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Alibaba and the Future of Business

Alibaba and the Future of Business

Alibaba hit the headlines with the world’s biggest IPO in September 2014. Today, the company has a market cap among the global top 10, has surpassed Walmart in global sales, and has expanded into all the major markets in the world. Founder Jack Ma has become a household name.

From its inception, in 1999, Alibaba experienced great growth on its e-commerce platform. However, it still didn’t look like a world-beater in 2007 when the management team, which I had joined full-time the year before, met for a strategy off-site at a drab seaside hotel in Ningbo, Zhejiang province. Over the course of the meeting, our disjointed observations and ideas about e-commerce trends began to coalesce into a larger view of the future, and by the end, we had agreed on a vision. We would “foster the development of an open, coordinated, prosperous e-commerce ecosystem.” That’s when Alibaba’s journey really began.

Alibaba’s special innovation, we realized, was that we were truly building an ecosystem: a community of organisms (businesses and consumers of many types) interacting with one another and the environment (the online platform and the larger offline physical elements). Our strategic imperative was to make sure that the platform provided all the resources, or access to the resources, that an online business would need to succeed, and hence supported the evolution of the ecosystem.

The ecosystem we built was simple at first: We linked buyers and sellers of goods. As technology advanced, more business functions moved online—including established ones, such as advertising, marketing, logistics, and finance, and emerging ones, such as affiliate marketing, product recommenders, and social media influencers. And as we expanded our ecosystem to accommodate these innovations, we helped create new types of online businesses, completely reinventing China’s retail sector along the way.

Alibaba today is not just an online commerce company. It is what you get if you take all functions associated with retail and coordinate them online into a sprawling, data-driven network of sellers, marketers, service providers, logistics companies, and manufacturers. In other words, Alibaba does what Amazon, eBay, PayPal, Google, FedEx, wholesalers, and a good portion of manufacturers do in the United States, with a healthy helping of financial services for garnish.

Of the world’s 10 most highly valued companies today, seven are internet 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 barely 20 years. Why has so much value and market power emerged so quickly? Because of new capabilities in network coordination and data intelligence that all these companies put to use. The ecosystems they steward are vastly more economically efficient and customer-centric than traditional industries. These firms follow an approach I call smart business, and I believe it represents the dominant business logic of the future.

What Is Smart Business?
Smart business emerges when all players involved in achieving a common business goal—retailing, for example, or ride sharing—are coordinated in an online network and use machine-learning technology to efficiently leverage data in real-time. This tech-enabled model, in which most operational decisions are made by machines, allows companies to adapt dynamically and rapidly to changing market conditions and customer preferences, gaining a tremendous competitive advantage over traditional businesses.

Ample computing power and digital data are the fuel for machine learning, of course. The more data and the more iterations the algorithmic engine goes through, the better its output gets. Data scientists come up with probabilistic prediction models for specific actions, and then the algorithm churns through loads of data to produce better decisions in real-time with every iteration. These prediction models become the basis for most business decisions. Thus machine learning is more than a technological innovation; it will transform the way business is conducted as human decision-making is increasingly replaced by algorithmic output.

Ant Microloans provides a striking example of what this future will look like. When Alibaba launched Ant, in 2012, the typical loan given by large banks in China was in the millions of dollars. The minimum loan amount—about 6 million RMB or just under $1 million—was well above the amounts needed by most small and medium-sized enterprises (SMEs). Banks were reluctant to service companies that lacked any kind of credit history or even adequate documentation of their business activities. As a consequence, tens of millions of businesses in China were having real difficulties securing the money necessary to grow their operations.

At Alibaba, we realized we had the ingredient for creating a high-functioning, scalable, and profitable SME lending business: the huge amount of transaction data generated by the many small businesses using our platform. So in 2010, we launched a pioneering data-driven microloan business to offer loans to businesses in amounts no larger than 1 million RMB (about $160,000). In seven years of operation, the business has lent more than 87 billion RMB ($13.4 billion) to nearly three million SMEs. The average loan size is 8,000 RMB, or about $1,200. In 2012, we bundled this lending operation together with Alipay, our very successful payments business, to create Ant Financial Services. We gave the new venture that names to capture the idea that we were empowering all the little but industrious, antlike companies.

Today, Ant can easily process loans as small as several hundred RMB (around $50) in a few minutes. How is this possible? When faced with potential borrowers, lending institutions need to answer only three basic questions: Should we lend to them, how much should we lend, and at what interest rate? Once sellers on our platforms gave us authorization to analyze their data, we were well-positioned 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 partners have high credit ratings, and so on.

Ant uses that data to compare good borrowers (those who repay on time) with bad ones (those who do not) to isolate traits common in both groups. Those traits are then used to calculate credit scores. All lending institutions do this in some fashion, of course, but at Ant, the analysis is done automatically on all borrowers and on all their behavioral data in real-time. Every transaction, every communication between seller and buyer, every connection with other services available at Alibaba, and indeed every action taken on our platform, affects a business’s credit score. At the same time, the algorithms that calculate the scores are themselves evolving in real-time, improving the quality of decision-making with each iteration.

Determining how much to lend and how much interest to charge requires analysis of many types of data generated inside the Alibaba network, such as gross profit margins and inventory turnover, along with less mathematically precise information such as product life cycles and the quality of a seller’s social and business relationships. The algorithms might, for example, analyze the frequency, length, and type of communications (instant messaging, e-mail, or other methods common in China) to assess relationship quality.

Alibaba’s data scientists are essential in identifying and testing which data points provide the insights they seek and then engineering algorithms to mine the data. This work requires both a deep understanding of the business and expertise in machine-learning algorithms. Consider again Ant Financial. If a seller deemed to have poor credit pays back its loan on time or a seller with excellent credit catastrophically defaults, the algorithm clearly needs tweaking. Engineers can quickly and easily check their assumptions. Which parameters should be added or removed? Which kinds of user behavior should be given more weight?


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