Rockford Mutual Insurance Client Project...New Customer 1: Age: 19-21, Marital Status: Single, Insurance score:654, Rate plan:7 Outcome for churn would be single females between the

Rockford Mutual Insurance Client Project

Report Prepared For:

Mr. Shane A. Heeren, Vice President of Marketing and Sales, Rockford Mutual Insurance

Report Prepared By:

Brent Brown, Karina Rodriguez, Margaret Sottosanto, Dan Warfield

Introduction to Marketing Analytics, Pui Ying Tong, Illinois State University

December 1st, 2017

Table of Contents

Introduction………………………………………………………………………………………..x

Data preparation steps……………………………………………………………………………..x

Descriptive Statistics and Visualizations of the Data

Set…………………………………………x

Customer retention/churn, Loss analysis………………………………………………………….x

Nearest Neighbor (KNN) Prediction……………………………………………………………...x

Discussion…………………………………………………………………………………………x

Appendix………………………………………………………….……………………………….x

Introduction

The purpose of this study is to display how Rockford Mutual Insurance is performing according

to their historical data. This information can help our client to make better business decisions in

the future. Our research objectives were to find customers that Rockford Mutual Insurance

should target based on meaningful insights from the auto policyholder dataset which included

variables such as age, insurance score, marital status, etc. We were able to group these customers

and profile them into categories such as top five best worst customer segments. Through this we

have provided insights for ideal customers as well as “loss” customers. We provided analysis on

customer retention as well as churn.

Data Preparation Steps

The dataset that was given to us was structured so that each row was a transaction. The policies

are renewed every 6 months and another dataset was structured so that every unique customer

represented each customer- this measured their total profits and total loss amounts during 2012-

2104. In the customer-based dataset, we created new fields and variables. One that indicated who

churned and who did not during this time period and one measuring who left in less than a year.

We added two new customers in which we added numbers in the variables with someone in

mind. We also added a new variable and we named it “Target”, it was 0’s all the way down the

row except for the new customers that we created, which was a 1.

Recency, Frequency, and Monetary (RFM) analysis is a technique used to quantitatively

determine what customers are the most valuable. A higher RFM score represents a higher quality

of customer. The RFM bar chart shows us that males and females ages 30 through 64 are

Rockford Mutual’s best customers.

Descriptive Statistics and Visualizations of the Data Set

Churn Rate Based on Territory

We see that the top churn territories include 17, 50, 87, 90, 92 and the low churn territories

include 1, 14, 57, 81. However, some areas (90 & 50) with the highest churn rates produce

higher profits for the firm. It is advised that Rockford Mutual focuses less on territories 17, 87,

and 92 as these areas have a very high churn rate with relatively low profit levels.

Between married females and males, females age 50-64 bring in the most total profit. Between

single females and males, females age 30-49 bring in the most total profit. The greatest total loss

paid was from married females between the age of 50-64 and from single females 50-64.

Between married females and males the highest average total profit came from males age 19-21.

Between married females and males the highest average total loss paid came from males age 30-

49 Between single females and males the highest average total profit came from females ages

19-21. Between single females and males the highest average total loss profit came from males

age 16-18.

Analyses for “Insurance Score Level” segmentations

The four charts above explain the insurance score levels in term of whether a female was married

or single and whether a male was married or single. All four shows the customer’s age in

relations to the insurance score.

Churn Customers versus Stay Customers

The data in this demonstrates that there is a high correlation between churn and insurance score.

This also shows the sex and marital status, it also shows that no matter if married- pleasure use

describes segments with the highest churn and insurance score.

Customers who left in less than a year

In the chart above, the data on the left demonstrates who left in less than a year. The pink bars

are females and the blue bars are males. The majority of people who stayed less than a year were

males age 19-21.

The Best/Ideal customers segment

The best customer segment are single males and females ages 30-64.

The worst customers segment

The worst customer segment would be married males and females age 16-18.

Nearest Neighbor (KNN) Prediction

Explanation of Nearest Neighbor Analysis and how it can be used to predict target outcome

Predicting Churn

The Nearest Neighbor Analysis is used to find the point in a given set that is the closest or most

similar to a given point. The closer the points, the smaller the function values. Nearest Neighbor

Analysis can be used to predict target outcome by choosing specific variables and running the

data which will present a chart in which the target outcomes will be colored red.

Demonstration of KNN using scenarios

New Customer 1: Age: 19-21, Marital Status: Single, Insurance score:654, Rate plan:7

Outcome for churn would be single females between the ages of 16-18 as shown with the red dot

in the image above.

New Customer 2: Age: 30-49, Marital Status: Married, Insurance score: 700, Rate plan: 5

Customer retention/churn, Loss analysis

Churn

In an assessment of the decision tree we found that the terminal node with the highest churn rate

is from node 107. These customers have attributes that resemble the highest rate plan possible

above 21. They also have made some sort of claim within the last 3 years and their insurance

score is lower than 683. This node contains 829 customers.

Less than a year

In an assessment of the decision tree we found that the terminal node with the highest rate of

clients that were insured under Rockford for less than one year was found in node 53. These

customers have a high rate plan (greater than 18) with low driver violation points (between 0 and

8). This node contains 1007 customers. In regards to long-term benefits, Rockford Mutual should

avoid customers with these attributes as they do stay with the firm for very long.

Total Loss Paid

In an assessment of the decision tree we found that the terminal node with the highest

amount paid out by Rockford Mutual was node 14. These customers are single and

aged 16-24. This tells Rockford that young, inexperienced drivers are more likely to

submit a claim resulting in Rockford paying. There are 2120 customers in this node.

Logistic Regression

Less than a year churn (0/1) prediction

Score dF Sig.

Step 0 Variables PolicyNum 45.707 1 .000

EffectiveDate 1312.224 1 .000

VehNum 123.466 1 .000

Territory 32.929 1 .000

VehicleAge 6.367 1 .012

BISymbol 8.462 1 .004

MPSymbol 18.058 1 .000

CompSymbol 7.225 1 .007

CollSymbol 6.173 1 .013

Use 16.579 4 .002

Use(1) 2.160 1 .142

Use(2) .128 1 .720

Use(3) 8.586 1 .003

Use(4) 6.961 1 .008

AssignedDriverNumber 2.531 1 .112

DriverSex(1) 5.391 1 .020

DriverAgeRange 208.081 7 .000

DriverAgeRange(1) 35.957 1 .000







DriverMarital(1) 31.832 1 .000

DriverViolationPoints 183.044 1 .000

AutoandHomeDiscount 86.404 2 .000

AutoandHomeDiscount(1) 5.030 1 .025

AutoandHomeDiscount(2) 80.039 1 .000

InsuranceScore 252.992 1 .000

InsuranceScoreLevel 345.063 15 .000

InsuranceScoreLevel(1) 51.026 1 .000







InsuranceScoreLevel(8) .068 1 .794








RatePlan 379.980 1 .000

TimesRenewed 598.166 1 .000

LossFreeYears 469.979 1 .000

EarnedPremium 6.131 1 .013

LossIncurred .470 1 .493

Profit .735 1 .391

LossPaid 1.597 1 .206

LossReserve .218 1 .641

SubrogationSalvage .006 1 .941

Expense .011 1 .916

Date_most_recent 1312.224 1 .000

Transaction_count 717.539 1 .000

TotalProfit 46.913 1 .000

Recency_score 1010.091 1 .000

Frequency_score 381.032 1 .000

Monetary_score 9.382 1 .002

RFM_score 1136.174 1 .000

TotalExpense .213 1 .645

TotalLossPaid .813 1 .367

churn 1451.099 1 .000

Churn Regression

B S.E. Wald df Sig. Exp(B)

Step

1a

PolicyNum .000 .000 .009 1 .926 1.000

EffectiveDate .000 .000 3.472 1 .062 1.000

VehNum -.314 7.570 .002 1 .967 .731

Territory .017 .188 .008 1 .930 1.017

VehicleAge .014 1.528 .000 1 .992 1.015

BISymbol .156 1.050 .022 1 .882 1.169

MPSymbol -.026 .612 .002 1 .966 .974

CompSymbol -.508 2.208 .053 1 .818 .602

CollSymbol .670 2.526 .070 1 .791 1.954

Use .067 4 .999

Use(1) 10.724 138.816 .006 1 .938 45448.445

Use(2) -26.542 5494.202 .000 1 .996 .000

Use(3) 3.873 19.584 .039 1 .843 48.106

Use(4) -7.219 98.828 .005 1 .942 .001

AssignedDriverNumb

er

-1.657 10.143 .027 1 .870 .191

DriverSex(1) 3.921 21.524 .033 1 .855 50.446

DriverAgeRange .153 7 1.000

DriverAgeRange(1) 7.334 209.557 .001 1 .972 1531.432

DriverAgeRange(2) 11.248 59.717 .035 1 .851 76737.908

DriverAgeRange(3) 14.362 79.335 .033 1 .856 1727190.743

DriverAgeRange(4) -1.820 64.900 .001 1 .978 .162

DriverAgeRange(5) 14.089 44.692 .099 1 .753 1314487.340

DriverAgeRange(6) 9.891 42.888 .053 1 .818 19750.103

DriverAgeRange(7) 13.454 45.273 .088 1 .766 696388.045

DriverMarital(1) -.013 16.415 .000 1 .999 .987

DriverViolationPoints 1.016 15.023 .005 1 .946 2.762

AutoandHomeDiscou

nt

.098 2 .952

AutoandHomeDiscou

nt(1)

5.427 27.802 .038 1 .845 227.451

AutoandHomeDiscou

nt(2)

-2.301 20.040 .013 1 .909 .100

InsuranceScore .067 .588 .013 1 .909 1.069

InsuranceScoreLevel .135 15 1.000

InsuranceScoreLevel(

1)

83.918 50681.202 .000 1 .999 2.786E+36


2)

96.146 50681.068 .000 1 .998 5.696E+41


3)

91.851 50680.974 .000 1 .999 7.768E+39


4)

22.308 50683.542 .000 1 1.000 4877870255.0

00


5)

94.693 50680.840 .000 1 .999 1.333E+41


6)

98.031 50680.814 .000 1 .998 3.752E+42


7)

99.889 50680.758 .000 1 .998 2.405E+43


8)

106.423 50680.662 .000 1 .998 1.656E+46


9)

96.340 50680.608 .000 1 .998 6.920E+41


10)

109.591 50680.556 .000 1 .998 3.933E+47


11)

109.404 50680.528 .000 1 .998 3.261E+47


12)

114.280 50680.519 .000 1 .998 4.278E+49


13)

109.313 50680.861 .000 1 .998 2.978E+47


14)

117.613 50683.793 .000 1 .998 1.198E+51


15)

155.272 50689.595 .000 1 .998 2.716E+67

RatePlan -.411 6.622 .004 1 .951 .663

TimesRenewed .170 1.250 .019 1 .892 1.186

LossFreeYears -.070 2.339 .001 1 .976 .932

EarnedPremium -.003 .052 .004 1 .950 .997

LossIncurred .002 .112 .000 1 .987 1.002

LossPaid -.001 .110 .000 1 .993 .999

LossReserve -.017 .114 .022 1 .883 .983

Expense .192 2.369 .007 1 .935 1.212

Transaction_count -.447 3.158 .020 1 .887 .640

TotalProfit .002 .008 .041 1 .840 1.002

Recency_score 115.619 195.026 .351 1 .553 1.631E+50

Frequency_score -.226 11.738 .000 1 .985 .798

Monetary_score .046 11.745 .000 1 .997 1.047

TotalExpense -.027 .382 .005 1 .943 .973

TotalLossPaid .002 .013 .021 1 .886 1.002

Constant 1427670.77

0

767849.34

0

3.457 1 .063 .

Discussion

Our overall conclusion to the client based on our interpretation of the data analysis results is

insurance score and churn have a high correlation. Recommendations to the client based on our

interpretation of the data analysis results would be to focus your advertisements and marketing

efforts on the territories and demographic with low churn and high profitability so that you are

not wasting your marketing dollars on people who are not very likely to buy. Also, Rockford

Mutual’s ideal customers lie within the 30 to 64 years of age range. Strengths of this study

included our team working together and learning how to use real data to interpret real insights for

this

Limitations of this study include Tableau not being cooperative. Suggestions for future research

would be to include more lifestyle variables (such as smoking) so that any margin of error will

decrease.

Documents

Rockford Mutual Insurance Client Project...New Customer 1: Age: 19-21, Marital Status: Single, Insurance score:654, Rate plan:7 Outcome for churn would be single females between the