Marketing in the era of the World Wide Web is still a growing field. Many great ideas came, worked for a while, declined in value, and disappeared. New technologies are always developing that provide additional opportunity and value in creating stronger methods of developing and marketing new products through understanding web conversation.
Web conversation is something we see everyday but don't really notice. It is everything people put on the web when they discuss products, needs, and other opinions about ideas and concepts. It is a type of user generated content that stays on the web and is good for analysis. A study by Tirunillai and Tellis (2014) show that user generated content is ripe for market analysis.
Consider the idea that you desire to understand what people are thinking about a particular product and its features. Popular products and features often develop forums that are used by interested members to analyze and discuss their ideas and opinions as they relate to that product. By reviewing the material it is possible to gain greater insight and value.
The problem is that it is time consuming to try and follow online customer chatter. Certainly it is better than nothing but it isn't the whole solution. By using analytical tools it is possible gather a wider array of information and use that information to understand products and services better. Scanning the web for a better sample of customer preferences is extremely helpful in decision making.
The process works a little like this.Using proper software and algorithms it is possible to calculate and analyze the types of works being used to describe products. If done over a longer period of time it is also possible to create trends that can be used in determining new product cycles, features, and overall brand exposure. The process is beneficial for those seeking to understand consumer sentiment.
New technology brings new opportunities for companies to develop higher levels of analysis. Brand image is the total collective perception of the product/service in public. In this case, consumer generate content acts as a type of speech while the analytical software seeks to break apart and understand the overall conversational trend. It is like listening to thousands of people at once to gain insight for future product and marketing development.
Tirunillai, S. & Tellis, G. (2014). Mining marketing meaning from online chatter: strategic brand analysis of big data using latent dirichlet allocation. Journal of Marketing Research, 51 (4).
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