Rickman, A.T., Consenza M. R. (2007). The changing digital dynamic of multichannel marketing: the feasibility of the weblog: text mining approach for fast fashion trending. Journal of Fashion Marketing and Management, 11, 604-621. Retrieved from http://search.proquest.com.proxy.library.vcu.edu/docview/235465209?accountid=14780
I think this post represents the major changes taking place in the industry. In particular this article focuses on Fashion Forecasting as well as street style. Fashion Forecasting allows brands to see what is trending and what to create and what not to create. Fashion Forecasters are in the field taking pictures and compiling data in the most trendy cities they go after the fashion innovators. Because everything in the fashion industry is recycled designers have to take an old look and make it new. This article also touches on data and how they are using new tools such as text mining which allows those in the field to sort through key words in a text to better understand the overall concept.
 Friedman and Furey (1999) define channel advantage as an advantage that does last. It is about reaching more customers by using sales channels to meet where and how they want to do business. Dynamic channel advantage is created through “fast” strategy that moves more stuff, reduces costs, improves customer retention and satisfaction, and grows market share and profits by giving customers flexible ways to do business with you. It is not surprising that the Internet has become the key action aspect to obtaining dynamic channel advantage. This occurs in two ways:
by optimizing supply chain interaction – the melding of partnerships to discover, develop, and deliver fast to market style; and.
This nugget is mainly focusing on fast fashion. But as in nugget #5 this paragraph touches on how utilizing every possible channel is what will get you ahead. The internet allows you to reach a larger audience, Facebook and Twitter will allow any company to interact with a large amount of people young and old. This is the fastest way to reach your desired target market within minutes.
Weblog mining is a special case of data mining ( Mena, 1998). The objective is to determine a variety of structural patterns to text data contained within weblogs. The models marry text mining algorithmic attributes and operations with the somewhat unstructured content from the internet blogsphere to determine present and future patterns.  Okumura (2005) presents a weblog mining methodology that captures trends on Japanese blogs. Okumura’s weblog mining system automatically extracts and mines “burstiness” (trend, frequency, time-span), “hot words”, and favorable and unfavorable opinions toward objects (i.e. fashion objects) from a collection of specified weblog pages. Although it is in rough form,  Fukuhara (2005) developed algorithms and methods that evaluate Chinese weblogs and real time social occurrences to find matches, lag, and leading indicators of social concerns. Building on the work of  Gruhl et al. (2004) and  Kumar et al. (2003),  Nakajima et al. (2005) proposed and tested a methodology to search weblogs to find important bloggers.
This nugget was particularly eye opening. Most of the time when we think of people monitoring key words your brain immediately jumps to Facebook. In Concept experience number 5 we discussed how Facebook conducted a study without us knowing. But it was interesting to see how the fashion industry too compiles data on trending words. The affluential words are compiled and used to see what is trending and what is not trending.