A Framework to Generate Optimal Keyword List for Pay-Per Click Advertising in an Organization

Web advertising is playing a key role making huge turnovers in terms of revenue for the retailers marketing various products online. Internet became a primary source of valuable information for everyone. The search engines are playing an important role as information providers over a decade. Online Advertising and Search Engine Optimization (SEO) became two primary sources for advertisers to implant their information online and keywords play an active role in driving traffic to the retailer’s website. Though there is ample literature available on search engine marketing, it is always a challenge for the advertisers to identify an ideal strategy for gathering keywords that would yield impressive results. This research article is an effort to provide an ideal framework that can be adopted by a retailer/advertiser to generate an optimal list of keywords that can drive good quality traffic to the website. The author refers an optimal list to state the quality of keyword list that can be generated using the proposed framework

Identifying Interesting Knowledge Factors from Big Data for Effective E-Market Prediction

Knowledge management plays an important role in disseminating valuable information. Knowledge creation involves analyzing data and transforming information into knowledge. Knowledge management plays an important role in improving organizational decision-making. It is evident that data mining and predictive analytics contribute a major part in the creation of knowledge and forecast the future outcomes. The ability to predict the performance of the advertising campaigns can become an asset to the advertisers. Tools like Google analytics were able to capture user logs. Large amounts of information ranging from visitor location, visitor flow throughout the website to various actions the visitor performs after clicking an ad resides in those logs. This research approach is an effort to identify key knowledge factors in the marketing sector that can further be optimized for effective e-market prediction

A Framework for Profiling Prospective Students in Higher Education

Prospective student acquisition is a prominent issue in higher education marketing. Noel-Levitz (2012) estimated that higher education institutions are losing as high as 75% of the prospects after receiving an inquiry. Another study reported that 80% of the students who decide to apply to a program were influenced by the post-inquiry communications they had received from the higher education institutions (Aarinen, 2012). This chapter attempts to study the underlying concepts from literature and design a framework to extract prospective student profiles and further extend a discussion on how these profiles can be used to address the prospect engagement