Research Methods and Tools
p-Hacking and False Discovery in A/B TestingOct 18, 2018 Ron Berman, Leonid Pekelis, Aisling Scott, and Christophe Van den Bulte, 2018, 18-130-10
Data on 2000+ commercial experiments show that 57% of online A/B experimenters p-hack, inflating the number of experiments that show significant effects. The result: incorrect decisions and foregone earnings for the company.
Identifying Customer Needs from User-Generated ContentAug 16, 2018 Artem Timoshenko and John R. Hauser , 2018, 18-124-08
UGC -- online reviews, social media, and blogs-- offers a rich source of information from which to identify customer needs. Combining machine learning and human analysis of UGC resulted in 46-52% gains over experiential interview-based analysis.
Slider Scale or Text Box: How Response Format Shapes ResponsesAug 7, 2018 Manoj Thomas and Ellie J. Kyung, 2018, 18-122-08
Seemingly simple interface changes can have a material effect on consumer payments: slider scales can change how consumers mentally visualize prices.
Making Words Speak: Leveraging Consumer Insights from Online Review Text to Improve Service QualityJul 25, 2018 Andrea Ordanini, Raji Srinivasan, and Anastasia Nanni, 2018, 18-119-07
Online review text analytics enabled managers to clearly and rapidly identify weaknesses in their offering.
Enhancing Power of Marketing Experiments Using Observational DataJun 5, 2018 Ron Berman and Elea McDonnell Feit, 2018, 18-116-06
This stratification approach may help marketers decrease the size of marketing experiments or gain more accurate insights from previously run experiments.
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