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How to Predict When You’re Going to Lose a Subscriber

Monday May 22, 2017. 04:34 PM , from The Apple Blog
No business likes to lose customers.
And today’s business world is more competitive than ever. Your customers have more options — and your competitors can reach them easier than ever before.
So customers are constantly juggling a decision around where to spend their money.
Consequently, developing a strategy to retain customers is now an essential part of any business.
But every customer leaves for different reasons, and an individualized retention campaign can be costly if you apply it to every one of your customers.
However, if you could predict in advance which customers are at risk of leaving, you could reduce those costs by solely directing efforts at folks that are at a high risk of jumping ship.
Fortunately, we can use artificial intelligence — or more specifically, a machine learning platform — to predict when a single customer is likely to leave based on their actions (or inaction). This is often called ‘churn.’
Although churn rate originally started out as a telecom concept, today, it’s a concern for businesses of all shapes and sizes — including startups.
And thanks to a number of cloud-based prediction APIs, accurately predicting churn is no longer exclusive to big businesses with deep pockets.
A.I.-Powered Churn Prediction
Churn prediction is one of the most popular uses for machine learning in business. It’s basically just a way of using historical data to detect customers who are likely to cancel their service in the near future.
In effect, we want to be able to predict an answer to the following question: “Is this particular customer going to leave us within the next X months?”
And of course, there are only two possible answers to that question — yes or no. Easy.
For this guide we’re going to use BigML to make those predictions.
BigML provides a convenient graphical interface for setup, visualization of the data, and the final predictions. Everything is point-and-click — no coding necessary.
So let’s get to it…
Looking for an on ramp?
This is a how-to guide intended for developers and tech-savvy business leaders looking for a proven entry point into A.I.-powered business systems.

What You’ll Need
Step 1: Create the Dataset
Step 2: Create the Model
Step 3: Test a Prediction

And the steps are really easy — it’ll only take a few minutes to run through this.
What You’ll Need
Right off the bat, let’s get the initial requirements knocked out.
Create an BigML account.
If you don’t already have a BigML account, go ahead and set one up.

Simply submit the form and activate your account — the service is free to use for datasets under 16MB (which our dataset is).
Step 1: Create the Dataset
To start, go to your BigML Dashboard.

If you’re signed in, you should see the “Sources” tab.
As a quick aside to help clarify what you’ll see in each tab:

Sources — view of the raw data sources you have in your account
Datasets — view of the processed data (from the original source)
Supervised — view of the supervised models you’ve generated
Unsupervised — view of the unsupervised models you’ve generated
Predictions — view of the predictions you’ve made from the models
Tasks — view of the jobs you’ve run

Click on the “Churn in the Telecom Industry” item. This dataset lists the characteristics of a number of telecom accounts — including features and usage — and whether or not the customer churned.
Next, click on the “1-CLICK DATASET” link. This will — as the name implies — process the raw source data into a properly formatted Dataset so we can start building models from it.

After a few seconds the job will complete and you should see the Datasets tab full of your new Dataset’s attributes and respective statistics.

And that’s it for the Dataset, so let’s start building models.
Step 2: Create the Model
To build your prediction model, click on the “1-CLICK MODEL” link.

After a few seconds the job will complete and you should see the Models tab with a colorful decision tree full of your new model’s decision nodes.

If you mouse-over one of the nodes, you’ll see its respective details.

And that’s it for the model, so let’s start making predictions!
Step 3: Test a Prediction
And now for the fun part.

Click on the “PREDICT” link.
As another quick aside, here’s what each prediction option does:

PREDICT QUESTION BY QUESTION — the system will ask you a series of questions then make a prediction based on your answers
PREDICT — provides a screen to adjust each attribute and get an instant prediction
BATCH PREDICTION — as the name implies, allows you to make predictions for a list versus just one

On the “Predict” screen you can start playing with different parameters to see which thresholds will predict whether a customer will churn or not.

As you adjust each attribute, you’ll an instant update of the prediction — including a score of how confident the system is for that respective prediction (100% = complete confidence, 0% = no confidence).
And that’s it!
What’s Next
You now have a powerful tool to help fine-tune your efforts at keeping your customers. However, this is just the tip of the iceberg. The real fun begins when you upload your own data.
And then all that’s left is for you to tie this service into your existing marketing planner or automation platform and you’ll be off and running.
Be sure to spend some time browsing the different features BigML provides; there’s a long list of useful things you can do — including some nice visualization tools to help drill into your data.
You can dig deeper into BigML’s API — including additional tutorials — in the developer documentation.

This post is part of our How to Apply A.I. in Your Business blog series. Be sure to check out our past issues:

Building Voice-Enabled Products With Amazon Alexa
Cognitive Customer Engagement Using IBM Watson
Harnessing Visual Data Using Google Cloud
Building a Recommendation Engine Using Microsoft Azure
Predicting Marketing Campaign Response Using Amazon Machine Learning
Unleashing A.I.-Powered Conversation With IBM Watson
Get into the Mind of Your Customer Using Google’s Sentiment Analysis Tools
Discover Your Customers’ Deepest Feelings Using Microsoft Facial Recognition
Give Your Products the Power of Speech Using Amazon Polly
Computers Are Opening Their Eyes — and They’re Already Better at Seeing Than We Are

And be on the lookout for future issues, they come out every other Monday.

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