Cell2Cell

Contents Business Understanding: Introduction ......................................................................................................... 2

Business Objective ........................................................................................................................................ 2

Data Mining Objective .................................................................................................................................. 2

Data Set ......................................................................................................................................................... 2

Data Preparation ........................................................................................................................................... 2

Data Modeling ............................................................................................................................................... 3

1. Decision Tree (Binary) ....................................................................................................................... 3

2. Decision Tree (Three-way tree) ........................................................................................................ 5

3. Logistic Regression ............................................................................................................................ 5

4. Logistic Regression with Transform Variables .................................................................................. 6

5. Neural Networks ............................................................................................................................... 6

6. Neural Networks after transform variables and variable selection .................................................. 7

Evaluation ..................................................................................................................................................... 8

Profitability of a Proactive Retention Plan .................................................................................................... 9

The key variables predicting churn: ............................................................................................................ 10

Possible Incentives Offered ........................................................................................................................ 10

Test Measures ............................................................................................................................................. 11

Profitability Matrix .................................................................................................................................. 11

Net Additions minus Existing Customers ................................................................................................ 11

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List of Figures

Figure 1: Process flow Diagram ..................................................................................................................... 3

Figure 2: 2-way decision tree that resulted from cell2cell data set ............................................................. 4

Figure 3: Variables in descending order of their importance helping in splits for 2-way Tree .................... 4

Figure 4: Result summary of 2-way decision tree ......................................................................................... 4

Figure 5: Variables in descending order of their importance helping in splits for 3-way Tree .................... 5

Figure 6: Result summary of 3-way decision tree ......................................................................................... 5

Figure 7: Result summary of Regression without transformation variables ................................................ 5

Figure 8: Result summary of Regression with transformation variables ...................................................... 6

Figure 9: Result summary of Neural Network without Variable transformation and selection ................... 6

Figure 10: Result summary of Network with Variable transformation and selection .................................. 7

Figure 11: Comparison of cumulative lift value for different techniques ..................................................... 8

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Cell2Cell: The Churn Game

Business Understanding: Introduction Cell2Cell is the 6th largest wireless company in the US, giving service to nearly 10 million subscribers,

serving more than 210 metropolitan markets & 2900 cities (covering nearly all 50 states). The company is

currently facing a major problem of customer churn.

We are using SAS EM 4.3 to develop a model for predicting customer churn at Cell2Cell.

Business Objective Reduce churn for the company

Improve profitability

Identifying incentives offered to the customers with high risk of churning

Data Mining Objective To develop an accurate predictive churn model (Lift value of at least 1.75)

To identify the factors that are important in driving subscribers churning

Data Set The given data set consists of 71,047 rows & containing a total of 78 variables (including a variable named

“CHURN”, signifying whether the customer had left the company two months after observation). One of

the variables named “CALIBRAT” was used to differentiate the validation dataset from training dataset.

Training dataset contained data of 40,000 customers and validation dataset contained 31,047 customers.

Data Preparation The dataset was divided in training and validation datasets, using “CALIBRAT” as the partition variable

(value of 1 was used training and value of 0 was used for validation).

Variable “CHURN” was set as target variable and some other variables (those not related to business

objective) were rejected.

Variable No.

Original Variable Changed Variable

22 Churn Target

26 CSA Rejected

30 Customer Rejected

77 Calibrat Rejected

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78 Churndep Rejected

Table1. Showing variables that were rejected

Data Modeling Total of 6 different models were used to predict the churn of customers. These models were:

Decision Tree (binary)

Decision Tree (three way tree)

Logistic Regression

Logistic Regression with Transform Variables

Neural Networks

Neural Networks after transform variables and variable selection

SAS EM 4.3 was used to run these 6 models. Snapshot of the model is shown below.

Figure 1: Process flow Diagram

1. Decision Tree (Binary) For both 2-way and 3-way tree gini-reduction method was used. The assessment criteria was set to be

“Proportion of event in top 10%”.

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Figure 2: 2-way decision tree that resulted from cell2cell data set

Figure 3: Variables in descending order of their importance helping in splits for 2-way Tree

As can be seen from above figure, EQPDAYS – Number of days of the current equipment (split at <302),

MONTHS – Months in service (Split < 11 months) are most important variables that resulted in splits.

Figure 4: Result summary of 2-way decision tree

As can be seen from figure above, with number of leaves greater than 34, no significant split happens.

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2. Decision Tree (Three-way tree)

Figure 5: Variables in descending order of their importance helping in splits for 3-way Tree

The important variables are very similar to that used in 2-way decision tree. First four variables are same

for 2-way and 3-way tree.

Figure 6: Result summary of 3-way decision tree

With number of leaves greater than 93, no significant split happens in 3-way tree.

3. Logistic Regression Here, no transformation of variable was carried out.

Figure 7: Result summary of Regression without transformation variables

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4. Logistic Regression with Transform Variables In this model, few variables were transformed. Details of transformation of variables are as below:

1. Variable MOU- Minutes of Usage was transformed using log transformation. The variable had high

skewness earlier.

2. CUSTCARE- Mean number of customer care calls was transformed into a squared variable. As high

number of customer care calls suggests high number of complaints, it can be a major cause for

churn. Sqauring the variable helps increase its influence.

3. Using decision trees, the 2 major variables EQPDAYS and MONTHS were identified and

transformed by creating 2 buckets. For EQPDAYS, the cut off value for bucket used is 301 days

and for MONTHS, the cut off value used is 10 months.

4. Other important variables like CHANGEM and CHANGER were transformed but they didn’t

improve performance.

Figure 8: Result summary of Regression with transformation variables

5. Neural Networks Here, no transformation and variable selection was done.

Figure 9: Result summary of Neural Network without Variable transformation and selection

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6. Neural Networks after transform variables and variable selection Here, the transformed variables were made to pass through variable selection process with default

settings. Initially, transformed variable CUSTCARE was rejected by neural network model. But, the same

was forced to be used. With this the performance of the neural network model improved.

Figure 10: Result summary of Network with Variable transformation and selection

As can be seen from figure above, the average error has come down a little bit and has less variation as

compared to that of neural network model without transformation and variable selection.

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Evaluation The lift chart of all the techniques is shown below:

Figure 11: Comparison of cumulative lift value for different techniques

Legend:

Tree : 2-way decision tree

Tree-2 : 3-way decision tree

Neural : Neural Network without transformations

Neural-2 : Neural Network with transformed variables and variable selection

Reg : Regression without variable transformation

Reg-2: Regression with variable transformations

Table below shows cumulative lift values for different techniques at different percentiles (deciles).

Table 2. Showing cumulative lift values at different deciles for different techniques

As can be seen from table, the performance of “Regression with transformed variables” is best among the

different techniques used. The lift value at first deciles with Regression with transformed variables is 2.102

Percentile 2-way Tree 3-way Tree Regression

Regression with

Transformation

Neural

Networks

Neural Networks

with

Transformation

10 1.931 1.904 1.658 2.102 1.511 1.691

20 1.706 1.699 1.642 1.79 1.42 1.593

30 1.451 1.573 1.5 1.604 1.336 1.489

40 1.353 1.444 1.4 1.457 1.318 1.416

50 1.294 1.325 1.343 1.411 1.297 1.346

60 1.254 1.263 1.253 1.314 1.21 1.284

70 1.236 1.192 1.203 1.217 1.159 1.194

80 1.127 1.131 1.131 1.133 1.127 1.131

90 1.056 1.07 1.067 1.071 1.076 1.062

100 1 1 1 1 1 1

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which is highest. It is also evident from table that “Regression with transformed variables” performs best

at most deciles and significantly better till top 3 deciles.

The performance of Neural Network technique is worse among the 3 techniques. Transformation and

variable selection does help in improving the performance of Neural Network technique.

3-way decision tree, though underperforms when compared to 2-way decision tree at first deciles,

performs better than 2-way decision tree at other deciles.

So, based on the table above, the best technique is “Regression with transformed variables” which gives

a lift value of 2.102 which is well above the target of 1.75.

Profitability of a Proactive Retention Plan Using regression model with transformed variables as inputs, the following values are calculated.

Assumption: Subscriber in the 1st deciles is targeted.

β = Base line churn rate= 1.96 %

λ= Lift = 2.102

γ = Success rate = λ-1= 1.102

LVC= Lifetime value of customer

C= Cost of incentive

Profit = Probability of Churner* Success Rate* LVC-C

= β* λ* γ* LVC –C

LVC = Monthly Revenues * (1+r)/(1+r-Retention Rate)

r= discount rate= 10%

Total average monthly churn rate= 2%

Retention Rate (annual) = (1-(.04*12))= 0.76

Average Monthly revenues per customer = 58.8528

Average LVC per customer = 58.8528*(1+.1)/(1+.1-.76) = 190.406

Thus Cell2Cell can spend a maximum of 190.406 on a customer.

Total number of customers = 10,000,000 subscribers

Using 1st deciles, who have highest probability of churning:

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Average incremental revenue = Number of customers contacted* β* λ* γ*(Average LVC)

= (10% of 10 million)*1.96*2.102*1.102*190.406

= 864.472 million

Thus Cell2Cell can afford a maximum incentive cost of 864.472million.

Using other monthly revenues we have:

Monthly revenues Lifetime Value of Customer Maximum Incentive Cost

30 97.05876356 440.6614469

50 161.7646059 734.4357448

70 226.4704483 1028.210043

90 291.1762907 1321.984341

110 355.8821331 1615.758639

130 420.5879754 1909.532936

150 485.2938178 2203.307234

The key variables predicting churn: EQPDAYS: Number of days of the current equipment

MONTHS: Months in service

CAHNGEM: % Change in minutes of usage

MOU: Mean monthly minutes of use.

RECCHRGE: Mean Total recurring charge

CHANGER: % Change in revenues

RETCALL: Customer has made call to retention team.

Variables like months, MOU, RETCALL, and Change in Revenues are Key performance indicator of telecom

businesses. It indicates longer the time a customer remains with a Cell2Cell lesser will be its churn. Also

number of calls made to the retention team has direct effect on the churn rate.

Variable like MOU, RETCALL, RECCHARGE are actionable. Cell2Cell can offer incentive plans to improve

minutes of usage as this will result in lower churn rate. Also Retention team can play an active role in

improving EQPDAYS, MONTHS and RECCHARGE through loyalty and service programs.

Possible Incentives Offered Based on above derived important variables, the following incentives plan can be offered to the customers

to reduce the possible churn

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From the model we got EQPDAYS as one of the primary factors for churn prediction. It makes

business sense, as a customer who changes his old cell phone is likely to churn, because many

mobile service providers gives new cell connection bundled with cell phone. From decision tree

we can see that after 302 days customers change their handset.

o So the company can come up with a plan of offering customers new cell phone (at a price

slightly higher than the cost price) without changing their connection. This offered at 280

days mark. This offer will allow the company to keep hold of their customers (ensuring

future revenue) without any incremental cost.

From the decision tree it is visible that the probability of churn increases if EQPDAYS is more than

301 and CHANGEM value is less than -131% (i.e. decrease in minutes by 131%). Hence the

company should try to increase the usage minutes by the customers having old cell phones.

o This can be done by providing discount plan to these customers.

Customers with CHANGEM value less than -131% along with MOU value less than 416mins and

the customers with CHANGEM value more than -131% along with MOU value less than 1875mins

have a very high tendency of churn.

o Such customers can also be offered with discounted calling plans to increase their minutes

of usage.

In case of LCV customers, the company should offer little more incentives to such customers. This

is because even if in the short term the company incurs more cost, but retaining such customers

will increase cash flow to the company in the long run.

Test Measures

Profitability Matrix Profitability in this case can be represented as shown below:

Decision (from Predictive Modeling)

Churn Not Churn

Actual

Churn True Positive (Profit = LCV - Incentive Cost) False Negative

Not Churn

False Positive (Loss = Incentive Cost) True Negative

Net Additions minus Existing Customers This is another measure that can be used to measure the success of the program. As negative net

additions minus existing customers is a concern for any company in the industry in question, a positive

number in mature market would suggest successful implementation of data analytics.