The advancement of computer networks and internet technology has prompted the rapid development of e-commerce. Essentially, e-commerce is a type of commercial operation model that allows consumers to buy products on online platforms and commercial tenants to sell products and receive electronic payment. E-commerce business operations often result in the accumulation of huge chunks of transaction-related data on web servers that develop into a transaction database with time. In order to track and store relevant data that give commercial value, online enterprises normally use a range of techniques including the documentation of traffic hits, log-analysis, clustering, collaborative filtering, customer profiling, and artificial intelligence (AI).
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One of the most basic technique that online businesses use to track data is the documentation of website traffic through counters. Although it is efficient in doing so, counter only measure activity on a site on a very basic level. If a counter records 20 hits, for example, there is no way of knowing whether those hits were as a result of 20 different visitors or fewer people who visited the website multiple times each. Moreover, website owners cannot use counter data to tell the specific locations from individual visitors come from. A more meaningful technique for tracking activity on e-commerce sites is through log-analysis applications. Long-analysis programs help online entrepreneurs to understand online behavior by revealing the number of unique visitors as well as how often visitors return to the site. They can also disclose the kind of FAQ that customers mainly click, which pages they stayed in longest, the sites that redirect them, and how the traffic volume affects their activity. Therefore, owners can infer what visitors think about their online stores, products, content, design, as well as other features. The feedback that long-analysis software collects functions as guidance on how to redesign the site, modify constant, and alter information.
E-commerce businesspersons can also resort to the use of clustering to observe visitors’ behavior on their websites. In essence, clustering is the process of grouping visitors into narrow data classifications, which can then help site owners to identify specific visitors by their characteristics and activity. Another alternative is collaborative filtering – this technique builds personalized recommendations on the particular website under scrutiny. Therefore, websites can match purchase patterns displayed by one shopper to other comparable shoppers or make recommendations based on prior online purchases. Collaborative approach utilizes customer segmentation to divide consumers into groups that share similar habits, just as in the case of clustering. Other tracking methods include customer profiling systems, which label individual visitors with the activity they perform on the site and artificial AI, which online marketers and traders to know the offers that specific customers prefer more, the time at which they are likely to respond, as well as the best way of presenting relevant offers.
In conclusion, E-commerce attacking allows online sellers to measure the quantity of transaction and revenue that a website generates. On a typical e-commerce website, once a consumer clicks the purchase option, their information is forwarded to the web server, which executes the transaction. If the process is effective, the server then redirects the consumer to a receipt or thank you page with transaction details and an acceptance message. E-commerce sites record this activity using a variety of tools that have been discussed in these paper. They include traffic hits, log-analysis, clustering, collaborative filtering, customer profiling, and artificial intelligence (AI).
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