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GUILT &
REDEMPTION
Phase Two
Kelly Foy, Tucker Hammel,
Dan McKenzie & Charles Whelan
Agenda
Q1:
Demographics
Geographic Analysis
Cluster Analysis
Q2:
Optimization Function
Price Elasticity/Sensitivity
Q3:
Pricing Strategy
Q1: Customer Demographics
$70,000-$79,999 income
73% Are “asian/pacific islander”
39% 4 year education
60% Employed for wages
66% Shopping up to 2-3 times a month
Q1:Demographics-Geographic
Presence highest in Northeast, Great
Lakes,Florida, and Northern &
Southern California metro regions
Modest in Piedmont-Atlantic
Low everywhere else
Low/Modest & high demand Texas
Triangle, Piedmont, Cascadia
GILT- WTP. & PURCH INT.
Non Gilt- WTP. & PURCH INT.
1: Younger, Low Income
2: High PI, Low Income
3: Younger, High Income
4: Low PI, High Income
5: Older, High Income
Q1: Clustering of survey participants
How to determine maximum revenue
Step 1: Determine unique WTP
Step 2: Calculate total revenue for each WTP in the following function
Price * Number of people = Revenue
Step 3: Find maximum revenue and set optimal price
Step 4: Repeat for each look
Q2: Price Sensitivity and the Elasticity of Demand
The demand elasticity attained for
any look can be attained by the
function (%change in demand)
The range of elasticities we attained
was between -.87 and -1.47
These elasticities are determined
between the values of optimal price
and its associated number demanded,
and the number demanded at gilt’s
(%change in price)
Q3: Maximized values within the given dataset
Product: Optimal Price; Optimal Revenue; Revenue in sample at Gilt price
Product 1: 99; $29601; $4792
Product 2: 24.99; $8821.47; $4275
Product 3: 49.99; $15746.85; $2933
Product 4: 29.99; $8487.17; $7020
Product 5: 75; $23700; $20493
Product 6: 50; $9850; $2904
Product 7: 49.99; $13597.28; $6557
Product 8: 39.99; $9917.52; $3490
Amount gained
$24809
$4546.47
$12813.85
$1467.17
$3207
$6946
$7040.28
$6427.52
Q3: Pricing Strategy
Continue gathering info on what maximum prices customers will purchase
a product
Survey customers before an item goes on sale
Implement optimization function
Guarantees minimum level of sales and would increase the amount of
money Gilt earns
All while decreasing the price of goods

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Analytics Competition Continued

  • 1. GUILT & REDEMPTION Phase Two Kelly Foy, Tucker Hammel, Dan McKenzie & Charles Whelan
  • 2. Agenda Q1: Demographics Geographic Analysis Cluster Analysis Q2: Optimization Function Price Elasticity/Sensitivity Q3: Pricing Strategy
  • 3. Q1: Customer Demographics $70,000-$79,999 income 73% Are “asian/pacific islander” 39% 4 year education 60% Employed for wages 66% Shopping up to 2-3 times a month
  • 4. Q1:Demographics-Geographic Presence highest in Northeast, Great Lakes,Florida, and Northern & Southern California metro regions Modest in Piedmont-Atlantic Low everywhere else Low/Modest & high demand Texas Triangle, Piedmont, Cascadia GILT- WTP. & PURCH INT. Non Gilt- WTP. & PURCH INT.
  • 5. 1: Younger, Low Income 2: High PI, Low Income 3: Younger, High Income 4: Low PI, High Income 5: Older, High Income Q1: Clustering of survey participants
  • 6. How to determine maximum revenue Step 1: Determine unique WTP Step 2: Calculate total revenue for each WTP in the following function Price * Number of people = Revenue Step 3: Find maximum revenue and set optimal price Step 4: Repeat for each look
  • 7. Q2: Price Sensitivity and the Elasticity of Demand The demand elasticity attained for any look can be attained by the function (%change in demand) The range of elasticities we attained was between -.87 and -1.47 These elasticities are determined between the values of optimal price and its associated number demanded, and the number demanded at gilt’s (%change in price)
  • 8. Q3: Maximized values within the given dataset Product: Optimal Price; Optimal Revenue; Revenue in sample at Gilt price Product 1: 99; $29601; $4792 Product 2: 24.99; $8821.47; $4275 Product 3: 49.99; $15746.85; $2933 Product 4: 29.99; $8487.17; $7020 Product 5: 75; $23700; $20493 Product 6: 50; $9850; $2904 Product 7: 49.99; $13597.28; $6557 Product 8: 39.99; $9917.52; $3490 Amount gained $24809 $4546.47 $12813.85 $1467.17 $3207 $6946 $7040.28 $6427.52
  • 9. Q3: Pricing Strategy Continue gathering info on what maximum prices customers will purchase a product Survey customers before an item goes on sale Implement optimization function Guarantees minimum level of sales and would increase the amount of money Gilt earns All while decreasing the price of goods

Editor's Notes

  1. Under the assumptions: They put at what point they turn a profit as a constraint They Automate the maximizing process Make sure that their survey results are truthful.