Introduction
Verizon is a key player in telecommunications, providing millions of users with mobile, internet, and cable TV. Verizon, like many other telecommunications providers, confronts the problem of customer churn when consumers either stop using the service altogether or engage with it less often. Loss of income and higher expenses involved with obtaining new customers are major issues made worse by client turnover. In this article, we will detail an analytical strategy for Verizon to reduce customer turnover. This strategy will include a plan for storing and analyzing customer data, discussing data risks and possible results, and an implementation schedule.
Brief History of Verizon and the Customer Churn Problem
In American telecommunications, Verizon Communications Inc., or simply Verizon, is a major player. Verizon began operations in 2000 and has since expanded into an international telecommunications behemoth serving a huge and varied consumer base worldwide (Verizon, 2016). The firm is a multi-faceted player in the telecommunications market, offering its customers wired and wireless phone, internet, and TV packages
However, as do many other telecom behemoths, Verizon faces the daunting obstacle of client attrition. This problem occurs when customers stop using the service or reduce their financial commitment to the business (Verizon, 2016). The company’s bottom and top lines will take a hit due to such turnover. Multiple causes contribute to client turnover, including unhappiness with service and attractive offers from competing businesses. Customers’ ever-evolving wants and desires add another layer of complexity.
Verizon has deliberately used sophisticated analytics better to understand consumer behaviour in light of this urgent problem. Using data analytics, the organization hopes to understand the complex factors that lead to client defection (Petroc Taylor, 2023). Verizon uses this information to inform proactive efforts to increase the company’s client base and keep current customers from defecting. The ultimate objective is to reduce churn’s negative financial effects and keep Verizon at the top of the increasingly competitive telecoms market.
Analytic Plan Recommendations
Data Storage:
Successful customer turnover research must begin with a solid and effective data storage foundation. Verizon has made a good choice in going with a conventional relational database system for this reason.
To begin, a relational database system offers a well-organized and systematic way to store massive volumes of information. In the methodical approach, important client data, such as subscription details, account information, payment history, and more, maybe stored standardized. By establishing connections between previously unrelated data elements, the relational model provides a complete picture of client behaviour and facilitates in-depth analysis (Ahmad et al., 2019). Additionally, the focus on maintainability ensures the database’s dependability throughout time. Data cleaning and validation procedures are examples of maintenance tasks crucial to ensuring continued precision and reliability. The accuracy of the analytics performed on this data is improved by these procedures, which assist in detecting and fixing errors.
Due to the sensitive nature of client data, telecom companies place a premium on security. Strong security measures may be added to a conventional relational database system to prevent data breaches and unauthorized access to sensitive client information. Finally, quickly getting and analyzing data is essential for making sound choices here and now. Verizon’s selected data storage solution will provide rapid querying and reporting, providing analysts with quick access to the necessary data for effective, in-depth churn research (Ahmad et al., 2019). Verizon’s approach to data storage is crucial to the success of its customer churn research because it guarantees data quality, security, and accessibility, providing a solid basis for data-driven decision-making and proactive churn mitigation efforts.
Data Analysis:
In order to get a full picture of why customers are leaving, the data analysis process will include many different kinds of analytics. Verizon’s plan to reduce customer attrition relies heavily on analyzing collected data. It incorporates several analytic methods, contributing significantly to learning why customers leave and how to keep them around.
Descriptive Analytics:
Descriptive information is the starting point for any analysis of customer turnover. It requires a thorough investigation of past data about customers in order to identify patterns and tendencies. Verizon will analyze crucial factors, including service quality, consumer satisfaction, and subscription options.
This enables the business to provide a historical context for client behaviour and attrition rates. Customers with certain types of subscription plans or service difficulties, for example, are more prone to churn, as determined by an examination of past data. Verizon can put its resources where it will do the most good if it can identify these tendencies. Seasonality and temporal patterns may also be shown via descriptive analytics. If Verizon notices a rise in churn during a given month, for example, they may take steps to fix the problem ahead of time.
Exploratory Analytics:
Exploratory analytics, which builds on descriptive analytics by identifying patterns in consumer behaviour, preferences, and loyalty, seeks to understand the data better. Cluster analysis and market segmentation are the most common statistical methods for dividing consumers into various subsets (Goworek, 2020). To identify high-risk client segments and improvement opportunities, this segmentation is important. Verizon may find that a subset of its customers, say those who often call customer service with concerns, are more likely to leave the company. With this information, the business may better target the at-risk demographics with retention initiatives like enhanced customer service responsiveness and individualized incentives.
A more detailed comprehension of client tastes is also made possible by exploratory analytics. Verizon may learn more about the preferences of its various consumer segments by evaluating collected data. This data may be used to better target certain demographics of consumers with targeted advertisements and product offerings.
Predictive Analytics:
Predictive analytics is a major step forward in reducing customer attrition. In this stage, Verizon aims to create predictive models that can identify clients who are most at risk of leaving the company shortly. Predefined algorithms or custom-built models from past churn data form the basis of these models.
In order to foresee the future, predictive models use the patterns and insights they discover in past data. For instance, customers who share traits with individuals who have churned in the past might be singled out. Predictive analytics yields actionable insights by identifying clients in danger of leaving or churn.
Predictive analytics may bring in various external and internal elements, including market competitiveness and consumer loyalty, to improve the precision of these forecasts. Verizon’s retention strategies are dynamic since they are always updated with fresh data.
Prescriptive Analytics:
The last and most proactive step in data analysis is known as prescriptive analytics. Prescriptive analytics is a data analysis that uses the results of other analytics to provide specific recommendations on how to use those insights best to increase customer loyalty and decrease churn.
These suggestions include anything from a more tailored approach to marketing to revised prices and enhanced offerings. Prescriptive analytics may prescribe specific actions to enhance the experience of high-risk consumers, such as providing them with special discounts or assigning them their account manager (Kapta, 2022). Verizon’s ability to quickly adjust its suggestions in response to changes in consumer behaviour is made possible by the fact that prescriptive analytics feeds on real-time data. It enables the business to stop reacting and start acting, providing individualized solutions that reduce the likelihood of losing customers.
Understanding past customer behaviour, segmenting consumers, forecasting turnover, and finally prescribing targeted actions is the data analysis step of Verizon’s customer churn reduction strategy. Verizon can reduce the financial toll of customer churn and strengthen its footing in the cutthroat telecommunications market with the help of this data-driven strategy for customer retention and satisfaction.
Data Risks
Inaccurate Data:
Inaccurate data is a constant threat to the validity of any data-driven study, but it is of paramount importance in the context of customer churn research. A company’s ability to forecast churn and execute an efficient retention strategy may be severely hindered by incomplete or outdated customer data (kapta, 2022). When customers’ contact information becomes outdated, it might hinder any efforts to contact and keep them as customers.
Verizon needs strong data validation and cleaning methods to reduce this danger. In order to keep the data reliable, it must be checked and updated often. Rapid detection and correction of errors are made possible by using automated data validation procedures. Also, the analytical process would benefit from constant data quality monitoring to ensure consistent quality checks.
Assumption Errors:
Core to churn analysis is predictive models, which are built with specific assumptions about customer behaviour and the variables impacting attrition. If these conditions are not met, the predictive power of these models may suffer. Inaccurate churn projections could result from a predictive model assuming that customers’ behaviours would be stable over time while ignoring substantial shifts in market dynamics.
Verizon has to take a strategy of predictive model continual validation and improvement to deal with this threat. As part of this process, models must be routinely checked against actual data and tweaked as appropriate (Lian Yan et al., 2014). Monitoring customer behaviour shifts and external variables affecting churn is crucial to keeping the models up-to-date and accurate.
Finally, Verizon’s customer attrition study’s efficacy is threatened by erroneous data and assumption mistakes. The best way for businesses to reduce their exposure to these dangers is to emphasize data quality by instituting stringent validation and cleaning procedures and to have a flexible approach to model validation and refinement that allows them to quickly adapt to changing client preferences and market circumstances. Verizon can improve its churn projections and retention efforts by tackling these threats.
Potential Outcomes
Improved Customer Retention:
One of the main advantages of executing the analytic strategy is the potential to improve client retention greatly. Verizon can foresee clients who may cancel their service thanks to accurate churn projections. With this information, the business may take preventative measures to keep these clients. Verizon may create compelling reasons for at-risk consumers to remain by delivering targeted incentives like discounts or enhanced services. Enhancing service quality and responding quickly to client complaints are two other ways to win over loyal customers (Lian Yan et al., 2014). This helps maintain income streams and builds goodwill among consumers, who are more inclined to become brand supporters.
Targeted Marketing Efforts
Verizon can better target its customers according to the analysis’s findings. The efficiency of the company’s marketing campaigns may be greatly improved by targeting certain customer groups based on their preferences, buying habits, and loyalty. Customers are likelier to interact with and buy from brands that provide relevant, personalized content and offers (Saias et al., 2022). Not only does this make the most of advertising dollars, but it also helps consumers feel heard and appreciated.
Cost Reduction:
It is possible to save much money by doing an accurate customer turnover study. Verizon may save money by not constantly attracting new customers and instead focusing on keeping its current ones. Moreover, targeted marketing is intrinsically more cost-effective since it focuses on clients more likely to react favourably rather than those less likely to do so. With fewer expenses, a business may focus its resources and energy where it will have the most impact, ultimately leading to higher profits.
In summary, Verizon gains a lot from implementing the recommended analytic approach, which includes better customer retention, more efficient marketing, and lower expenses. These results improve the company’s bottom line, its standing in the market, and its connections with customers, elevating Verizon to the forefront of the increasingly competitive telecommunications sector.
Timeline for Implementation
Verizon’s analytic plan implementation requires a methodical schedule to guarantee the success of each step. A well-thought-out schedule enables modifications and enhancements to be made in a timely manner and promotes a methodical approach to churn reduction. The suggested schedule is explained in further depth below.
Data Collection and Storage Setup (2-3 Months):
The first step is to establish a reliable system for storing data and implementing data-gathering procedures. Verizon’s data discovery, pipeline construction, and database architecture will occur during this time. Spending the time necessary to guarantee reliable, safe, and productive data collecting is essential. To ensure data integrity and availability, sufficient time is made aside for thorough testing and validation of the data storage system.
Descriptive and Exploratory Analytics (3-4 Months):
After setting up the necessary data infrastructure, the next step is to conduct descriptive and exploratory analyses of past customer data. This phase aims to single out certain client subsets, trends, and patterns (Simpson, 2015). In-depth data examination, categorization, and insight extraction are all made possible by the timeline’s design. Spending sufficient time on this step is essential for gaining a deep comprehension of client behaviour.
Predictive Analytics Model Development (3-4 Months):
It is essential to construct churn prediction models. The process entails using past churn data to build, test, and refine these models. Models are trained, validated, and optimized at this stage. Due to the intricate nature of predictive analytics, dedicating enough effort to model building is essential.
Prescriptive Analytics and Strategy Implementation (4-6 Months):
An essential step is creating prescriptive analytics and putting the suggested tactics in place. In this phase, Verizon will practice tactics to increase client loyalty and decrease turnover gleaned from earlier analytical phases (Simpson, 2015). These tactics can be developed and implemented with due diligence, given the time frame. Creating new marketing strategies, reevaluating prices, and refining offerings fall under this category.
Ongoing Monitoring and Optimization (Continuous):
The time of the last stage, which consists of constant monitoring and optimization, is unknowable. Effectiveness evaluation requires constant tracking of current tactics and analytics models. It also entails adjusting and perfecting in real-time in response to shifting client behaviour and market circumstances (Zhong & Li, 2019). During this stage, Verizon is kept nimble and responsive so that the churn reduction strategy has maximum long-term effect.
In summary, this organized calendar provides Verizon with a clear roadmap for the execution of the analytic strategy, making it possible to give each stage the time and focus it needs to be implemented effectively. By sticking to this schedule, Verizon can effectively reduce customer loss, adjust to new conditions, and refine its retention tactics.
Conclusion
In conclusion, Verizon’s long-term performance depends on deploying a thorough analytic strategy to reduce customer turnover. Verizon can better understand its customers, lower churn rates, and boost loyalty using data storage, analysis, and predictive and prescriptive analytics. Data accuracy and model assumptions pose hazards, although they may be reduced with cautious deployment and constant monitoring. Verizon, a major player in the highly competitive telecommunications industry, benefits greatly from implementing this analytic approach.
References
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Zhong, J., & Li, W. (2019). Predicting Customer Churn in the Telecommunication Industry by Analyzing Phone Call Transcripts with Convolutional Neural Networks. Proceedings of the 2019 3rd International Conference on Innovation in Artificial Intelligence – ICIAI 2019. https://doi.org/10.1145/3319921.3319937
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