Technology transformation has made almost everything possible. From accurately predicting weather to pin-pointing a single customer’s preference at any given point of time. Network operators are reaping benefits such as improvement in targeting the right audience and hence the reduction in churn through implementation of advanced analytics and machine learning techniques.
A recently commissioned McKinsey survey, covering some of the world’s leading network operators and technology companies, had 90 percent of its respondents say that they had some form of centralized advanced-analytics capability in place, whereas only a quarter of respondents reported an increase in revenues, decrease in costs, or other outcome as a result of this analytics activity.
Effectively targeting an audience through machine learning
Predictive models help telecom operators estimate the likelihood of its customers’ actions beforehand. For example, with the help of predictive modelling, network operators will be able to identify actions such as which customer is planning to exit the network or cancel their data plan.
To create these models, solution providers use ML techniques. These techniques include Machine Learning algorithms being trained to find natural patterns in data and generate insights that help you make predictions using regression and classification techniques when fed with a huge amount of data.
This gives insights into customer behavior and identifies preferences such as data to voice or vice versa, the percentage of customers who like or dislike offers proposed to them and can also determine which customers are happy with their network offerings. These insights when further processed can be converted into campaigns and help in targeting effectively.
These fine patterns in customer behavior will enable network operators to uncover misalignments between customer usage and service plan, and proactively suggest an appropriate product. The analysis generated in real-time about customer-specific data will also enable the network operators to precisely align promotions and campaigns along the entire customer journey matching the customer sentiment. For example, the solution can predict churn from a customer through his actions at each stage of interaction with the network. With this, if the customer sentiment will be classified as ‘unsatisfied’, churn can be predicted. Similarly, the models are created using different patterns and processes that can help network operators deepen customer relationships by providing tailor-made offers, deliver outstanding customer experience by learning customer behavior patterns and likewise maximize revenue through opening revenue streams for cross-sell and upsell.
Implement Machine Learning techniques and embrace the change
Machine Learning provides network techniques a sure-shot means to manage and draw accurate insights into their customer behavior from a large tract of unstructured data collected every day. With this predictive knowledge, the telecom industry can transform the way it works. Decision makers will have a continuous, real-time view into the performance of campaigns, allowing them to make mid-course corrections at a much earlier stage.
The goal is to be more proactive, and to understand what action or direction is the best for a favorable outcome.
