










![]() | ![]() | ||||||
| |||||||
| |||||||
The surge in user generated content, and specifically "user reviews" has been the backbone of many sites such as Tripadvisor and Amazon. What could have been a wonderful tour for John and Mary could be a nightmare for Trixie and Noah. What could have been a fun experience for Trixie and Noah could have been hard work for the Schmidt parents. On every level each customer would view their experience with different rating scales on different criteria. User reviews need to evolve to enable customers to filter reviews so that they can see reviews just from "people like me". This is particularly important in the case of experiences. Experiences include things like destinations, hotels, restaurants, theatre, travel and other services. The use of a tangible product (e.g. hairdryer, camera) is less affected by personal preferences. With experiences, preferences do count. Peer reviews can actually be misleading rather than assisting. The challenge in filtering peer review for experience-type products is three fold: 1. Identify the customer as they arrive on-site 2. Gathering customer profile information to store against the review 3. Filtering the display of reviews so that they match a customer Imagine how powerful this could be. - I want summer holiday recommendations, but only from vegetarians travelling with children under 2 - I want restaurant reviews, but only from business users - I want ski resort reviews, but only from people that enjoy skiing off-piste If you can deliver this content at the right time, you can provide real relevancy. It does however require some clever customer segmentation and profiling to get right. Retailing is often cited as providing the right deal to the right person at the right time. I would argue that for online retailing of services, it also involves giving the right information to the right person for the right product. That means giving the customer reviews "from people like me". This is where the future of user generated content lies. It's a massive challenge and I look forward to seeing who can get it right and how they do it. It would be web 2.1. Does anybody have any examples of people doing this well already? | |||||||
Any contributed content above is the subjective opinion of that member or external author, and not of Gooruze.com Pty Ltd. View our House Rules for more details. | |||||||
Related Articles![]() No related articles available | Bookmarks![]() No bookmarks available | ||||||
Related keywords: influence, retailing, reviews, 0, content, generated, user, 2 | |||||||
| ARTICLE RATING | |||||||
Thankyou for your vote (you can change your vote at any time). Please leave some helpful comments about this article using the box below. | |||||||
![]() Add a comment | |||||||
Add a comment on this article. | |||||||
![]() Comments | |||||||
November 2007 i think stumbleupon is doing this kind of.
ie: is suggests sites that you may like based on your previous votes and based on what others that vote similar to you like. Scouta is also doing this for video and podcasts. Reply
Report
| |||||||
Home | Read News | Post News | Read Articles | Write Articles | Q & A | Groups | Activity | Members | More
Privacy Policy | House Rules | About Us | Contact Us | House Blog | FAQ | Advertise With Us
© Copyright 2007 Gooruze ™ | Built by Market United