Reducing Strategic Uncertainty in High-Value Transactions through Customer Intelligence Systems

Abstract

Transactions of high value in the real estate, energy, and B2B fields are prone to strategic uncertainties owing to decision volatility, long negotiations, and asymmetric information problems. In this paper, the researcher presents the Customer Happiness Intelligence System (CHIS), a commercial intelligence solution that tackles the above issues by timely identification of behavioral risk factors. Developed through executive experience, CHIS consists of data integration, behavior recognition, and insight generation as its three components, based on a patent-pending architecture, as well as a future MVP software. The research methodology follows a mixed-method design, including a theoretical framework and a longitudinal case study on 150 multimillion-dollar transactions from Sunlocate Properties. The results indicate the reduction of churn rates by 25%, the increase of the closing rate of deals by 20-30%, better forecast accuracy (by 28%), and lower revenue variance (by 35%) from analyzing communication, engagement and hesitation. From the theoretical perspective, CHIS represents an intersection between artificial intelligence and behavioral economics. As for practical implications, it provides executives with scalable approaches to solving the problem. Limitations are related to the specific context.