The Business Book

(Joyce) #1


Market research is valuable, but it
can be very time consuming to gather
data that is representative of the age,
gender, and background of consumers.
Computer models do the work faster.

See also: Managing risk 40–41 ■ How fast to grow 44–45 ■ Organizational culture 104–09 ■ Avoid groupthink 114 ■
Good and bad strategy 184–85 ■ Forecasting 278–79 ■ Marketing mix 280–83 ■ Benefitting from “big data” 316–17


marketers and others in an
organization can measure projected
product growth, or return on
investment, and make informed
decisions on how to optimize the
combination of factors most likely
to generate market success.
Gathering the required data for
modeling is crucial. Information
is needed from all areas of the
business so that every step in the
process of getting the product from
the drawing board to the customer
is factored in. When David Packard,
the co-founder of Hewlett-Packard,
said that “marketing is far too
important to leave to the marketing
department,” he was implying that
the plans made by marketers can
come to nothing if the rest of the
organization is not fully engaged. In
addition to getting approval on plans
and budgets, marketers should
communicate with all departments
to gather data and share it once
decisions have been made.
Using the data, the marketer
can simulate product tests and
input variations using different
assumptions about elements of
the marketing mix, such as market
conditions and consumer behavior.
The greater the amount of relevant

data and the longer the historical
period it covers, the more accurate
the results will be. Models reassure
members of the business that every
scenario has been investigated.
Marketers can choose from
different models or design their
own, but the key to making the
model work is data.

Gathering and using data
Consumer goods maker Procter &
Gamble (P&G) has invested heavily
in data gathering and modeling,
implementing digital processes
from the factory to the shelf in
order to capture data and feed it
back. The data can be used to
make immediate adjustments to
product planning and distribution,
as well as added to a massive
database for future use. According
to CEO Robert McDonald in 2011,
“Data modeling, simulation, and
other digital tools are reshaping
how we innovate.”
P&G focuses on internal data-
gathering processes and also relies
heavily on market information from
external partners. The leadership

team around the world confers once
a week to examine data and make
decisions in response to buying
behavior. As McDonald says, “it’s
the data sources that help create
the brand and keep it dynamic.” ■

The origin of marketing models

Models of consumer behavior
date from the 1960s. They grew
out of a need to make marketing
more scientific and less driven
by instinct or unproven ideas.
In the 1960s US scholar
Robert Ferber advocated the
use of mathematical simulation
techniques and models. These
became known as measurement
models because they were
devised to measure demand
for a product as a function of
various independent variables—
for example, if the selling price

is raised by one percent how
might this affect demand? Then
in 1969 Stanford University’s
Frank Bass devised his Bass
model, which is still used to
predict how fast new products
will be adopted and spread
through a market.
Decision Support Systems
(DSS) use measurement models
to project the outcome of new
decisions, adding variables—
such as previous outcomes in
similar contexts—to help
marketers make optimal choices.

Marketing is inherently
about producing results.
Geoff Smith
VantagePoint Marketing (1962–)
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