More than Words: The Influence of Affective Content and Linguistic Style Matches in Online Reviews on Conversion Rates

TLDR

Customers increasingly rely on other consumers’ reviews to make purchase decisions online. The study investigates how the semantic content and linguistic style of verbatim customer reviews influence online retail conversion rates. The authors use text mining to extract changes in affective content and linguistic style properties from Amazon book reviews. The panel model shows that while increases in positive affective content yield diminishing returns on conversion rates, negative affect changes do not taper, and that both positive affect shifts and alignment with the product group’s typical linguistic style jointly boost conversion rates, suggesting managers should highlight influential reviews, guide reviewers, and tailor editorial language to the category.

Abstract

Customers increasingly rely on other consumers' reviews to make purchase decisions online. New insights into the customer review phenomenon can be derived from studying the semantic content and style properties of verbatim customer reviews to examine their influence on online retail sites' conversion rates. The authors employ text mining to extract changes in affective content and linguistic style properties of customer book reviews on Amazon.com . A dynamic panel data model reveals that the influence of positive affective content on conversion rates is asymmetrical, such that greater increases in positive affective content in customer reviews have a smaller effect on subsequent increases in conversion rate. No such tapering-off effect occurs for changes in negative affective content in reviews. Furthermore, positive changes in affective cues and increasing congruence with the product interest group's typical linguistic style directly and conjointly increase conversion rates. These findings suggest that managers should identify and promote the most influential reviews in a given product category, provide instructions to stimulate reviewers to write powerful reviews, and adapt the style of their own editorial reviews to the relevant product category.