Competitive Intelligence: The Latest Trend in BI
Let me elaborate: The general population has increased its use of chat rooms, blogs, wikis, and online communities such as Myspace.com. In such pages, they are talking about anything and everything including your organization, your products and your services. So, a new opportunity has started for organizations to gather the true feeling of their customers about their organization and their products (and their competitors) by monitoring and reading such pages.
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Obviously, there are so many of these pages (in many sites and languages) that reading them manually becomes almost impossible. On the other hand, categorizing and presenting such sentiments is a monumental task in itself. CI products, processes, and services make this process much easier.
CI products search pre-designated sites, chat-rooms, and communities (in many languages) and utilize specific taxonomy and ontology to identify good and bad sentiments of consumers about a specific product (or group of products), the competitor (and its products), and/or the organization in general.
After the search is completed, the business intelligence (BI) component of a CI product would categorize, and tabulate the search results and present it in a format that enables the user to see the summary and yet have the ability to drill down to the actual pages or documents.
For example, imagine that an auto manufacturing organization (org-A) has delivered the latest model of its hybrid vehicle. At the same time, its competitor (org-B) has delivered its own version of a hybrid vehicle. Logically, both organizations would like to know the general populations reaction to the vehicles, the service, and the brand in general for both vehicles.
Either organization could implement a CI platform that will automatically explore specific websites, online communities, chat-room, blogs, industry online magazines and articles, etc. in English and Spanish. It could also check out internal documents including focus group results, market research reports, and even emails sent to the company by existing or potential customers about its and org-Bs new hybrid vehicles.
A CIs search engine would utilize search words and patterns that would determine the content of the website is referencing either organizations hybrid vehicle. The categorization engine of the CI product will then utilize a pre-defined taxonomical and ontological set of tables to determine a) the content is about the horsepower, speed, or interior design of the vehicle and b) whether the writer has written positively or negatively about the vehicle. The presentation engine would then tabulate the results and present it in an easy to understand format.
In this case, for example, org-A might find out that, although the population finds the power and the speed of its hybrid vehicle superior to org-Bs vehicle, they find the interior design of org-Bs vehicle more friendly and inviting. Both organizations could also find unsolicited wish-lists for the products such as extra cup holders and memory seats.
All-in-all CI provides an insight into minds, thoughts, and attitudes of existing and potential customers that has never been available in such broad options in history. As the usage and innovations of online communities increase, it will become important to organizations to harvest the sentiment of the masses to create products that are enjoyed by consumers.
Majid Abai is president and CEO of Seena Technologies, an enterprise information management and architecture consulting firm. Majid co-authored Data Strategy (Addison-Wesley, 2005) and teaches classes in Business Intelligence and Enterprise Data Architecture at UCLA.