Burns Insurance Agency Data Analysis

University of Indianapolis
Analytics Project
Burns Insurance Agency
Noah Pugh: Brody Conner: Ethan Whitaker
CIS 151-01
Jerry Flatto
April 29th, 2016

Executive Summary
Ethan Whitaker
Brody Conner
Noah Pugh
THIS REPORT EXAMINES data provided by Burns Insurance Agency covering the years
2015/2016.
Issues Examined
The main issues that Shane wanted to be examined were agent productivity and the data
collection procedure along with possible ideas on how to improve customer relationships and
management decisions. In the data there were problems that included spelling errors and also an
inconsistency in some of the formatting and the capitalization of city names along with states
names. There was also missing data entries with the “Service Team” section column.
Background Information
What we did for the project was analyze the data given to us by Burns Insurance. We
were given concerns from Justin Jones about productivity in the workplace, employee retention
to information, and how to maximize efficiency. What we did was go into Tableau and look at
our given data in depth. We analyzed which agents had the highest number of records they were
responsible for, which agent had the highest average written premium per applicant type, and
where Burns Insurance does most of their work. One thing we particularly looked at in the
beginning is how many applicants Burns Insurance for each Commercial and Personal policy
type. We then evaluated what we did and put our findings into the separate reports shown in the
Final Document.

Analysis
● With the data given we constructed graphs using the number of records for each agent,
this allowed us to examine agent productivity.
● We also looked the correlation between the numbers of records for effective dates per
month. With that information, we are able to determine what months would be the most
effective to advertise in.
● We examined more specifically the type of policy (personal or commercial) and
compared that to the agents to better understand which agents did more or performed well
with the type of policy.
● We looked at what carriers are aligned number of records, this being helpful in deciding
which companies to prospectively do more business with.
● We examined the days that policies have previously gone into effect and how long they
last, this helping in deciding whether or not to get involved in a case depending on Burns
Insurance's preference.
Conclusion
In conclusion, by modeling the data, we were able to find and address all of the original
issues and questions that Shane appointed. We showed which agents were the most effective by
graphing the correlation of the annual and written premiums each agent had been assigned. We
also showed where Burns insurance could be more efficient with their advertising from time of
year and who to target. We graphed which months are the most effective months to obtain more
clients, and which months need to more work on and advertisement for other policies. Then we
showed which Insurance companies Burns Insurance deals with, and how that can affect their
overall decisions. We showed which insurance companies need to be given discounts as far as
benefits go to show loyalty, and which companies need to be dealt with more. We then
extrapolated on how to get more personal or commercial policies throughout the year and see
when Burns Insurance is getting those personal or commercial types. This information is useful
when trying to be more efficient with when they would use their free capital to advertise to the
right groups in the right times.

Future Recommendations
With the analysis performed it was found that there are improvements to be made in
collecting data. This includes gathering more specific and additional information which each
case. Additional Information that would be useful to Burns Insurance Agency includes the
collection of, but is not limited to; client first and last name, client phone number and e-mail,
insurance type (example: motorcycle, car, or home), client feedback/satisfaction rating, time
spent on each case, and client gender. With the additional collecting of some of these things
Burns Insurance will be able to more accurately track progress of agents, maintain a healthy
customer relationship, and be more efficient in advertising and marketing.

Business Understanding
Ethan Whitaker
Noah Pugh
Brody Conner
Burns Insurance Agency is an independent agency that has been in operation since June
1st, 2008 and represents many different insurance companies including Allstate, Progressive,
MetLife, and many others. Independent insurance agencies are offices that take the hassle away
from buying insurance. They observe the information of the client and attempt to get the
customer the best coverage for the best price. Insurance agencies often prevent people from over-
paying for too much coverage or not getting enough of the proper coverage that they need. Burns
Insurance Agency was established by a man named Shane Burns with the help of his family in
2008 to help people with process of getting insured.
The company now strives to educate the community on the best options for their
insurance needs. Their one-on-one service approach is their #1 goal for all their clients. Burns
Insurance agency not only helps cover individuals, but assist in commercial insurances needs as
well. Their personal insurance includes but is not limited to; Home, Auto, Flood, ATV, and
Recreational vehicles. While their commercial insurance includes but is not limited to;
Restaurants, Trucking, Church, numerous types of liabilities, and Builders. Burns Insurance
Agency is a company that is always dealing with different people all of which have different
concerns, making customer service their #1 priority. Insuring their customers that they are in
good hands is the first step in the process of getting the client insured properly.
Our sponsor Justin Jones, of Burns Insurance, is a third-party consultant under Divergent
Consulting Solutions LLC (Limited Liability Company). Divergent Consulting Solutions
provides Mr. Burns with alternative business and marketing solutions to assist him in broadening
his company’s reach across the Midwest. Justin Jones’s role in Burns Insurance is running the
Google AdWords campaigns to capture any additional streams of revenue for the agency via
online. In addition to managing multiple ads running online, he assists in giving advice on how
to improve office processes and offer alternative solutions to better the interactions Burns
Insurance Agency has with and potential and current clients. His official title at the company is
Risk Management Analyst and his primary responsibility is to measure the risks; by means of

statistical and analytical interpretation, associated with offering a policy to an individual, family,
or business. For example, he wanted to know how to produce a better time frame for allowing his
clients to find the right insurance company faster, which has to do with turn-around time to pitch
the new policy. He has suggested to time stamp these procedures to capture a more accurate
time this new client should expect to hear back from them, with their new insurance policy.
Justin has concerns about how the interior processes take place and how effective current
procedures are done. When a potential client makes a submission on the website or is captured
through direct marketing, he would like to know what time frame it take to actually produce the
better alternative in a policy to this client and he would like to know what time frame should be
expected. This has to do a lot with turn-around time to pitch the new policy. He has suggested to
time stamp these procedure to capture a more accurate time this new client should expect to hear
back from us. He would also like to know how these new client feel about the agency. Burns
Insurance have request and offered give-away to gather feedback, along with incorporating a
“Tell Us How We Are Doing” tab on the website. To date they have received zero feedback.
Burns insurance has an issue with efficiency within the company that needs to be solved
to increase productivity and profits within the company. This is both a problem and an
opportunity to Burns insurance because of the fact that it holding the company back, but is also a
pathway (if fixed) to lead them to future success. The way that this inefficiency is a problem is
because it is holding the company back from being able to progress like it should. If the company
could clean up its processes, fix the simple mistakes, and finish putting in the data like they
should, this would result in significant less time fumbling around and wasting time fixing those
mistakes. This may mean getting rid of policies that don’t make enough money for the time spent
on them, or possibly having to fire an employee because they aren’t doing enough work. The
impact in reducing inefficiencies and increasing productivity within the company would result in
making the company grow and become stronger overall. This would help everyone’s workload
become lighter by not having to waste time fixing the simple mistakes. This would also make
sure their customers happier by the agents being able to pay more attention to them. Another way
that Justin wanted this company to become more efficient ways by increasing employee retention
rate, and increasing communications with Google AdWords. We simply do not have the
information to tell how communication with Google AdWords would help Burns Insurance as a
whole, but that would definitely help promote their business. We can look at employee retention

rate as a guesstimate of which employees are more productive with their time and the policies
they deal with. If Burns Insurance was able to help the agents who had a lower retention rate,
that would increase productivity throughout the company, reduce inefficient workers, and
promote growth throughout.

Data Understanding Phase
Ethan Whitaker
Noah Pugh
Brody Conner
Burns Insurance Agency sent us their information involving all of their applicant types,
talking about whether the applicants were personal or commercial users. The data provided also
gives us the address of the applicant, stating the city, state, and zip of each applicant. The data
also lists what insurance policy is the best option for the each specific applicant type. The data
assigns the applicant a specific insurance policy and agency. The data provides an individualized
policy number for each applicant. The data then proceeds to describe each limit of benefits the
applicant has, which is based on their coverage. The date their policy goes into effect, when it
expires, and how much premium each policy has to pay is also in the data given along with the
specific assigned agent, and service team (if there was one). We were given 622 records of data,
and 13 fields, in tabular form.
Column
Name
Data Role Data type Consolidation
Type
Definition Comments
Applicant
type
Dimension Boolean Count Classifies
the type of
application
(personal or
commercial
)
For most cases
applicants are
either personal
or commercial
City Dimension Geographic Count Which city
the
applicant
lives in.

State Dimension Geographic Count The state
the
applicant
lives in.
Zip Dimension Text,
Geographic
Count The county
the
applicant
lives in
Carrier Dimension Boolean Count Which
Insurance
company
the
applicant is
paired with.
Ex: Geico,
Allstate, State
Farm. (Who
provides the
coverage)
Policy
Number
Dimension Text Count The
specific
identificatio
n number
for the
specific
applicant
LOB Dimension Text Count Limit of
Benefits
The amount of
benefits you
can receive
based on your
coverage.
Effective date Dimension Date Count The date
the policy
comes into
effect.
Expiration
date
Dimension Date Count Date the
policy
expires.
Written
premium
Measure Numerical-
nominal
Sum The amount
of premium
the
customers
are required
to pay in a
Policy amount
can change
depending on
how often
those insured
pay- quarterly,

specific
period.
semi-annually,
or annually
Annualized
premium
Measure Numerical-
nominal
Sum The total
amount of
premium
paid
annually
Assigned
Agent
Dimension Text, Flag Count Specific
agent
assigned to
each policy.
Service Team Dimension Text, Flag Count The names
of the
people who
worked on
this
applicant
together.
● There are no special values in the data, meaning there are no fields that are related to
emotion or preference. The data from Burns Insurance Agency is purely raw data.
● There are also blank entries in the documents in most of the “Service Team” column,
which is data that has not been entered in yet.
Basic statistics of data:
Column names Mean Standard
Deviation
Minimum Maximum
Written premium 1,831.43 6,670.62 0.00 101,636.00
Annual premium 2,215.19 6,671.15 0.00 101,636.00
● We were only provided one document, so we cannot obtain any common fields between
different data sets.
● The data that was provided also contained no free text entries.

With the data from the Business Understanding phase we calculated how much revenue
is coming in for the company base on the type of insurance policy and applicant type. With the
information we have, we can try to find productivity for the sales representatives/workers. The
data we need to observe the productivity of the agents at Burns Insurance Agency would be the
dollar amount of commission for each policy sold for every agent. This would give us the
number of policies sold by each agent and how much money they were making via commission
for the agency. With this data we could use a primary key such as a policy number to connect the
data and then divide the total number of policies by the total amount of commissions earned to
give the productivity of each agent.
Term definitions:
● Column Name- Name of the column in the document.
● Data Role-
○ Dimension- Those things you want to track. They're referrers, pages, country of
origin, product category and other items whose attributes are often non-numerical.
○ Measure- The quantities you want to measure. Visits, page views, hits, bounce
rate and other items that can be quantified numerically.
● Data Type - Describes the kind of information in the column.
● Consolidation Type- How should the data be summarized when displayed.
● Definition- The meaning of the column in non-technical terms.
● Comments- Any additional comments that may help explain the values.
Definitions of Acronyms:
● AUTOB – auto business policy
● BOP – business owner’s policy
● BOPGL- business owner’s policy general liability
● BOPPR – business owner policy property
● CFIRE – commercial fire policy
● CGL – commercial general liability policy
● GARAG- garage policy
● PROP – property policy
● WORK – workers compensation policy
● AUTOP – auto personal policy

● BOAT – boat policy
● DFIRE – dwelling fire policy
● HOME – home owner’s policy
● PUMBR – personal umbrella policy
The Burns Insurance Agency has operated independently since November 2014 and has
changed to a commonly used software in the industry called EZLynx. This agency management
platform is capable of its share of analytics, but fails to provide the Burns Agency a geared
marketing strategy. While this company grows, the Burn Agency will need to develop a more
targeted marketing tactic be competitive in the new downtown Indianapolis location. The dataset
exported from EZLynx consist of six files. Premium Change Exceptions – Captures processes
that occur in the office. Identifies the assigned agent whose customer needed an amendment to
the policy and is assigned a transaction code. In addition to providing a policy overview. Policy
Change – Along with providing a policy overview, this report points out policy premiums
differences made in the policy. Inactive Customer – Includes a list of customers who have
churned along with a brief description of the policy. Different than the other reports, this shows
monies refunded by carrier to customers who have left the agency. Customers With No Policy –
List agents who have received information from potential customer and the type of policy
inquired about. Book of Business All Lines – Describes all current clients of the Burn Agency and
who the policy is written by. Some basics about the policies are provided. Active Customer
Policies – Similar to the Book of Business All Lines file, but assigns transaction codes to each
policy.
Transaction Codes:
● PCH – policy change
● RWL – policy renewal
● XLC – policy cancelled
● SYN – initial sync
● NBS – new business
Monthly Sales Tracker:
● Issue date – date policy was issued

● Effective – date policy is effective
● Policy type – describes the type of policy issued
● Units – measures number of policies issued
● Company – list carrier policy is written with
● Premium – shows the annual or bi-annual dollar amount owed by client
● Agent – list agent who wrote policy
● Lead source – shows how client was acquired
● D or AP – D is that we wrote with our contract AP means we brokered it much less
commission on AP 65% of overall commission instead of 100%. Average commission on
a policy 15%.

Data Preparation Phase
Ethan Whitaker
Brody Conner
Noah Pugh
Renaming Column Headings:
Original Column Name New Column Name
Applicant Type Personal/Commercial
City City (No Change)
State State (No change)
Zip Zip Code
Carrier Insurance Company
Policy Number Policy Identification Number
LOB Amount Of Receivable Benefits
Effective Date Start Date of Policy
Expiration Date End Date of Policy
Written Premium Premium Paid Periodically
Annualized Premium Premium Paid Yearly
Assigned Agent Assigned Agent (No Change)
Service Team Assigned Agents
The column that can be ignored is the column name “State” and lists the state the
applicants are from. We cannot remove any columns from the data table that we have because
we can use and create relationships between and with all of the data. We are almost able to

remove the column “State” because all of the applicants are from Indiana, except 4 applicants.
We have only 2 applicants from Ohio, 1 from Georgia, and 1 applicant from California. We do
not have any use for “code” in our data because we do not have any textual entries. All of the
data we have is “tabular”, so there is no need to give a code for anything. There are also blank
entries in the documents in most of the “Service Team” column, which is data that has not been
entered in yet. We were only provided one document/one table, so we cannot obtain any
common fields between different data sets. We do not have any special values in the dataset, all
of the data that was given is structured, straight-forward data. There is no data that translates into
something different. We do not have more than one table provided, we are only working with the
singular table we were given to analyze, so there is no need to get rid of any repeating fields.
Adding up all of the annualized premiums we have will get us the total amount of revenue that
Burns Insurance has made in a year. When dividing the annual premiums by the number of
applicants we have, we can see what the average premium is. We can also add up all of the
premiums done per individual agent, then divide that by the number of applicants the agent had
to get the average amount of annual premium each agent brings in shown below.
Other Relationships that can be found include the average premium per city to conclude what
city generates the largest and smallest premiums, whether it be a commercial or personal policy.

The inconsistent data encodings that we have are the ways that the cities are spelt. The
majority of the cities are written in all capital letters, where some of the cities are written with
only the first letter capitalized. All of the cities should be written in the same format as all of the
rest, not written up differently. We do not have any suspicious data because all of our data
doesn’t have any free responses, so the suspicious data probability is minimal, and is none with
our dataset.
We have 4 outliers for Written and Annualized Premium. 3 of the outliers are on the top
tier, and 1 was from the bottom tier. What this means is that the top 3 policies are commercial
type of applications, which usually cost 5 times more than an average personal policy, obviously,
those 3 are extreme examples. The bottom tier listed is a personal applicant type.
Policy Number Written Premium Annualized
Premium
XA 2071456 $101,636.00 $101,636.00
02642040-0 $100,372.00 $100,372.00
02618911-0 $65,867.00 $65,867.00
134563216 $0.00 $0.00
The additional data preparation that was performed was separating the data into useful and non-
useful sets, then manipulating it to find useful information. Then we also simplified the values to
become easier read and comprehend in order to use, manipulate, and to work with.
Compared with the original issues of not being able to calculate the productiveness of each
agent, the data provided strains the accuracy of this because the data provided solely gives the
average premium per agent and it lacks the service team information, which could change the
outcome of the results.
Data needed or missing data in the document provided would be the service team for each policy
so that the productivity of teams could be evaluated. We could obtain this data by contacting
Burns Insurance and requesting the data for the service teams on each policy. We could also
contact them and ask them who is on the service team for each policy.

Issues with the data are inconsistency in the spelling of the city in which the policy is
located. For example, Indianapolis is spelled INDIANAPOLIS, and Indianapolis on the
document provided. This could be fixed in the data collection phase by having a set of rules for
imputing city names. For example, there could be a general rule to capitalize the first letter only
to help with the organization of the data.

Modeling Phase
Ethan Whitaker
Noah Pugh
Brody Conner
To analyze the data from Burns Insurance Agency further, we will be using “Tableau”.
This is because Tableau’s software has capabilities such as the ability to depict maps
corresponding to the data. Tableau is also good at comparing averages and producing a visual of
the comparison. Tableau’s ability to do this is helpful as it links well with the questions and
problems we have surrounding the data given to us by Burns insurance such as who is the most
productive agent and what areas produce the highest net income for Burns Insurance.
We will use techniques such as modeling the data with graphs to make comparisons
between the agents and their productivity. We will also use maps to see where the majority of the
policies come from. We will also be able to tell when most policies go into effect, telling us
when to advertise or hire help. These techniques will tie back with our original questions
surrounding agent productivity shown in the graphs we will create and explain what Justin is
looking for. This is the only software package we will be using so we won’t be repeating any of
what we’ve done.
Our initial analytical approach for evaluating the data is to put the data into Tableau and
create various graphs to obtain a visual representation of the data to find useful correlations. This
being our only logical method due to our software limitation of Tableau.
We are comparing the applicant type, city, state, zip, carrier, LOB, expiration, and
effective dates to annualized premiums, written premiums, sum value, average value, sum of
records, and average records. The parameters we are dealing with include the limit to the data
that we are presented with, and the software we are presented with. We cannot make
assumptions, or generate more data to fill the questions that we are asking, and tableau cannot
make more comparisons or other comparisons than its software allows it to do. Input parameters
that we are dealing with are Months, Years, Annualized premiums, Written premiums, number
of records, States, City, Zips, Overall value, Average value, Service teams, Agents, Carriers,
applicant types, and any other special values given.

The issues and errors that exist in data provided from Burns Insurance included errors in
spelling and entry inconsistency. Which are fixed by spellchecking a cross referencing our data
with what it is supposed to be. These errors can cause us to get different data for ‘Indianapolos’
from “Indianapolis” or “INDIANAPOLIS”. Some characteristics that on our current model that
might be useful for the future would include consistency in the data that we have like changing
the incorrect spellings of Indianapolis instead of taking out all of the inconsistent spellings.
Organization of the data is also key for the dataset we were given. We needed to make sure we
could find all of the information easily and without wasting time.
With the data that we were given, we are seeing that they have a lot more personal
policies than they do commercial. We can see which policies have made more money, and which
ones they should be putting more time into. The data that we were given shows where most of
the policies have come from as well, and where they need to branch out to. We are also shown
which employee’s handle more policies, which are more productive than others, and which
handle more specific policy.
With this information and looking at the past, we can see that the greater number of
personnel policies mean that they are easier to get than commercial policies. The main reason for
that is because commercial policies are usually more expensive than personal policies. The fact
that it’s more competitive to obtain a commercial policy, example being the 2 policies out in
Edinburg, means that Burns Insurance needs to make it easier to obtain a commercial policy.
Part of the reason Michelle Madsen’s Commercial policy average is much higher than everyone
else’s (averaging about $100,000 per policy) is because she would have to make it easier for the
big commercial agencies to obtain the policy that they like. The reason that Burns Insurance has
the majority of policies in the Indianapolis area is because they are only stationed in that area,
and have done little to no advertising outside this area. We are also shown that the reason for
some employees being more productive than others relates back to what we said earlier, the
agents who do the most business are the ones who make it easiest for the policyholders to obtain
the specific policy that they want.
With the data we have, Burns will continue to do most of their business in Indiana. There
is a possibility of doing more work outside of Indiana and expanding, but for the most part most
business will be done within Indiana, and specifically within Indianapolis. We can also predict
that most of their policies will continue to be personal applicant types. Right now there are about

5 times as many personal policies as there are commercial, even though the sum monetary
amount for personal policy is only a little more than the sum commercial amount. One thing we
were really interested in looking at was agent efficiency. Due to the data that we have, we were
not able to truly look into that variable that is already hard to define and determine.
With the data we were given, we see a lot of areas and places that are available for
expansion, and that’s where we want to see this company going. We can see that the commercial
policies bring in the most money, yet they are the minority when it comes to the other policies. If
Burns Insurance could advertise to more commercial policy buyers, then we believe that they
would see the most profitability through that. They also have great potential to expand outside
the Indianapolis area, the majority of Burns Insurance’s policies have come from that area, so if
they could at least expand outside that city, we believe that they would also see great financial
growth. From the data, we can see which agents are the most efficient and productive, so with
that information, we can also grow the company. If one agent is using a different technique than
others, or is doing something else better, than they could implement that into more of their agents
so more of them become more standardized in their processes and easier to change agents or
clients so all agents could easily see what the other agents are doing.. Or, we could see which
agents are just not getting the job done, and they could ether decide to let them go, or to train
them more so they can become more effective and efficient.
We are working with structured data, all of the data that we have is tabular, and can be
easily put into tabs and spreadsheets. With the structured data that we have, we are able to run
easier diagnostics on it like finding the means, standard deviations, and structured data makes
constructing a graph easier. We are not working with unstructured data, we do not have any data
that involves open ended responses or non-tabular information.

All of these graphs were created via the tableau software.
1. This graph above shows the sum number of records per agent.

2. This graph above shows the average of the written and annualized premiums per each
agent.

3. This graph above shows the date (month) in which the policy came into effect.
4. This graph above shows the number of records per carrier.
5. This graph above shows the effective date day vs. expiration date day broken down by
applicant type.

6. This graph above shows the total number of policies per each agent broken down by
applicant type.

7. This graph above shows the average written and annualized premium value broken
down by applicant type.

Evaluation Phase
Ethan Whitaker
Noah Pugh
Brody Conner
● This first graph is showing the total number of premiums (annualized and written)
per agent. Combined with second the graph showing the average cost of each
premium the two graphs together can serve as a useful tool when observing the
original issue of agent productivity. The graph showing the average amount is
valuable to a company to help it understand how much it makes per premium
average. By comparing which agent has done the most clients to the price of each
premium, we can then start to use this data to see how effective their agents are
and how much money is being generated on average per premium. This
information could be useful in figuring out which agents have the most customers
and highest average output for each case. With that information you could
strategically place you best and most productive agents in areas where there are
more potential customers and perhaps a higher average income to produce the
highest potential profit for Burns Insurance. This could also be a singular to which
agents need more training and which agents should recognition for their above
average performance. In the future to help make the information on the agents
more accurate and to get better more in depth results I would recommend
documenting how often each agent works, which would provide an exact dollar
amount the agent generated per hour worked.

● The third graph is showing when our claims have come in, based on a month to
month scale. This could possibly help the company when it comes to advertising
for specific months it needs to focus on such as January and June, and when to
focus on each specific claim. This graph might also show a seasonal trend when
more people are insuring their valuables such as boats and cars, just to name a
couple. As you can see for the months of May, August, and October the number
of insurance premiums increase dramatically. For the month of May, this might be

because more people are insuring boats for the summer, and for August, more
people might be insuring cars or other valuables. We are not entirely sure. This
also might be helpful in the hiring process in deciding when to hire temporary or
permanent help and when it might be okay to cut back on hours worked by
employees like in the months of January and June when there’s a low amount of
new insurance policies. In the future, Burns Insurance might want to show how
much they advertise during specific months of the year and see if that has a
correlation with the number of insurance premiums secured for those months.
Burns Insurance could also have a new column asking how their customer found
them, whether it be through a radio advertisement or they found them on Social
media.
● The fourth graph is showing the number of records for each carrier. This is
extremely useful for a company to know because they can use this information to
extrapolate their profits, and significantly improve customer relationships. By
knowing which company Burns Insurance is doing the most business with, they
can therefore provide possible benefits and deals with the companies they work
with the most. So for example, Burns Insurance has over 170 records with Grange
Insurance, so to keep that insurance company happy, giving them a discount of
some sort would be a good sign of loyalty and building the relationship with that
insurance company. Another way that this graph is beneficial to Burns Insurance
is because of the fact that they can see which companies aren't dealing with. And
knowing which companies don't have a lot of records, they can further research
into if they should even be doing business with that insurance company in the first

place. They can also look into if those insurance companies which they have not
been doing a lot of business with flew under the radar and are actually gold mines.
With this information at hand, Burns insurance can see which companies are
effective and not effective to be dealing with. If some company like rider
insurance, do not have a lot of records, Burns Insurance can analyze why they are
not doing a lot of business with them, and that will allow them to further expand
and grow their company. In the future, I would recommend for Burns Insurance
would collect the data more specifically and give a reason why each Insurance
company has more or less records.
●
The fifth graph is showing the days that policies have been going into effect. For
commercial applicants, the top line shows 1 year policies that have been put into
effect in 2015. There is one outlier below that trend line, and that just shows the
one commercial policy that is 6 months long instead of one year long like every
other commercial policy. The middle graph shows personal policies and the days
they went into effect. The top line, similar to commercial, shows policies that will
be in effect for 1 year, while the line below shows the policies that will be in place
for 6 months. The two are pretty even in terms of number of policies. There are
three outliers, however. Those three outliers are policies that were only in effect
for one month. We don’t know why they were only in effect for one month. The
reasons could be they found a different insurance provider, or they sold what was
insured and didn’t need to insure it anymore. The graph on the right simply shows

the combined graph of commercial and personal policy effected dates. The reason
I chose this specific format was because it shows annualized premiums for both
commercial and personal applicant types. It shows how long each policy lasts,
whether it be one year, 6 months, or one month, and how much each of the
policies cover based on the size of the circle for each premium. For the future it
would be wise of Burns Insurance to state what kind of personal or commercial
policies those policies were, whether they be health insurance, motorcycle, boat,
property, or crime insurance.
● This sixth graph is showing the total number of premiums specifically for
personal policies and commercial policies (annualized and written) per agent.
Combined with the seventh graph showing the average cost of each premium
(personal and commercial) the two graphs together can serve as a useful tool to
observing the original issue surrounding agent productivity. The graph showing
the average amount is valuable to a company to help it understand how much it
makes per premium average. By comparing which agent has done the most clients
to the price of each premium, we can then start to use this data to see how
effective their agents are and how much money is being generated on average per
premium. Because the graphs highlight the specific policy type, Burns Insurance

Agency is able to see which agents are more productive with each policy type.
This information could be useful in figuring out which agents have the most
customers and highest average output for each case specifically. With that
information you could strategically assign you best and most productive agents in
areas where they are the most productive and places where there are more
potential customers based on what they are good at. This could also be a singular
to which agents need more training in certain areas and which agents should
recognition for their above average performance. In the future to help make the
information on the agents more accurate and to get better more in depth results I
would recommend documenting how often each agent works, which would
provide an exact dollar amount the agent generated per hour worked for personal
and commercial cases

In conclusion, by modeling the data, we were able to find and address all of the original
issues and questions that Shane appointed. We showed which agents were the most effective by
graphing the correlation of the annual and written premiums each agent had been assigned. We
also showed where Burns insurance could be more efficient with their advertising from time of
year and who to target. We graphed which months are the most effective months to obtain more
clients, and which months need to more work on and advertisement for other policies. Then we
showed which Insurance companies Burns Insurance deals with, and how that can affect their
overall decisions. We showed which insurance companies need to be given discounts as far as
benefits go to show loyalty, and which companies need to be dealt with more. We then
extrapolated on how to get more personal or commercial policies throughout the year and see
when Burns Insurance is getting those personal or commercial types. This information is useful
when trying to be more efficient with when they would use their free capital to advertise to the
right groups in the right times.
One major recommendation in the data collection process that would be very useful to
Burns Insurance to collect the type of insurance policy they collected from each policy, whether
it be boat, car, or motorcycle insurance for a personal applicant type, or crime and property
insurance for commercial applicant type. This additional data would result in the ability to be
more specific in assigning agents to cases and more precise advertising based on the time of the

year and location. This being because research shows that there are more motorcycle and boat
policies in the summer months and months leading up to. Another recommendation that we
would suggest to Burns Insurance would be collecting customer feedback. With this feedback
from the clients, Burns Insurance could change their operations and policies to fit the customer’s
needs and wants becoming more effective.

Appendix
● This graph above shows the number of records for each city.
● This graph above shows the trend of sum number of records for the effective date month

● This graph above shows which agents have policies in different states.
● This graph above shows the trend of number of records for expiration date.
● This graph above shows the sum of records for each LOB.

● This graph above shows the number of records for each zip code.

● This graph above shows the sum of records for each service team.
● This graph above shows the sum of annualized premium for each carrier.
● This graph above shows the sum of annualized premium for each state.
● This graph above shows the sum of the written premium for each state.
● This graph above shows the sum of number of records for each state.
● This graph above shows the sum of records for each applicant type.

● This graph above shows the broken down view of carrier vs. Assigned agent for the
policy.
● This graph above shows the sum of annualized premiums for each applicant type.
● This graph above shows the sum of written premiums for each applicant type.
● This graph above shows which service team is with each carrier.
● This graph shows the sum of the written premiums for each carrier.

● This graph shows the sum of the annualized premium and the sum of the written
premium for each applicant type.
● The graph shows the sum of annualized and written premiums corresponding with their
zip-codes.

● The graph shows the number of records in each zip-code. (The bar on the right shows the
sum of records)
● The graph shows the sum of monetary-amount for policies per zip code.
● The graph shows which zip codes yield the most amount for policies and where they
come from.

● This graph shows the total number of records for each zip code Burns Insurance services.

Burns Insurance Agency Data Analysis

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