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Customer churn is one of the most important metrics for a growing business to evaluate. It is a business term used to describe the loss of clients or customers. In the retail sales and marketing company, customers have multiple choices of services an
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5
PREDICTING CUSTOMER CHURN AT QWE INC. Group10: Richard Ely, Yuchen Luo, Xinyu(Frank) Meng, Yijia He, Simeng Yin
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Agenda ■ Ex Exec ecu uti tive ve Su Summ mmar aryy ■ Met eth hodology – Multiple-variable Logistic Regression (MLR)
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Executive Summary ■ Problem: how to estimate the probability that a given customer would leave and identify the drivers that contributed most to that customer’s decision ■ Decisions to make: - methodology - ident identify ify the the 3 most most influent influential ial variab variables les related related to probabi probability lity of churn churn
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Relationship between Age and Churn does not align with Mr. Wall's belief Mr. Wall’s Belief of Age vs. Churn
■ Ag Age e 6 and and 14 are are no nott good good cut cutof offf poin points ts ■ On Only ly cus custom tomer erss age age > 35 les lesss like likely ly to to leav leave e
Customer Age (in month)
Likelihood to Churn
<6
Less Likely
6-14
Most Likely
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Top 3 factors in Multiple-variable Logistic Regression - “CHI Score in Dec” , “Change in Login Recency”, “Change in CHI Score” ■ Be Best st fa fact ctor orss be beca caus use: e: Smaller p-value
Statistically Statistic ally significant
Variable
Standardized Coefficient
P-value
CHI Score in Dec.
-0.37
1.87e-07 ***
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MLR with Five Variables Is Not Good at Predicting Churn Customers Methodology: Five variables with statistically significant coefficient CHI Score in Dec
∆Days since Last Login
(Dec-Nov)
∆CHI
Score (Dec-Nov)
Customer Age
∆ Views
(Dec-Nov)
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Reasons:
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Top 3 Predictors in Decision Tree - Change in Login Recency, Change in Login Frequency, Customer Age
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Change in Login Recency < 18
Condition met Condition unmet
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Decision Tree-An Extract of Predicted Churn Customers
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Decision Tree Excellent in Avoiding
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