Every decision-maker faces risk and uncertainty, now more than ever. The Palisade Risk Conference provides a unique opportunity to help you deal with risk in tangible, practical ways: Learn how decision-makers in similar situations deal with risk. Apply Monte Carlo simulation and other powerful techniques to your problems. Ask questions of experts in risk analysis and your field – bring your models! Make important connections to stay in touch with after the conference.

1. RETAIL PRICE
OPTIMIZATION
Palisade Risk Conference
Chicago, IL
July 17th, 2014
Presented by:
Andrew Pulvermacher

5. Case: Retail Pricing

7. Average
Fundamental Lack of Understanding
Forward-Looking
Decision Making -Patrick Leach, author
“Why Can’t You Just Give Me the Number”
REASON FOR BEING

8. 0
1
2
3
4
5
6
7
8
9
Avg 1 Avg 2

10. New MATH
2 + 25
II + XXV

13. DECISION
SCIENCES

14. CASE:Retail Forecasting
$100 million $7 billion

15. PROFIT
Sales Costs
Variable Fixed
Unit
Cost
Sales Volume
(units)
Sales Volume
(units)
Price

16. PRICE cost
Profit
Competitive
Analysis
Customer
Analysis
Product
Positioning
Marketing Strategy
Perceived
Customer Value
Competitive
Offerings

17. OPTIMIZATIONComplexity Theory: Limits of Feasible Computation
$325k
First Month
Cash Flow
$5 million
First Year
Cash Flow
Improvement
Product
Margin
Improvement
500bps

18. PRICING MODEL
CLEAN PPT ZONE

19. Structure
DEPT
VP
Dir.
Mgr.
A/O
DEPT
Analyst
DW
• Corp 1:
Top 3 in Industry
$19 mln ERP
Excel
• Corp 2:
$5bln Retailer
Excel / Access
• Corp 3:
Top in Industry
Data Warehouse
Excel

20. Optimization
Engine
Forecasting
Engine
Forecast:
• Units
• Revenue
• Profit
• Optimal Price
• Discount
• Offer
• Timing
• Parameters
• Coefficients
Multivariate Bayesian Modeling
INPUTS
Parameter
Models

21. Demand Elements
• Sales & Price History
• Channel
• Store Availability
• Events
• Holidays
• Weather
• Demographics
Constraints
• Price Families
• Pricing Frequency
• Pricing change min/max
• Margin Targets
• Competitive Policy
• Store Labor Limits
• Product Turnover / Age
INPUTS
Model Inputs & Constraints

22. $6.7 Billion in Retail Revenue
• 421,570 records
• 44 Retail Locations
• 99 Product Departments
Weather Impact

23. Modeled Parameters
• Elasticity
• Promotional Lift
• Seasonality
• Holiday Lift
• Transference
Parameter
Models
• Economic Changes
• Inflation
• Demographic shifts
• WEATHER
Wallet Optimization

24. CREATING A PRICING MODEL

26. Andrew Pulvermacher
LinkedIn/drewpulvermacher