How to reduce customer churn? It’s simple. Delight your customers with the help of Advanced Analytics.
Analytics for telecom organizations
It is highly likely that you are sitting on a goldmine of knowledge about your customers’ likes, dislikes, and preferences. The question is: how do you make your customers stay loyal to you and prevent them from seeking other services?
What sets most businesses apart today is how they take care of their customers, e.g., picking up the phone to call a customer with a solution before the customer calls in to report a problem. By taking the time to incorporate analytics in telecom and understand customer data insights, you are empowering your business to offer customers value, which in turn delights them and provides them with a great experience.
The telecom challenge
Telecommunication companies often encounter situations where customers leave their service network for another provider.
The decision to switch from one vendor to another is usually triggered by the entrance of a new player to the market. Competitors are offering better solutions at more attractive prices, while the customers’ current provider might have problems such as poor customer service and grievances handling, faulty bills, and ongoing unresolved issues.
It has then become absolutely vital for Telcos to understand their customers and respond quickly to minimize customer churn.
Why delight customers?
For every business, current customers are a valuable source of ongoing business. They need as much nurturing as you would nurture a new prospect. If a customer exits a service network, the costs of acquiring a new one is significantly higher.
Great customer experience is a key factor in customer delight. Remember, happy customers recommend your business to their friends and colleagues, while unhappy customers seek out alternative services or leave bad reviews about your business with friends, colleagues, and on the web. This can damage your reputation quite a bit.
What if you can predict customer churn?
Predict which customers are at risk of leaving in advance and take action before it’s too late. Take advantage of artificial intelligence techniques where “intelligence” is built by referring to historical data. In other words, leverage machine learning to predict the likelihood of customer churn.
Having data that predict customer churn can change how businesses approach its customers. What if you could contact a customer with a personalized offer when there is a billing error, even before the customer notices it? Or upgrade the customer to a better subscription when you notice they frequently get disconnected from their network?
Being able to proactively follow up with a customer may, by all means, eliminate the likelihood of the customer switching due to reasons out of your control.
Integrate all data to forecast customer churn and improve loyalty
Telecom companies must acquire a deep understanding of customer attitudes, behavior, likes, dislikes, preferences, and actions by adopting analytics solutions to turn their data into valuable business insights.
Unify data from different sources that are specific to your customers, e.g. customer profile, device and network data, usage patterns, location data, downloaded apps, clickstream data, call data records, customer care reports, agent performance reports, costing and billing reports, and network service quality—for a 360-degree view of your customer.
For companies that sell a service for a monthly fee, whether it is a B2C or B2B, it is important to provide your customers with personalized service and know when it is the right time to approach them for cross selling, upselling, promotion, and retention.
Why a BI & Advanced Analytics platform?
Business Intelligence and Advanced Analytics, platform, plays a critical role in helping Telcos meet business objectives, promote growth, and drive efficiencies and profitability across the entire Telecom value chain.
By monitoring all relevant data sources to understand customer data in real time, Telcos can identify potential occurrence of issues and execute context-specific actions to address them proactively, in a manner that is personalized and specific to the individual customer.
For companies in telecom, predictive analytics can help access and analyze massive amounts of data in real-time to help get ahead of the curve and turn a potential customer dissatisfaction scenario into one of customer delight.
