Crescent Electric Sales Forecasting
A Summer Project by Intern Bo Anderson
www.cesco.com
How do you Forecast Sales?
• Find variables outside company related to sales– Outside company, economic factors
• Measure effect of variables on sales– So X is ↑, do sales go ↑ or ↓?
• Adjust for company– Seasonality– Size of company (branches)
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How do you find those variables?
• Find ones that make logical sense• Find ones that trend with sales
– Correlation• “the degree to which two or more attributes or measurements
on the same group of elements show a tendency to vary together”
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Correlation Types
• Positive Correlation– If one thing goes up/down, so does the other
• My weight goes up and so does my waist size.
• Negative Correlation– If one thing goes up/down, the other does the
opposite• I spend more time playing video games and my GPA goes
down
• No Correlation– If one thing goes up/down, the other doesn’t respond
• I eat more cheetos but my shoe size stays the same
Correlation Examples
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Correlation Examples
• Hypothetical Examples– Left Shoe & Right Shoe
• Correlation = 1.0
– # of Apples Person 1 & Person 2 if sharing• Correlation = -1.0
• Real Life Examples– Unemployment Rate & # of Hires
• Correlation = -0.93
– MLB 2011: Wins & Saves• Correlation = -0.03
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No Correlation
Regression
• Linear Regression:– the relation between variables in an equation that
measures how certain independent variables effect a dependent variable
• Measure the effect of variables while holding “everything else constant”
• Hand size & weight relationship, really it’s just height and weight– Must hold height constant to really see a relationship
Let’s do our own Linear Regression
What is the relationship between the Economy and Crescent’s Total Sales?
Variable Real Sales Real Sales Log
Job Openings: Total Nonfarm 0.7416 0.7444
Industrial Production Index 0.7264 0.7281
Real Private Nonresidential Fixed Investment 0.7263 0.7227
Quits: Total Nonfarm 0.7006 0.7025
Unemployment Rate -0.6947 -0.6947
All-Transactions House Price Index for California 0.6799 0.6749
S&P Case-Shiller 20-City Home Price Index 0.6774 0.6720
Manufacturers' New Orders: Nondefense Capital Goods Excluding Aircraft 0.6734 0.6724
Manufacturers' New Orders: Durable Goods 0.6708 0.6707
All Employees: Goods Producing Industries 0.6593 0.6589
Real Personal Income Excluding Current Transfer Receipts 0.6449 0.6374
Real Retail and Food Services Sales 0.6330 0.6404
Natural Gas Price: Henry Hub, LA 0.6239 0.6158
Real Private Nonresidental Investment
• Measured in billions of 2005 dollars. This means it’s adjusted for inflation
• Measures business investment purchases (buildings, inventory etc.)
• The majority of our sales count under this
Real Personal Income Excluding Current Transfer Receipts
• Measured in Billions of 2005 Dollars, meaning adjusted for inflation
• Measures individuals ability to consume
• Our customers livelyhood
# of Branches
• Measured in… well branches. Closings and openings are adjusted by the year/quarter of change so decimals are used (Close in late June then .5 branches for year)
• Help measure an increase in our available market
S&P Case-Shiller 20-City Home Price Index
• Index with January of 2000 as a base of 100
• Increase in demand for a housing, means increases in complimentary products (ex: lights, wiring, etc.)
• Type 1 Customers are effected by housing
All Employees: Goods Producing Industries
• Measured in thousands of persons
• As companies expand payrolls they’ll also be in a position to expand inventories and builidings
Our Model
• Q1 Sales =- $35,2464,272.30
+ ($46,758.22)*RPNFI
+ ($55,242.36)*SPCSHHPI
+ ($7,492.30)*AEGPI
+ ($32,962.60)*RPIECTR
+ ()*Branches
Seasonal Adjustments: Q2 + $29,606,440.82; Q3 + $36,307,769.30; Q4 + $29,550,914.42
Efficiency
Year Actual Sales Predicted Sales % Error
2004 $743,203,941.31 $740,336,170.05 -0.39%
2005 $826,969,138.75 $825,148,749.58 -0.22%
2006 $975,724,449.00 $1,002,965,878.32 2.79%
2007 $987,896,799.86 $956,907,932.59 -3.14%
2008 $1,034,431,846.94 $1,043,719,368.00 0.90%
2009 $752,005,333.88 $743,286,006.38 -1.16%
2010 $768,882,644.62 $762,552,247.79 -0.82%
2011 $885,405,400.95 $895,815,601.36 1.18%
2012 ? ? ?
Average (+/-)1.32%
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How accurate is our Model?
• 95% of the time it is within 3%. Once every 20 years it will be off by more
• The odds of it being off are the same as the odds the Dallas Cowboys have to win the Super Bowl this year according to Vegas
• Think about it the Cowyboys COULD win the Super Bowl, but we are pretty sure that won’t happen. if anybody wants to bet heads-up with 1 to 1 odds, I’ll take your bet right now
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