Predictive Analytics Techniques: How to Accelerate Your Business

The telecommunications sector is changing, and there are now more providers, data, and opportunities to expand value-based service offerings, making this industry more dynamic and very competitive. So how does a business stay ahead of the game and accelerate its business growth? The answer lies in predictive analytics techniques, which use data analysis techniques that include machine learning, data mining, and statistics to identify patterns and predict ways for a business to reduce customer churn, manage possible risks and ultimately enhance profitability.

What Is Predictive Analytics?

To understand the impact of predictive analytics on a business, we need to first define what it is. Predictive analytics utilises big data, algorithms and machine learning to make predictions on future events and outcomes based on historical data that has been collected. This process converts data into insights that you can then act.

This becomes increasingly important when determining what elements could impact the business. Such elements include identifying new business trends and risks, opportunities for diversifying services, alerting to problems relating to customer churn and experience, as well as detecting factors affecting productivity and profitability.

These analytical techniques can help businesses predict what could happen in the future. This will give businesses sufficient time to solve problems or create solutions and services that could impact customer loyalty and experience. This enhances accuracy and gives you the competitive edge needed to increase your bottom line.  

Why are Predictive Analytics Techniques Valuable?

There are several benefits related to the use of predictive analytics techniques that add value to a business and assists in solving challenging and complex problems using data, these include:

• Detecting fraudulent activity– by using data mining and predictive analytics techniques, data analysts can identify, detect, and prevent cybersecurity issues and fraud. The algorithms can process data and find patterns in real-time to assist with identifying vulnerabilities, threats, and fraud, therefore allowing you to act fast and protect your business and customers.
• Optimising marketing activities – these analytics enable businesses to determine customer responses to product offerings and cross-sell services. The insight from this data collection helps businesses create marketing campaigns that target the right customer base to enhance profitability.
• Improving network operations monitoring and management – predictive analysis enables a Telco to identify traffic patterns, peak use times, and forecast inventory needs and demand. This allows the business to put measures in place that ensure the network runs smoothly during these times and that customers have access to the products and services that they need. These analytics also assist in managing workflows, supply chain, optimising maintenance activities, provide alerts to equipment failure, and provide real-time updates on the network.
• Churn prediction and improving customer experience – predictive analytics provides historical data on customers who have already churned as well as insight into their behaviours and reasons for the churn. This data can be used to identify patterns and take preventive measures, like tailor-making effective packages or offering more competitive pricing to retain subscribers. By addressing the reasons for customers leaving, you will also identify opportunities to enhance the customer experience.

Choosing the Right Models for the Job

The value that predictive analytics holds for Telcos is evident but for it to be successful and gain an accurate insight into future outcomes, trends and opportunities, the business needs to have the right predictive modeling techniques in place for the job. There are several predictive models available, these include:

• Regression analysis – this predictive analytics model is often used to predict demand during various months and how operations can meet this demand. It is also often used for determining the best target process for an offering. This is done by using and comparing previous sales patterns and data.
• Clustering – this model is often used for customer segmentation as it involves grouping data, based on similarities, into clusters. This enables identification of patterns in relation to customer demographics, locations etc. This information and insight become important for marketing campaigns and service or product offering targeting.
• Classification – this model establishes the shared characteristics of large data sets and determines the category of a new piece of data based on its characteristics. This becomes important when identifying customers’ lifetime value, how much they are worth, if they are loyal customers or if they have churn potential.

Many businesses choose to use a combination of different predictive analytics models because a single model does not provide a full view representation of all the data available. These models also often utilise different predictive analytics techniques.  

Four Predictive Analytics Techniques

There are four main predictive analytics techniques used to assist businesses in predicting trends, identifying relationships as well as patterns impacting risk and customer churn. These techniques include:

• Decision Trees – this technique is based on machine learning (ML) and uses classification algorithms from data mining to identify possible risks and rewards for different actions. The potential outcomes are then represented visually, using flow charts, to help company executives better understand the data and patterns.
• Text Analytics – makes use of ML, statistical and linguistics techniques to process data collected from different sources, including emails, social media, web pages, call centre enquiries etc., into descriptive analytics. Topic modelling is also used to examine larger clusters of text data and determine the specific topics covered in the sources. This technique also uses sentiment analysis to identify the opinions and feelings related to the sources. This provides insight into customer experience, problems, or issues that customers are experiencing with their services or offerings.
• Simple Statistical Modeling – utilises multiple linear regression models and deep learning to build models that identify the strength of the effect of independent variables on dependent variables. This assists with predictions on future trends or in the forecasting of sales.
• Neural Networking – neural networks operate the same way as a human brain by identifying layers of relationships within data. Neural networking techniques can be applied for predicting time-series information and identifying anomalies in data, therefore allowing for better predictions on the information.

The Future of Predictive Analytics

Predictive analytics is set to significantly impact the telecommunication industry and business as we know it. This analytical tool gives Telcos a competitive edge in an increasingly competitive market where many service providers are fighting for the same piece of the pie. Predictive analytics allows a business to identify patterns and gain essential insight into issues relating to customer churn, fraud detection and risk-related issues, operations, and marketing activities which all impact customer experience and the business’s bottom line.

By using predictive analytics, Telcos can amplify decision making, identify opportunities for improvement, offer more tailored services and products as well as retain more customers. Predictive analytics will become key to delivering on business objectives and growth within this dynamic industry.