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Choice Modeling
What is Choice Modeling?
Consumer call tell us what they like and do not like. They can tell us what
they will buy and what they will not buy. But rarely can they tell us why they
buy one brand over another. They do not know the roles that price, brand image,
package color, brand name, promotional offers, and media advertising play in
thier purchase decisions. But with controlled scientific experiments and advanced
multivariate analyses, we can implicitly measure the marketing variables that
underlie the consumers' purchase decisions.
Discrete choice modeling, volumetric choice modeling, and conjoint modeling
are analytical methods used to simulate real-world consumer purchasing behavior.
Our Advanced Analytics Consultants set up carefully controlled experiments in
which consumers are simply asked to choose how many of each product to buy,
given predetermined sets of realistic conditions. Consumers are not consciously
aware of what is being measured. The brands are presented visually (including
3D simulation), if possible, in the context of advertising, pricing, packaging,
features, promotion, and other variables. The importance of each marketing variable
is then derived mathematically.
The “choice” experimental design is tailored to the specific objectives,
constraints, and variables of the project. Customization is the key to success
because every category/brand has critical idiosyncrasies. The resulting data
are used to create a DecisionSimulator™ in which potential sets of marketing
variables, product features, and marketing conditions can be evaluated.
Choice modeling techniques can help marketing researchers:
- Optimize product designs
- Analyze price sensitivity
- Bundle product and service features
- Optimize brand strategy
- Improve product line planning
- Maximize media advertising effectiveness
- Improve promotional offers
- Optimize advertising messages
Conjoint Analysis
Conjoint analysis is ideal for optimizing new product designs by identifying the most appealing sets of features. Conjoint analysis, sometimes referred to as trade-off
analysis, is a multivariate technique that quantitatively measures the relative importance of different marketing variables, attributes, or product features related to a
brand, product, or service. The distinguishing feature of this technique is that each variable’s importance is determined implicitly or indirectly. That is, the
respondent is not consciously aware of what is being measured.
Discrete Choice Modeling
Discrete choice modeling is ideal for (a) product categories where only one purchase is made over a longer period of time (for example, durable goods, credit cards, cellular
phones, etc.) and (b) complex products (i.e., products with many different possible features). In these carefully controlled experiments, current and potential
customers are asked which one product they would buy, given a realistic scenario including all of the products or services that compete with one another in the marketplace. In
each scenario, the respondent is presented with a different set of marketing stimuli and asked which brand or product would be purchased. The type of decision that the respondents
make in each scenario is designed to mimic the real market, and again each variables’ importance is being determined implicitly.
Volumetric Choice Modeling
Volumetric choice modeling is ideal for product categories where (a) multiple
products are purchased over relatively short periods of time and (b) repeat
purchase volume is an important consideration. In these experiments, current
and potential customers are asked how many of each product they would buy, given
a realistic scenario including all of the products or services that compete
with one another in the marketplace. The type of decision that the respondents
make in each scenario is designed to mimic the real-world marketplace, where
varying quantities of multiple brands might be purchased (including volumetric
measures such as units purchased, dollars spent, etc.). The overriding goal
is to create realistic scenarios that properly represent the buying behavior
that we are striving to model. And the modeling is designed to implicitly measure
the role and importance of each marketing variable.
Logician® Simulated Shopping with 3D Animation
The more realistic the experimental stimuli, the more accurate the results.
That is why we often recommend Logician® Simulated Shopping with 3D Animation.
We can create 3D shopping environments that take consumers on a realistic online
visit to a retail store, expose them to advertising and promotional stimuli,
and allow them to purchase products with the click of the mouse. 3D models can
be created for lawn mowers, cell phones, cars, etc., that permit consumers to
see a product from many visual perspectives.
Analytical Consulting Services
Decision Analyst is a leading international marketing research and analytical
consulting firm with over 32 years of experience in state-of-the-art modeling,
simulation, and optimization. A team of Ph.D.s heads up Decision Analyst’s
choice modeling work. They publish many white papers on advanced analytical
methods and speak frequently at marketing research industry conferences. They
program choice models in SAS, Sawtooth, and the R-Language.
If you would like more information or would like to discuss a possible project,
please contact by emailing Jerry W. Thomas, President/CEO (jthomas@decisionanalyst.com),
or John Colias, Ph.D. (jcolias@decisionanalyst.com),
or by by calling 1-800-ANALYSIS (262-5974) or 1-817-640-6166.
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