Copart is a provider of online vehicle auction and remarketing services in the United States, Canada and United Kingdom. It provides vehicle sellers with a range of services to process and sell savage and clean title vehicles using its virtual auction technology. The challenge was to analyze their data and come up with useful recommendations for their business. We performed the following three analysis on the given dataset primarily using SAS, R and excel: 1. Time series analysis to forecast their revenue for the next year. 2. Copart's revenue model is built around both seller and buyer fees. We developed an optimization model to arrive at the optimum value of buyer fees at the given rate of seller fees, to achieve the target revenue in the future years. 3. We computed an alignment metric to help Copart balance their yard alignment across the different states in the USA to meet the rising demand for auto auction vehicles and at the same time maximize their profit.

1. DataCogit

2. University of Texas at DallasDataCogitate!
AGENDA
TEAM INTRODUCTION
COPART BUSINESS UNDERSTANDING
SCOPE OF THE DATA PROVIDED
ANALYSIS ON REVENUE GROWTH
YARD SIZING / ALIGNMENT

3. University of Texas at DallasDataCogitate!
TEAM INTRODUCTION
VIBHORE AGARWAL
An analytics professional with nearly 3 year of
experience in business analytics and project
management.
Masters in Business Analytics – UT Dallas (’17)
Past Companies – Avaya Inc., ZS Associates,
Samsung
Our obsession for DATA is INSATIABLE…
Engineering graduate with 3 years of celebrated
professional experience with client facing roles,
business negotiations, requirement gathering
and quality assurance.
Masters in Business Analytics – UT Dallas (’18)
Past Companies – Bureau Veritas, Accenture
A big Data enthusiast and an aspiring Business
Intelligence Professional with 2 years of
experience as a Decision Scientist
Masters in Business Analytics – UT Dallas (’18)
Past Companies – Mu-Sigma Business Solutions
A data driven analytics professional with an
educational background in Economics and
Statistics and nearly 3 years of experience in
Actuarial and Financial Analytics.
Masters in Business Analytics – UT Dallas (’17)
Past Companies – Aon Hewitt, Brillio
SAISIRI INDRAKANTI JATIN GARG
ANUJA SARCAR

4. University of Texas at DallasDataCogitate!
TEAM INTRODUCTION
COPART BUSINESS UNDERSTANDING
SCOPE OF THE DATA PROVIDED
ANALYSIS ON REVENUE GROWTH
YARD SIZING / ALIGNMENT
AGENDA

5. University of Texas at DallasDataCogitate!
COPART BUSINESS UNDERSTANDING
Accident
Assignment
Dispatch
Receiving
Title
Processing
$ $
Auction
Settlement
Marketing
COPART PROFIT = (BUYER FEE + SELLER FEE) – OPERATIONS COST
Founded in 1982, Copart Inc. helps sell so-called salvage vehicles over the internet. The company offers its virtual bidding auction-style sales
technology to vehicle suppliers and chiefly insurance companies, which in turn sell to licensed dismantlers, rebuilders, repair shops etc.

6. University of Texas at DallasDataCogitate!
TEAM INTRODUCTION
COPART BUSINESS UNDERSTANDING
SCOPE OF DATA PROVIDED
ANALYSIS ON REVENUE GROWTH
YARD SIZING / ALIGNMENT
AGENDA

7. University of Texas at DallasDataCogitate!
SCOPE OF DATA PROVIDED
1,286,506
127 43
0
200,000
400,000
600,000
800,000
1,000,000
1,200,000
1,400,000
Closed Open Pending
Distribution of LOT Status
1,244,547
36,834 4,067 1,226 2
0
200,000
400,000
600,000
800,000
1,000,000
1,200,000
1,400,000
Distribution of Salvage Type
categories
1,226
1,223,678
46 20,802 7 7,448 29,060 163 96 80 558 2,824 686 2
0
200000
400000
600000
800000
1000000
1200000
1400000
Distribution of Vehicle Type
108,345 (8%)
209,332 (16%)
458 (0%)
964,356 (75%)
4,185 (0%),
Distribution of Bid Type
LIVE KIOSK BID PRELIMINARY BID FINAL SEALED BID
VIRTUAL BID (VB3 LIVE) NA
Distribution of Transactions based on Auction Month-Year

8. University of Texas at DallasDataCogitate!
TEAM INTRODUCTION
COPART BUSINESS UNDERSTANDING
SCOPE OF DATA PROVIDED
ANALYSIS ON REVENUE GROWTH
YARD SIZING / ALIGNMENT
AGENDA

9. University of Texas at DallasDataCogitate!
ANALYSIS OF REVENUE GROWTH
Yearly Revenue Growth Projected = 2.03%
Avg. linear monthly growth forecasted of 2.05%
BUYER FEE 2010 = LATE FEE + ADMIN FEE + MEMBER FEE + INTERNET FEE + STORAGE FEE
SELLER FEE 2010 = COPART CHARGES + ADVANCE CHARGES
Used Stochastic and Deterministic Time Series Modeling; took the model that gave the best growth rate
AIM is to increase
it further by ~10%
$807 M$792 M

10. University of Texas at DallasDataCogitate!
ANALYSIS OF REVENUE GROWTH contd..
OBJECTIVE : To increase the Buyer Fee while hedging the increment in Seller Fees
Late Fee Admin Fee Member Fee Internet Fee Storage Fee
42 30 75 49 45
Source for 2010 buyer fee -> https://secure.copart.com/c2/pdf/BasicFees.pdf
Average Fee based on distribution
TYPE OF BUYER FEE ->
MULTI OBJECTIVE TRADE-OFF ANALYSIS (SIMULATION)
$241
50 75 100 55 55
~ 19% ↑ ~ 150% ↑ ~ 33% ↑ ~ 12% ↑ ~ 22% ↑
$335
AVG. % ↑
AVG. INCREMENT ON BUYER FEE ->
Approx. 40% increase in Buyer Fee
TOTAL BUYER FEE (2010) = $203.5M
TOTAL SELLER FEE (2010) = $588.5M
TOTAL BUYER FEE (2011) = $300M
TOTAL SELLER FEE (2011) = $588.5M
~$ 792M
12% ↑
TOTAL PROJECTED REVENUE FOR 2011 ~$ 807M
10% ↑
C
O
N
S
T
R
A
I
N
T
A
I
M
~$ 888M
2% ↑

11. University of Texas at DallasDataCogitate!
ANALYSIS OF REVENUE GROWTH contd..
MULTI OBJECTIVE TRADE-OFF ANALYSIS (SIMULATION)
Fee Type Late Admin Member Internet Storage
Orignal Distribution 80740 1285586 1285586 1285586 53870
% increase in Distribution in 2011 2.00% 3.00% 3.00% 2.50% 2.00%
Expected Distribution 82355 1324154 1324154 1317726 54948
Variable Cells 50 73 98 53 55
Min Variable Max
42 Late 50
30 Admin 75
75 Member 100
49 Internet 55
45 Storage 55
OPTIMUM VALUES OF
BUYER FEE RATES
SUM = $ 329
RECOMMENDATIONS
Revenue could be increased by 12 % by increasing the components of buyer fees to
the realized optimum values
OR
COPART could keep all the components of buyer fees constant and introduce a new
value component ~ $ 88 wiz. $ 329 - $ 241

12. University of Texas at DallasDataCogitate!
TEAM INTRODUCTION
COPART BUSINESS UNDERSTANDING
SCOPE OF DATA PROVIDED
ANALYSIS ON REVENUE GROWTH
YARD SIZING / ALIGNMENT
AGENDA

13. University of Texas at DallasDataCogitate!
YARD SIZING / ALIGNMENT
WEST SOUTH NORTH
EAST
CENTER
OBJECTIVE: To balance the Yard Quantity,
existing sales volume and market
opportunity by using “Index Based”
Approach
Our approach to address the above disparate goals is by
incorporating multiple variables into a single “index”, and
then balance the index across all the yard states. Multiple
variables are used to design an index plan. This index
approach provides several benefits. Most importantly, the
index method could help meet the future demand and
increase revenue.

14. University of Texas at DallasDataCogitate!
YARD SIZING / ALIGNMENT contd..
Effective Yard Balance Design:
1) Create more uniform coverage of existing business, thus
helping customer retention
2) Results in better coverage of new growth opportunities
3) Reduces travel requirements for sellers
4) Diminishes or even eliminates state inequalities, which
eradicates unequal opportunities
Typical variables used in aligning the state yard’s count are:
1) Number of Sellers
2) Bidders Count
3) Distinct Count of Yards
Maximizing each state’s potential
Potential (Sellers) Existing Qty. (Yards) Demand (Bidders Count)
1,271,908 139 7,617,023
27,650 3 165,587
29,212 3 170,421
1.05 1 1.03
Total in US
Average per State
State A
State A vs. Average State
Weights 20% 35% 45%
X 1,000
1031.5An alignment index with a value greater than 800 would mean that the state
has a higher growth potential and would require more # yards in the future.

15. University of Texas at DallasDataCogitate!
YARD SIZING / ALIGNMENT contd..
RECOMMENDATIONS
~22 % states require re-alignment of the number of yards. Hence, we need an increase in # yards in the highlighted states to
fulfill the rising demand.

16. University of Texas at DallasDataCogitate!
THANK YOU
“ If you torture data long enough, it would confess to anything you’d like. “
- R.H. Coase,
British Economist
Gracias Dankie շնորհակալություն Vielen Dank Thank-You Tack Ngiyabonga Terima Kasih 고맙습니다 धन्यवाद ありがとうございました