and source variations.
    • Extracting insights from unstructured data required tools their infrastructure lacked.

 

Approach

  • The approach included identifying KPIs, implementing data collection mechanisms, and utilizing analytics tools for analysis.
  • Select processing tools, develop algorithms for organization, and integrate analysis capabilities into existing infrastructure.
  • Normalize data by assessing formats, developing pipelines for standardization, and implementing quality checks for consistency and accuracy.
  • Extract insights from unstructured data by selecting NLP tools, integrating them into pipelines, and training analysts on their effective use.
  • Leverage Databricks by evaluating, deploying, developing workflows, and training personnel for optimizing service operations.

Solutions

Innover designed and developed a transformative data solution that helped unlock insights from unstructured customer data, empowering optimization of service operations through comprehensive analysis of technician and customer behavior KPIs.

1.Utilized 26 data sources to create a nested loop data model.

2.Employed an iterative approach for model development to tackle business challenges.

3.Addressed unproductive truck roll optimization.

4.Performed customer sentiment analysis and churn prediction.

5.Conducted chronic caller analysis.

6.Implemented field force capacity planning and optimization.

7.Worked on repeat ticket reduction.

7.Developed an interactive PowerBI dashboard featuring key KPIs related to technician and service performance.

Impact Delivered

7X

Time to develop model reduced

5X

Return on investment with the
deployment of each analytical model

Tech Stack