Insights and Data Trends in Telecom Analytics

Data analytics is key if you want to understand customers today, but what challenges surround its implementation in the telecom industry, what tech is driving it, and how is it being used in the real-world?

With the rise of social media and smartphones over the last two decades, it has never been easier, or more important, to leverage data analytics in the telecom industry to predict customer needs and behavior. But you cannot interpret data without having the correct telecom software solutions and data sciences development services in place.

Additionally, you need to have real discussions around automation and software architecture if you want your analytical efforts to be as simple and as accurate as possible. Conversations that focus on questions like the role of automation in telecom and whether you should go with an umbrella architectural pattern to achieve your goals.

This blog will discuss the value data analytics can deliver to telecom providers, including the challenges that come with it, the key technologies behind it, how it is being used in real-world situations, and current trends around it in the industry.

What is telecom analytics and why does it matter?

Telecom analytics is a specialized process enabling communication service providers to analyze extensive data for actionable insights. It focuses on collecting, sorting, and analyzing information to enhance service delivery, optimize network functions, and improve customer satisfaction. This analytical approach drives efficiency and innovation in the telecommunications industry.

But how does data analytics in the telecom industry differ from other sectors? What are the challenges in implementing telecom analytics solutions? What technologies are driving innovation in telecom data analytics, and can telecom analytics help in reducing operational costs?

Challenges of data analytics in telecom

To discuss these challenges, we first need to understand how data analytics in the telecom industry differs from other sectors. In short, it is used to measure metrics unique to the telecommunications sector such as:

• Customer Lifetime Value (CLV): a data prediction model used by telecom providers to help them offer the right services to the right existing customers, target ideal customers for the future, identify issues quickly to increase customer loyalty and retention, and reduce customer acquisition costs.
• Average Revenue Per User (ARPU): a measure of how much revenue telecom providers generate from each user that reflects their operational performance and influences how much they can spend in any given year based on how much revenue they acquire from all their customers collectively.
• Minutes of Usage (MOU): a metric that measures the total time in minutes, used by customers on their mobile phones within a particular timeframe, which helps telecom providers design promotional campaigns that can be tailored to certain groups, and help them understand how interested their customers would be in voice pack bundles.
• Subscriber Acquisition Rate (SAR): a measure used to figure out the total average cost of adding a new subscriber to a service that considers all marketing costs including dealers’ commission, sales cost, marketing, and advertising costs and many more to acquire a new customer.

These metrics are just some of the unique ways data can be analyzed within the telecom industry. However, implementing it correctly comes down to the same thing every industry now faces when trying to leverage data to improve their services: data and system quality.

Therefore, your data analytics, and the services you derive from them, such as tailor-made customer experiences or increased network security, or complexity are only as good as the data driving them.

So, make sure the data you use to train your systems comes from as many trusted sources as possible if you want to minimize the effects of bias or fallacy in your data. Leveraging poor data to improve your network or customer service could lead to loss of customer trust or even lawsuits down the line if you are not careful.

But what technology is driving all this forward?

Core technologies behind telecom analytics

The technology behind telecom analytics enables your systems to interpret a wide array of data by drawing connections between data points and predicting future trends based on how your customers, or your systems, have interpreted this information in the past. It does this by grouping things into nodes, similar to how the human brain works.

Additionally, many telecom providers also leverage a digital twin to ensure any data analysis their new algorithm will perform produces the results they want by first running it in a similar, but closed-off, environment from their main operations. This is what is meant by the term “twin” in digital twin.

This approach ensures that any abnormalities generated by their new algorithm are caught well before they integrate it into their main operations – regardless of the technology driving it. This, of course, produces a better algorithm at go-live leading to better offers being presented to customers and a more secure and complex data network as a result.

Data analytics in the telecom industry: use cases

What does implementing data analytics in the telecommunications sector look like, what technologies support it and what results has it produced to date?

• Vodafone: has been leveraging big data and AI to better understand their customers so that they can deliver services that customers will be interested in. By integrating data analytics into their ways of working, Vodafone has been able to track voice and data consumption habits amongst their users and offer the most appropriate plan or pack options to them based on their findings
• Reliance Jio: has acquired 130 million customers within one year with the help of big data. They are turning to big data analytics to understand where their customers are located globally in real-time, enabling them to ultimately enhance customer experience by offering them location-based promotions based on past MOV.

There can be no doubt then, that implementing data analytics into ways of working can deliver a host of benefits to a telecommunications provider. So how can we expect the market to react to this fact in the coming years?

Trends in data analytics in the telecom industry

According to Valuates Reports, the global big data analytics market is projected to reach $ 684.12 billion USD by 2030 – up from $198.08 billion USD in 2020 growing at a CAGR of 13.5% during the forecast period.

This growth in the big data market is being driven by increased data analytics adoption across sectors in order to reduce costs and deliver enhanced decision-making at speed. This, in turn, enables many industry leaders to analyze and act on information in a timely manner, giving them the opportunity to give their customers what they want as soon as they need it across geographies.

The transformative power of telecom analytics and data in telecom

It is safe to say that with the rise of social media and smartphones, it has never been easier to acquire data on customer behavior in the telecom industry.

Gone are the days of painstakingly creating email surveys or cold calling customers, everything you need – including how they really feel about your offerings and customer service – is readily available to you if you are brave enough to seek it out. All of which means, of course, that analyzing data in the telecom industry is here to stay.

At Software Mind, we know that implementing data analytics correctly can be challenging. But we also understand the benefits this technology can deliver to your business and how to implement it into your way of working at speed. Which is why our experienced software team is happy to talk about what data analytics can do for you wherever you are.