Do you think data analytics was a complex and time-consuming task? This is no longer true. OpenAI has recently unveiled Advanced Data Analysis. Now, with Advanced Data Analysis, anyone can perform data analytics in just a few minutes. Without further ado, let’s dive in.
I have this massive data set containing 6,000 rows of Telecom customer data, including churn rates. Next, I’ll upload this CSV file to the Advanced Data Analysis, which can handle file sizes of up to 512 MB. First, I’m going to ask, can you describe this data? Advanced Data Analysis thinks that the data relates to Telecom users, with each row representing an individual customer. It understands the data very well.
Then, I’m going to ask it to perform exploratory data analysis on this data and create visuals. As you can see, it performs the data analysis and creates some graphs. But it complains about 10 missing values in the total charges, which we need to address before building any models. So, our next step is to clean the data. Advanced Data Analysis can fill these missing values with the mean or average value, which is a common approach in data cleaning.
Next, let’s ask it to perform exploratory data analysis on this data again and create visualizations. Let’s take a look at those graphs. The distribution of tenure shows peaks at around zero to five months, which represents new customers, and around 70 months, which represents loyal customers. The distribution of monthly charges peaks at around twenty to thirty dollars. The distribution of total charges indicates that a large number of customers have relatively low total charges.
Now, let’s analyze the churn rates. Churn by gender does not seem to significantly differ by gender. Both genders show similar churn rates. Churn by senior citizen status shows a higher proportion of senior citizens churn compared to non-senior citizens. Churn by internet service type shows that customers with fiber optic service churn at a higher rate than those with DSL or no internet service. Churn by contract type shows that customers with a month-to-month contract churn at a much higher rate than those with one-year or two-year contracts.
Now that we have rough ideas about what factors influence the churn rate, let’s ask Advanced Data Analysis to show us the significant factors that drive customer churns in descending order. Advanced Data Analysis creates a bar chart indicating that total charges, monthly charges, tenure, customer ID, and contract type are the top five factors. Please note that tenure and customer ID are somewhat duplicated or highly correlated. Older customers usually have smaller customer IDs, and newer customers have bigger customer IDs.
Advanced Data Analysis also alerts us that these factors don’t indicate whether they have a positive or negative effect and suggests using logistic regression. So, let’s ask it to use logistic regression and create the visualizations again. It generates a new graph showing the positive and negative impact on those factors. Not only that, but it also provides details such as the negative coefficient of contracts suggests that certain types of contracts, for example, longer-term contracts, are associated with a lower likelihood of churn. The positive coefficient of internet service indicates that certain types of internet service are associated with a higher likelihood of churn. This gives you a clear picture of how these factors impact the churn rates.
Isn’t that amazing? Without actionable insights, data analysis is useless, right? Indeed, we can also ask Advanced Data Analysis to advise us on how we can improve customer retention. It will generate a list of suggestions. Let’s evaluate whether these suggestions make sense.
For contract length, Advanced Data Analysis reveals that customers with longer contracts are less likely to churn and suggests the company to promote longer contracts by offering incentives for customers to sign up for them. I think this is a great suggestion.
For internet service, Advanced Data Analysis identifies that internet service customers have a higher churn rate and suggests the company to probe into the reason behind it and possibly improve service quality.
As a result, Advanced Data Analysis also finds that customers with paperless billing are more likely to churn, indicating potential difficulties or issues with the paperless system that may be causing customer frustration. It suggests the company to investigate this and make necessary improvements.
I think these are all brilliant suggestions, and Advanced Data Analysis can indeed be a powerful tool to improve your business or sales.
If you are interested in this data set, it will be available in the description below. If you’re looking for more data sets to explore, you can visit kaggle.com under data sets. Here, you will find many different data sets.
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