Consumers and Their Online Shopping Behavior
Success of any business depends on how best it understands the consumer behavior and reaction towards their various online contents aimed at attracting their attention to the products offered or services. A use of mixture of the proposed techniques is the most optimum method of mapping the consumer trends and prediction of their online purchase habits. Introduction This study will center on the processes used in assessing the consumers and their online shopping behavior in relation to the marketing techniques used by organizations. Additionally, the study will show how the contemporary techniques have evolved and are mostly based on the Internet, where consumers are sourced from various online platforms. The study will only focus on the campaigns conducted through the digital or internet to reach out to consumers and how the consumer behavior relates to them.
In addition, the study will provide business organizations with important information about various techniques of measuring and tracking success of their marketing campaigns to decide their most preferred one. Literature Review Search Engine Advertising Click-Through and Conversion When dealing with search engine advertisements, there are two different aspects relating to the measurement of success of their advertisement statements. These include; the conversion rates, and the click-through. Even though the two aspects are related, they are quite different and have been presented as such through the literature. The method of search engine advertising is currently the leading form of advertising done through the internet. Low involved users see such evidence as too complex, while highly involved users take their time to process the content presented in the ad more carefully, and when they click on the advert, their conversion likelihood is quite higher.
Recency and Frequency of Page Views As the number of businesses that operate e-commerce sites increase to offer their products through the internet, it is crucial to analyze ways through which the customers compare and buy their products over these sites. Consumers are also increasingly making purchases without having to go to the brick-and-mortar premises only by using the information available on the e-commerce sites. Businesses are building profitable relationships with the consumers through these sites, and the effectiveness of this process is measured using the clickstream data, which simply implies the record of the page view for each visitor, and can be used to understand the behavior of a consumer(s). In a research conducted by Iwanaga et al. According to the study, it is suggested that the dynamic models relating to time-related character of data are better than the static ones.
Therefore, the model-based clustering which should be depended upon is the mixture of the first order Markov models. Association Rule Mining In 2005, a collaborative filtering technique was developed by , Kim, Yum, Song, and Kim based on the behavioral and navigational patterns of the consumers who visit the e-commerce sites. A research was conducted by Kim et al. (2005) to improve the methods already developed and in turn create a new and advanced recommender system. The authors argue that the Big Data has been applied in marketing by largely focusing on the assessment of consumer preferences, prediction of what the consumers are most likely going to buy next, improvement of the targeted advertising, to understand the brand perceptions, and finally in the description and understanding of the competitive landscape.
The researchers provide a new insight into the Big Data and how their investigations can help to inform more psychological aspects associated with the consumer behavior, with the aim of enhancing an understanding instead of just predicting, the attitudes and emotions of the consumers. The advantage suggested for the holding of huge data about the consumers is that it helps the business in knowing more about their customers but may not necessarily help these businesses know these consumers themselves. By the use of the Big Data, businesses can get to understand the consumers better with regard to the stable psychological traits in addition to their psychological states which are more malleable. Realistic Customer Journey Map Mark, Mauricio, and Germa´n (2017) suggest a different model of dealing with the consumer behavior prediction and effectiveness of the marketing through the customer journey mapping (CJM).
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