Taking a new product to market: What price are consumers willing to pay? What is the optimum configuration?
SCENARIO:
The inventors and manufacturers of a new Electric Car propose to take it to market in two years. They ask Future and Simple to predict:
optimum product configuration;
feature trade-offs;
and willingness to pay, and market share for various configurations.
Features studied will include brand, information attributes, and existing and hypothetical product attributes.
F&S Approach
We propose a full-life-cycle Stated Preference Discrete Choice Modelling project, but with a few important process and operational enhancements that greatly increase the cost efficiency, speed, and accuracy of the process.
These include:
Using our software to easily construct experimental tasks, and deploy them on computer. Deployment may be online, in controlled labs (or shopping mall intercepts at internet cafes), or on stand alone computers (laptops in remote, internet unfriendly places) using executable versions of our tasks, which later ‘dock’ with the central database. This is no mean technical achievement, seeing that every task is different, driven by an experimental design.
Using a Configurator task. There are a number of problems with Discrete Choice Modelling which are ameliorated by modelling the results of a configuration exercise, prior to specifying the Attributes, Levels and Experimental Design These include: the “menu” or “bundling” problem; and the inability of a practical-sized experimental design to cover all the 2 way interactions in a permutation set, let alone 3 and 4 way interactions.
Plan and Deliverables
Literature Review (including the client’s own data and intelligence)
Qualitative work with consumers to ascertain attributes of interest
Workshop with client Product, Marketing and R&D personnel. Here we ascertain product and information (marketing) attributes – existing and proposed.
Best-Worst (max-diff) fieldwork. We put together the long list of attributes steps 2 and 3 have produced, and use data from a Best-Worst study to produce a rank order of attributes, whittling the list down to a manageable number of important ones.
Configurator fieldwork. Collecting and analysing the data from a Configurator task will further expose: the relative importance of attributes; the existence of important interactions between attribute levels; and the pertinent range of levels for certain attributes.
Choice Study fieldwork. We use the polished set of attributes and levels in a Discrete Choice Modelling exercise. The Experimental Design will be specified to study main effects and a number of important interactions (observed via Configurator).
Deliver a Choice Model, in the form of an easy to interrogate, interactive DSS (Decision Support System).
