The KISS of Death (a commentary on chain store sales forecast modeling)

Read our latest blog post from guest columnist Jim Stone, Chain Store Advisors

Jim Stone is the principal consultant of Chain Store Advisors, a consulting firm in Reading, MA.  Chain Store Advisors works with
businesses who operate chain stores including retailers, restaurants,
and service businesses. 

The KISS of Death 
Most
chain store modeling experts will tell you that a good sales
forecasting model will estimate sales +/- 20% in 80-90% of the cases.
Most chain store real estate dealmakers believe that they need a model with no more than +/- 15% error 85% of the time.
Most
people don’t agree on how this error is measured or what the role of
human judgment should be in determining the official sales estimate
used in calculating the projected return on investment.
Everyone
wants to “Keep It Simple Stupid” because it’s hard to make decisions
when you are confused about the facts or their implications.  This
definitely applies to real estate planning and site selection for chain
store operators.  However, most sales forecasting models are anything
but simple and are often intimidating to those without strong
backgrounds in statistics (which includes the CEO, CFO, and VP Real
Estate).

 There
is a huge push these days to use technology and mathematical models to
increase the quality of business decisions.  From the rigorous
discipline of Six Sigma in the late 80’s to the recent business
analytics wins of companies such as Capital One and Harrah’s, there
seems to be an unbridled confidence in the application of computers and
statistics to financial analysis.
The problem is that some situations cannot be modeled with enough precision to be useful.
 Chain store sales forecasting is one example.  
The
reason is simple:  historical data about the retail marketplace are
not static and therefore cannot be used to reliably estimate future
sales.
A Framework for Complexity
Let’s consider some different decisions that face chain store operators ranging from simple to complex.
A simple
problem is one that can be reduced to an equation and applied
repeatedly with very similar results.  An example would be the
selection of the size of a steel beam to support a roof in a building.
 The force of gravity is consistent and can be used to compute the load
requirements of structural steel.  Even if the equation is complicated
(to those who are not structural engineers), it is simple,
straightforward, and reliable.
A complicated
problem is one in which the relationship between cause and effect
requires analysis and expertise.  Many business problems fall into this
category such as staffing for checkout lines to minimize wait times
for customers, logistics for deliveries in the supply chain, and
inventory management based on seasonality of demand.  In these cases,
historical data provide a reasonable basis for predictive models and
can provide a solid foundation for planning and investment decisions.
A complex problem
consists of a situation where the relationship between cause and
effect can only be determined in retrospect, not in advance.  This is
due to large number of variables that influence the outcomes, the
changing values of these variables, and the non-linear interactions
among the variables.
Chain store sales forecasting is a complex problem.

Although
our use of statistical models in sales forecasting has outstripped its
usefulness, it would be a mistake to simply revert to “gut feel”.  The
chain store industry has a great opportunity to build upon the
advances in technology, data, and analytical methods and create a new
approach that uses the best of “art and science”.

The best tool for integrating art and science in real estate decisions is the oldest tool:  analogs.
Analogs
allow decision-makers to look at a new opportunity, find similar
situations from past experience, and use them as a guide to estimating
the future performance of trade areas and sites.  Computers and market
data can be used to present the “patterns” for comparison and the human
brain can be used to assess the similarity of the analogs and adapt
them to the new situation.
In
chain store sales forecasting, the analog method was first formalized
by William Applebaum in the 1930’s.  Since then a vast array of methods
have been used to create classification schemes for markets, trade
areas, stores, competitors, and customers.  The frustration of this
effort is that no two entities are exactly alike, and any attempt to
fit them into a scheme will result in a large number of cases near the
boundaries of the categories.  For example, let’s say that we define “urban” stores as those with a a population density of 5,000 people per
square mile within a 2 mile radius.  Does that mean that a store with
4,999 people per square mile is not urban?
 
The
computer can easily compute population density for any location in a
second; a human being can’t do this in a year.  However, a human can
look at a map of an area and instantly classify it based on a variety of
attributes:  its density, proximity to major highways, the presence of
retail activity, traffic congestion, and relationship to surrounding
cities and towns; a task that a computer program would find daunting,
generating comical results in many cases.
Over
the next few months we will further explore some new ways to integrate
art and science for better real estate planning and site selection.
See Jim’s original post and others at  Real Analogies 



NY Deal Making 2018