No full-text available
To read the full-text of this research,
you can request a copy directly from the authors.
Evolution of Online Marketing Tools, Approaches, and Strategies With Associated Challenges: A Survey
International Journal of Technology Diffusion
Abstract
Marketing tools like organizing campaigns, banners, TV, and radio are traditionally designed for making the product more appealing and creating a need in the customers' minds. With the increased use of internet in our day-to-day life, these traditional tools are being replaced rapidly by online marketing media. Online marketing is a two-way communication that can reach a large number of relevant people in less time by providing more information and removing geographical constraints. The companies can respond to the customers' requirements more speedily with better quality by making direct contact with them. Most of the online development work supports itself with the revenue earned through advertisements rather than subscription. Online marketing is one of the largest, most effective, and fastest advertising media. Today, it is an essential medium of advertising for meeting the desires and needs of the customer. This paper explores the history of online marketing along with its basic tools with a special emphasis on keyword-based search, recommendation engines, and real-time bidding.
... The offline algorithms provide the best result as the complete data is available initially. But the data nowadays is streaming and this approach fails with streaming data [11]. ...
Sponsored search has emerged as a prominent form of advertising on the internet and acts as a major source of revenue for various search engines. In this, the attention of the user is drawn towards the ads, presented as sponsored links, along with organic search results, to the entered query, on a given search engine. Advertisers bid on keywords (also referred to as bid terms) of possible future search queries and pay accordingly on getting clicked. It is observed that normally the advertisers bid on frequently occurring keywords in the search queries which often leaves the revenue space of search engines underexplored. The paper presents a novel technique for maximizing the revenue of a given search engine by an adaptive keyword-weighted approach. The proposed approach ensures weight assignment to keywords based upon their impression-winning capability adaptively and progressively. It then couples the weights assigned with the rarity factor of the keywords leading to a revenue-maximizing ranking mechanism. Advertisers with lower bid values but relevant rare keywords are explored over the higher bidders. Quantitative analysis results show that the proposed algorithm is efficient and it shows significant improvement compared to the generalized balance algorithm.
On the internet, with the increased number of users, its use for recommending products and services is also increasing. But there is a need to filter that data and provides only relevant recommendations. Recommendation systems help in solving this problem by providing personalized recommendations from a large pool of data. This paper provides an overview of the recommendation system along with its various filtering techniques. The paper also discusses various challenges faced by the current recommendation systems and the possible research areas in this field that can improve its efficiency.
KeywordsRecommendation engineCollaborative filteringContent-based filteringHybrid filtering
One of the most prominent fields of online advertising is Sponsored search and for various search engines, it acts as one of the main sources of revenue. This paper focuses on sponsored links displayed to the user along with search results when a query is fired by the user. Bidding on keywords is done by the advertiser for the expected future queries and accordingly, payment is done if clicked. A novel technique is proposed in this paper which aims to maximize the revenue earned by a search engine by using an Adaptive keyword weighted approach. Normally, the advertisers focus on keywords with a high frequency which leads to underexplored revenue of search engines. The approach proposed in this paper assigns weight to the keywords based on their winning probability. It also merges the assigned weight with the rarity factor leading to more revenue. With this approach, advertisers with relevant keywords which are rare are explored even if the bid value is low. Experimental results are shown in this paper for proving the improvements over the generalized balance algorithm.
ResearchGate has not been able to resolve any references for this publication.