How Online Shopping Companies Perform Data Collection for Big Data Analytics
Tim Ly
If you’re living in the 21st century, chances are that you’ve been victim to the one mindset that we’ve all been battling recently — laziness. Whether it’s because of the pandemic, exhaustion, or heaps of work, many of us just don’t want to spend time going out to buy groceries or drive to a shop that has the item that we want. Since the rise of the internet, one service and economic sector has grown vastly — online shopping.
As we all may know, online shopping applications or sites often try to enhance customer experience or offer the best deals so that we, the consumer, can maximize their profit. One way that shops often do this is through Big Data Analytics — by collecting various data types through different methods, companies can predict the items that they want to buy and personalize the experience. By doing so, customers are likely to buy more, shop efficiently, and enjoy the service. But how exactly do these companies collect user data?
1. Transactional Data
Transactional Data refers to data about time, money, and products that are gathered whenever a consumer makes a purchase. This data is primary data and is usually collected through the point-of-sale and sent to a data archive. As shown in Figure 1, data is collected through the transactional systems and is used for analytics. This type of data is generally collected by companies to predict other items that the consumers may be interested in to increase sales. In addition, analysis of transactional data could forecast changes in demand which could minimise the supply and demand gap and waste.
2. Network Data
Network Data refers to data and information collected about a user’s activity on an application or website. Once again, this is an example of primary data, as it is collected by the application run by the organization. In our case of online shopping, information about how long a customer stays on a product page could be indicative of their interest in that specific product. When used in conjunction with transactional data, companies can accurately gauge what a specific consumer may want to purchase. This type of data is usually collected through websites or applications that track your activity.
3. Regional Data
Lastly, online shoppers often reveal their IP address and rough location when using a shopping service or application. Major companies often find data on temperature, precipitation, wind speed, or natural disasters to alter product suggestions and close the supply and demand gap. This is an example of secondary data — data that isn’t directly collected by the organization. For example, in Figure 2, a large online shopping company may notice a decrease in temperature in the west of the United States and increase the supply of winter coats and hats, which would both increase sales and prevent a shortage.
Although there are various types of data, many online shopping companies collect transactional data, network data, and regional data to increase sales and close the supply-and-demand gap. Companies use programs on websites, transactional systems, and data from weather stations to accurately predict the desired products of consumers and personalize their experience. Through data collection, online shopping companies provide those of us who just want to stay at home with an effective, reliable, and customized way to purchase the products we want.
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