Telecom churn case study python

Long story short — in this article we want to get our hands dirty: building a model that identifies our beloved customers with the intention to leave us in the near future. We do this by implementing a predictive model with the help of python.

Customers turning their back to your service or product are no fun for any business. It is very expensive to win them back once lost, not even thinking that they will not do the best word to mouth marketing if unsatisfied. Learn all about the basics of customer churn in one of my previous articles. The basic layer for predicting future customer churn is data from the past.

Hands-on: Predict Customer Churn

By fitting a statistical model that relates the predictors to the response, we will try to predict the response for existing customers. This method belongs to the supervised learning category, just in case you needed one more buzzing expression.

In practice we conduct the following steps to make these precise predictions:. First of all we use Jupyter Notebook, that is an open-source application for live coding and it allows us to tell a story with the code. Furthermore we import Pandaswhich puts our data in an easy-to-use structure for data analysis and data transformation. To make data exploration more graspable, we use plotly to visualise some of our insights.

Finally with scikit-learn we will split our dataset and train our predictive model. One of the most valuable assets a company has is data. As data is rarely shared publicly, we take an available dataset you can find on IBMs website as well as on other pages like Kaggle : Telcom Customer Churn Dataset. The raw dataset contains more than entries.

All entries have several features and of course a column stating if the customer has churned or not. To better understand the data we will first load it into pandas and explore it with the help of some very basic commands. This section is kept quite short as you can learn more about general data exploration in better tutorials. Nevertheless to get initial insights and learn what story you can tell with the data, data exploration makes definitely sense. By using the python functions data.For the last few articles we have been working on a telecom case study to create customer segments Part 1Part 2 and Part 3.

In this case, you are the head of customer insights and marketing at a telecom company, ConnectFast Inc. Recall, in the first part, you have created cluster centroids through iterative calculation of Euclidean distances. Remember, the objective of iterative calculations was to adjust the centroids to place them at the center of the cluster members see Part 1. Have a look at the animation below you have seen this data in part 1 of this series ; with each iteration Standard Sum of Error SSE is reducing.

This is absolutely the objective to iteratively reduce SSE till it gets stabilized — and voila! As discussed in the previous article most machine-learning algorithms try to iteratively converge to an optimal solution. For cluster analysis the idea is to minimize SSE iteratively. As a telecom company ConnectFast offers several services on top of their existing cellphone plan with prepaid and postpaid billingsome of them are listed below. Before moving further, let us try to generate some intuitive feel for customer segmentation using cluster analysis.

For simplicity let us consider just 3 different services i. This is displayed in the adjacent figure.

Prediction of Customer Churn with Machine Learning

Theoretically, there could be 4 3 or 64 maximum clusters that can be formed. However, after our analysis for customer segmentation we have generated just 4 clusters displayed as orange customer segments.

Let us take a pause and think about it for a while, there are 64 difference locations where customers could be found based on their services usage behavior. However, the major density of customers is located at 4 clusters detected through cluster analysis. I hope you could see some relationship with universe and galaxies discussed in Part 1 here, the mass is concentrated in limited areas with majority of white space.

For ten variables that you will be using in your analysis for ConnectFast, with 4 levels each you could have a little more than one million clusters i.

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Now one of the biggest challenges with cluster analysis, as also discussed in a previous article, is to choose the right number of clusters before the analysis.

That is you need to know the exact number of clusters that you are going to form before you run your cluster analysis through K-mean algorithm K is the number of clusters one wants to form or the number of initial cluster seeds you provide to the algorithm.

The best solution to the above challenge is a mix of analytical methods and business acumen to arrive at the initial number of cluster seeds. Business acumen is something you generate over a period of time by developing intuitive feel about the business.

In the next segment, let us focus on the analytical procedure to form optimal customer segments. One of the useful analytical methods to choose the optimum value of K is to plot stabilized SSEs vs. An illustration for this is shown in the adjacent figure Graph A. On a technical note, an outer loop that performs cluster analysis with incremental value of K generates the values for this kind of plot. You have plotted the same with number of clusters seeds on the horizontal axis and minimum or stabilized SSE on the Y axis.

There is a significant drop in the value of SSE as you have moved from 9 initial cluster seeds to Your business sense justifies the presence of 10 customer segments hence you have decided to stick with it. You are feeling good because you know you were lucky with the output plot.

The definitive clues you have got from plotting SSEs and cluster numbers may not have been as clear i. In this case you may have to rely completely upon your business sense. Now you are left with a final task for customer segmentation of naming these clusters based on their attributes.

You have completed the task of naming the 10 customers segments. The customer segments are arranged in the descending order of value to the company. The following are the highest and lowest value customer segments. For the last few years there is a special emphasis on customer attrition or churn rate — a concern for the industry after implementation of number portability by the telecom regulators.

The chief operating officer COO of the company was set on the task to keep a close check on the churn rate as a major part of his responsibility when he joined four years ago. There is a constant communication to product managers on the field to keep a close tab on customer churn.

telecom churn case study python

On top of the things their effort is certainly showing positive influence as the churn rate is gradually decreasing shown in the adjoining figure.Long story short — in this article we want to get our hands dirty: building a model that identifies our beloved customers with the intention to leave us in the near future. We do this by implementing a predictive model with the help of python.

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Customers turning their back to your service or product are no fun for any business. It is very expensive to win them back once lost, not even thinking that they will not do the best word to mouth marketing if unsatisfied.

Learn all about the basics of customer churn in one of my previous articles. The basic layer for predicting future customer churn is data from the past. By fitting a statistical model that relates the predictors to the response, we will try to predict the response for existing customers.

This method belongs to the supervised learning category, just in case you needed one more buzzing expression. In practice we conduct the following steps to make these precise predictions:. First of all we use Jupyter Notebook, that is an open-source application for live coding and it allows us to tell a story with the code.

Furthermore we import Pandaswhich puts our data in an easy-to-use structure for data analysis and data transformation. To make data exploration more graspable, we use plotly to visualise some of our insights. Finally with scikit-learn we will split our dataset and train our predictive model. One of the most valuable assets a company has is data. As data is rarely shared publicly, we take an available dataset you can find on IBMs website as well as on other pages like Kaggle : Telcom Customer Churn Dataset.

The raw dataset contains more than entries. All entries have several features and of course a column stating if the customer has churned or not. To better understand the data we will first load it into pandas and explore it with the help of some very basic commands.

This section is kept quite short as you can learn more about general data exploration in better tutorials. Nevertheless to get initial insights and learn what story you can tell with the data, data exploration makes definitely sense.

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By using the python functions data. This is important to know so we have the same proportion of Churned Customers to Non-Churned Customers in our training data.

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Be aware that the better we prepare our data for the machine learning model, the better our prediction will be. We can have to most advanced algorithm, but if our training data sucks, our result will suck too.

telecom churn case study python

For that reason data scientists spend so much time on preparing the data. And as data preprocessing takes a lot of time, but is not the focus here, we will work through a few transformations exemplary. Missing Values Furthermore it is important to handle missing data. After identifying the null values it depends on each case if it makes sense to fill the missing value for example with the mean, median or the mode, or in case there is enough training data drop the entry completely.

In the data set we are working with there is a very unusual case — there are no null values. Lucky us for today, but important to know usually we have to handle this.It's a critical figure in many businesses, as it's often the case that acquiring new customers is a lot more costly than retaining existing ones in some cases, 5 to 20 times more expensive.

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Understanding what keeps customers engaged, therefore, is incredibly valuable, as it is a logical foundation from which to develop retention strategies and roll out operational practices aimed to keep customers from walking out the door. Consequently, there's growing interest among companies to develop better churn-detection techniques, leading many to look to data mining and machine learning for new and creative approaches.

But modeling churn has wide reaching applications in many domains. For example, casinos have used predictive models to predict ideal room conditions for keeping patrons at the blackjack table and when to reward unlucky gamblers with front row seats to Celine Dion.

Similarly, airlines may offer first class upgrades to complaining customers. The list goes on. So what are some of ops strategies that companies employ to prevent churn?

Well, reducing churn, it turns out, often requires non-trivial resources. Specialized retention teams are common in many industries and exist expressly to call down lists of at-risk customers to plead for their continued business. Organizing and running such teams is tough. From an ops perspective, cross-geographic teams must be well organized and trained to respond to a huge spectrum of customer complaints. Customers must be accurately targeted based on churn-risk, and retention treatments must be well-conceived and correspond reasonably to match expected customer value to ensure the economics make sense.

The good news is that we live in the data age and have some pretty great tools at our disposal to help answer these questions. John Forman of MailChimp summarizes this well:. We help customers send e-mail newsletters to their audience, and every time someone uses the term 'e-mail blast,' a little part of me dies.

telecom churn case study python

Because e-mail addresses are no longer black boxes that you lob 'blasts' at like flash grenades. No, in e-mail marketing as with many other forms of online engagement, including tweets, Facebook posts, and Pinterest campaignsa business receives feedback on how their audience is engaging at the individual level through click tracking, online purchases, social sharing, and so on.

This data is not noise. It characterizes your audience.Learn how to use Python to analyze customer churn and build a model to predict it. You also accept that you are aware that your data will be stored outside of the EU and that you are above the age of Churn is when a customer stops doing business or ends a relationship with a company.

This course will provide you a roadmap to create your own customer churn models. Begin exploring the Telco Churn Dataset using pandas to compute summary statistics and Seaborn to create attractive visualizations. With your data preprocessed and ready for machine learning, it's time to predict churn! Learn how to build supervised learning machine models in Python using scikit-learn. Having explored your data, it's now time to preprocess it and get it ready for machine learning.

Learn the why, what, and how of preprocessing, including feature selection and feature engineering. Learn how to improve the performance of your models using hyperparameter tuning and gain a better understanding of the drivers of customer churn that you can take back to the business.

He has worked on a variety of big data and machine learning projects across the US and Latin America including customer churn, part failures, smart cities, and NLP. He's interested in using AI to improve business processes and lives. Pricing See our plans. Plans For Business For Students. Create Free Account.

Sign in. If you type We will search for Community Projects Podcasts. Start Course For Free. Loved by learners at thousands of top companies:. Course Description Churn is when a customer stops doing business or ends a relationship with a company. Churn Prediction.Machine Learning is the word of the mouth for everyone involved in the analytics world. Gone are those days of the traditional manual approach of taking key business decisions.

Machine Learning is the future and is here to stay. However, the term Machine Learning is not a new one.

Machine Learning is the art of Predictive Analytics where a system is trained on a set of data to learn patterns from it and then tested to make predictions on a new set of data. The more accurate the predictions are, the better the model performs. However, the metric for the accuracy of the model varies based on the domain one is working in. Predictive Analytics has several usages in the modern world. It has been implemented in almost all sectors to make better business decisions and to stay ahead in the market.

In this blog post, we would look into one of the key areas where Machine Learning has made its mark is the Customer Churn Prediction. For any e-commerce business or businesses in which everything depends on the behavior of customers, retaining them is the number one priority for the organization.

telecom churn case study python

Customer churn is the process in which the customers stop using the products or services of a business. Customer Churn or Customer Attrition is a better business strategy than acquiring the services of a new customer. Retaining the present customers is cost-effective, and a bit of effort could regain the trust that the customers might have lost on the services.

On the other hand, to get the service of the new customer, a business needs to spend a lot of time, and money on to the sales, and marketing department, more lucrative offers, and most importantly earning their trust. It would take more recourses to earn the trust of a new customer than to retain the existing one. There is a multitude of reasons why a customer could decide to stop using the services of a company.

However, a couple of such reasons overwhelms others in the market. A study showed that nearly ninety percent of the customer leave due to poor experience as modern era deems exceptional services, and experiences.

Another study showed that almost fifty-nine percent of the people aged between twenty-five, and thirty share negative client experiences online.

Thus, poor customer experience not only results in the loss of a single customer but multiple customers as well which hinders the growth of the business in the process. Even a good marketing strategy would not save a business if it continues to lose customers at regular intervals due to other reasons and spend more money on acquiring new customers who are not guaranteed to be loyal.

There is a lot of debate surrounding customer churn and acquiring new customers because the former is much more cost-effective and ensures business growth.I am working in a telecom company, which is interested in developing a churn prediction model. I want to know the which steps should I follow in order to develop such kind of model. Any help regarding the problem is highly appreciated. Tags: ChurnDataPredictionTelecomcompanyminingmodelprediction.

Share Tweet Facebook. Views: We did this for a financial company a while ago. It depends highly on the context you want to bring along. I would say that starting with the current CRM database is a solid base. Most likely, the number of customer care calls, the number of complaint e-mails etc. Just counting will most likely not be sufficient though, you will need to analyze the content of the e-mail, audio from the conversations with customer care, web behavior and perhaps even social network analysis.

You are right, the most important place to dig is in Customer Care system or better say CRM database. What I want is that what are the steps in an order way to design the prediction model and of course which model best suits for analyzing telecom data.

After researching a lot in whitepapers and articles in scholar. Here is my findings:. Step1: find as much attributes in telecom data as you can, and make a dataset of those data. JanFebMar and extract those customers in this period of time JanFeb and March which leave the company Am i right? I want to know whether I am doing right or not? Am I right? After finding some assumptions or hypothesis or rules Am i right with this word?


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