industry comparison
5 pages
Industry overview
· What is e-Commerce
With the rapid development of the internet, cloud computing, and the Internet of Things, ubiquitous mobile devices, RFID, wireless sensors are generating data every minute, and hundreds of millions of users' internet services are generating vast amounts of interactions all the time. Based on these, a large number of structured and semi-structured visual data generated by the e-commerce industry. Data mining, data analysis, and comprehensive considerations could help e-commerce enterprises be global and systematic decision-making, looking for optimal solutions and operational decisions.
E-commerce is the process of buying and selling products through electronic devices, such as mobile applications, on the internet. In the past few decades, the popularity of e-commerce has dramatically increased, and to some extent, it is replacing traditional physical stores since it allows people to buy and sell products on a world scale, 24 hours a day, without incurring the same overhead as running a physical store.
· Major types of e-Commerce business model
The e-commerce industry can generally be divided according to transaction methods: business institution-to-business e-commerce (B2B), business institution-to-consumer e-commerce (B2C)), business institution-to-government management department e-commerce (B2G), Consumer-to-government e-commerce (C2G), consumer-to-consumer e-commerce (C2C). It can also be divided into B2B e-commerce, online shopping, online travel, and O2O according to its main subdivisions. At present, the huge data sources for e-commerce are mainly B2B e-commerce and online shopping. For example, the transaction scale of e-commerce reached 12.3 trillion at the end of 2014. While the amount of e-commerce data is increasing day by day, big data of e-commerce has gradually entered a period of rapid development from the initial stage.
· Benefits of e-Commerce
· Global market: Going from a local customer base to a worldwide market at no additional cost is one of the most significant advantages of trading online. Physical stores are geographically limited to nearby markets, that is, if you have a store in New York and want to sell in New Jersey, you need to open another physical store. E-commerce does not have this restriction. Instead, people can sell it to anyone anywhere in the world through a digital e-commerce business.
· Around-the-clock availability: Physical business hours are usually limited, but online e-commerce stores open 365 hours with 24 hours a day, seven days a week, which is very convenient for customers and an excellent opportunity for merchants.
· Reduced costs: Compared with physical stores, the operating expenses of e-commerce businesses are significantly reduced. There is no rent, no staff need to pay, and limited fixed operating costs, making e-commerce stores very competitive.
· Automated inventory management: automation is much easier inventory management through the use of electronic online tools and third-party vendors. It has saved e-commerce companies billions of dollars in inventory and operating costs.
· Targeted marketing: Online merchants can collect large amounts of consumer data to ensure that the right people are targeted for their products, thereby reduces the cost of acquiring customers and keeping the online e-commerce business extremely agile.
· Niche market advantages: Due to the lower operating costs, being able to target ideal customers, and covering the global audience brought by e-commerce sites can ensure the profitability of online businesses.
Application of business analytics in e-Commerce
· Customer shopping pattern analysis: data analytics are changing how businesses interact with customers nearly on every level. E-commerce is using real-time cloud computing to consider thousands of different factors, including past purchase records, demographic analysis, and customer comparison, to predict shopping behavior. With the forecasting analysis, online stores could customize the customer’s shopping experience and make personal recommendations, also increase customers’ likelihood of making a purchase and improve customer engagement through the purchase.
· Effective customer service: predictive analytics is revolutionizing the marketer-customer relationship, increasing shopper satisfaction, and reducing customer churn rate. The customer service not only reflects in the post-purchase follow-up and response, but it also starts before the purchase. Hyper-personalized marketing serves customers the right message at the right time on the right channel, and it knows even before the customer knows. Virtual concierge creates instant gratification, so it will change suggestions based on whatever customer clicks and watches. Post-follow-up allows customers to get help and support to resolve the problem quickly and accurately.
· Predict future operation plans: data analysis in the supply chain uses quantitative methods to improve decision making, inventory management, and operation planning. Modeling and simulation help run “what-if” analysis under different scenarios to enhance decision-making, such as selecting the right and optimal vendor. Application of unstructured data could be used in inventory management; for example, digital cameras are used to monitor stock levels and provide alerts when restocking is needed.
· Ease of online payments: big data analysis in digital platforms enable greater and easier payments and lending. As technology has transformed into Point-of-Sale, people prefer mobile platforms to make purchases. Data science can help to fight fraud in payment processing. PayPal is building new and modifying existing fraud analytics systems by incorporating with Hadoop and Spark, identifying susceptive activities, applying online catching with machine learning, and processing payments through cloud computing safely and securely.
Company selection
1. Amazon
Amazon currently owns more than half of the e-commerce market share in the United States, and its revenue is increasing by 15% every year. The number of Primes reached 124 million in 2019 and is expected to reach 133.1 million in 2020 and 143 million in 2022.
· Dynamic pricing improves competitiveness
Amazon updates product prices every 10 minutes on average according to consumers' purchase intention, competitor prices, and inventory, helping each product price stay at the most reasonable value.
· Personalized recommendation system
Amazon's recommendation system recommends products that consumers want to buy by analyzing their past browsing behavior, income levels, and spending preferences. Simultaneously, Amazon has the most accurate prediction algorithm, which predicts the customer's order before they checkout and realizes anticipatory shipping.
· Online and offline collaboration
In addition to its online platform, Amazon owns offline physical stores like Whole Foods and Amazon Go. Amazon uses data to help bricks-and-mortar stores adjust the placement of goods and collect information about people's offline shopping behavior to predict online sales.
2. Alibaba
Alibaba is the largest e-commerce company in the world, which had the largest IPO in 2014 and ranked the ninth of the highest global brand value. Its business includes B2B(Alibaba.com), C2C (Taobao) and B2C (Tmall) markets.
· Customize webpage
Alibaba has an advanced recommendation system. As users browse the webpage, the E-commerce Brain system analyzes consumers' browsing behavior in real-time and predicts their preferences and needs. Traditionally, it is taken as a function of historical recording data for artificial analysis, which is no longer suitable for today's fast-paced business model. Each consumer's purchase history, browsing history, and online activities, including conversations with chatbots, will be used to update the AI algorithm
· Chatbot
Ali Assistant solves the most common problems, such as delivery status, return process. They self-study relevant product and policy information, constantly correct themselves through machine learning. During Alibaba's biggest sales day in 2017, the chatbot handled more than 95% of customer problems and answered about 3.5 million questions.
· The application of business analysis in double 11
Alibaba uses business analytics to predict the items that consumers are looking ahead of and are likely to be interested in buying and calculating the price range they will most likely accept. They also inform the sellers which goods are likely to be popular and stock them in advance. The Shipping department will pre-collect the inventory required by each region and distribute the goods to warehouses in advance to increase logistics efficiency.
· Application of business analysis in Ant Financial Services
Based on data-driven microfinance, Ant Financial Services committed to helping companies that lack a credit record and lack a business record. They use automatic real-time analysis of transactions and exchanges between buyers and sellers to decide whether to lend money to the company. By comparing the performance of borrowers, they collect patterns for each group and use these features to calculate credit scores to improve the quality of the algorithm. The microlending operation has a default rate of about 1%, far below The World Bank's 2016 estimate of an average of 4% worldwide.
3. EBay
eBay generates billions of dollars in revenue and operates in about 33 countries. It has expanded beyond early auction sales to online classified advertisements and online event ticket trading.
· Optimize search engines
When consumers describe the products according to their wishes, eBay needs to find the corresponding products through a powerful sentence association algorithm. It continuously optimizes its search capabilities by tracking the process by how consumers find products and buy them.
· Optimize the company's internal IT structure
eBay's most prominent data application is that IT not only analyzes user behavior and website traffic patterns but also integrates the resources of its internal IT system to mobilize all servers and devices to increase speed fully. This helped eBay save millions of dollars a year.
· Apply business analysis to finance
eBay believes that business analytics can help finance improve agility, efficiency, and forward-looking decisions. This helps the finance team to be more accountable and to rationalize each part of the business needs.
Comparison
a. Analytics Strategies
● The strategy used for selling products in amazon allows sellers to do their selling in its platform, eBay does its business transactions through auctioning products. Alibaba on the other hand can predict the needs of consumers through the use of software developed by itself called the e-commerce brain.
● Alibaba and eBay do not participate in direct sales of products with consumers, they create a market for both buyers and sellers. Amazon on the other hand conducts direct sales to consumers.
● Amazon operates through focusing on building a customer foundation whereas eBay entirely focuses on looking for sellers. Alibaba on the other hand focuses on improving company products to assist those who sell to sell more and those who buy to buy more (Darvazeh, Iman, and Farzane)
b. Analytics Process
● In terms of business revenues, eBay does not have a large capacity to produce revenues the way amazon and Alibaba can. Between Alibaba and amazon, Amazon generates the most revenues. In 31st march 2020 Alibaba recorded revenues of about 72 billion USD lower than what amazon recorded which was approximately 75.45 billion USD. In the previous year Amazon recorded 87.44 billion USD. eBay on the other hand generates approximately 10.8 USD as was recorded in 2019, a lower amount compared to what the other competitors recorded.
● Businesses need to offer other essential services to customers rather than just selling products. The importance of doing that is to attract a large group of consumers. eBay operates through offering online retail but does not provide other services. Amazon allows people to sell products and to add on that, it offers amazing shipping services that take place in 2 days at a subscription price of 99 USD to consumers. Amazon also goes out of its way to present movies and music and other benefits. Alibaba also allows buyers and sellers to conduct their businesses and also improves the branding of companies to create new products to attract more people.
● All these three companies operate through online platforms making use of websites and ecosystems. They have taken advantage of the fast-growing technology to capture more consumers (Akter and Wamba 173-194).
c. Organizational Capability
● All these huge companies use similar technologies such as cloud computing and their differences in operations is brought about the use of unique technologies.
● Alibaba can assist sellers to make more sales in both small- and large-scale businesses and fastens the process of innovation in the business. It uses a number of technologies to improve its business such as cainiao, cloud and the tracking system of determining the authenticity of products called the blockchain technology. Amazon on the other hand focuses on building warehouses making the company require more employees to provide product deliveries. This makes the company provide employees with very little profits. eBay on the other hand is characterized by making less profit because it does not participate in offering other services thus attracting fewer people.
● Alibaba businesses include the use of very important infrastructure such as cloud computing, it also offers consumers with entertaining programs and many other innovative items. Amazon specializes in selling products through its websites and does not have efficient infrastructure like those of Alibaba. eBay on the other hand operates using a computing system called grid that helps in business growth and correction of possible errors. This technology has allowed the auctioning process to be smooth and better over the years.
References
1. Akter, S., & Wamba, S. F. (2016). Big data analytics in E-commerce: a systematic review and agenda for future research. Electronic Markets, 26(2), 173-194.
2. https://link.springer.com/article/10.1007/s12525-016-0219-0
6. https://www.dezyre.com/article/big-data-use-cases-how-paypal-leverages-big-data-analytics/231
7. Wu, X., & Gereffi, G. (2018). Amazon and Alibaba: Internet governance, business models, and internationalization strategies. International business in the information and digital age, 327-356.
8. https://www.alibabacloud.com/blog/the-big-data-platform-behind-alibabas-e-commerce-systems_595931
9. http://atomthought.com/how-alibaba-uses-data-to-revolutionise-e-commerce-retail/
10. https://hbr.org/2018/09/alibaba-and-the-future-of-business
11. https://insidebigdata.com/2019/11/30/how-amazon-used-big-data-to-rule-e-commerce/
12. https://medium.com/dataflair/how-data-science-has-taken-amazon-on-top-360043e146ec
13. https://digital.hbs.edu/platform-digit/submission/amazon-and-big-data/
14. http://www.digitalinnovation.pwc.com.au/ebay-uses-big-data-to-drive-operational-efficiencies/
Work Cited:
https://link.springer.com/article/10.1007/s12525-016-0219-0
https://www.dezyre.com/article/big-data-use-cases-how-paypal-leverages-big-data-analytics/231