Analysis of Predictive Modeling: A Case Study of PayPal
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This article analyzes the use of predictive modeling in PayPal, focusing on its impact on customer experiences, fraud detection, personalized ads, and more.
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Running head: PREDICTIVE MODELLING1 Analysis of Predictive Modeling: A Case Study of PayPal Name Institutional Affiliation
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PREDICTIVE MODELING2 The recent market capitalization ranks PayPal among the top 5 companies for payment. Besides, PayPal has become common for online processing of payments over the web using strategies like Data Science and Big Data Analytics in accessing the buying habits of customers based on their location. The two strategies have been fruitful in enriching her costumers through every day experiences. Merchants remain the real customers of PayPal though their customers indirectly consume services of PayPal hence are customers as well. PayPal makes it easier and comfortable for customers to shop. Besides, it achieves this by safely and securely processing the payments of vendors and auction websites through cloud computing(DeZyre, 2016). To achieve its ends, PayPal indulges in provision of improved capabilities in prediction helping the merchants improve the experience of their customers. For example, a merchant like Walmart can have the desire to efficiently market her products. In this case, PayPal through her data scientists can create the list of target customers in a way that help with prediction hence the success of the company. The data scientists use transactional data in predicting the purchasing behavior and patterns of consumers realized by analyzing online data such as customers views, location, and visited websites(DeZyre, 2016). According to DeZyre (2016), PayPal uses the HBase and Hadoop technology; tying a knot with traditional databases with the Hadoop being helpful in running exploratory queries while BI analysis helps with the identification of answers through the uses of memories such as SAP HANA. However, Hadoop lacks advanced security. To help fight fraud, the company identifies cases of fraud, relying mostly on the analytical systems and technologies like spark which make use of human detectives, online caching, and algorithms to combat fraud. Moreover, analyses of past payment also play a role in identifying scam.
PREDICTIVE MODELING3 In addition, PayPal uses advanced big data analytics in delivering personalized ads and offers of a company. Today, there are numerous communications taking place on websites, smartphones, and tablets whereby customers are enticed with offers and advertisements based on location. Current shopping trends challenge advertisers and marketers in placing their ads. In this case, PayPal leverages big data thus sending the data to several customers. Analysis of past shopping trends helps connect merchants and customers thus saving on money as well as driving the volumes of transactions. For example, PayPal has the information that customers who undertake their shopping at a given depot are likely to enjoy their meals at the nearby Subway thus sending those customers ads of the nearby Subway. PayPal is also vital in enriching the experience of customers by providing insights from the conversations made by other customers on the feeling and love of a given product(DeZrye, 2016). Being a consumer of online advertisement, there is a high tendency of my browsing and shopping history being used for predictive analysis. Besides, reading of business ads aids the possibilities of use of browsing history. Also, participation in various surveys also has higher chances in the inclusion of views and opinion entered in these databases. As a consequence, the economy will further grow owing to the modern marketing patterns which has reduced the globe into a mere market place. Predictive analytics help companies gain popularity as well as saving on money. It helps companies to forecast on the requirements thus improving on their sales. Also it will improve demand, pricing, maintenance, and the discovery of new applications(LaRiviere, McAfee, Rao, Narayanan, & Sun, 2016).However, the predictive capabilities have low chances in promoting privacy of the customer such their tastes, preferences, locations, and buying habits will be exposed while predictions are made.
PREDICTIVE MODELING4 References DeZyre. (2016, March 12). Big Data Use Cases: How PayPal leverages Big Data Analytics. Retrieved from https://www.dezyre.com/article/big-data-use-cases-how-paypal- leverages-big-data-analytics/231 LaRiviere,J., McAfee,P., Rao,J., Narayanan,V.K., & Sun,W. (2016, May 25). Where Predictive Analytics Is Having the Biggest Impact. Retrieved from https://hbr.org/2016/05/where-predictive-analytics-is-having-the-biggest-impact