Enhancing customer experience with Analytics in Telecom

Users share their data through various modes in Telecom. With different ways to collect user experience data, telecom operators can use technology advancements to improve customer experience. One such prominent technology that can be used by telcos to enhance user experience is through the usage of Analytics. 

According to Forbes, 90% of global Executives claim that using data analytics has increased their capacity to provide excellent customer service.

Discussed here are a few methods and technologies by which customer experience can be enriched by deploying Analytics.

Sentiment Analysis

The continual evolution of the telecommunications industry is a result of the growing significance of Internet services. This may be seen by each telecom firm as a big area to analyze and grasp more about the users.

A variety of techniques are used for information processing in customer sentiment analysis. This analytics enables the evaluation of the customer’s positive or negative response to the service or product. Analysis of the collected data enables valid responses to customer problems.

Take an example discussed in Forbes. A company using sentiment analysis monitors customer service conversations in real time, looking for signs of human engagement and providing behavioral guidance to enhance the user experience. Their technology has been demonstrated to enhance customer feedback by an incredible 90%, reduce call handle time by 15%, and improve customer satisfaction by almost 30%.

Predictive Maintenance

Businesses may monitor equipment, draw lessons from historical data, forecast equipment failure, and make necessary corrections before it occurs by using data-driven insights. A further significant area where AI can be useful is network optimization. Self-organizing networks (SONs) driven by artificial intelligence can assist networks in adjusting and rearranging in response to changing demands. It is helpful for building new networks as well. Networks with AI capabilities are better suited to deliver consistent service.

Predictive Care was introduced by Nokia as a general use case for next-generation services. It uses big data analytics processing to identify patterns that are out of the ordinary, pointing to a component that might be deteriorating or failing, and it forecasts where problems will occur so that preventive measures can be done. Deep element level visibility is used by predictive care services to identify problems more quickly and fix them. It quickens the identification of the new critical operational warnings before they have an impact on customers when combined with the experience of Nokia care professionals.

They use automated machine learning (ML) technology to identify previously undetected behavioral inconsistencies that may indicate more severe performance issues. Early detection of these problems enhances the performance, accessibility, and connectivity of mobile networks.

Cohort Analysis

To better understand the product, marketing, and other aspects of business, telecoms can use cohort analysis to derive insights from your cohorts.

Additionally, increasing client retention is one of the most typical purposes of cohort analysis.

By looking at consumers as cohorts, firms may gain valuable insights into things like early subscription cancellation rates, the impact of product or business changes on churn, and the revenue impact of each cohort.

Take an example. A data-driven mobile consumer acquisition firm made its Actionable Cohort Analysis which expanded and has 1.1 billion active mobile profiles.

Based on their usage habits and behavior, the firm has the ability to group users into cohorts. Travel, Entertainment, Gaming, Shopping, Utilities, and Lifestyle are just a few of the categories that the firm has divided up its cohorts into. Through this, they enhance their customer experience by better understanding.

Root cause analysis

Root cause analysis involves evaluating all of your data to determine the true source of the issues your users are facing. With root cause analysis, firms can identify the main causes of the problem with the customer experience. It provides the opportunity to correct any issues by revealing what is actually impacting them.

Take an example discussed in Mckinsey. Over 50 variables that affected customer churn were uncovered by a top operator using the analytical method known as “feature discovery”. These characteristics contained specified criteria that, when achieved, accurately predicted customer attrition, such as combinations of phone type, data consumption, and call center history. The firm was able to then identify reasons for customer concern, such as problems at particular call centers and dropped calls, once it had a list of these root causes.


Telcos deal with vast volumes of client data. Analytics aids in a variety of tasks, including customer segmentation, preventing customer churn, estimating customer lifetime value, product creation, boosting profits, price optimization, and more. Hence, Telecoms can use Analytics to extract valuable business insights and use those insights to make quicker and wiser business choices.