Demand in Business Forecasting
“What’s missing is often good measurement
and a commitment to follow the data. We
can do better. We have the tools at hand.”
Bill Gates
Use of the law of demand is not simple or we
would not be here. Successful application
can have a significant impact on profits.
Traditional forecasting process
• The sales manager asks sales reps for forecast.
• The reps make guesses for next year, then subtract 10%.
• The sales manager takes the forecasts and raises them
because he knows the reps are fudging.
• The sales manager gives the forecast to top management
which changes the numbers to match analyst expectations.
• Manufacturing ignores the numbers and orders raw materials
based on last year’s actual sales.
• Actual sales may bear no relation to any of the above.
• Prior to earnings announcements, change the books so that
they resemble the forecast.
Better forecasting process
• Build a model that predicts future buying behavior
based upon previous years’ sales, seasonal
changes in buying patterns, historical impact of
marketing campaigns, overall state of the economy,
fluctuations in currency exchange rates, and other
relevant factors.
• Test the model against historical data to confirm
that if it had been in place in the past if it would
have predicted sales.
• Goal sales and marketing on providing information
that hones the accuracy of the model.
Why Demand Is Not Easy to Measure
• Changes in the design of products and entry of new
products mean limited lifecycles. Changes make
forecasting more difficult because you use data from
previous products and time periods.
Study from Chicago and Columbia Business Schools:
40% of household expenditures are on goods created
in the last 4 years and 20% of expenditures are on
goods that disappear in the next couple years. That
is, in many markets there is rapid product entry and
exit.
Accurate Measures Difficult
• Difficulty in interpretation of historical data:
Sales, orders, shipments and invoices are
historical data that can cause confusion.
• This may be innocent; but—may be
evidence of theft, evidence of bad record
keeping, or other internal problem.
• Besides trying to measure demand; also an
opportunity to understand company costs
and operations better.
One Size May Not Fit All
• Expanding business to new markets
means new demographics (customer
base), facing new competitors, different
seasonal factors, packaging requirements,
and distributional channels. Seller of a
product is likely really in multiple minimarkets that each require analysis.
Measurement Difficulties, But Big Benefits
• Demand usually underestimated. Were sales actual
demand or did stock run out, thereby cutting sales?
• Most data is old; look for more “real time” data. YRC
Worldwide (transport) quickly reduces its fleet and
employee base when it sees shipments shrinking.
Checks weight and frequency of shipments to look for
changes in industry conditions.
• HP works with Walmart to forecast PC demand. By
getting earlier orders, HP saves on manufacturing
costs, which are lower if ordered far in advance.
Walmart gets better price.
Demand Forecasting
• Forecasts are statistical estimates for the future.
They can be improved by determining probability
distributions for demand points by location and for
specific times.
• How much did actual demand deviates from prior
demand forecasts? Improve the model. Revision a
good idea as time passes—were the estimates
made six months ago for next year still the best
estimate?
• Based on experimenting with data, determine
relevant time period. Example: Anheuser-Busch
uses five-year historical data to better understand
product lifecycles and seasonal demand.
One Company’s Application of Data
Schwan Food—6,000 sales reps deliver
frozen foods to 3 m. customers at home. They
looked at 6 weeks of orders to decide what to
suggest to customers. Sales flat for years.
More sophisticated: match customers’ buying
patterns; offer new products and discounts via
hand held devices used by reps. Revenues up
3-4% because understand demand better.
Improving Demand Measures
• More measures of possibly relevant factors:
competitor prices, regional events,
demographics, and weather. Some of this
information is low cost.
Use info from bar codes & RFID chips.
Think of how to use new information sources,
such as social media. Insurance companies
beginning to exploit life style information
revealed in Facebook and such.
Data Shows Relationship
between Weather and Sales
• Bottled water sales rise in Los Angeles in
the winter when temperatures are below
average and there is less wind than usual.
• Bottled water sales rise in Los Angeles in
the fall when temperatures are above
average and there is less wind than usual.
• In other areas—different factors are
related to bottled water sales rates.
 Google Maps for Inside the Store
 Mobile Integration
 Shopping List
 Checkout using Smartphone's
 Directions using
Smartphone's
 Using Security Camera’s in the
store
 Goal is to be Local in a Global
Market place
 Demographic Data
 Purchasing Pattern’s
 Purchasing Reason’s
 Impact on Demand.
 Increase Sales at Shelf.
 Weather Information
CONTAINS CONFIDENTIAL & U.S. PATENT PROTECTED INFORMATION
PAGE 12
Goal
 Identify Shrink
 Identify Phantom Inventory
•
•
POG, shelf allocation & display
compliance
Standards compliance
•
•
Promo & trade spend compliance
Full category/competitor visibility
•
Extensive Reporting
 SKU & custom groups
 Product on display
 Quantity on display
 Store location
 Retail price
 POS accuracy
 Asset placement & tracking
•
Stores linked to reporting hierarchy
CONTAINS CONFIDENTIAL & U.S. PATENT PROTECTED INFORMATION
PAGE 13
After Hurricane Katrina: Drinking
Water, Batteries, Cleaning
Supplies, Ready-to-Eat Food
Amazon VP of Digital Video and Music: “we let
the data drive what to put in front of customers;
we don’t have tastemakers deciding what our
customers should read, listen to and watch.”
Best Buy gets data on multiple offerings of products
—who is buying and using electronics?
What they learned: Many DVD players bought for
young children. A store-brand with rubberized
edges and spill resistant became good seller.
Private label models do fine if have special features.
Match.com—better algorithms for matching men and
women.
Wide Range of Applications
• Pricing restaurant meals an drinks; drinks
have higher margins. Experiment with
changing the mix of these services.
• Inventory control—Walgreen cut number of
products carried about 20%. Eliminate low
value goods; focus on profitable goods.
• Amazon runs many A-B experiments—two
versions of websites appear to matched sets
of customers to see reactions.
• Google runs 100+ experiments a day.
Successful Practice
One form of this is in “price-optimization
software” that looks to past sales to
determine where to set initial prices today
and when to begin to discount.
This helps avoid panic discounting if initial
sales are weaker than expected.
Nordstrom’s attributed much of its increase
in profit margin from 5.2% to 10.6% in two
years to impact of the software.
Rapidly Changing World
Harrah’s casino was second rate. New CEO made it
first tier as Caesar’s. Focus on data about
customers from Total Rewards loyalty cards.
Tested new promotions, price points, services,
workflows, employee incentive plans and casino
layouts. Let the customers tell you want they want.
Ron Kohavi (Microsoft software architect): Objective
data are replacing HiPPOs (Highest Paid Person’s
Opinions) as the basis for decision making—better
cost control and better customer service.
For many companies data doubles every year now.