APEX™ MARKET
FORECASTING
Introduction
With unprecedented accuracy, CEO’s, marketers and program managers
now have the power to:
- Forecast specific market or product marketing performance
- Identify and anticipate immeasurable or unobservable variables
that affect sales and demand for products and/or services (e.g.,
expectations of market participants)
- Forecast balance sheet results, including:
- Return on Investment (ROI)
- Return on Assets (ROA)
- Net Profit (as a percentage of sales)
- Utilize any combination of marketing mix and external
variables in classic "what-if" scenario modeling to accurately
forecast outcomes
- Predict new product marketing performance
- Simulate test marketing
Following more than 13 years of research, testing and application
to various product and service environments, APEX™ offers leading
edge market forecasting and performance prediction. APEX™ – connoting
the “highest point” of possible return on marketing investment – is
the name we use to describe a proprietary marketing analysis, optimization
and forecasting technology that delivers unparalleled power and accuracy
to 21st Century marketing models. The acronym is derived from this
tool’s underlying technology, Adaptive Prediction Estimation
and Control (“X”).
APEX allows an executive to use the
modeling tool to reach “apex” (or peak) company
performance by optimizing all variables necessary to achieve
maximum profit
and marketing objectives.
This technology combines the latest market forecasting
techniques and assessment technology with the leading edge
of state
space engineering science to deliver powerful improvement
to marketing
models. APEX
technology allows marketers to quantify vital marketing
variables which cannot be directly measured. The result
is improved
understanding of marketplace dynamics, forecasting accuracy,
and the ability
to optimize controllable variables to achieve maximum return
on marketing
investment.
Background Brief
After several years research and model development,
APEX technology was first deployed in 1993 by Dr. Rod Freed,
who currently
serves as Chairman of the Department of Economics and member
of the
Department of Mathematics at California State University,
Dominguez Hills.
A widely published author and speaker at national mathematics
and economics
forums, Dr. Freed combined his doctorate degrees in math
and economics with a “hobby horse” love of
engineering (inherited from his father, a professional
engineer) to discover
and apply
ground-breaking
approaches to market forecasting and analysis.
APEX has been applied to marketplaces as complex as those
served by IBM, and as variable as produce commodities with
equal success.
Currently, one form of the APEX model is being considered
by the USDA for use as a reference standard for quantifying
marketing
effectiveness
and performance evaluation by federal marketing orders
across the country.
In the early years, some of the APEX technology description
was published in academic briefs for peer review. This
documentation was thorough
enough to support results of one series of APEX analyses
to be
used in a federal court case. However, as effectiveness
became proven,
subsequent description and detail has remained confidential
and will not be disclosed further (except as may be necessary
for
validation,
and then only under strict confidentiality and non-disclosure
agreements).
Marketing Applications
Techniques employed in APEX models offer significant advantages
over the forecasting techniques usually used in the business
world, which
include Linear and Non-Linear Regression, Analysis of Variance,
Discriminant Analysis, Canonical Correlation, Principal
Components, Factor Analysis,
and traditional economic forecasting, market prediction
and estimating methods. APEX forecasting techniques have
been
effectively applied
in the following areas:
MARKETING PLAN OPTIMIZATION
APEX models determine the optimal marketing mix needed
to achieve specific marketing goals. Marketing optimization
through APEX
can evaluate any "what-if" scenario desired,
incorporating internal or external variations of the total
product or service
environment. As current, real marketing data flows in,
the model design allows
for continuous updating to account for evolving external
and internal variables and to maintain long-term forecasting
accuracy.
TARGET OPTIMIZATION,
POSITIONING OPTIMIZATION,
PRODUCT/SERVICE OPTIMIZATION
APEX will identify optimum levels of marketing (line item)
focus and budget for individual key variables to generate
desired purchase
behavior. Resulting forecasts offer exceptional accuracy,
often within a range of 1%-1.5% of actual performance.
ENVIRONMENTAL ANALYSIS
APEX modeling and forecasting techniques offer accurate
assessment of various observable and unobservable or immeasurable
factors
(e.g., buyer expectations, market psychology, etc.), including
evaluation
of both internal and external variables.
PRICING OPTIMIZATION
APEX modeling techniques provide thorough price elasticity
analysis, testing pricing levels in combination with all
other variables
to suggest the optimum price in relation to marketing budget
and mix.
This allows marketers opportunity to deliver maximum profitability
against specific objectives.
NEW PRODUCT MARKETING OPPORTUNITIES
The APEX model can be adapted to predict the likely purchase
response for a new product or service – in spite of having no sales
track record – based on sophisticated market evaluation
techniques.
SIMULATED TEST MARKETING
APEX techniques offer low cost simulated test marketing
with immediate results, providing similar quality of projected
results similar in
quality to those that would otherwise have to be drawn
from
actual, more expensive test market campaigns. This application
can offer
significant competitive advantages through cost and time
savings and a supplier's ability to keep marketing intentions
concealed.
APEX Functions Detailed
APEX analyzes and evaluates variables related to product
or service market performance.
Once an APEX model is constructed using a company’s or product’s
environmental variables, the model identifies the impact and relative
importance of each variable – including those which
cannot be directly observed or measured. The model tests
the value and
relative importance of each resource related to a product/service
mix, and
suggests the optimum marketing mix to achieve specific
objectives. After performing its analysis of various known
and unknown
variables, APEX can predict probable outcomes of any combination
of variables
utilized in a strategic or marketing plan. These techniques
offer an executive unlimited capability to test or anticipate
the outcome
of any marketing course proposed.
APEX can use balance sheet targets set by management to
identify optimum arrangement of marketing mix variables.
If a company has a specific balance sheet objective (such
as a Return on Assets [ROA] ratio), APEX will suggest an
optimum
mix of variables
under possible macro-economic or competitive scenarios
to achieve those specific objectives. In addition, APEX
models
offer the
relative probability of occurrence for any scenario. For
example, if a company
wants to achieve a 10 percent ROA, APEX might suggest an
optimum strategy and marketing mix which offers a 98 percent
probability
of achievement. Targeting is fully scalable along a probability
curve. For example, if a company wanted to achieve a 30
percent ROA, APEX
will suggest the best possible arrangement of variables
to achieve this objective while noting that even the best
combination
of
variables might have only a 15 percent probability of achievement
versus the
98 percent probability to achieve a 10 percent ROA. An
executive can therefore choose an appropriate level of
risk/reward
to achieve given targets.
APEX recognizes and forecasts the law of diminishing returns.
The model recognizes the limits inherent in a particular
marketing environment and will identify limits to the revenue
increase
possible through each variable. For example, if the model
determines that
within an optimum marketing mix each advertising dollar
spent generates $4.32 in increased revenue, APEX can also
show
how additional increases
in the advertising investment will subsequently generate
less revenue (e.g., $3.00, then $2.00, then less than $1.00
in additional
revenue
as advertising spending rises). Additional increases would
reflect further declines in revenue-generating value. The
cost/benefit
analysis of any variable can be tracked to any spending
level. Unlimited evaluations
can be conducted testing the limits of various marketing
mix options.
Results to date demonstrate the value of APEX as a marketing
planning and prediction tool. No limitations to potential
applications have
yet appeared as the model's value to a broad range of industries
and company sizes is being demonstrated. The effectiveness
and results of APEX modeling to date are described briefly
below.
Marketing and Missile Guidance – How APEX Works
The distinctive techniques employed in APEX modeling have
been utilized over the past 40 years with great success
by aerospace
engineers
in aircraft and missile guidance systems. In addition,
the APEX modeling technology has been applied by electrical
engineers
to filter “noise” out
of complicated electromagnetic radiation analysis, as well
as to design controllers for robots and sophisticated prosthetic
devices.
Until now, these techniques have had few applications in
marketing, management, and economics – mainly because
marketing executives and managers do not often communicate
with mathematicians and
engineers. Dr. Freed, however, has bridged this gap with
a modeling technique
that offers a true break-through in applied information
science.
Because the complexity and sophistication of APEX models
can be difficult to grasp in a marketing or management
context, a
simplistic review
of missile guidance applications might aid in understanding
the model's marketing application logic:
Marketing, Management and Missiles Seek to Hit Targets
As demonstrated in the Gulf wars, a Cruise missile targeted
to hit a building in Baghdad and launched from its mother
ship over
1000
miles away had only partial “understanding” of the forces
it would encounter along its intended flight path. However, it was
equipped with the resources needed to reach its target and accomplish
its mission, including: fuel, a flight plan and its “APEX-type” missile
guidance system.
The missile's program anticipated most of what could be
expected en route to its target: gravity, atmospheric resistance,
curvature of the earth, and so on. However, wind could
prove
to be a very
significant “environmental
variable” which had to be incorporated into the missile's guidance
on a constant basis – even though the missile had
no way of measuring wind speed or direction. It could only
detect
the
wind's
effect, i.e., course deviation, or how some group of variables
was affecting its ability to accomplish its mission of
reaching its desired
target.
The missile relied on its APEX-type guidance system to
tell it that something was having an effect on its mission.
By
measuring
progress
against its course, the missile discovered if this “something” was
affecting its objective (reaching its goal/target) and by how much.
It then incorporated this “learning” into current and
continuing corrections needed to conclude its mission. The missile
adjusted its strategy (long-term trajectory) or tactics (short-term
fin and rudder adjustments) in response to its “environment” or “situation” – hundreds
or thousands of times per second.
As a result of its remarkable market-type intelligence
system, the Cruise missile can travel hundreds of miles
only a few
feet off the
ground, dodging hills, mountains, structures or other obstacles
while it measures and anticipates the effect of some forces
it cannot see
or otherwise observe, and adjusts its execution to accomplish
its mission: hitting a target within inches of center.
APEX uses the same analytic logic for assessing market
and product internal and external environments – especially those which
cannot be directly measured – as it zeros in on specific
marketing and management objectives. Application to marketing
environments represents a logical and rational innovation
of this engineering
principle to the world of marketing.
Recent Examples of the Model's Effectiveness
- Faced with devastating red ink, IBM used APEX modeling
during 1993-94 to optimize the $500 million business
of its Personal
Systems Solutions/Western
Division group. Using the strategy and tactics prescribed
by the model, the PSS group increased revenues by 17%
as the rest
of IBM
continued to lose money. Only a massive reorganization
resulting in thousands of lay-offs interrupted plans
to roll out and
apply APEX to the entire personal computer group of IBM.
- Since
1993, APEX has been used to create a crop return forecasting
and marketing optimization model for California’s
growers of peaches, plums and nectarines, represented
by the California
Tree
Fruit Agreement. The model has correctly predicted
actual market returns by applying its techniques
to data on
crop size, price
and marketing mix, as well as to data on behavior
of consumers, retailers
and wholesalers and the national economy. The model
has forecast crop returns within 1.5% of actual
per month,
in spite of
dramatic price swings over 12-month periods.
- In
Los Angeles' “South Bay” region, it
is impractical to try to create a retail price index
or an
index of quantity
of goods sold by retailers. However, APEX modeling
produced an accurate
price index and quantity index by applying its techniques
to the retail sales data which is available for Los
Angeles' South
Bay.
Result: APEX identified the unobservable factors and
accurately forecast (+/- 1.5% per month) economic
performance for
the South Bay area.
- An APEX model created an index of investor expectations.
This test model has proved highly predictive by
applying its techniques
to
data on investors' observed behavior, along with
data on technical factors, market fundamentals, and other
economic
data to predict
investor behavior under various conditions. Application
of APEX modeling in the equities marketplace is
continuing.
- In addition, APEX models have been used to:
- Construct a portfolio choice model which optimizes
investment groupings (stocks, bonds, etc.) to achieve
targeted rates
of return
- Develop a financial model which a firm can use to
simultaneously control profit (as a proportion
of sales), return on
assets, and return on equity
- Predict regional macroeconomic performance
of Los Angeles' South Bay.
Possible applications seem vast at this point
and the model is currently being applied to
a wide range of
goods and
services providers.
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