Retaining existing customers is vital for organizations looking to grow their business without relying too heavily on the significantly higher cost of acquiring new customers. Marketing, sales, and customer retention departments need to work to make sure customers are satisfied, provide them with incentives, and present offers at the right time to reduce churn. MindsDB can aid churn reduction by analyzing the massive amounts of customer data and interactions necessary to identify and measure customer turnover risk. MindsDB Machine Learning can help marketing, sales, and customer retention teams determine the best incentive and the right time to make an offer to minimize customer turnover.
In this tutorial you will learn how to use SQL queries to train a machine learning model and make predictions in three simple steps:
Using SQL to perform machine learning at the data layer will bring you many benefits like removing unnecessary ETL-ing, seamless integration with your data, and enabling predictive analytics in your BI tool. Let's see how this works with real world example to predict the probability of churn for a new customer of a telecom company.
Note that you can follow up on this tutorial by connecting to your own database and using different data - the same workflow applies to most machine learning use cases.
We will be using MindsDB Cloud in this tutorial. The first step would be to create a free account on cloud.mindsdb.com. After successful login, you will be redirected to MindsDB Studio.
Alternative installation instructions are available on the MindsDB Getting Started page
First, we need to connect MindsDB to the database where the Customer Churn data is stored. In the left navigation click on Database. Next, click on the ADD DATABASE. Here, we need to provide all of the required parameters for connecting to the database.
Then, click on CONNECT.
We have successfully connected MindsDB to the database. Now, we need to create a datasource which requires that we connect MindsDB to the Customer Churn table. Click on the NEW DATASET and add:
After filling in all the values click on CREATE. Now, we have successfully created a new Datasource that is connected to the database. The next step is to use the MySQL client to connect to MindsDB’s MySQL API and train a new model to help us predict customer churn.
I will use MySQL command line client in the next part of the tutorial but you can follow up with the one that works the best for you, like MySQL Workbench, Dbeaver, etc. The first step we need to do is to use the MindsDB Cloud user to connect to the MySQL API:
In the above command, we specify the hostname and user name explicitly, as well as a password for connecting.
If you got the above screen that means you have successfully connected. If you have an authentication error, please make sure you are providing the email you have used to create an account on MindsDB Cloud.
In this tutorial, we will use the customer churn data-set. Each row represents a customer and we will train a machine learning model to help us predict if the customer is going to stop using the company products. Below is a short description of each feature inside the data.
Now, we will train a new machine learning model from the datasource we have created using MindsDB Studio.
Switch back to mysql-client and run:
You will notice there are 2 tables available inside the MindsDB database. To train a new machine learning model we will need to INSERT a new record inside the predictors table as:
The required values that we need to provide are:
To train the model that will predict customer churn run:
What we did here was to create a model called customer_churn to predict the Churn and also ignore the gender column as an irrelevant column for the model. Also note that the ID columns in this case customerId will be automatically detected by MindsDB and ignored. The model training has started. To check if the training has finished you can SELECT the model name from predictors table:
The complete status means that the model training has successfully finished.
The next steps would be to query the model and predict the customer churn. Let’s be creative and imagine a customer. Customer will use only DSL service, no phone service and multiple lines, she was with the company for 1 month and has a partner. Add all of this information to the WHERE clause.
With the confidence of around 82% MindsDB predicted that this customer will churn. One important thing to check here is the important_missing_information value, where MindsDB is pointing to the important missing information for giving a more accurate prediction, in this case, Contract, MonthlyCharges, TotalCharges and OnlineBackup. Let’s include those values in the WHERE clause, and run a new query:
Now, we can see that this customer will still churn but MindsDB is more confident with around 98% for this prediction.
In this tutorial, we saw how easy it is to bring Machine Learning directly to the Database layer and train/query Machine Learning models using SQL queries and demonstrated how to use SQL queries to build a machine learning model and make predictions in three simple steps.
For a limited time, you can try MindsDB to connect to data sources, train models, and run predictions in the cloud. Simply create an account, it’s free (final pricing to be announced later this year), and our team is available on Slack and Github for feedback and support. Check it out and let us know what predictions you come up with.
