DIGITAL MARKETING ESSENTIALS CASE EXAMPLE 2.2 Moving customers from pre-purchase to purchase with Reviews.io 0 Consumer reviews help potential customers move from the pre-purchase to purchase touchpoints and are part of online word of mouth or eWOM, which is any positive or nego stage by reading or watching feedback from other customers. These are social or external tive statement made by potential, actual or former customers about a product or company, which is made available to a multitude of people and institutions via the Internet (Hennig- Thurau et al., 2004, p. 39). video. These are gathered post-purchase and often via email or text message. Reviews can Typically, a customer review includes a star rating, some comments or a show-and-tell be applied to the whole business (usually about customer service) or individual products. Companies are notified about new reviews and access the data via a dashboard, which allows companies to respond. Reviews.io wasn't the first platform that enabled companies to collect verified customer feedback, but their focus was making it easier and collecting different types of reviews. In addition to the star ratings and written remarks, Reviews.io captures feedback via SMS and video. Collecting comments by text message works well where people are shopping via an app or mobile and don't use email as often Reviews.io encourages video reviews and although these are used less frequently, as real customers upload videos to share how they use, wear and consume. Another innovative service is automatically turning reviews into social media posts. In the company dashboard, companies can select great reviews, choose the social media platform and posts are automatically created, ready to share. they are powerful steps the customer journe Companies such as Reviews.io try to combat this weak practice by only accepting veri- fied reviews which are collected through automation after a purchase is made. This aims to reduce inauthentic and deceptive comments as they are a 'verified purchase. However, as reviews have become so valuable, one of the challenges is the growth of fake reviews and many major organisations have fallen foul of this practice, including Tripadvisor, Amazon and tech product company HTC (Wu et al., 2020). An entire business based on trying to combat fake reviews has emerged, mainly led by researchers seeking algorithms to better identify the fakes (see Digital Tool Review Skeptic). Fake reviews can be created by companies, by people paid by companies to make an organisation look good, or automated where online programs use machine learning to add positive words to describe products. Case questions Have you ever made a decision to buy - or not to buy- an item because of a review? What do you think are the key components of a good and a fake review? In addition to written reviews, video testimonials and star ratings, what other formats could be used to create reviews