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• W4306166191 abstract "• A continuous-time, intensity-based modeling approach for agent-based models (ABMs). • ABM including both agent-to-agent interactions and external effects. • Inference for model parameters based on individual behavior in the training period. • Computationally efficient algorithms for calibration and prediction. • Exogenous sources of influence distinguished from viral, word-of-mouth spread. • Macro- and micro-validation on a real data set and what-if scenario simulations. Understanding the spreading process of new products provides valuable knowledge that can be used for effective marketing. The ability to make early prediction of success or failure is a great advantage in innovation processes. Extending current literature in a novel way, we propose a data-driven agent-based methodology that accomplishes this task. Inference and predictions are based on short-time observations of the product adoption history and knowledge of the social network of consumers. We model and predict adoptions at the agent level as driven by unobserved peer-to-peer influence and external factors such as marketing. The method compares interaction between consumers and general campaigns, and quantifies the importance of characteristics of customers and their social relations. Our computationally efficient method is demonstrated by analyzing real data, predicting the process far into the future using data from a short period after launch, and validated by simulation experiments on a true full-scale communication network." @default.
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• W4306166191 date "2022-11-01" @default.
• W4306166191 modified "2023-10-18" @default.
• W4306166191 title "An agent-based model with social interactions for scalable probabilistic prediction of performance of a new product" @default.
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• W4306166191 doi "https://doi.org/10.1016/j.jjimei.2022.100127" @default.
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