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The document analyzes customer data from "Big Cellular" to understand why customers are leaving. The analyst clusters the data using attributes like gender, age range, payment amount, location and reason for closing the account. Further clustering with additional attributes like marital status and renewal month produced more dispersed clusters. The analysis found trends that most customers leaving were males aged 20-30 from certain regions who had a 200 minute rate plan and were billed for non-payment. Recommendations include removing contract fees in renewal months, offering discounts to certain regions, and targeting advertising to males aged 20-30.
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A bank would like to understand the demographics and other characteristics associated with whether a
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that often the company sees only those who respond to an offer. To get around this, the bank designs a
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know who does and does not respond to the offer, and to use existing demographic data that is already
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Customer Number: A sequential number assigned to the customers (this column is hidden and
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Reward: The type of reward program offered for the card.
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Classification Trees
From Building Better Models with JMP® Pro,
Chapter 6, SAS Press (2015). Grayson, Gardner
and Stephens.
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see community.jmp.com/docs/DOC-7562.
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Key ideas: Classification trees, validation, confusion matrix, misclassification, leaf report, ROC
curves, lift curves.
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A bank would like to understand the demographics and other characteristics associated with whether a
customer accepts a credit card offer. Observational data is somewhat limited for this kind of problem, in
that often the company sees only those who respond to an offer. To get around this, the bank designs a
focused marketing study, with 18,000 current bank customers. This focused approach allows the bank to
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available on each customer.
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offer combination isn’t confused or confounded with the demographic factors. Because of the size of the
data and the possibility that there are complex relationships between the response and the studied
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Because the response is categorical (either Yes or No) and we have a large number of potential predictor
variables, we use the Partition platform to build a classification tree for Offer Accepted. We are primarily
interested in understanding characteristics of customers who have accepted an offer, so the resulting
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Customer Number: A sequential number assigned to the customers (this column is hidden and
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Reward: The type of reward program offered for the card.
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situation.
3
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BI Assignment
- 1. ASSIGNMENT 2BUSINESS INTELLIGENCEPROF. TREMBLAY Understanding trends and patterns to determine why customers are leaving “Big Cellular.” By Ryan Paton
- 2. Understanding the data first DATA MINING IS “AN ART” AS IT “A SCIENCE” (DEPENDING ON YOUR VIEW) UNDERSTANDING WHAT THE COLUMNS (ATTRIBUTES) MEAN WHAT QUESTION DO WE WANT TO HELP ANSWER? THE QUESTION IS “WHY ARE CUSTOMERS LEAVING “BIG CELLULAR?”
- 3. WHAT ATTRIBUTES I CHOSE AT FIRST TO CLUSTER R_CHURN_REASON GENDER TOTAL_PAID_AMOUNT R_CITY AGE_RANGE “These are the attributes I believed that we would help differentiate the trends from the customers” My Reasons – The attributes deal with location, type of customer, age of customer, the amount paid, and the reason we closed their account.
- 4. CLUSTER DIAGRAM By testing the 30% of the trained data, the data mining tool automatically produced 10 clusters. By looking at this diagram, you can see that each cluster is related to each other
- 6. FROM MY FIRST ANALYSIS I NOTICED SOME GOOD ATTRIBUTES R_CHURN_REASON GENDER AGE_RANGE RATE PLAN MY NEXT CLUSTER I ADDED THREE MORE ATTRIBUTES MARITAL STATUS RENEWAL MONTH REGION
- 7. New cluster diagram With this cluster diagram, you can see that only 6 clusters were created and they weremore dispersed from each other.
- 9. The attributes I like R_CHURN_REASON AGE_RANGE GENDER RATE_PLAN REGION RENEWAL MONTH
- 10. The cluster diagram with the attributes I like This new cluster diagram shows cluster 2 by itself which means cluster 2 is unique in some way and (cluster 8, cluster 3 and cluster 6 are related.)
- 11. In conclusion from my analysis From my analysis, I noticed that trends can be derived from the clusters which are:- Are of the ages between 20-30 Had a contract fee of $20 Are based in either South Atlantic and East Southern Atlantic Are mostly male Billing non-pay is reason we mostly likely closed their account Had a renewal month of either June or December. Had a rate plan of 200 minutes
- 12. My Three recommendations My first recommendation would to void contract fees in the months of June and December when customers upgrade their rate plan from 200 minutes to 300 minutes. Since the majority of the people were from east southern Atlantic or south Atlantic, we could give special discounts to these areas. The majority were mostly male. With that said, we should target our advertising campaign towards males of the ages of 20-30.
Editor's Notes
- By looking at this diagram, it is difficult to decipher what these numbers mean. However, if you look at the column “Population (All)” you can see specific trends like “ most customers are between 30s & 20s, most are male, billing non-pay is the most likely reason we closed their account, missing is most city (not a good attribute) and “other” is highest rate plan (but the second is 200 minutes) and the average of total amount paid is $879 with a standard deviation of $913 (not a good attribute) because of the high standard deviation.
- From looking at the Population (All) column, you can notice some patterns of the majority of the people who had left- 1. Had a contract Fee of $20 2. Martial Status of Unknown – Not a good attribute, 3. Lived either in South Atlantic or Eastern South Central and 4. Renewal Month was either in June or December