Análisis de pérdida de clientes con ChatGPT

Strategies and detailed analysis to understand the phenomenon of churn in a telecommunications company are an area of inescapable relevance in the business world. Delving into this field not only helps to predict and prevent customer churn, but also to design more effective tactics for customer loyalty.

What is churn and why is it critical to understand it?

The term 'churn' is used in the business context to refer to the moment when a customer ceases their business relationship with a company. In the case of Platz, the fictitious platform for this exercise, churn occurs when a subscriber stops making payments. This action is not exclusive to one industry; it manifests itself in various sectors, and it is crucial to understand the variables that contribute to this phenomenon in order to counteract its effects.

What are the relevant demographic and contractual variables?

The study identified several variables that can influence churn, such as:

• Gender of the subscriber.
• Age, focusing on whether the subscriber is a senior citizen.
• Duration of the relationship with the company.
• Type of services contracted with the telephone company.
• Modality and conditions of the contract.
• Methods of payment and periodicity of charges.
• Total amount billed to the customer.

Examining these variables leads to a detailed knowledge of the customer's profile and preferences, allowing the development of action plans aimed at solving the critical points of the commercial relationship.

Why is dataset cleansing and analysis essential?

Data cleansing plays an indispensable role, as evidenced by inconsistencies such as an untitled column and the need to correct the format of the 'Total Charge' column from categorical to numeric. Examples of corrective actions include:

• Removing irrelevant or untitled columns to avoid distortions in the analysis.
• Transform data types to reflect their nature and facilitate correct interpretation.
• Replacing null values with statistical measures, such as median or average.

What analytical methods can be applied to a churn dataset?

The implementation of different analytical techniques facilitates a deeper understanding of the causes of churn. Among these:

• Exploratory Analysis: For initial understanding of the distribution and characteristics of the data.
• Treatment of Inconsistencies: Resolution of null or erroneous data that could bias the results.
• Correlation between Variables: Evaluation of the degree of relationship and its impact on attrition.
• Machine Learning Models: Such as logistic regression or decision trees to predict future customer behavior.

What do the graphs and analytics provided by Chad GPT reveal?

The visualizations provided by Chad PT provide enlightening clues:

• Gender is not identified as a significant predictor of 'churn'.
• Customers with month-to-month contracts exhibit higher churn rates.
• Variables such as customer seniority and monthly charge amounts may influence customer loyalty.
• Additional services and demographic characteristics, such as being a senior citizen, may be relevant to customer decision making.

Data-driven strategic recommendations

To mitigate churn, it is imperative to focus efforts on:

• Improving the experience and conditions of the contracts most likely to cause attrition.
• Adjust the pricing structure so that monthly amounts do not become an exit factor.
• Value the introduction of additional services that can improve customer satisfaction and retention.
• Consider demographic characteristics and tailor loyalty strategies to segments most likely to abandon services.

Companies can benefit greatly from this type of analysis, as it provides them with a clear understanding of the critical aspects of customer retention and how data-driven strategies can redirect the churn trend towards a longer and more productive relationship with their users.