Telecom Data Analytics use cases

1. Telecom Billing Reconciliations

Billing recon is very important to avoid financial losses to organisation and maintain goodwill in the market.

2. Price Optimization

Acquiring as many subscribers as possible remains a critical goal for any organization because the number of users has been growing at a breakneck pace in recent years. Pricing arose as a means of reducing congestion while also increasing revenue.
Dynamic pricing approach strives to map lifetime values, tariffs, channels to calculate price elasticity at the intersection of device, channels, and pricing plan and to mix this data. Basing on these insights the interdependencies between pricing, promotion, and future revenues is also defined.

3. Improve customer experience

A complete analysis of telecom data can help businesses to spot factors that impact customer experience. Telecom analytics helps companies collate and analyse data obtained from call centres, CRM systems, and other sources to know their customers' most crucial pain points.

4. Reduce truck rolls via proactive customer care

Using telecom analytic solutions, businesses can create customized reports based on data clustering algorithms and what-if scenarios, thereby avoiding unnecessary service calls and in-person appointments, which could cost them several dollars.

5. Analyse the potential of new offerings

Telecom analytics solutions that affect predictive modelling serve as a crucial enabler of business success by assisting telcos in developing innovative offers that are supported by client preferences, resulting in new revenue streams.

6. Lesser customer churn rate

Telecom analytics uses big data and advanced analytics tools to spot factors or events that impact churn. Using such insights, organizations can deploy suitable strategies to stop churn and improve satisfaction rates.

7. Real-time analytics

As the rapid expansion of the internet and the evolution of 3G, 4G, and even 5G connections, telecommunication firms face continually changing client demands. Subscribers are becoming increasingly demanding, and traffic is becoming more active by the day.
Modern streaming analytic systems are designed to continually ingest, analyse, and correlate data from many sources while also generating real-time response actions. Customer profiles, network, location, traffic, and usage data are all combined in real-time analytics to offer a 360-degree user-centric perspective of the product or service. It also records and analyses inter-dependencies.

8. Recommendation engines

The recommendation engine is a collection of clever algorithms that describe client behaviour and make predictions about future product or service demands. Collaborative filtering is based on analysing user behaviour or preferences and anticipating what they will like based on their similarities to others.
As a result, the system suggests products and services that are comparable to those previously purchased.

9. Network management and optimization

Customers' interaction process and internal channels are often seen by telecommunication businesses as a guarantee of smooth operation. The ability to specify the score points in operations to discover the fundamental causes of these issues is provided by network management and optimization. For telecom providers, looking into previous data and projecting potential future problems or, on the other hand, favourable scenarios is a huge benefit.

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