How to Increase Profits Through Predictive Analytics When Only Few Competitors’ Bids Are Known

Abstract

The clear majority of pre-existing work in the published domain of competitive bidding requires large sample sizes for reliable econometric, probabilistic or game-theoretic modelling techniques. Such unrealistic large data requirements have prevented the successful application of bid modelling in managerial practice. This article presents a new predictive analytics method for very small samples of historical bidding data. Requiring as few as nine competitive bid prices for a group of pooled/aggregated competitors over a 30-month period is the standout differentiator of this research from any previously published research. This minimizes the demands on competitive intelligence and, therefore, realistically enables its application in the real world of practice. Maximum likelihood estimations are used to evaluate two new, revolutionary bid strategies against a range of evaluation criteria, taking into account the pricing judgements made by competitors, including a degree of competitive reaction among them. Using off-the-shelf analytics software, a case study of a bidder from the telecommunications infrastructure sector demonstrates how commercial outcomes can be improved substantially: A 400 per cent improvement in win ratio, an 86 per cent increase in contribution margin and 76 per cent revenue growth. In addition, the difference between the submitted bids and the lowest-priced competing bids (which is an opportunity cost, sometimes referred to as the ‘spread’ or ‘money left on the table’), has been reduced to 2 per cent on a total revenue of US$210 million.

Keywords

Competitive bidding pricing small sample statistics regression predictive analytics

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