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Telcos are in a state of flux. In the 2022 State of Customer Churn in Telecom survey, it was found that customer loyalty to telecom providers is down 22% post-pandemic, with customer stickiness being impacted more by the customer experience than ever. Further, customers are now more price sensitive, with 58% perceiving telco offerings as expensive.
It takes only a cursory look at the online discussion around telecom companies to see that customer frustration has exploded. Telco users are tired of slow network speeds, poor customer service, and a lack of self-service channels. As a result, churn rates remain high, with nearly a quarter of customers indicating that they are likely to switch providers at the end of their contract period or in just the next few months.
Machine learning can be a powerful tool for telcos to predict customer churn and keep their customer base. ML is used across many industries, and its application in the telecommunications industry is no different. In this article, we’ll explore how ML is used in the telecommunications industry and how it can be applied to customer churn prediction.
The telecom industry is plagued by a high average churn rate. While the broader utilities market has a churn rate of approximately 12-15% for Western European markets, the telecommunications industry has an average churn rate of 30% to 35%.
Reasons for this may include poor customer service, product failure, and price. Customer churn can have serious consequences, such as higher product and acquisition costs, lost referrals, and CLV.
In our changing economy, marked by rising interest rates, the sky-high cost of goods due to rampant inflation, and a weakening job market, it is more important than ever to tame the beast of churn.
Telcos that fail to do so will find themselves having to constantly invest more in user acquisition, leading to a vicious cycle of costs spiraling out of control. Ultimately, untempered churn has only one outcome: death by a thousand cuts for telecom providers.
Even a relatively "good" churn rate of 25% means a few crushing realizations for telcos. To make up for lost customers, these companies must devote more of their resources to customer acquisition and marketing.
This can lead to a focus on short-term growth and can leave telcos unable to invest in long-term strategies such as product innovation and customer experience improvements. In addition, telcos that keep their churn rate low will have more resources to devote to customer retention, leading to higher customer lifetime value (LTV).
Naturally, firms with a lower churn rate also benefit from greater word-of-mouth referrals and more loyal customers. Word-of-mouth is the most powerful marketing tool, and telcos with loyal customers are more likely to reap its rewards.
All this is why a Bain & Company analysis finds that reducing customer churn by just 5% can increase company profits by up to 25%. Instead of trying to fill a leaky bucket of customers, telcos should focus on plugging the leak in the first place.
As we've explored, telcos are currently navigating a difficult economy. Customer churn is one of the biggest challenges they face, and reducing it requires creative solutions. Fortunately, there are several working strategies that companies can use to reduce customer churn and improve customer loyalty.
As telcos struggle with customer churn, it's important to understand why customers leave. To do this, companies must be proactive in collecting customer feedback.
This can include implementing customer surveys, conducting customer interviews, and leveraging analytics to better understand customer needs and preferences. This information can then be used to personalize the customer experience and develop products and services that better meet customer needs.
For instance, telcos could include surveys at the bottom of each customer's monthly bill, asking customers to rate their experience with the service. This could provide invaluable feedback on customer satisfaction and highlight areas where improvements could be made.
They could also use an on-site customer service representative to conduct interviews with customers, getting an in-depth understanding of their needs and preferences. This could provide a more personalized experience, allowing telcos to tailor their offerings to better meet customer expectations.
Once telcos have a better understanding of their customers, they can then use this information to identify those who may be at risk of churning. This could include customers who have had a bad customer service experience, those who have been with the company for a long time, or those who are showing signs of disengagement.
Telcos can then target these customers with personalized offers and better customer service. This could include offering loyalty discounts or special promotions to encourage them to stay. For customers who have had a bad customer experience, telcos could offer additional support or incentives to try to win them back.
Telcos should also look to optimize their billing processes to reduce customer churn. This could include simplifying the billing process and making it easier for customers to understand their bills.
The goal should be to make the billing process as transparent as possible. Customers should be able to easily see how much they are paying, what services they are getting, and what promotions they are eligible for. This will help to reduce customer confusion and frustration, making them more likely to remain with the telco company.
Finally, telcos should consider offering incentives and rewards to loyal customers. This could include discounts on services or special promotions.
Incentives can be a great way to show customers that they are valued, making them more likely to remain with the telco. This can also be a great way to differentiate a telco from its competitors, as customers may choose to stay with the telco that gives them the best deals.
These are just a few of the strategies that telcos can use to reduce customer churn in the current economy. By taking steps to understand customer needs, identify at-risk customers, optimize billing processes, and offer incentives to loyal customers, telcos can reduce customer churn and improve customer loyalty.
World-class firms are featured in countless business school case studies for their ability to use data to gain insight, inform decisions, and drive success. This is no manual approach, either: They're using machine learning algorithms to do the heavy lifting. Netflix, for example, uses machine learning algorithms to predict what movies its users will like based on their previous movie preferences and those of other users with similar tastes.
But how can machine learning be applied to reducing churn in the telecommunications industry? Data is the essential starting point for any ML model. This data can come in the form of a CSV, a Python Pandas DataFrame, a Kaggle dataset, or any other format. Telecom service providers should collect a dataset of their customers, including demographic data such as age, gender, marital status, dependents, and geographic location.
Additionally, telecommunications companies should also collect information about the customer's device protection, internet service, online backup, online security, phone service, streaming movies, streaming TV, and tech support services. Furthermore, telecom providers should also collect billing data including payment methods, monthly charges, total charges, and paperless billing.
But before we can start with our analysis, we must first clean and prepare the data. This includes dealing with missing values, numerical and categorical variables, and false positives. We can also create a subset of the dataset which will be our training data.
Once the data is clean and ready, we can begin our analysis. Data visualization is often the first step, as this will help us to gain a better understanding of the data. After that, we can start to use big data and data mining techniques such as logistic regression and decision trees, to analyze the dataset. We can also use a random forest and a classifier to determine which customers are churners.
Finally, we can use validation to test our model and tune the parameters. Once the model is tuned, we can use it to predict churn for new customers. For example, our ML-powered churn analysis could be used for the segmentation of customers into low-risk and high-risk categories.
To show how easy it is to use predictive analytics to identify customer churn risk, we've embedded an Akkio churn prediction model below, trained on sample data. You can enter your own input data to see how the model works. Give it a try!
Not long ago, the only option for predicting customer churn was the manual analysis of customer data. It was time-consuming and often did not yield the desired results.
Then came machine learning, but few businesses had the deep pockets, bench strength, and technical know-how to make the most of it.
Enter Akkio, a machine learning platform that is helping telco companies reduce customer churn. Akkio uses machine learning algorithms like classification trees, neural networks, and regression analysis to analyze historical data from customer devices.
Akkio deploys a technique called Neural Architecture Search (NAS) to automatically design the best algorithms for predicting customer churn and other customer behavior. For instance, if you're using a relatively small dataset of, say, 5,000 customers and just 5 columns of data, you may only need a shallow neural network.
On the other hand, if you have a large dataset with many columns of data, you may need to use a deep neural network with many layers to get better accuracy.
With Akkio, telco companies can upload their data in moments, or directly connect with data sources like Salesforce and BigQuery. You then select the column of data that you wish to predict, such as churn. Once the data is in, Akkio automatically builds a customer churn prediction model that can predict whether or not a customer will leave their provider.
The best model is chosen based on the accuracy of the predictions, as measured by performance metrics like the Root Mean Squared Error (RMSE) metric. You can then deploy the model in any environment, such as in a web application or mobile app, or on a server.
Telcos are facing a multi-headed hydra of rising interest rates, high costs, weakening consumer confidence, increasing competition, and, of course, high customer churn rates. Without action, churn can become an insurmountable challenge that cuts deeply into profits.
Thankfully, telcos have an invaluable ally in machine learning. By understanding customer needs and preferences, identifying at-risk customers, optimizing billing processes, and offering incentives to loyal customers, telcos can reduce churn and improve customer loyalty.
With Akkio, telcos can now easily use machine learning to understand their customers and build powerful churn prediction models. Ultimately, this can help them to reduce churn, increase customer loyalty, and improve their bottom line. Check out our 5-star reviews on Product Hunt, where we ranked as the #1 product of the day, or read a case study of a customer who saved 6 months of development time using Akkio.
If you’re ready to use ML to strengthen your telecom business, sign up for a free trial of Akkio.