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What is Customer Segmentation in Telecom Industry

Customer segmentation is known as market segmentation, the process of dividing customers into different groups. Based on common characteristics, such as demographic, psychographic, geographic, behavioral, and firmographic. Organizations market to each group effectively and appropriately. Customer segmentation is an increasingly important part of a strong marketing strategy. The division into varied segments allows marketers to adapt their marketing efforts to various audience subsets. These efforts can relate to both product and communications development. Classification helps a company to create and communicate with properly targeted marketing messages that will resound with specific groups of customers. It selects the best communication channel for the segment, which might be social media posts, radio advertising, email, or another approach, depending on segmentation. It identifies ways to improve old products or new products or service opportunities. It helps in establishing a better customer relationship and testing different pricing options by focusing on the most manageable customers by amplifying customer service. It helps in upselling and cross-sells other products and services. A company has to gather specific information data about customers to analyze & identify patterns that can be used for creating segments.

Customer segmentation plays a very important role in the telecom industry. It helps in customizing the services to meet the needs, to select a service that suits their budget so that they can get maximum satisfaction. Segmentation also helps telecom companies to identify the appropriate distribution channel for their services. In this super-competitive market, it plays a critical role in this increasing the return on investment by minimizing the investments in marketing, resources, production facilities, etc.

In today’s data-driven market, companies need to implement different tactics to ensure that their attempts aren’t going to waste. The key to all these marketing attempts is data collected from the consumers. Managing, understanding, and applying this data allows for evaluating marketing’s successes and failures while also planning the strategy. With an accurate understanding of the customer segmentation models, the telecom industry can suitably develop its strategy to make sure it works.

Customer segmentation importance

Customer segmentation can allow marketers to address each customer most effectively. Using a large amount of data available on potential customers. Customer segmentation analysis allows marketers to identify distinct groups of customers with a high degree of accuracy based on behavioral, demographic, and other indicators. Since the marketer’s goal is usually to maximize the revenue, from each customer, it is crucial to know in advance how any particular marketing action will influence the customer. Preferably such “action-centric” customer segmentation will not focus on the short-term value of marketing activities, but rather on the long-term customer lifetime value (CLV) impact that such a marketing activity will have. Thus, it is necessary to segment or group, customers according to their CLV. The proper approach to segmentation analysis is to segment customers into groups based on predictions regarding their total future value to the company. Approach each group or individual in the way that is most likely to maximize that lifetime or future value.

Correct customer segmentation involves tracking dynamic changes and frequently updating new data. There are many types of customer segmentation models, but segmenting customers according to their CLV is the recommended approach. Some common types are RFM segmentation, segmentation via cluster analysis, and longevity. Some marketers to reach their goals might even combine one or more segmentation models. Customer segmentation can be performed by all businesses regardless of size or industry and whether they sell in person or online. It begins with gathering and analyzing data and ends with acting on the information collected in a way, that is appropriate and effective.

Types of Customer Segmentation

As market conditions are unpredictable, companies are evaluating the effectiveness of the market segment solution by validating the solution with the help of the market segmentation criteria. Five ways the current trends in the telecommunications sector segmentation are as follows:

1. Customer Value Segmentation: Customer loyalty has been termed as the most critical aspect of marketing. Hence, companies invest significant time and effort to retain customers. Telecom companies are willing to work themselves to segment profitable customers by calculating their value. The ‘decile analysis’ is the standard approach used, which calculates a value measure for each customer and divides the entire customer base into ten equal-sized groups. With millions of customers, for large-scale companies, there may be more than 10 value segments. This approach hinges on several issues, the precise measure falls on the availability and quality of data.

2. Lifetime Value Segmentation: identifies the predicted contribution to overall organizational profitability based on expected lifetime relationships with the organization. The current approach of value segmentation focuses on identifying the contribution that a customer makes to overall organizational profitability based on relationships with customers currently with the organization.

Report customer profiles to monitor their needs carefully and develop an appropriate marketing strategy that answers their needs. In applying these solutions, organizations need to be clear about their definitions of contribution, profit, revenue, and so on.

3. Customer Behavior Segmentation: The third type of segmentation that most marketers will be familiar with is segmentation according to customer behaviour. In recent times telecom services have drastically increased customer service centres due to the increase in users. To manage such large amounts of customers and understand their needs, telecom companies have to capture every action performed by customers, building huge storage containing customers’ behavioral data. As people in their daily lives use telecom services extensively, telecommunication companies monitor to determine customer needs and develop relevant strategies from the collected customer behavioral data.

4. Customer Lifecycle Segmentation: The fourth approach to segmentation is more properly referred to as Life Stage Marketing, and is almost similar to the life cycle used by financial organizations. Modern age telecom companies need to keep customer-centric market segmentation techniques. The fundamental of this technique is to determine and understand customer behaviour and improve customer loyalty. Customer lifecycle segmentation considers a picture of the current life stage of customers and performs market segmentation by analyzing their needs and interests. There are 2 types of customer life stages in this type of segmentation, firstly stages related to their association with the company and secondly stages of their individual lives.

5. Customer Migration Segmentation: This is another form of customer segmentation, and is probably the one least considered by many marketers. But it can provide great value. Due to increased competition, the telecom industry is experiencing a high customer churn rate. These high levels of customer attrition have negative impacts on various aspects such as loss of average revenue per customer, difficulty acquiring new customers, and decreasing sales and profit. Thus, modern age telecom companies prepared themselves to understand and analyze the possible factors that cause customer defection. This leads to the growing importance of customer migration behaviour. The value measure of each customer is observed at various time frames. It is very usual for customers to increase or decrease their loyalty along the way. Therefore, customer satisfaction and loyalty patterns can be identified if customers migrate between different segments. Such loyalty patterns and identification of satisfaction can help companies to predict churn before it occurs. Hence, telecom companies can design and develop convincing activities for customers who have high chances of switching to other competing companies.

Customer segmentation and machine learning Python

Another approach to customer segmentation is leveraging machine learning algorithms to discover new segments. Machine learning customer segmentation allows advanced algorithms to provide external insights that marketers might find difficult in finding out on their own. Moreover, marketers that create a response loop between the segmentation model and campaign results will have ever-improving customer segments. In these types of cases, it will not only be able to refine its definition of segments, but also be capable of identifying if a specific subset of the segment is outperforming the rest, optimizing marketing performance. Artificially intelligent models are powerful tools in decision-making. There are many machine learning algorithms, each fit for a specific type of problem. The need of implementing machine learning for customer segmentation is very imperative.

Time-consuming - More time is taken if manually we try to derive customer segmentation as takes months, even years to analyze a mass of data and find patterns. Also, if done un-skeptical it may not have the accuracy which is expected.

Ease for re-training customer segmentation is not that it can be developed for once and can be re-used forever. Data is dynamic, trends swing, and everything keeps replacing after your model is deployed. Usually, more labelled data becomes available after development and it is a great source to over improve the overall performance of your model.

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Better Scaling - Machine learning models which are deployed in production support scalability, thanks to cloud infrastructure. These machine learning models are quite flexible for future changes and feedback.

Higher Accuracy- To find the value of an optimal number of clusters for given customer data is easy by using machine learning models. Not only optimal number but also the performance of the model is far better when we use machine learning where there is no need to write codes.

A more intelligent and automated approach to networks will increase margins and customer satisfaction. For this reason, telecom operators should seek scalable machine learning backed with no-code cloud-agnostic AI-powered solutions. While transitioning legacy systems to more modern infrastructures, saving money, time and effort when deploying AI-based and data-science solutions.

Python is a computer programming language that is used for conducting data analysis, building websites, software, and automated tasks. K- means clustering in Python is used in Customer segmentation and is an efficient machine learning algorithm to solve data clustering problems. It is an unsupervised algorithm that is quite suitable for solving customer segmentation problems. It is also quite different from supervised machine learning. Unsupervised machine learning is a special kind of algorithm that discovers patterns in the dataset from unlabelled data. It can group data points based on similar features in the dataset. One of the main types of models is the clustering model.

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Advantages of customer segmentation in the Telecom Industry

Segmenting the customer base in the telecom industry and analyzing the performance of those segments can improve marketing, sales, and customer service efforts. The segmentation advantages are listed below in the telecom industry.

Increase Marketing Efficiency - This is one of the biggest benefits of well-implemented customer segmentation. marketers can identify more effective tactics for recognizing better customers’ needs. Because of improving customers’ interactions and experience with the company marketing efforts have become even more effective. Targeted marketing enables better returns on investment and wastes less money on marketing that reaches the wrong audience.

Expand New Market Opportunities - During the process of customer segmentation, if a new market segment is identified, then it can alter the whole marketing strategy to fit the new market segment. This segmentation research may help companies recognize areas that had not been the focus yet in the market. This leads to the new product development, explicitly designed for these markets.

Improve Brand Strategy – Once the key customers are identified, products can be branded appropriately. The main goal of market segmentation is not only to reach out to the targeted market but also to see the true value of the company. Promoting the product with a well-altered brand strategy allows placing the company’s head above competitors.

Advance the Product – By knowing what are their needs and who wants to buy the product, which differentiates the company as the best solution on the market. Such practice will result in increased satisfaction and better performance against competitors. With more insights, the benefits extend beyond core product offering allowing companies to offer better professional services, customer support, and any services that guarantee the complete customer experience.

Decrease Customer Retention - Thanks to customer segmentation, now marketers can identify customers’ needs or who require extra attention. Those with the highest potential value, and those that churn quickly. It can also assist in creating targeted strategies that capture customers’ attention and create a positive, high-value experience with the company.

Gain competitive advantage – currently, with the amount of competition in the telecom sector, the market segmentation techniques can ensure revenue streams. For example, by combining behavioral and geographical segmentation, companies can gain insights into the customers’ behavioral treads located at different geographical locations.

How FutureAnalytica can help in this journey?

FutureAnalytica is the only holistic automated machine-learning, no-code AI platform providing end-to-end seamless data-science functionality with data-lake. Artificial Intelligence app-store & world-class data-science support, thus reducing time and effort in your data-science and Artificial Intelligence journey.

We give you insights to learn how telecommunication companies can gain by giving excellent customer experience and leveraging customer reviews, predictive analytics, and more.

We at FutureAnalytica, keeping privacy regulations in mind, help you to estimate the current and future value of customers, and collect relevant data from all touchpoints of as many customers and their behavior as possible over multiple years. This helps the corresponding analytical models which are dependent on the availability of sufficient amounts of information to identify relevant patterns. The greater the volume of data available, the more meaningful and accurate the analysis. It helps in streamlining the operations, maximising profits, builds effective marketing and business strategies.

With FutureAnalytica’s advanced solutions dividing a customer base into specific groups, marketers can use predictive analytics to make forward-looking decisions to tailor content to unique and special audiences.

Conclusion:

Customer segmentation is very essential. It is not wise to serve all customers with the same product price model. They have different needs, and requisites, and machine learning models can give you insights over the complete process. These machine learning algorithm models give us insights into a customer’s brain and help us to know precisely what they need, enhancing their participation and expanding profits. It improves customer experience and boosts company revenue. That’s why segmentation is a must if you want to exceed your competitors and get more customers. Machine learning is the right way of doing it.

With our AI-based platform you can create the best models, for example, the descriptive model which tries to calculate CLV using historical customer data and identifies behavioral patterns of consumer groups through simple manual analysis.

The predictive model gives you a deep understanding by using historical data patterns to determine future CLV. The results are more accurate and meaningful as the individual profile is taken into consideration along with their remaining time as a customer.

The operative model goes one step further as it automatically predicts CLVs using machine learning and makes initial recommendations for decisions, amplifying the CLV effect. To work with all three models, continuous updating of data and calculations is necessary.

So here you need to adjust CLV after each customer purchase, but the (Customer Acquisition Costs) CAC value must be increased if, for instance, a marketing campaign is launched for a specific customer group. It is essential that the data and the associated analytics results are utilized for future campaigns. For more insights to stay ahead of your competitors, get connected and request a free demo.

We hope this article was insightful and helped you to understand Customer segmentation in the Telecom industry and how its advantages are helping the sector. Thank you for showing interest in our blog and if you have any questions related to Customer segmentation, Retention, cross-selling and upselling, Machine Learning, or No-code AI-based platform, please send us an email at info@futureanalytica.com.

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