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New
Product Development: Stages and Methods
By Rajan
Sambandam, TRC
The new product
development process has the potential to be haphazard because of
the inherent uncertainty in the process, as well as the myriad methods
available for product development. Setting up an organizing framework
to identify the stages in the process, and the methods applicable
to each stage, should help in bringing order to the process.
Our purpose
in this article is to lay out a framework and identify key methods
that are most likely to be useful in each stage. The focus here
is on methods that use quantitative data collected mainly through
the web thus bringing more validity and flexibility to the process
along with speed to market.
We envision
the new product development process as an iterative multistage process
as shown in Figure 1.
This is a straightforward
way of looking at the process that starts with idea generation,
moves to development of individual features and then to full product
development and finally into product testing. Of course, this is
one example of how the process can be viewed and not a rigid framework.
There has to be considerable fluidity in the system to accommodate
feedback, skipping of stages, use of new methods and perhaps introduction
of new stages.
Idea
Generation
Many methods are available for the idea generation stage such as
brainstorming, Delphi and focus groups. The basic approach is to
harness creativity in some form for the development of new ideas.
While there is much to recommend for the more qualitative approaches,
one of the drawbacks is the lack of quantitative validity to the
ideas at this stage. That is, the ideas have not been shown to have
popularity in the constituency that matters – the customers.
We have found that the Smart Incentives approach can provide both
creativity and validation in the same step. Respondents to a survey
compete with each other to produce ideas thus introducing creativity
into the process. The generated ideas are then evaluated by a peer
group to provide the required market validation. This approach can
be useful for generating ideas on both whole products and individual
features.
Feature
Development
Feature development is the process of identifying features that
would be of interest to customers. Traditional methods such as Importance
Scales can be used, but may not provide sufficient discrimination
between features. Pairwise comparisons of features are a straightforward
method for identifying feature importance. The task is simple, but
can be tedious if a large list of features needs to be culled. More
recently developed methods such as Max-Diff scaling can provide
a better alternative. Max-Diff is similar to pairwise comparison,
except that more than two features are evaluated at a time (3-5)
and the most and least preferred alternative is chosen from each
set. Some advanced statistical analysis on the back end provides
a score for each feature that is generally more discriminatory than
a regular importance scale.
Another alternative
is the Kano method where the positive and negative aspect of each
feature is rated in order to distinguish the “must have“
features from the “nice to have” features. The final method
in this stage (that straddles this and the next stage) is the Self-explicated
Method (SEM). Respondents rate the desirability of each level of
each attribute as well as the importance of each attribute. Combining
these two pieces of information gives attractiveness scores (similar
to conjoint utilities) for each attribute level. Although all attributes
and levels are rated by respondents (as in conjoint analysis), since
they are presented individually, this method may be more appropriately
seen as useful for feature development.
Product
Development
In this stage, combinations of features are used to build or evaluate
the product. The Configurator allows survey respondents to build
their ideal product by selecting from a list of available features.
Usually prices are provided at the feature level to ensure that
respondents make realistic decisions. As respondents build their
own ideal products, the most popular features and feature combinations
rise to the surface, resulting in the automatic development of preference
based market segments. The Optimizer is different in that respondents
make choices from among fully formed products. Information from
their choices is taken into account in creating successive products
that are more preferred till the process finally converges on the
respondent’s ideal product. This method is more appropriate
when the design and packaging (i.e. the visual element) is more
important. As with the Configurator, the market segments itself
into preference based segments.
The various flavors
of conjoint (such as traditional, discrete choice, adaptive) can also
be used in this stage to identify feature importance. But care has
to be taken to ensure that the basic assumptions are met and that
the right type of conjoint is used.
Product Testing
Conjoint analysis can be fruitfully used in this stage also to estimate
the interest in various product combinations and especially in running
market simulations. The latter ability is very important in cases
where a strong competitive market exists and reasonable estimates
of take rates and ability to choose the ideal combination for the
market are requisites. Concept testing is much more limited than
conjoint and is usually used when the product is almost set except
for perhaps one or two questions, often relating to price.
In short, the
chaos of the product development process can be structured, and
appropriate methods applied, to gain maximum benefit at different
stages.
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About
the Author: Rajan Sambandam, Chief Research Officer
My primary job is to oversee research activities at TRC.
Ergo, I’m usually involved in the design and statistical
analysis of pretty much every project that goes through the
shop. I can honestly claim to have spent many years grappling
with data searching for insight. In my time here I have worked
with more companies than I can recall, many of which are household
names.
I consider knowledge development to be an important dimension
of my work. One part of that is publishing articles based on
my research. Another part is the organization of conferences,
lectures and presentations to further the development and exchange
of knowledge. Collaborating with academic researchers is a particular
interest of mine and those relationships span several universities
such as MIT, Yale, Columbia, Wharton, Rice and University of
Illionois. I have found that interacting with smart minds in
academia helps find ways to apply theory to practice and, equally
important, practice to theory.
As a personal
interest that conveniently overlaps work, I write the Insightology
blog on this website. It provides an outlet for thoughts
that form in my head when reading subjects that are of interest
to me. The topics I write about tend to be about research
or analytic approaches to problems in various subject areas,
approached in a user-friendly manner.
Reading
is perhaps the most enduring passion of my life. Behavioral
economics, sustainable living, sports analytics and popular
physics – these are a few of my favorite things to read.
I also think Seinfeld and Star Trek are particularly entertaining
ways of losing track of time.
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