|
Assess interaction across
multiple initiatives
Managing innovation in today’s complex marketplace often
requires that you understand what is happening across a
portfolio of brands, not just a single new initiative.
Understanding interaction across a portfolio is a challenge,
since new items or re-launches interact with each other,
parent brands, and competitors. Choices you make to optimize
a single brand may not produce the best outcome for the
broader portfolio. BASES DecisionPoint services help you
navigate these interactions and make decisions that will
help you set up your portfolio for success.
Flexible
services for diverse portfolio needs
Portfolios vary in
size and complexity, and different objectives require
different research. Depending on your specific needs, your
BASES consultant may suggest one of these approaches:
-
BASES Portfolio Optimizer – Which new items
should I launch to grow portfolio sales?
-
BASES Portfolio Pricing Simulator – How does a
price change on one brand affect sales for my portfolio?
-
BASES Competitive Response Lab – What is the
impact of a competitive launch on my portfolio?
-
We can
combine elements to create a BASES DecisionPoint
Custom Service tailored to your questions.
Each includes pricing and
in-store promotion models by default, so you always have the
ability to explore those objectives, without extra effort or
investment.
Flexible
Solutions for Portfolio Questions
Address your portfolio assortment, pricing strategy, or
competitive response decisions with a customized study.
Integrated with BASES Forecasting
When detailed predictions are needed, we can incorporate the
results into a BASES forecast to provide estimates of sales
and incremental sales for one or more of the specific new
product or restage initiatives included in the study.
Even without a forecast, BASES DecisionPoint services can
help guide your portfolio decisions by estimating the
underlying consumer
potential of various scenarios. |
|
BASES DecisionPoint
To model portfolio interactions, BASES DecisionPoint
technology combines the best of several research traditions:
state-of-the-art discrete choice (conjoint) models
in-context data collection via “virtual shopping” the BASES
forecasting system
Together, these elements
permit creation of models customized to your category and
objectives. By modeling consumer choice dynamics at the
individual level, we can explore numerous scenarios,
seamlessly incorporating interaction effects even when
exploring multiple types of initiatives or in-store
decisions.
Shelf View Example: Consumers can click on any product
for an enlarged view, as
well as rotate the product to view the back label. Multiple
products can be purchased in a single shopping trip.
|
