Data Analytics: The basis of Extracting Insights

Data analytics have become significantly important in recent times, thanks to digital transformation. The shift towards digital technology has meant that MNOs now have access to more and more data but what they do with that data is what will ensure business growth and success. This is where data analytics comes in. We examine the importance of data analytics and how MNOs can utilise this technology to gain a competitive edge in the market, reduce customer churn and increase profitability. 

What is Data Analytics? 

In simple terms, data analytics refers to the science of analysing raw data to provide insights, statistics, patterns, predictions, future outcomes and conclusions about that information. During this process, data analysts utilise a number of different automated techniques, such as mechanical processes and algorithms, that take raw data and make it understandable and give it context.                        

In this way, data analytics is changing how information is being analysed and utilised by businesses across sectors, including MNOs. The insights provided by data analytics can help organisations optimise their performance, efficiency, and ultimately profitability. They say knowledge is power, and that is exactly what data analytics provides. 

By being able to analyse large amounts of data related to your organisation, you can identify patterns, trends, customer segments, opportunities, and issues that can be affecting your business. This not only assists with decision making but also gives you the insight needed to make changes to your business model that will ultimately promote growth and success. 

Data Analytics Technology and Types

A data analytics program makes use of a number of different data science technologies that fulfil an essential role in analysing data. These technologies include: 

• Data storage – refers to computing and storage technology that collects and manages large data sets and databases, which  allows for real-time data analytics. 
• Data mining – is the process of sorting through big data sets to identify patterns and relationships. 
• Big data analytics – is the process of examining big data to identify insights, hidden patterns, trends, and customer preferences that will ultimately improve decision making.
• Data visualizations – is the graphical representation of information and data through the use of graphs, imagery, maps, platforms etc. that facilitate an efficient understanding of the data at hand.

There are also a number of different types of data analysis that can be utilised to provide valuable insight. These types of data analytics include: 

• Descriptive analytics – descriptive analytics helps data scientists to answer the question “what happened?”. This technique helps to summarise large datasets to describe outcomes to stakeholders. This is governed by key performance indicators (KPIs) and can help track successes or failures. This process utilises artificial intelligence for the collection of relevant data, the processing of this data, data analysis and data visualisation. 
• Diagnostic analytics – this technique helps to answer the question “why has this happened?”. This type of data analytics takes the findings from descriptive analytics and investigates them to identify anomalies in the data, information related to these anomalies as well as the relationships and trends that explain these anomalies
• Predictive analytics –  answers the question, “what will happen in the future?”. Predictive analysis is a business intelligence tool that utilises machine learning techniques to compare historical data to identify trends and determine if they are likely to recur. This provides valuable insight into what may happen in the future, allowing for effective decision-making and planning. 
• Prescriptive analytics – helps answer questions about what should be done. Prescriptive analytics techniques utilise machine learning strategies to find patterns in large datasets. By analysing historical decisions and events, you can create different outcomes. 

Impact of data analytics in MNOs

Data analytics plays a central role in assisting MNOs to develop new business models that allow them to stay ahead of the competition and retain customers in this very saturated market. The insight provided by the data analytics process enables MNOs to provide a differentiated value proposition to their customer base based on personalised offerings.  Data analytics provide MNOs with: 

• Accurate and complete information related to revenue monitoring and reporting, interconnecting traffic between networks, churn tracking, KPI performance, and customer and market segmentation. 
• Advanced Analytics data models capture and audit raw information to ensure the accuracy of insights. 
• Provides insight into what services and offerings customers are looking for, patterns of consumption, churn tracking, customer behaviour, market segmentation, and more. 

MNOs can utilise these accurate and valuable insights to identify opportunities to improve their current services, offerings as well as develop relevant and targeted marketing campaigns, services, and offerings that add value to their customer base, which boosts revenue and profitability.  

Advanced Analytics for data analytics

From the above, it is clear that data analytics is an MNOs biggest digital asset and Advanced Analytics solutions take this a step further. For example, Adapt IT Telecoms Advanced Analytics solutions outlines a business’s performance data and highlights opportunities for improvement, growth, and success. This solution not only gives you data but interprets this data into valuable and actionable insights that you can use to enhance all aspects of your business.

Adapt IT Telecoms Advanced Analytics solution allows you to: 

• Extract and obtain relevant business analytics that are simple to interpret in almost real-time
• Improve your reporting capabilities by presenting call data records in a simple and understandable way 
• Access accurate and relevant information relating to network revenue contributors, churn management, KPIs, spend and more. 
• Gain actionable insights into customer behaviour and create profiles of your customer segments, their user patterns, revenue, and more. 
• Empower your business with the knowledge and information needed to adapt and better target your products and services offerings to increase profitability
• Identify high value and low-value contributors at a glance, enabling you to drive action and target the right customers.
• Amplify customer acquisition and retention opportunities by reporting on customer value indicators and drivers to develop cost-effective product and service strategies that talk to your customers’ needs and requirements and increase profit margins.
• Track your business Average Revenue Per User (ARPU), and Average Spend Per User (ASPU) against monthly targets effortlessly

Conclusion 

Data and advanced analytics play a vital role in equipping MNOs with the critical information that will enable business growth, optimise operations, enhance customer experience, reduce customer churn and grow revenue. We expect data analytics and advanced analytics solutions to gain even more momentum in the coming years as technological innovations continue to gain traction, especially where 5G and IoT are concerned and the amount of data these innovations bring with them. For more on how our Advanced Analytics solution can improve your business download the CDRlive White Paper.