New Product Research

Marketing research plays a crucial role in making the best decisions throughout the product development process.  The understanding of the consumer wants and needs and of how your new product could fill those needs in a meaningful way is at the heart of any product development program.

Since the beginning of the 1990s MACRO Consulting, Inc.has guided clients throughout product development in different industries such as CPG, sports products, software,  financial services, pharmaceutical and retail.  We are well-recognized for our ability to develop creative solutions to business challenges.

Typical new product development process:

1. Market understanding
2. Idea generation
3. Idea screening
4. Concept development
5. Business analysis
6. Product development and marketing mix
7. Market testing
8. Commercialisation
9. Product life management

Issues we address:

• How many of the new product can you expect to sell in the first year?
• What product features are most desirable?
• Which features will increase sales the most?
• What price will maximize profits?
• What should the new product look like?
• Who will be the primary purchaser of this product, i.e. target segment?
• Who will be the secondary segment?
• Why will they buy?
• How does your brand affect sales?

The MACRO Approach to New Products

What can you expect your first year sale volume to be?
What product features are most desirable?
Which features will increase sales the most?
What price will maximize profits? 

Conjoint analysis, as well as a broad family of techniques, is used to address many of the above issues:

• We forecast new product sales by combining conjoint analysis with actual sales data.  The primary difficulty in forecasting sales based on survey data is that respondents don’t always do what they say they’ll do.  Some mechanism must be developed that “translates” survey data into real world sales forecasts.  We’ve been successfully forecasting new product sales since the late 1980s.
• Identifying the most desirable or, alternatively, the most impactful product features is a fairly straightforward result of virtually any form of conjoint.  Selecting the appropriate form of conjoint for your specific problem will increase the accuracy and relevancy of your results.  There is no single form of conjoint that is right for all situations.
• Optimizing price to maximize profit involves running simulations of real world scenarios over a range of prices, estimating gross revenues (unit sales multiplied  by price) and, based on those estimates, calculating gross profits.  A key element of successful price sensitivity analysis is building a conjoint simulator that truly reflects the marketplace.

MACRO offers a full range of conjoint techniques:

• Choice-based conjoint
• Unconstrained allocation
• Partial profile
• Menu-based conjoint
• Cake Method©
• Max/diff scaling

What should the new product look like?

MACRO used the association test to measure the conscious as well as subconscious communication of visual images.  The association test has proven effective in identifying powerful visual motivations to human behavior.

Who will be the primary purchaser of this product, i.e. the target segment?
Who will be the secondary segment?

MACRO uses a variety of techniques for segmenting the marketplace for a new product:

• Choice-based Conjoint can be used as a basis for an insightful, actionable segmentation.  By using Latent Class Choice Modeling and the raw data from the choice exercises, segments are identified that have maximally different feature and pricing preferences.
• Cluster Ensemble Analysis has proven to be an extremely effective method for combining existing segmentation solutions into one comprehensive solution.
• Decision Pathway Segmentation (DPS) is a relatively new segmentation technique that involves building an initial Structural Equation Model (SEM) and then using the most relevant predictor variables from the SEM as the basis for a traditional segmentation.  DPS solutions tend to develop segments with dramatically different purchase drivers.

Why will they buy?

MACRO has developed a technique called DriverConjoint©.  DriverConjoint combines attitudes and perceptions data with conjoint utilities in a Structural Equation Model.  The result is typically a deeper understanding of why customers make the product choices, and product trade-offs, that they do by identifying key attitudes and perceptions that drive utility values.

How does your brand affect sales?
What messaging should you stress?

Brand imagery data have numerous well documented problems, including collinearity, scale usage bias and brand halo.  MACRO uses Structural Equation Modeling to minimize the effects of such data limitations and more accurately identify key brand drivers.

MACRO is also actively researching new ways to collect brand imagery data that will eliminate the problems currently plaguing brand measurement studies.