Professor Lee G. Cooper

Anderson School at UCLA

110 Westwood Plaza, Suite B518

Los Angeles, CA 90095-1481

This project outlines an approach to strategic marketing planning for radically new products, disruptive or discontinuous innovations – those new products or services that change the dimensionality of the consumer decision process. The planning process begins with an extensive situation analysis that pays particular attention to environmental change coming from political, behavioral, economic, sociological, and technological sources. These environmental forces are looked at from the points of view of the company, the business ecosystem or value network, and the infrastructure. The factors identified in the situation analysis are woven into the economic webs surrounding the new product. The webs are mapped into Bayesian Networks. This involves a combination of knowledge engineering and specification of focussed research projects. The Bayesian nature of the planning document enables planners to update information as events unfold and simulate the impact that changes in assumptions underlying the web have on the prospects for the new product. The method is illustrated using the historical case concerning the introduction of video tape recorders by Sony and JVC.

This research is supported by grants from Intel Corporation and software donations from Microsoft Corporation.

This project develops and illustrates a new knowledge discovery algorithm tailored to action requirements of management science applications. The problem is to develop planning forecasts at the SKU level. We use traditional market response models to extract the information components associated with essentially continuous variables and use the data mining technique on the residuals to extract the information from the many-valued nominal variables such as manufacturer or retail category. This combination means a more complete array of information can be used to develop planning forecasts. The method is illustrated using a sample of 1.3 million promotion events from a 95-store retail chain in a single trading area.

This research is supported by grants from Intel Corporation and software donations from Microsoft Corporation.

This project involves the implementation of a promotion-event forecasting system, PromoCastä, in two test markets: 95 stores in one southwestern city belonging to one national chain, and 185 stores in an eastern city belonging to another national chain. The goal was to provide forecasts useful for planning promotions for any of the over 150,000 UPCs in each store’s item-master file. Since the unit of analysis is the promotion event, neither store tracking data nor consumer panel data are the optimal input. We describe the promotion-event databases and the statistical model developed using these databases. We argue that the best historical averages provide a superior benchmark to the industry standard "base-times-lift" method. PromoCastä is three to eight times more accurate than these best historical averages.


This class will have a high-tech flavor particularly in the first block of sessions. Brand planning for radically new products is a superset of the issues involved in planning for all products. So we focus radically new products and the planning process. In the second part of the course we deal with more mature products, but also bring in how technology is changing the nature of product management. We focus on the Efficient Consumer Response initiative as a way of organizing the issues facing brand managers and retailers now and for the mid-term future. The class will be centered on cases and class discussions of work I'm currently doing with Intel (in strategic marketing planning for radically new products) and with EMS, Inc. (a company driven by the ECR initiative).

Team Projects: Self-selected teams (of approximately 6 people) will undertake the planning process for a radically new product or service.

Steps in the Planning Process:

  1. The overall goal involves planning for a radically new product. The first step involves articulating this goal as simply and comprehensively as possible. The top-level objectives that flow from that goal should also be developed at this stage.
  2. Filling out the Critical Issues Grid. First develop a general description of the product or service. Then list the issues prompted by each cell title. This becomes an outline for short written statements about the issues in each cell. Sharing this document within the planning group is a good way to find out if there is agreement on the critical issues.
  3. Mapping the Critical Issues into a Network. What affects what is the central issue at this stage. Since the network can be progressively refined, the preliminary work is just in seeing what issues are interconnected. Finding the nodes that are the key to decision making is an important part of this step.
  4. Knowledge engineering and specification of research projects. The rough network at this stage contains nodes and arcs. Each connection between two nodes represents something that is either known by the planning group or researchable. This step fills in the known conditional relations and sets up the research projects to fill in the rest.
  5. Test the sensitivity of the key nodes to changes in the underlying assumptions.
  6. Revise and refine the network, as new knowledge becomes available.

The emphasis in this course is on marketing research as an aid to management decision-making. The required skills fall into four broad groups.

1. The planning of a marketing-research project. These skills include:

2. The design skills that that help insure you get the data that can answer your research questions.

3. The data-analysis skills that transform the data you collect into the answers you seek.

4. The application of research skills to marketing problem areas.