Document Type

Closed Project

Publication Date

Fall 2012


Timothy Anderson

Course Title

Operations Research

Course Number

ETM 540/640


Data envelopment analysis, Oscilloscopes -- Pricing, Technology -- Management, Operations research


The research in this paper proposes a new technique for pricing products in competitive markets taking into account the features and prices of competing product offerings. This technique is based on a methodology known as Data Envelopment Analysis (DEA) and is referred to as Competitive Pricing using Data Envelopment Analysis (CPDEA).

The speed of innovation and new product development in competitive markets continues to accelerate at a rapid pace. In this environment, when new products are introduced they do not necessarily disappear, they often remain in the market at a price that decreases over time to reflects the market’s perception of reduced value – the latest and greatest products often command a price premium. Providers of products and services need to be able to price their products competitively: if they price too high then volume can drop to zero; price too low and money is left on the table.

There are a number of analytical techniques that are used to determine optimal pricing. These include: Conjoint Analysis, Van Westendorp Price Sensitivity Meter, Gabor Granger, Brand vs. Price Trade-off, and Expert Session. However these techniques rely largely on direct customer survey research which is complicated and time consuming to setup; as well as subject to bias based on individual opinions. This bias can be reduced given a large sample size; however, this does not eliminate the gap that exists between buyer perception (what potential customers say they would do) and buyer behavior (what customers actually do). This bias can be removed by looking directly at the market.

This study uses CPDEA to examine the actual prices and features currently available in the oscilloscope market and find those offerings that are state of the art (SOA). In addition, given the results of this analysis, and assuming an efficient market, it is also possible to adjust prices and create an efficient offering even when the “feature set” is not SOA. This provides one more analytical tool to assist with competitive pricing, especially for those offerings with complex feature sets. This, in turn, can boost profit margins for the most advanced and assure competitive pricing for older models.


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