A Decision Support Tool using Machine Learning Techniques for Strategic Investment Planning Based on Customer Evaluation Criteria
Open AccessEarly identification of anticipated customer needs is critical to winning new business in competitive environments. This allows companies to make strategic investment decisions that will improve their core competencies to meet those customer needs. These strategic decisions target enhancements that allow contractors to offer their best proposed solution to meet the anticipated customer’s needs, inferred by the customer’s prioritized selection criteria. However, contractors do not know the customer’s final prioritized selection criteria until release of the final Request for Proposal. A contractor’s ability to strengthen their proposed solution decreases by 60% by the release of the customer’s selection criteria, reducing their probability of winning.Current methods leverage limited samples of comparable historical Request for Proposals in conjunction with subject matter experts that have customer insights to predict a customer’s prioritized needs and selection criteria. These approaches are subjective, costly, and time-consuming. This research applies supervised machine learning techniques, which are an underutilized approach within strategic management research. This research yields a decision support tool using supervised machine learning techniques to predict customer needs deduced from historic customer evaluation criteria. This research contributes a robust process for strategic investment predictive modeling, a newly established database of customer evaluation criteria, and a predictive model with an over 80% accuracy and test quality performance measure. The impact of these contributions spans various industries in competitive environments and will offer strategic decision-makers the confidence of a robust and rigorous approach that current methods are unable to provide.
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