Business Model Of Alibaba – Summary. Alibaba is not a retailer in the traditional sense. It does not originate or store goods, and logistics services are managed by third-party providers. Instead, Alibaba is what you get if you take all the work related to marketing and coordinate them online into a network of buyers, merchants, service providers, logistics companies, and manufacturers. Yes, Alibaba does what Amazon, eBay, PayPal, Google, FedEx, all wholesalers, and a good portion of manufacturers in the U.S. do, with a healthy helping of financial services for capital. Alibaba achieves this by using the latest technologies of network coordination and data intelligence. It uses the work of thousands of Chinese businesses to create an ecosystem that is faster, simpler and more efficient than traditional business structures. This is a new business model that Ming Zeng, chairman of Alibaba’s Academic Council, calls smart business. The players in the ecosystem share data and use machine learning technologies to identify and best serve customer needs. This article provides a framework for turning a company into a smart business.
Alibaba is the model of tomorrow’s “intelligent business”: a technology platform that organizes many business players in an ecosystem.
Business Model Of Alibaba
Alibaba hit the headlines with the world’s largest IPO in September 2014. Today, the company has a market share among the top 10 in the world, surpassing Walmart in international sales, which have increased in all the major markets of the world. Founder Jack Ma has become a household name.
Comparing Alibaba, Jd, And Pinduoduo
Since its inception, in 1999, Alibaba has seen tremendous growth on its e-commerce platform. However, the world-beater did not look the same in 2007 when the management team, which I had joined full-time the year before, met for an outside strategy. of location at a seaside hotel in Ningbo, Zhejiang province. Over the course of the meeting, our views and opinions on e-commerce trends began to coalesce into a larger vision for the future, and ultimately, we agreed on a vision . It will “promote the development of an open, coordinated and efficient e-commerce ecosystem.” This is when Alibaba’s journey began.
Alibaba’s unique innovation, we realized, is that we are truly creating an ecosystem: an ecological community (businesses and consumers of many kinds) that interact with each other and with the environment (the online platform and out-of-line physical elements). Our strategic objective is to ensure that the platform provides all the resources, or access to resources, that an online business needs to succeed, thereby supporting the development of the ecosystem.
The ecosystem was simple at first: We connected buyers and sellers of goods. As technology has advanced, many other business functions have moved online—including established ones, such as advertising, marketing, logistics, and finance, as well as emerging ones, such as affiliate marketers, product developers, and social media influencers. And as we expanded our ecosystem to include these innovations, we helped create new types of online businesses, reshaping China’s retail sector along the way.
Alibaba today is not just an online shopping company. What you will get if you take all the activities related to marketing and coordinate them online in a network of customers, traders, service providers, logistics companies, and manufacturers. In other words, Alibaba does the work of Amazon, eBay, PayPal, Google, FedEx, merchants, and a good part of the manufacturers in the United States, with the healthy help of financial services for capital.
Product And Services Networking Comparison Of Different Types Of Businesses
Of the 10 largest companies in the world today, seven are online companies with business models similar to ours. Five of them—Amazon, Google, and Facebook in the United States and Alibaba and Tencent in China—have been around for almost 20 years. How much value and market power did it generate so quickly? Because of the new capabilities of network coordination and data intelligence all these companies use it. The ecosystems they protect are far more economical and customer-oriented than traditional industries. These businesses are following a path that I call smart business, and I believe this is a sign of how businesses will be thinking in the future.
Smart business occurs when all players involved in achieving a common business goal—marketing, for example, or car sharing—are coordinated in an online network and use machine learning technology. in order to use the data in real time. This technology-based model, in which most of the management decisions are made by machines, allows companies to adapt quickly and quickly to changing market conditions and customer needs, and obtain significant competitive advantage over traditional businesses.
Abundant computing power and digital data are the fuel for machine learning, of course. The more data and the more cycles an algorithmic engine goes through, the better its output will be. Data Scientists come up with potential predictive models for specific tasks, and then an algorithm crunches the vast amount of data to produce better decisions in real-time and with each iteration. These predictive models form the basis of most business decisions. So machine learning is more than a technical exercise; the nature of business management will change as human judgment is replaced by algorithmic output.
Ant Microloans is an amazing example of what this future could look like. When Alibaba launched Ant, in 2012, the typical loan issued by major banks in China was in the millions of dollars. The minimum loan amount—about 6 million RMB, or just under $1 million—is above the amount required by most small and medium-sized enterprises (SMEs). Banks didn’t like service companies that didn’t have some sort of credit history or proper documentation of their business practices. As a result, tens of millions of businesses in China are having a hard time keeping the money they need to grow their business.
Bu121 Lecture Notes
At Alibaba, we knew we had the ingredients to build a high-quality, viable, and profitable SME lending business: the wealth of transaction data generated by many small businesses using our position. So in 2010 we launched a data-driven microfinance business to provide loans to businesses no larger than 1 million RMB (about $160,000). In seven years of operation, the business has provided more than 87 billion RMB ($13.4 billion) to nearly three million SMEs. The loan amount is 8,000 RMB, or about $1,200. In 2012, we combined this lending business with Alipay, our most successful payment business, to form Ant Financial Services. We gave the new agency that name to capture the idea that we’re empowering small but hardworking, ant-like companies.
Today, Ant can easily make loans from as little as several hundred RMB (about $50) in a few minutes. How is this possible? When dealing with borrowers, lending institutions have to answer only three basic questions: Who should lend them money, how much money to lend, and how much interest will be charged? When customers on our platforms have given us the right to analyze their data, we are happy to answer those questions. Our algorithms can look at transaction data to assess how well a business is doing, how competitive its offerings are in the market, whether its peers have high credit ratings, etc.
Ant uses that data to compare good borrowers (those who pay on time) and bad borrowers (those who don’t) to separate the characteristics of the two groups. Those characteristics are used to calculate credit scores. All lending institutions do this in some way, of course, but at Ant the analysis is done automatically for all borrowers and all their behavioral data in real time. All transactions, all communications between the buyer and seller, all links and other services available on Alibaba, especially all activities on our platform, will affect the credit score of the business. At the same time, the algorithms that calculate the scores are evolving in real time, improving the quality of the decision with each iteration.
Determining how much debt and how much interest to pay requires checking many types of data generated in the Alibaba network, such as gross profit margins and inventory turnover, and low more mathematical information such as product life cycles and customer quality. social and business relationships. Algorithms can, for example, analyze the frequency, length, and type of communication (instant messaging, e-mail, and other common methods in China) to assess the quality of the relationship.
Quick Snapshot Of Alibaba Business Model
Alibaba’s data scientists are instrumental in identifying and testing which data points provide the insights they are looking for, and then develop algorithms to mine the data. This role requires a deep understanding of business and expertise in machine learning algorithms. Consider Ant Financial again. If the customer is considered to be a poor creditor and will pay his debt on time, or a customer with excellent credit risked bad credit, the algorithm change is clear. Machines can quickly and easily check their ideas. Which parameters should be added or removed? Which type of user behavior should be given more weight?
As the revised algorithms’ predictions become more accurate, risk and Ant costs decrease, borrowers get the money they need, when they need it, at based on the interest rate they can afford. The result is a very successful business: The microfinance sector has a default rate of 1%, far below the World Bank 2016
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