Demand Based Management

Demand Based Management
Phoenix, AZ
May 5, 2003
Krishna Venkatraman
Chief Scientist
DemandTec, Inc.
TM

Demand Based Management
A brief history of DBM.
Principles of DBM.
DemandTec’s DBM Platform.
D’Agostino’s, NY.
Conclusions.
2

Demand Based Management
Understanding consumer demand and
applying that knowledge to every
merchandising decision
Price Optimization
Promotion Optimization
Placement Optimization
Assortment Optimization
3

DBM Aligns Objectives and Strategies
Corporate Objectives
Sales
Profit
Price Image
perfect
Find the perfect balance
balance
Deliver results
Merchandising Strategies
Price
Promotion
Product Assortment
Placement
4

The Evolution of Enterprise Software
Revenue
Cost
CRM
Sales, Marketing,
Service Automation
Blue Martini, Siebel, Oracle,
SAP, PeopleSoft
ERP
Financials, HR, Manufacturing
DBM
Price, Promotion,
Assortment, Placement
DemandTec,ProfitLogic,
Manu/Talus, KSS
SCM
Purchasing, Logistics,
Inventory, Planning, Distribution
SAP, PeopleSoft, Oracle Retek, i2, Manugistics, SAP,
NONSTOP
Efficiency Optimization
5

Why understanding consumer demand matters
Consumer response to a product is context-sensitive.
Marketing levers affect consumer response
dramatically.
Marketing levers affect true supply chain costs.
6

Convention Consider … …with we would the two new Wisdom: recommend individual demand, Net items, P adjusting Profit moves C in and Customer to both P,
…and … so And But the now the Profit trick the Best is entire Actually to Items push assortment Varies are demand in the Widely is to Upper more the by upper SKU Right profitable.
right!
Products using prices the right, hypothetical slightly, and Retail C to Is to send Under the numbers left demand 2%… from C to P.
Net Net Profit
Profit
$ $ per per SKU
SKU
12%
10%
8%
6%
4%
2%
0%
-2%
-4%
-6%
-8%
-10%
-12%
C
P
BEST
BESTP
WORST
WORST
C
Sales Sales $ $ per per Store Store per per Week
Week
- 0.01 c
+0.02 c
7

Of course you don’t just do it with two items!
Net Profit
$ per SKU
12%
10%
8%
6%
4%
2%
0%
-2%
-4%
-6%
-8%
-10%
-12%
Sales $ per Store per Week
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The Principles of DBM
Consumers benefit.
Deliver immediate,
sustainable and measurable
results.
Model consumer response at
the appropriate level.
Deliver strategies that
maximize objectives subject
to business constraints.
Model temporal effects.
Capture the interdependent
effects of marketing
instruments.
Include supply chain impact.
Adapt and evolve to
changing and dynamic
needs.
Integrate.
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A DBM Solution Architecture
Web-based
Interface
Enterprise
Applications
DBM
Platform
External
Data
Reports & charts Business logic Scenario management Promotion calendars
Price Promotion Placement
Product
Assortment
Optimization Engine
Demand Engine Financial Engine
POS data
Product data
Promotion calendars
Data
Warehouse
Back
Office
Competitive data
Cost data
Store data
Supply
Chain
Seasonal data
Causal data
Legacy
Systems
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The Demand Engine’s goal is to understand
volume under various merchandising conditions
Volume
150
100
50
0
1
Example: Price/Volume Trade-offs
3
5
Ad and Display Activity
Driving volume
7
9
11
Week
13
15
What happens
to volume at
various prices?
17
19
Actual Model
Forecast Everyday Price
$2.20
$2.00
$1.80
$1.60
$1.40
$1.20
$1.00
Base Price/Unit
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Output of the Demand Engine: a set of algebraic
equations defining relationships between variables
Conceptual
Conceptual
Estimated Prediction Equation
= X ˆ β + X ˆ β
ˆ 1 1 2 2
y
Predicted
dependent
variable
(Sales, share)
Predictor variables
(Price, Promotion, Week,
Cannibalization, Seasonality)
+
...
Estimated
Parameters
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The Demand Engine specification
Models consumer response at
the appropriate decision making
level
Works for all products
Accurate, robust and stable
Handles environmental changes
Cost-effective
Objectives Decisions
Supports multiple applications
Model form
Estimation level
Estimation method
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Demand Engine Specification: Model Form
Multivariate, nonlinear, multistage
Quantifies many merchandising effects simultaneously
Estimates demand across relevant products
simultaneously
Limits the impact of outliers
Attribute-based
Leverages data in a sparse data environment
Provides estimates for new items
Adaptive
Forecasts can be adjusted if necessary
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Product Level of Model
Dept. Cat. SKU
Demand Engine Specification: Estimation Level
Goal: Provide maximum insight that data allow
Few Models x
Millions of
Models
Chain Zone/region Store
Store Level of Model
Option #1:
Estimate aggregate models
for all stores and products
Option #2:
Estimate a single model for
each account-market or
store and product
Option #3:
Something in between: can
we exploit specificity but
manage data sparsity?
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Demand Engine Specification: Estimation Method
Manage Data Scarcity
Modeling Approach Implementation
Pooled Data
or Aggregate
Models
DemandTec
Method
Bayesian
methods
Account-
Market or
Store Models
Exploit Specificity
Market-structure
Internal domain expertise
Data-derived relationships
Customer expertise
(optional)
Attribute-based methods to
leverage information
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Optimization
Objectives Rules
Maximize Profit.
Maximize Same-Store Sales
Maximize Unit Volume.
Large size items must cost more than
small size items.
Large size items must cost less per oz
than small size items
Brand X must cost more than Brand Y
Stores must have the same/similar
prices on like items
Overall, average prices may not
increase more than x%
Prices must be within x% of
competitor price
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Optimization Challenges
Problem Size: The average grocery category has
50,000+ products and price zones of 200 stores.
Nonlinear Model Form:The demand functions for the
products and the constraints on them are nonlinear.
Response Time: Users expect fast response times.
Infeasibilities: Infeasibilities are easy to create (e.g., a
solution where volume and profit both increase by 50%
probably does not exist)
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DemandTec’s approach to optimization:
Nonlinear Programming with Decomposition
Methodological Objectives Implementation
Handle a wide variety of
business rules and
objectives.
Deal with very large-scale
(real world) optimization
problems.
Guarantee fast response
time.
Support “what-if” analysis
User defined hierarchical
structure for business rules.
Infeasibility checking,
correction and reporting.
Advanced nonlinear
programming models.
Parallel computing
environment.
Heuristics to identify
reasonable initial solutions
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D’Agostinos at a Glance
Family-owned grocery chain founded in 1932
23 grocery stores in New York City and Westchester county
Sales $200 million
Employees (2001): 1,150
Previous pricing system: Homegrown
D'Agostino not only showed a big boost in sales and
profit, but can now enhance customer satisfaction and
loyalty by tailoring its selection to be highly responsive
to their needs.
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The Challenge
Has not competed on price - motto is "exceptional quality and
superior service at the right price"
Finding “Right Price” was very hard
How did their customers
perceive price?
Did price sensitivity change from
location to location?
Was there a way to automate pricing
to respond to these sensitivities
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What D’Agostino’s Was Looking For…
A solution that could be implemented easily,
without disrupting operations
Quick results, without dedicating too many
resources
D'Agostino had a homegrown pricing system,
with no optimization
22

The Results…
DemandTec Trial: 8-week price-optimization and price-image
trial involving seven categories across 10 stores.
Revenue
Unit volume
Gross profit
Net profit
+ 9.7%
+ 6.2%
+ 16.1%
+ 1-2 %
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The Results…
“[Customers used to say:] 'I paid too much for this,' or 'I'm
surprised this one's so low.' With DemandTec, we can have
prices matched to demand, so customers can go in and say,
'Everything is fairly priced.' … they're happy, and that's what's
important. We now have a reliable and effective way to
significantly raise profit and strengthen our price image. I couldn't
be happier with the financial results from implementing
DemandTec."
Nick D'Agostino III
Executive Vice President
D'Agostino Supermarkets
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What Retailers Are Saying
HEB improved price image and profitability
“DemandTec Price demonstrated to H-E-B that it delivers accurate
information enabling us to execute our merchandising strategies,
while meeting our business objectives and maintaining price
image.”
- Scott McClelland, Chief Merchandising Officer, H-E-B
Longs Drugs reversed a year-long negative
sales trend
“Optimizing pricing strategies is an important part of the company's
growth and overall business performance. Using DemandTec's
software brings a new science and logic to pricing areas that drive
profitability in our stores and helps us meet business objectives by
addressing both competition and consumer reaction."
- Terry Burnside, Former SVP & COO, Longs Drug Stores
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Summary
Demand-Based Management is the ultimate
way to create great values in the 21 st century.
DBM integrates demand and supply
management.
Turn data to intelligence, using smart analytical
technologies.
The value potential is great, and the time to act
is NOW.
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