Unlocking Predictive Analytics in Telecom Billing: 5 Powerful Ways to Optimize Revenue & Customer Experience

Predictive Analytics in Telecom Billing is a tech savvy branch of the Telecom Billing & Revenue Management world, which has not been used enough until recently. With fast paced developments in the field of AI, more and more telecom operators are understanding the power of data and how using AI and predictive analytics in Telecom Billing can give them an edge in a highly competitive environment. The telecom billing systems store huge amounts of rating and charging data, which is very useful to study and analyse various usage patterns, revenue patterns, customer churn, possible fraudulent attempts or usage, service issues and resultant customer satisfaction (or dissatisfaction).

The predictive analytics helps telecom operators to predict some of these events or fall-outs before they take place and provides inputs that allow them to act proactively to resolve the issues before they occur. The predictive analytics combines AI and machine learning and when used with huge amounts of data, it allows the prediction models to provide accurate forecast of the likely events before they actually occur. Since this system uses AI and machine learning algorithms, it is a system that becomes more and more accurate with the passage of time as it learns continuously from its experience (more data) as the humans would do.

The telecom billing system contains various kinds of data. It contains customer information, service and subscription information, product catalogue with pricing information, asset inventory, service usage data, rating and charging data, network provisioning information, payment instruments, invoice history, payment history and more. This data is crucial in providing the seamless services to the end-customers. This billing system data is also important to ensure the rating and charging accuracy which in turn helps build trust with the telecom service provider.

Predictive analytics in telecom billing leverages this billing data and uses it to enhance decision making, improve customer experience and detect fraudulent usage or purchases. Let us first look into how the billing data is leveraged for usage pattern analysis in telecom billing.

The most useful data-store in a telecom billing platform is the usage consumption data-store. This data-store is a gold mine for application of data analytics. The usage consumption data is a properly conditioned data where all the key fields such as the user account, the service number, the event details such as type of service or call used, source number, destination number, duration or quantity – all these fields are resolved and clearly identified.

Such even data is also free of errors and the raw usage data received from a telecom switch (or the network component) is stripped of any non useful or erroneous records that cannot be leveraged or processed further. This data is therefore a perfect store for carrying out the Usage Pattern Analysis.

The line graphs below show examples of how such an events store can be used for predictive analytics in telecom billing.

The first line graph shows the predictive analytics applied to the usage consumption revenue. The system has used the call and other usage consumption events stored in the database and produced a graph that is predicting the year ahead in 3 different sectors: a) the premium customer sector (with monthly usage revenue above $200 per month), b) the mid sector (usage revenue from $75 to $200 per month) and c) the low usage sector (for customers with month usage revenue of less than $75).

These sectors can be defined and adjusted based on your usage plans. The system would study the usage patterns and trends that occurred in the last 4 to 5 years and predict the revenue for each sector for the year ahead. This gives clear indication of which sector will have the potential growth and the telecom operator can focus on that sector more. The company can make decisions that better align with the expectations of the particular sector and divert more resources and more budget to services in that particular sector.

The second line graph is predicting the usage patterns for various key services. The services can be divided based on various levels of granularity. For example, the above graph groups services based on whether they are domestic usage services or international usage services. This graph also groups international roaming service separately. Such data analysis can give meaningful insights into the consumption patterns across the services and help management make the right decisions.

The business may want to find out which customer segment is driving growth in a particular service. They could ask for a combination predictive analysis to be developed that provides the ‘customer segment’ wise and ‘service’ wise line graph. Such predictive analytics will give a peek into which segment+service combination is likely to grow in near to medium tenure. To boost such targeted growth, the business could come up with new offerings and pricing plans for a segment+service combination. For instance, a decision could be made to launch a new plan in the premium sector that provides international calls usage up to certain minutes free for certain countries.

Leveraging the Usage Pattern Analysis in Telecom Billing has the potential to boost revenue by targeting specific business segments and services.

Predictive Modeling in Telecom includes various techniques that can be applied to the billing and usage data, assess the usage patterns, find out the anomalies and provide specific insights with a prediction for the upcoming period. These telecom billing predictive models can lead to predictions with 80 to 85% accuracy if implemented well. These predictions are applied in the areas such as customer churn, suggesting customers with plan upgrades or downgrades based on usage patterns, fraud detection, revenue forecasting, revenue assurance and peaks and low usage predictions.

There are different telecom billing predictive models in use and these rely on various mathematical, statistical and AI based predictive models. Some of these models are discussed here along with their applications in Telecom.

This prediction model is used for predicting the customer churn. It relies on data related to the billing plan, usage patterns including call duration, number of calls, also number of customer complaints and interactions on those complaints. Let us look at some predictive models that can be used to predict the customer churn:

This model provides a way to predict the customer churn using weighted features. A set of variables such as type of billing plan, number of months of active service usage, any plan downgrades, number of service disruptions and a number of customer complaints are used as weighted features in a logarithmic equation. This equation then gives a value between 0 and 1. The business can decide that a value greater than 0.5 indicates a high probability of churn, whereas a value less than 0.5 could indicate that the customer will not churn. Thus, this model gives a binary probability of whether the customer “will churn” or “will not churn”.

This model builds a decision tree for each of the decision parameters that is used in predicting the churn. For example, the monthly plan fees, duration of the service, total usage charges per month and number of service complaints. Each decision tree will make its own prediction and the aggregate or an average from all the votes is used to provide a final score. This means if more trees vote as a customer will churn, then there is more probability of the churn. This model also predicts the probability of churn, for example it could predict a 70% probability of churn.

Gradient boosting is another effective decision tree based model, but it adopts a different approach than random forests. It uses prediction from each tree along with the prediction error probability and refines this decision with the next decision tree in the sequence. This model gradually improves or ‘boosts’ the result with every iteration. This model can give more accuracy than a random model if configured well.

Neural networks are used with more complex datasets, when the requirement is to detect more subtle changes in the usage behaviour which other statistical data models cannot capture. The networks use the input features such as duration of service, type of billing plan, monthly usage amount, daily usage amount, low usage, peak usage and number of complaints etc and resolve them into abstract representations. This is followed by non-linear learning in which the model is trained to establish non-linear relationships amongst the features. An iterative training is followed with back propagation and activation functions used to reach a churn probability.

In telecom, the fraud could occur in the day to day service through various means such as SIM cloning, false plan transfer, abnormal call routing, or sudden higher usage of international calls. A fraud detection model could be trained to find any anomaly in the normal usage pattern for a service. An example could be that a service that never used international calling, has a sudden spur in international calling. Another example could be SIM cloning and then using the number to send spam messages. Let us look at some modeling approaches which can be used for fraud detection:

The decision trees evaluate data based on decision rules, for example they can red flag a credit card purchase made from a foreign location with a large amount. Or red flag back to back transactions done from an unusual geographical location.

This model can be used to detect subtle variations in usage patterns or purchase patterns with a credit card. Even if the purchase with a credit card looks like a normal pattern, this model can pick up subtle variations and red flag such purchases and help the system block the transactions.

Long Short-Term Memory neural networks are useful when the context matters over a long period of time to understand the usage or purchase behaviours by users. It is a Recurring Neural Network (RNN) that uses a system of memory cells and gates to keep or discard the information. It can study the usage behaviour over a long period of time and pick up any subtle anomaly and red flag the user or the account.

For example, if the user has a roaming usage in particular geographic locations, then it can quickly pick up an entirely different location especially if it is a totally different location from the usual one. It also continues to learn based on data and avoids red flagging any normal usage variance. For credit card purchases, it can pick up not only geographical anomalies but also pick variations in amounts, sequential transactions etc.

Revenue forecasting using the historical data, subscriber growth trends and various usage patterns, can give a telecom company very good insights into expected revenue streams. This allows the business to fine tune their pricing strategy for the upcoming period, after taking into account the market competition and seasonal fluctuations in revenue. There are various Telecom Billing Predictive Models that are used for revenue forecasting, some of them are discussed here:

This forecasting model analyses the past revenue data and predicts the future trends using the historical data. Time Series Forecasting could be done based on models such as ARIMA (AutoRegressive Integrated Moving Average) or Prophet which use the parameters such as seasonality, holidays, trends, and events such as major sport events to study the impact on the revenue and make a prediction. An LSTM neural network could also be used to use additional parameters such as billing cycles, usage consumption and user retention to fine tune the predictions.

In the regression based forecasting, relationship between the billing data and the revenue could be set up using various regression models such as the linear regression, multiple regression or polynomial regression. The linear regression could be used to predict the revenue growth using the parameter such as increasing subscriber base. The multiple regression model could be used to factor in additional parameters such as plan type, promotions and regions etc. The polynomial regression is used to consider revenue fluctuations caused by the market competition and variations due to seasonal demands.

The machine learning and neural networks use more complex predictive modeling in telecom with variables such as the churn rate, the usage trends and the customer segment based usage patterns. They provide more accuracy in revenue forecasting as the model continues to learn based on multiple iterations and fine tuning of weights and biases.

Telecom billing predictive models can be leveraged to make usage predictions for various services that a telecom operator is providing. The predictive analytics in telecom can be used to forecast subscriber behaviour, network loads and service consumption. These predictions help the service provider to enhance the customer service, offer improvised pricing and avoid problems for the network for peak load conditions. For example, by identifying the peak load condition more accurately, the telecom operator could add more resources on a need basis to handle the load condition. This proactive action improves the service and results in higher customer satisfaction.

The telecom billing predictive models like time series forecasting (ARIMA, Prophet and LSTM), machine learning algorithms and regression based models are used for predicting usage consumption.

Telecom operators use predictive modeling in telecom to predict the errors that can occur in billing, rating and charging the customers. This is done by analysing historical data patterns, detecting anomalies and flagging potential errors before they impact the customers. For example, a revenue leakage could be detected for a particular day’s billing job by comparing the revenue for that day in the previous months and if there is a larger deviation in any stream, it could amount to an error.

Anomaly detection can be taken down to a user level. If a particular user’s monthly bill is between $35 to $50, and the latest bill generates a high value of $200 and then such an account could be flagged for a review. The model used can be fine tuned to avoid false positives by looking at usage patterns over the last several months and detecting a variation in usage carefully. 

Using predictive billing trends can help reveal billing errors in a particular type of service. For example, if the predictive trend shows high international roaming usage for the month of December but the actual usage charges generated from international roaming are much lower than the trend predicted despite a high number of calls, then it could be a case of a billing error leading to a leakage in the revenue.

Telecom billing systems handle enormous volumes of data that includes usage data, invoices, payments history, pricing plans, products, inventory and more. The traditional telecom billing implementations and live sites face a large number of issues such as billing errors, support calls, revenue reporting delays and delay in revenue forecasts. All these issues affect the decision making by management because the organisation’s focus moved to resolving errors and supporting issues, rather than focusing on coming out with innovative business growth strategies and implementing them.

With the power of AI, data analytics and predictive modeling in telecom, the telecom operator can change this scenario and achieve more control of business operations. Let’s explore the key innovations that Predictive Analytics in Telecom Billing brings to the fore:

1. Proactive Billing Management – The telecom billing predictive models provide predictions related to billing errors, revenue forecasts, usage patterns at finger tips of the management. The business team is equipped now with data to make decisions about pricing strategies, coming up with promotions and targeting the right segments for business growth.

2. Dynamic Pricing and Automated Billing Adjustments – Imagine the AI is working for you to correct the billing errors before they impact the customers. So the AI can be used to not only detect and flag the accounts in error, but it can also be trained to fix certain errors. For example, if a plan is always sold with a discount, and if for some reason a set of accounts is not given the discount, then the AI can add the discounts to such invoices on the fly based on billing rules configured in the system.

3.  Improved Customer Experience – AI based chatbots and call answering systems can be trained to handle the billing support issues in an effective manner. This reduces the workload on the human teams who can work on much more innovative works and contribute further to business goals of the company. The AI can also use the data from the support tickets and billing disputes to make an assessment of the customer sentiment and provide recommendations to the management on certain services that are not providing on-par service per the market expectations.

4. AI Driven Billing Operations – The AI can be trained to take care of day to day billing operations such as executing, tracking and monitoring the daily invoicing and payment collection jobs, review the generated reports and provide automated updates to the relevant stakeholders.

Do also check out our blog on Impact of AI in Telecom Billing Systems here. 

To Summarise:

Telecom operators are increasingly using AI and machine learning to build the predictive analytics in telecom billing. The usage pattern analysis in telecom is helping the business to drive growth and predict patterns before they reveal themselves as business problems or frauds. The telecom billing predictive models are hugely beneficial for the telcos to invest in and reap benefits. This blog discussed the 5 Powerful Ways to Optimize Revenue & Customer Experience using Predictive Modeling in Telecom. It also touched upon the Future Trends in AI and Predictive Analytics in Telecom Billing.

AI in Telecom Billing Systems is driving innovative solutions, contact the EarnBill team here to know more.