Adaptive Idea Screening Using Consumers
Adaptive Idea Screening Using Consumers
复制标题
使用消费者进行自适应创意筛选
DOI：
--
复制
发表时间：
2007
期刊：
影响因子：
--
通讯作者：
Laurent Florès
中科院分区：
文献类型：
--
作者：
Olivier Toubia;Laurent Florès
在一次成功的创意生成活动之后，公司可能很容易留下专家、员工或消费者提出的数百个创意。下一步是筛选这些想法并确定那些最有潜力的想法。在本文中，我们提出了一种让消费者参与创意筛选的实用方法。
尽管想法的数量可能非常大，但要求每个消费者评估多个想法是不合理的。这就提出了有效选择每个消费者要评估的想法的挑战。我们描述了几种想法筛选算法，这些算法根据先前消费者的评估自适应地执行此选择。我们使用模拟来比较和分析算法的性能并了解它们的行为。性能最佳的算法侧重于根据之前的评估最有可能被错误分类为“顶部”或“底部”想法的想法，并通过向错误分类概率添加随机扰动来避免过快丢弃想法。我们通过现场实验证明了该算法的收敛有效性，这也证实了模拟预测的收敛模式。
Following a successful idea generation exercise, a company might easily be left with hundreds of ideas generated by experts, employees, or consumers. The next step is to screen these ideas and identify those with the highest potential. In this paper we propose a practical approach to involving consumers in idea screening.
Although the number of ideas may potentially be very large, it would be unreasonable to ask each consumer to evaluate more than a few ideas. This raises the challenge of efficiently selecting the ideas to be evaluated by each consumer. We describe several idea-screening algorithms that perform this selection adaptively based on the evaluations made by previous consumers. We use simulations to compare and analyze the performance of the algorithms as well as to understand their behavior. The best-performing algorithm focuses on the ideas that are the most likely to have been misclassified as “top” or “bottom” ideas based on the previous evaluations, and avoids discarding ideas too fast by adding random perturbations to the misclassification probabilities. We demonstrate the convergent validity of this algorithm using a field experiment, which also confirms the convergence pattern predicted by simulations.