Calvert Gemma A, Brammer Michael J
Nanyang Business School, Nanyang Technological University and the Institute for Asian Consumer Insight, Singapore.
IEEE Pulse. 2012 May-Jun;3(3):38-41. doi: 10.1109/MPUL.2012.2189167.
Advances in machine learning as applied to functional magnetic resonance imaging (fMRI) data offer the possibility of pretesting and classifying marketing communications using unbiased pattern recognition algorithms. By using these algorithms to analyze brain responses to brands, products, or existing marketing communications that either failed or succeeded in the marketplace and identifying the patterns of brain activity that characterize success or failure, future planned campaigns or new products can now be pretested to determine how well the resulting brain responses match the desired (successful) pattern of brain activity without the need for verbal feedback. This major advance in signal processing is poised to revolutionize the application of these brain-imaging techniques in the marketing sector by offering greater accuracy of prediction in terms of consumer acceptance of new brands, products, and campaigns at a speed that makes them accessible as routine pretesting tools that will clearly demonstrate return on investment.
应用于功能磁共振成像(fMRI)数据的机器学习进展,为使用无偏模式识别算法对营销传播进行预测试和分类提供了可能性。通过使用这些算法来分析大脑对品牌、产品或在市场上失败或成功的现有营销传播的反应,并识别表征成功或失败的大脑活动模式,现在可以对未来计划的活动或新产品进行预测试,以确定由此产生的大脑反应与期望的(成功的)大脑活动模式的匹配程度,而无需言语反馈。信号处理方面的这一重大进展有望彻底改变这些脑成像技术在营销领域的应用,通过以一种使其能够作为常规预测试工具使用的速度,在消费者对新品牌、产品和活动的接受度方面提供更高的预测准确性,从而清楚地证明投资回报率。