develop reports that showed traffic levels from ravitejafe's blog

Earlier fraud systems only used to describe unusual activities. The traditional fraud systems used to develop reports that showed traffic levels, changes in call duration and so on. And experts would determine what was really happening and find the root cause. With the development of big data techniques and statistical analysis, fraud systems have evolved into diagnostic and predictive analytics.

To prevent churn, data scientists are employing both real-time and predictive analytics to:

Combinevariables (e.g., calls made, minutes used, number of texts sent, average bill amount, the average return per user i.e.ARPU) to predict the likelihoodof change.

Know when a customer visits a competitor’s website changes his/her SIM or swaps devices.

Use sentiment analysis of social media to detect changes in opinion.

Target specific customer segments with personalized promotions based on historical behavior.

React to retains customers as soon as the change is noted.

Predictive models, clustering would be the ways to predict the prospective churners.

Using big data and python, I have developed the solution to find the upcoming network failure before it takes place. 

In the past, telecom companies have handled this problem by putting caption data and developing tiered pricing models.

But now, using real-time and predictive analytics, companies analyze subscriber behavior and create individual network usage policies.

When the network goes down, every department (sales, marketing, customer service) can observe the effects, locate the customers affected, andimmediately implement efforts to address the issue.

When a customer suddenly abandons a shopping cart, customer service representatives can soothe concerns in a subsequent call, text, oremail.

More info: field engineer