Discover how B2Metric's AI-based marketing solutions can transform the telecom industry. Read our blog to learn more about the benefits of data-driven insights.

Murat Hacıoğlu
B2Metric AutoML - Telecom Industry
7 Minute Read
Machine Learning
Automated Machine Learning
B2metric Machine Learning Studio
Telecom Marketing
Predictive Marketing
Campaign Optimization
Customer Micro Segmentation

Table of Content

  1. Predictive Marketing Analytics
  2. Machine Learning Uses Cases for Telecommunication Industry
    1. Personalization
    2. Price optimization
    3. Consumer Behavior
    4. Improving Customer Service
    5. Increasing Efficiency and Effectiveness 
  3. B2Metric AUTO-ML Platform for Telecom Marketing


The telecom industry has experienced rapid development in the 21st century and is still growing day by day. In the past, the telecom industry needed agencies to be able to sell and market products, but with machine learning, the telecom industry has become able to reach millions of customers at low cost without being dependent on agencies.

Machine learning detects customers' preferences with algorithms and reaches customers with similar tastes through similar advertisements or other sales channels. Personalized offers from machine learning are beneficial for both customers and the telecom industry. For example, using machine learning, we can predict what kind of product a customer may need and how much budget this customer can allocate for this product. 



Because there is a lot of data to make sense of in marketing studies, marketing teams are having a hard time deriving insights from it. AI allows marketing teams to make the most of the data at hand using predictive analytics that leverages a variety of machine learning, algorithms, models, and datasets to predict future behavior. This can help marketing teams understand the types of products a consumer will be looking for and position campaigns more accurately.

In addition to these, machine learning can optimize the campaign management of the telecom industry including functional, regional, product-based campaigns. features such as these are instrumental in the growth of the telecom industry as they increase sales and enable more customers to be reached faster and at a lower cost. Therefore, machine learning has become one of the most important tools of the telecom industry.

In one of the studies carried out with artificial intelligence in the telecom sector, millions of personalized offers were created using a small amount of classified customer information, and afterward, thanks to these offers, the interaction with repeat customers were beneficial. Thanks to the project carried out in customer interaction during the year of the study, an increase in revenue was clearly observed in the telecom company. Similar to this study can be applied in many sectors such as insurance, retail, and banking.

There is intense potential in the telecom sector. For this reason, they offer almost the same services as the operator with similar prices. Thus, by switching between operators, the demand for the highest possible offers with low prices increases. It is seen that, among these operators, producing the best performing and smart solutions that will ensure customer satisfaction is through personalized offers. Thanks to machine learning techniques, it learns algorithm errors and sharpens its prediction power by taking into account customer tastes with similar features. In this way, it is possible to reduce costs and make offers that the customer will be interested in.

Despite all this, machine learning should be compatible with people who benefit from it. In this regard, machine learning should serve with almost zero error margin with the collected data and analysis.


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Let's examine the benefits of machine learning to the telecom industry one by one.

  1. Personalization

The ads and content is shown are customized for each customer thanks to machine learning algorithms. In this way, each different type of customer is appropriately addressed. Since the offers offered to customers are addressed to the customer's preferences, this increases the possibility of the customer buying a product. For example, Google Adsense.

  1. Price optimization

It is not enough to just offer customers products that they can prefer or need because as each customer has different preferences, each customer has a different budget. Therefore, it is necessary to offer the customer the most suitable product for her/him, both according to her/his preferences and considering the potential price he/she will spend on this product. 

  1. Consumer Behavior

Even though customers can reach the appropriate product at an affordable price thanks to machine learning, customers are also human. Therefore, machine learning should work in accordance with consumer behavior. It should be combined with people who benefit from it. Machine learning should be able to better analyze customers' interests and manage the company's campaigns at the same time. Machine learning provides all of these with the data collected and algorithms and offers the most appropriate service to the customer.

  1. Improving Customer Service

With machine learning, it is possible to respond to customers' problems 7/24 with advanced customer service. It is possible to find solutions to customers' problems that can be solved in a short time, faster, and more practice with machine learning For instance, a chatbox of a telecommunication company can be very beneficial to respond to customer questions quickly. Moreover, fast customer service also makes the company look professional and gain the trust of the customers.

  1. Increasing Efficiency and Effectiveness 

When we bring together all we explained above, we clearly see that machine learning increases the profitability of companies. Machine learning personalizes and customizes the data of customers to improve marketing campaigns. Machine learning predicts the budget of customers with the data collected and offers the best service to customers with advanced customer service. While machine learning does all of these, it actually provides companies with great convenience, contributes to companies saving time and money, and increases the sales of the company with effective campaign management.

Here is the list of telecom companies using AI-driven campaign optimization:

1. AT&T

2. Turkish Telecom 


4. Deutsche Telekom

5. Turkcell

6. Globe Telecom

7. Vodafone

8. ZBrain Cloud Management

9. Tier 1 Telecom Companies

10. NetFusion

11. Nokia




To sum up, considering the benefits of machine learning, we can observe that the campaign optimization of telecom companies can be achieved with the highest efficiency. Machine learning collects information using many algorithms and interfaces and enables telecommunication companies to manage and organize their campaigns according to the predictive analysis results using this information and data. When we examine the telecom companies using ML above, we observe the successful campaign management of these companies as a common feature. In addition to these, B2Metric provides high service quality to the telecom industry customers with the services it provides using AutoML. B2metric contributes to the development of campaigns with its customer-oriented AutoML, with the solutions it produces for Turkey’s leading telecom companies, one of the most well-known names of the telecom industry.

When we tried this out at a leading telecom operator, we realized that the machine-learning tools alone were not enough. Our experience shows that AI is a great tool, but that it needs to be combined with deep changes in the working methods of people who use it — that is, the staff who come into contact with customers, whether as sales agents or giving technical advice. To implement this shift, the traditional role of product managers has to be supplemented by a customer base management practice, which brings together functions such as IT, business intelligence, marketing, and sales.

These implementations are expected to increase revenue by about 50 percent, one-year customer acceptance rate by 15 percent, and finally, active customer base size by 10 percent. We are convinced that similar results could be achieved by applying these tools and principles in a wide range of industries where service providers have long-term relationships with customers, retail banking. 


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