Acme Telephonica (AT) is a mobile phone operator that has customers across every state of the U.S.A.
AT struggles with customer churn prediction—customers leaving AT for other mobile phone operators.
AT hired us to take a new approach to reducing customer churn.
This case study is to develop a machine learning solution to this business problem.
AT did not approach us with a well-specified machine learning problem. Instead, the company approached us with a business problem—reducing customer churn.
Our first goal is to convert this business problem into a machine learning problem and develop a concrete solution.
To evaluate the available data, we have the data definitions.
Feature
Description
BILLAMOUNTCHANGEPCT
The percent by which the customer’s bill has changed from last
CALLMINUTESCHANGEPCT
AVGBILL
AVGRECURRINGCHARGE
AVGDROPPEDCALLS
PEAKRATIOCHANGEPCT
AVGRECEIVEDMINS
AVGMINS
AVGOVERBUNDLEMINS
AVGROAMCALLS
PEAKOFFPEAKRATIO
month to thismonth
The percent by which the call minutes used by the customer has changed from last month to this month
The average monthly bill amount
The average monthly recurring charge paid by the customer The average number of customer calls dropped each month
The percent by which the customer’s peak calls to off-peak calls ratio has changed from last month to this month
The average number of calls received each month by the customer
The average number of call minutes used by the customer each month
The average number of out-of-bundle minutes used by the customer eachmonth
The average number of roamingcallsmade by the customer each month
The ratio between peak and off peak calls made by the customer thismonth
NEWFREQUENTNUMBERS
How many new numbers the customer is frequently calling this month?
Feature
Description
CUSTOMERCARECALLS
The number of customer care calls made by the customer last
NUMRETENTIONCALLS
NUMRETENTIONOFFERS
AGE
CREDITRATING
INCOME
LIFETIME
OCCUPATION
REGIONTYPE
HANDSETPRICE
HANDSETAGE
NUMHANDSETS
SMARTPHONE
CHURN
month
The number of times the customer has been called by the retentionteam
The number of retention offers the customer has accepted
The customer’sage
The customer’s credit rating
The customer’s incomelevel
The number of months the customer has been with AT
The customer’soccupation
The type of region the customer lives in
The price of the customer’s current handset
The age of the customer’s current handset
The number of handsets the customer has had in the past 3 years
Is the customer’s current handset a smart phone?
The targetfeature
