Gal Naftal

How can we determine the economic value of data when pricing a data-driven Decision Support System (DSS)?
Many data-driven DSS, particularly in agriculture, represent emerging solutions for which pricing practices are still evolving. Even when comparable products exist, the economic logic underlying their pricing, especially the role of data quality and data generation costs, is often not transparent.

This challenge is especially relevant in agricultural DSS, where system performance depends not only on data sharing, but also on the adoption of the technology that generates the data itself. In Prof. Menachem Moshelion’s lab, we are developing an agricultural DSS that provides actionable recommendations for yield optimization and resource reduction. Unlike many existing systems that rely primarily on environmental data, this system integrates plant physiological data, which are widely considered ground truth in AgTech R&D for early stress detection and precision agriculture. While such data substantially improve DSS accuracy and decision quality, they are costly to produce, raising fundamental questions about economic value and pricing.

To address this challenge, the research follows a two-step economic evaluation framework.
The first step characterizes user adoption, segmentation, and data-sharing feasibility. Using survey data from 290 respondents across 46 countries in the AgTech R&D ecosystem, I estimate price sensitivity, elasticity, and user segments related to technology adoption and data sharing. Econometric and segmentation models are used to predict heterogeneous adoption behavior, defining the realistic scale and structure of decentralized data generation.

The second step integrates these empirical insights with external pricing benchmarks and alternative solution costs to construct a supply–demand–based pricing model for data-driven DSS. On the supply side, data generation is modeled based on the economic cost of producing high-quality environmental and plant physiological data. On the demand side, willingness to pay for DSS outputs is derived from estimated marginal gains and cost reductions for different stakeholders, such as farmers and AgTech R&D users.

The novelty of this research lies in providing a transparent economic framework for evaluating and pricing data-driven DSS in emerging markets where prices are still forming. While empirically grounded in agriculture, the framework is applicable to other data-intensive sectors facing similar valuation challenges.