데이터 스토리텔링_아마존의 애널리틱스, 빅데이터 적용 사업 제안서_Columbia Univ_Brief Proposal for Analytics and Data Science in Amazon
분야
docxOur company, Amazon.com, Inc.1), has gained the large amount of the customer data in grocery segment by acquiring Whole Foods. The purpose is to customize the grocery shopping experience to each individual user (i.e., upselling), and to discover other opportunities. To achieve this goal, Amazon must utilize analytics to process the data and gain insights.
Opportunity
Customizing shopping experience
The company has well-established image of customization, tailoring the online shopping experience to each customer. And the customers would have the same or more expectation on grocery shopping with Amazon. In what manner should we apply the customizing features in the grocery product lines? We can find answers from the data. If the company can satisfy their expectation, not only it can provide convenience to the customers, but also it will effectively make them to see Amazon as their favorite supermarket.
Enhancing AmazonFresh
Not all Prime members2) upgrade to Prime Fresh (subscription for AmazonFresh3)). With the available data, we need to find out ways to improve AmazonFresh, and what encourages customers to do onsite shopping instead of signing up for AmazonFresh. The latter will help us to figure out how to increase AmazonFresh subscription.
Rationale
The following three analytical techniques help us achieve the goals above.
Exploratory Data Analysis (EDA)
To extract value from the large data, one must thoroughly explore the data. The analytics team will perform EDA to understand it and find useful information such as seasonality or trend in grocery sales. EDA would be performed in the following manner:
