This Small Business Innovation Research (SBIR) Phase I project is to develop a data-driven optimization-based approach to retail market modeling and strategic pricing. Retail companies are faced with the persistently renewed challenge and burden of designing sales promotions. The company's baseline technologies in retail market model building and strategic pricing have field-demonstrated the potential to turn large volumes of individual purchase history records into profitable decisions. The objective is to extend the reach of the baseline technologies to address a broader variety of promotion design problems. Specific technical objectives are: 1) Time-sensitive market models (Derive a market model of time sensitive behavior. Desirable elements include seasonality, timed follow-on and sequential purchase analysis.), 2) Multidimensional market models (Derive a market model of multidimensional associations in market behaviors, such as customer attributes, catalogue alignment, or store differentiation), and 3) Streamlined promotion optimization (Enable an "automation" of the promotion design process through the data driven extraction of market parameters, such as the expected response to joint product offers or multiple promotion offers, for mathematical promotion optimization). These advances to the baseline company's technology can form the foundation of a commercial product for strategic promotion design.
This project will have a broader impact on education and career training. As part of its dissemination activities the company will offer a course on "Scientific Marketing" in the MIT Industrial Liaison Program. The added value specific to this project lies in its highly interdisciplinary nature, involving scientific marketing, large-scale optimization, pricing theory, and data analysis. Results from such activities inevitably allow cross-fertilization of concepts that catalyze new educational or research initiatives. Indeed it is in small companies where new economic theories emerging from academia have an opportunity to be evaluated in a true data driven environment. Regarding career training, Infolenz will continues its regular hiring of summer interns in a stock compensated arrangement to get first hand startup company experience.
