Using Customer Behavior Data to Improve Customer Retention

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Using Customer Behavior Data to Improve Customer Retention

Telco Customer Dataset
This demo uses the the sample data within Watson Analytics. Please use the sample dataset.

 
What’s in the Protect Your Customer data set?

This data set provides info to help you predict behavior to retain customers. You can analyze all relevant customer data and develop focused customer retention programs.

A telecommunications company is concerned about revenue and the number of customers leaving their landline business for cable competitors. They need to understand who is leaving. Imagine that we are analysts at this company and we have to find out who is leaving and why.

The data set includes information about:

  • Customers who left within the last month –this column is called Churn
  • Services that each customer has signed up for – phone, multiple lines, internet, online security, online backup, device protection, tech support, and streaming TV and movies
  • Customer account information – how long they’ve been a customer, contract type, payment method, paperless billing, monthly charges, and total charges
  • Demographic info about customers – gender, age range, and if they have partners and dependents

Getting the data                         

  1. Under the Data tab in Watson Analytics, tap + New Data button.
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  2. Tap Import > Sample Data and then select and import the Protect Your Customer dataset.
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The data set appears as a tile under the Personal folder within the Data tab and you’re ready to get to work. It may take a couple of seconds as Watson Analytics is analyzing the data to aid your journey in using this dataset.

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Which customers have high value?

  1. To find the answer to this question, tap the Protect Your Customers CSV data set tile.

You want to know where the revenue comes from and what you want to protect.   To better understand the business you may want to look at total charges by internet service type by asking “What is the average TotalCharges by InternetService?”

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You will want to select the first tile as best represents the line of inquiry.  Note that the image on the tile indicates you should expect a bar chart for this comparison.  In looking at the results, we see that Fiber Optic is clearly  the main internet service that gets the bulk of the revenue.

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Next, you want to find out about the total charges by contract type. Press the Plus button circled below and we will add another tab to your discovery set.

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We want to investigate the total charges by contract type.  Enter the question “What are the average TotalCharges by contact?” and select the first suggestion (tile) from Watson Analytics.  We see the result that 2 year contracts generate more income whereas month-to-month is the lowest.  Typically, I would have thought average charges would be lower with longer contracts.  This is a little surprising.

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Clearly we want to protect customers with Fiber Optic and longer service contracts.  Lets add to the discovery set again with the plus button and find out how long customers stay with the services for each contract type by asking “What is the average tenure by contract type?”  Again the first suggestion from Watson Analytics is exactly the line of inquiry we want to explore, so we will select the first tile.

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When reviewing the results, we see that month-to-month contracts stay with the service on average 18 months whereas customers stay 42 months and 56 months on average for one year and two year contracts respectively.  You can hover over the bars of the chart to get the actual numbers.  The month-to-month contracts are not leaving immediately, but we should be thinking about how we can move these customers into longer term contracts.

What drives customer tenure and churn?

In thinking this through, we want to nail down the factors that drive customer tenure.  Let’s add to the discovery set and ask “What drives Tenure?”.  The first suggestion fromWatson Analytics brings you to a spiral diagram which highlights TotalCharges and InternetService as the key factors for Tenure with a predictive strength of 91%.

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Looking at the relationships further down the list, I see that churn also affects tenure. This makes a lot of sense.

Let’s see what drives churn by adding to the discovery set and asking “What drives Churn?”.  This time we will look at the second tile as it shows a decision tree for Churn.  By scrolling downward on the decision tree and hovering over each of the tree nodes, we can see that  customers with a month-to-month contract with less than six months tenure and Fiber Optic services churn 75% of the time.

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This occurrence is very high and we need to understand this better.  Perhaps the service is weaker than what our competition is providing and these new customers see the difference.  In any case, we need to speak to customer services and our hardware team with this finding as this directly impacts Fiber Optic revenue which is key to our business.

Again, you can watch the narrated video for this use case here.