RETAIL PRICE OPTIMIZATION

Palisade Risk Conference Chicago, IL

July 17th, 2014

Presented by:

Andrew Pulvermacher

Case: Retail Pricing

Average

Fundamental Lack of Understanding

Forward-Looking

Decision Making -Patrick Leach, author “Why Can’t You Just Give Me the Number”

REASON FOR BEING

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Avg 1 Avg 2

New MATH 2 + 25 II + XXV

DECISION SCIENCES

CASE: Retail Forecasting $100 million \ $7 billion

PROFIT

Sales Costs

Variable Fixed

Unit Cost

Sales Volume (units)

Sales Volume (units) Price

PRICE cost

Profit

Competitive Analysis

Customer Analysis

Product Positioning

Marketing Strategy

Perceived Customer Value

Competitive Offerings

OPTIMIZATION Complexity Theory: Limits of Feasible Computation

$325k First Month Cash Flow

$5 million First Year

Cash Flow Improvement

Product Margin Improvement

500bps

PRICING MODEL

CLEAN PPT ZONE

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

Optimization Engine

Forecasting Engine

Forecast: • Units • Revenue • Profit

• Optimal Price • Discount • Offer • Timing

• Parameters • Coefficients

Multivariate Bayesian Modeling

INPUTS

Parameter Models

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

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

Weather Impact

Modeled Parameters

• Elasticity • Promotional Lift • Seasonality • Holiday Lift • Transference

Parameter Models

• Economic Changes

• Inflation

• Demographic shifts

• WEATHER

Wallet Optimization

CREATING A PRICING MODEL

Andrew Pulvermacher LinkedIn/drewpulvermacher