Automated Marketing Research Using Online Customer Reviews

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Web Appendix D. Interpreting the Correspondence Analysis dimensions. Correspondence Analysis is an approach to dimension reduction when analyzing high- dimensional, two-mode, two-way count data (Everitt and Dunn 2001). To interpret the dimensions in the reduced space, we regressed each brand’s factor scores on the derived attributes. For each figure in the paper, we report the results of the stepwise regression on F1 and F2. The reported results assume a probability for entry of .05 and a probability of removal of .1. To make the dimensions both interpretable and actionable, we follow the marketing literature in relating customer needs, as represented by user generated reviews, to actionable manufacturer specifications, as in the "House of Quality" (Hauser and Clausing 1988). Specifically, product attributes elicited from online reviews include different granularities ("zoom" versus "10x optical zoom") or reflect different levels of technical sophistication ("low-light settings" vs. "iso settings"). Consulting the set of professional buying guides, we manually mapped our 39 automatically generated attributes onto a coarser but actionable categorization of specifications. The visualizations are generated and interpreted using these meta-attributes. lens cap/cover resolution manual focus lens rechargeable battery image resolution/print qual feel durability price movie/video zoom image stabil manual controls start-up time pc connect navigation weight software picture types shutter-lag flash bult - in storage type size low-light/light control service/warranty shot-delay camera type white balance lcd autofocus Table WD4.1 Actionable meta-attributes for regressing explaining CA dimensions Figure 3. Mapping the market using customer reviews Summary of attribute selection for dimension F1. No. of Adjusted variables Attribute MSE R² R² 1 navigation .000 .897 .882 2 storage type .000 .992 .989 3 movie/video .000 1.000 .999 Summary of attribute selection for dimension F2. 20
No. of Adjusted variables Attribute MSE R² R² 1 low light .000 .913 .900 2 lens .000 .988 .983 3 price .000 1.000 .999 Figure 5. Market structure by Cons with Pros as supplementary points Summary of attribute selection for dimension F1. No. of Adjusted variables Attribute MSE R² R² 1 image resolution/print qual .000 .929 .918 2 start-up time .000 .972 .963 3 lens/cap cover .000 .989 .982 4 shutter-lag .000 .996 .992 5 service-warranty .000 1.000 .999 Summary of attribute selection for dimension F2. No. of Adjusted variables Attribute MSE R² R² 1 lens .001 .689 .644 2 navigation .000 .933 .911 3 size .000 .973 .956 21
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Page 4 and 5: [3] Phrase clustering We cluster th
Page 6 and 7: More formally, we assume that phras
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Page 10 and 11: Web Appendix B. Automatically gener
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Page 16 and 17: 38. Picture quality extremely, swit
Page 18 and 19: VOC Only Survey attribute Auto Fami

Automated Marketing Research Using Online Customer Reviews

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