Alibabaand the Future
 of Business
libaba 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 manage￾ment 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, 
Lessons from China’s innovative digital giant MING ZENG 
Chairman, Academic Council 
of the Alibaba Group
Art by Harry Campbell
A
88 HARVARD BUSINESS REVIEW SEPTEMBER–OCTOBER 2018
Alibabaand the Future
 of Business
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 
off-line 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 
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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 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 
► A NEW BUSINESS MODEL
Alibaba is an example of 
tomorrow’s “smart business”: 
a tech-enabled platform that 
coordinates multiple business 
players in an ecosystem.
► HOW IT WORKS
Players in the ecosystem share 
data and apply machine-learning 
technology to identify and better 
fulfill consumer needs.
► HOW TO BUILD IT
Automate decision making by:
• making sure every interaction 
yields as much data as possible
• ensuring that all business 
activities are mediated by 
software
• using APIs and other interface 
protocols to ensure smooth 
interaction among software 
systems
• applying machine learning to 
make sense of data in real time
► Idea in Brief
Alibaba and the Future of Business
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dynamically and rapidly to changing market conditions and 
customer preferences, gaining 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-size 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 name 
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 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, 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?
As the recalibrated algorithms produce increasingly 
accurate predictions, Ant’s risk and costs steadily decrease, 
and borrowers get the money they need, when they need it, at 
an interest rate they can afford. The result is a highly successful 
business: The microlending operation has a default rate of 
about 1%, far below the World Bank’s 2016 estimate of an 
average of 4% worldwide. 
So how do you create that kind of business? 
AUTOMATE ALL OPERATING DECISIONS
To become a smart business, your firm must enable as many 
operating decisions as possible to be made by machines fueled 
by live data rather than by humans supported by their own 
data analysis. Transforming decision making in this way is a 
four-step process. 
Step 1: 
“Datafy” every customer exchange. Ant was fortunate to 
have access to plenty of data on potential borrowers to answer 
the questions inherent in its lending business. For many 
businesses, the data capture process will be more challenging. 
But live data is essential to creating the feedback loops that are 
the basis of machine learning. 
Alibaba’s Major 
Businesses 
at a Glance 
CHINESE RETAIL 
MARKETPLACES
Taobao Marketplace
Tmall
Rural Taobao
CROSS-BORDER AND 
GLOBAL MARKETPLACES
AliExpress
Tmall Global
Lazada
WHOLESALE COMMERCE
1688.com 
(China)
Alibaba.com 
(global)
DIGITAL MEDIA AND 
ENTERTAINMENT*
Youku Tudou 
(online video)
Alibaba Pictures
Alibaba Music
Alibaba Sports
UC (mobile browser)
OTHER SERVICES*
AutoNavi 
(mapping and navigation)
Koubei 
(local services)
Ele.me 
(delivery)
FINANCE*
Ant Financial 
(includes Alipay)
MYbank
LOGISTICS*
Cainiao Network
CLOUD COMPUTING*
Alibaba Cloud
* Major investee companies 
and cooperative partners of 
Alibaba Group
Consider the bike rental business. Start-ups in China have 
leveraged mobile telephony, the internet of things (in the form 
of smart bike locks), and existing mobile payment and credit 
systems to datafy the entire rental process. 
Renting a bike traditionally involved going to a rental 
location, leaving a deposit, having someone give you a bike, 
using the bike, returning it, and then paying for the rental by 
cash or credit card. Several rival Chinese companies put all 
of this online by integrating various new technologies with 
existing ones. A crucial innovation was the combination of 
QR codes and electronic locks that cleverly automated the 
checkout process. By opening the bike-sharing app, a rider 
can see available bicycles and reserve one nearby. Once the 
rider arrives at the bicycle, he or she uses the app to scan a QR 
code on the bicycle. Assuming that the person has money in 
his or her account and meets the rental criteria, the QR code 
will open the electronic bike lock. The app can even verify the 
person’s credit history through Sesame Credit, Ant Financial’s 
new online product for consumer credit ratings, allowing the 
rider to skip paying a deposit, further expediting the process. 
When the bike is returned, closing the lock completes the 
transaction. The process is simple, intuitive, and usually takes 
only several seconds. 
Datafying the rental process greatly improves the consumer 
experience. On the basis of live data, companies dispatch 
trucks to move bikes to where users want them. They can also 
alert regular users to the availability of bikes nearby. Thanks in 
large part to these innovations, the cost of bike rentals in China 
has fallen to just a few cents per hour.
Most businesses that seek to be more data-driven typically 
collect and analyze information in order to create a causal 
model. The model then isolates the critical data points from the 
mass of information available. That is not how smart businesses 
use data. Instead, they capture all information generated during 
exchanges and communications with customers and other 
network members as the business operates and then let the 
algorithms figure out what data is relevant. 
Step 2: 
“Software” every activity. In a smart business, all activities—
not just knowledge management and customer relations—are 
configured using software so that decisions affecting them can 
be automated. This does not mean that a firm needs to buy or 
build ERP software or its equivalent to manage its business—
quite the opposite. Traditional software makes processes and 
decision flows more rigid and often becomes a straitjacket. In 
contrast, the dominant logic for smart business is reactivity 
in real time. The first step is to build a model of how humans 
currently make decisions and find ways to replicate the simpler 
elements of that process using software—which is not always 
easy, given that many human decisions are built on common 
sense or even subconscious neurological activity.
Alibaba and the Future of Business
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The growth of Taobao, the domestic retailing website of 
Alibaba Group, is driven by continuous softwaring of the 
retailing process. One of the first major software tools built 
on Taobao was an instant message tool called Wangwang, 
through which buyers and sellers can talk to each other easily. 
Using the tool, the sellers greet buyers, introduce products, 
negotiate prices, and so on, just as people do in a traditional 
retail shop. Alibaba also developed a set of software tools 
that help sellers design and launch a variety of sophisticated 
online shop fronts. Once online shops are up and running, 
sellers can access other software products to issue coupons, 
offer discounts, run loyalty programs, and conduct other 
customer relationship activities, all of which are coordinated 
with one another. 
Because most software today is run online as a service, an 
important advantage of softwaring a business activity is that 
live data can be collected naturally as part of the business 
process, building the foundation for the application of 
machine-learning technologies.
Step 3: 
Get data flowing. In ecosystems with many interconnected 
players, business decisions require complex coordination. 
Taobao’s recommendation engines, for example, need to 
work with the inventory management systems of sellers 
and with the consumer-profiling systems of various social 
media platforms. Its transaction systems need to work with 
discount offers and loyalty programs, as well as feed into our 
logistics network.
Communication standards, such as TCP/IP, and application 
programming interfaces (APIs) are critical in getting the data 
flowing among multiple players while ensuring strict control of 
who can access and edit data throughout the ecosystem. APIs, 
a set of tools that allow different software systems to “talk” 
and coordinate with one another online, have been central to 
Taobao’s development. As the platform grew from a forum where 
buyers and sellers could meet and sell goods to become China’s 
dominant e-commerce website, merchants on the site needed 
more and more support from third-party developers. New 
software had to be broadly interoperable with all other software 
on the platform to be of any value. So in 2009, Taobao began 
developing APIs for use by independent software suppliers. 
Today, merchants on Taobao subscribe to more than 100 software 
modules, on average, and the live data services they enable 
drastically decrease the merchants’ cost of doing business. 
Getting the technical infrastructure right is just the 
beginning. It took tremendous effort for us to build a common 
standard so that data could be used and interpreted in the 
same way across all of Alibaba’s business units. Additionally, 
figuring out the right incentive structures to persuade 
companies to share the data they have is an important and 
ongoing challenge. Much more work is needed. Of course, 
the degree to which companies can innovate in this area will 
depend in part on the rules governing data sharing in the 
countries they’re operating in. But the direction is very clear: 
The more data flows across the network, the smarter the 
business becomes, and the more value the ecosystem creates.
Step 4: 
Apply the algorithms. Once a business has all its operations 
online, it will experience a deluge of data. To assimilate, 
interpret, and use the data to its advantage, the firm must 
create models and algorithms that make explicit the 
underlying product logic or market dynamics that the business 
is trying to optimize. This is a huge creative undertaking that 
requires many new skills, hence the enormous demand for 
data scientists and economists. Their challenge is to specify 
what job they want the machine to do, and they have to be 
very clear about what constitutes a job well done in a particular 
business setting. 
From very early on, our goal for Taobao was to tailor it to 
each individual’s needs. This would have been impossible 
without advances in machine learning. Today, when 
customers log on, they see a customized webpage with a 
selection of products curated from the billions offered by our 
millions of sellers. The selection is generated automatically by 
Taobao’s powerful recommendation engine. Its algorithms, 
which are designed to optimize the conversion rate of each 
94 HARVARD BUSINESS REVIEW SEPTEMBER–OCTOBER 2018
visit, churn data generated across Taobao’s platform, from 
operations to customer service to security. 
A milestone in Taobao’s growth, in 2009, was the upgrade 
from simple browsing, which worked reasonably well when 
the platform had many fewer visits and products to handle, 
to a search engine powered by machine-learning algorithms 
and capable of processing huge volumes of inquiries. Taobao 
has also been experimenting with optical-recognition search 
algorithms that can take a photo of a desired item supplied by 
the customer and match it to available products on the platform. 
While we are still in the early stages of using this technology 
to drive sales, the function has proved very popular with 
customers, boasting 10 million unique visits daily. 
In 2016, Alibaba introduced an AI-powered chatbot 
to help field customer queries. It is different from the 
mechanical service providers familiar to most people that are 
programmed to match customer queries with answers in their 
repertoire. Alibaba’s chatbots are “trained” by experienced 
representatives of Taobao merchants. They know all about 
the products in their categories and are well versed in the 
mechanics of Alibaba’s platforms—return policies, delivery 
costs, how to make changes to an order—and other common 
questions customers ask. Using a variety of machine￾learning technologies, such as semantic comprehension, 
context dialogues, knowledge graphs, data mining, and 
deep learning, the chatbots rapidly improve their ability to 
diagnose and fix customer issues automatically, rather than 
simply return static responses that prompt the consumer 
to take further action. They confirm with the customer that 
the solution presented is acceptable and then execute it. No 
human action by Alibaba or the merchant occurs. 
Chatbots can also make a significant contribution to a 
seller’s top line. Apparel brand Senma, for example, started 
using one a year ago and found that the bot’s sales were 26 
times higher than the merchant’s top human sales associate.
There will always be a need for human customer represen￾tatives to deal with complicated or personal issues, but the 
ability to handle routine queries via a chatbot is very useful, 
especially on days of high volume or special promotions. 
Previously, most large sellers on our platform would hire temp 
workers to handle consumer inquiries during big events. Not 
anymore. During Alibaba’s biggest sales day in 2017, the chat￾bot handled more than 95% of customer questions, responding 
to some 3.5 million consumers. 
These four steps are the basis for creating a smart 
business: Engage in creative datafication to enrich the pool 
of data the business uses to become smarter; software the 
business to put workflows and essential actors online; 
institute standards and APIs to enable real-time data flow 
Alibaba and the Future of Business
Alibaba by the 
Numbers
The Alibaba Group went public in the United States in 
September 2014 and has grown at a blistering pace, 
now boasting a market cap of more than $500 billion. 
The group’s e-commerce platforms now have more 
than 550 million annual active consumers. These 
numbers don’t include Ant Financial, which reports 
financial results separately. 
In the fiscal year ending March 2017, Alibaba 
Group reported profits of more than $15 billion on 
nearly $40 billion in revenue. Ant reported profits of 
$814 million on revenue of $8.9 billion and is currently 
valued at over $100 billion. Ant pays Alibaba 
royalties, which amounted to $332 million in 2017. 
$8.4
$39.9
Revenue (in US$ billions)
2014 2015 2016 2017 2018
$15.5
EBITDA
Source: Alibaba Group
$4.9
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SEPTEMBER–OCTOBER 2018 HARVARD BUSINESS REVIEW 95
1 2 3 4 5 6 7 8 9
and coordination; and apply machine-learning algorithms 
to generate “smart” business decisions. All the activities 
involved in the four steps are important new competencies 
that require a new kind of leadership.
THE LEADER’S ROLE
In my course on smart business at Hupan School of 
Entrepreneurship, I show a slide of 10 business leaders and 
ask the students to identify them. They can easily pick out 
Jack Ma, Elon Musk, and Steve Jobs. But virtually no one can 
identify the CEO of CitiGroup or Toyota or General Electric. 
There is a reason for this. Unlike GE, Toyota, and CitiGroup, 
which deliver products or services through optimized supply 
chains, digital companies must mobilize a network to realize 
their vision. To do that, their leaders have to inspire the 
employees, partners, and customers who make up that network. 
They must be visionaries and evangelists, outspoken in a way 
that the leaders of traditional companies do not have to be. 
At the highest level, the digital evangelists must understand 
what the future will look like and how their industries will evolve 
in response to societal, economic, and technological changes. 
They cannot describe concrete steps to realize their companies’ 
goals because the environment is too fluid and the capabilities 
they will require are unknowable. Instead, they must define 
what the firm seeks to achieve and create an environment 
in which workers can quickly string together experimental 
products and services, test the market, and scale the ideas that 
elicit a positive response. Digital leaders no longer manage; 
rather, they enable workers to innovate and facilitate the core 
feedback loop of user responses to firm decisions and execution.
In the smart business model, machine-learning algorithms 
take on much of the burden of incremental improvement by 
automatically making adjustments that increase systemwide 
efficiency. Thus, leaders’ most important job is to cultivate 
creativity. Their mandate is to increase the success rate of 
innovation rather than improve the efficiency of the operation.
DIGITAL-NATIVE COMPANIES such as Alibaba have the advantage 
of being born online and data-ready, so their transformation 
to smart business is quite natural. Now that they have proven 
the model works and are transforming the old industrial 
economy, it is time for all companies to understand and 
apply this new business logic. That may look technologically 
intimidating, but it is becoming more and more feasible. 
The commercialization of cloud computing and artificial 
intelligence technologies has made large-scale computational 
power and analytic capabilities accessible to anyone. Indeed, 
the cost of storing and computing large quantities of data has 
dropped dramatically over the past decade. This means that 
real-time applications of machine learning are now possible 
and affordable in more and more environments. The rapid 
development of internet-of-things technology will further 
digitize our physical surroundings, providing ever more data. 
As these innovations accumulate in the coming decades, 
the winners will be companies that get smart faster than the 
competition. HBR Reprint R1805F
MING ZENG is the chairman of the Academic Council of the Alibaba 
Group, an e-commerce, retail, and technology conglomerate, based 
in Hangzhou, China, and the author of Smart Business: What Alibaba’s 
Success Reveals About the Future of Strategy (Harvard Business Review 
Press, September 2018). He is also the dean of Hupan School of 
Entrepreneurship, a private business school founded by Alibaba chairman 
Jack Ma and other leading Chinese entrepreneurs in Hangzhou. 
Alibaba and the Future of Business
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Further Reading
96 HARVARD BUSINESS REVIEW SEPTEMBER–OCTOBER 2018
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In response to the above article, the following essay was written to "Critically assess Alibaba's data-driven model of smart business in the Chinese market".

Introduction
Alibaba has established a prominent position among international rivals in the nascent epoch of commercial digitalization, by constructing and maintaining a prosperous operational framework predicated on the notion of a data-centric commercial ecosystem. Zeng (2018) asserts that the Alibaba group's innovation is characterised by its emphasis on ecosystem development as its primary focus. Therefore, a meticulous assessment of this data-centric approach raises the inquiry of how enterprises have fared within the given environment. This essay endeavours to scrutinise Zeng's data-driven model of intelligent business for the Chinese market, with the aim of addressing various inquiries.
Summary of the Business Ecosystem Model
Zeng (2018) underscores the business ecosystem of Alibaba as its principal component and model in his article entitled "Alibaba and Lessons from China's Innovative Digital Giant." From his perspective, the business ecosystem is a communal network that facilitates the linkage between commercial entities and their respective clientele, with the aim of fostering mutual engagement within the business milieu. Su, Zheng, and Chen (2018) argue that contemporary discussions on strategic business management must necessarily incorporate the concept of 'ecosystem'. According to them, scholars have delineated the term in diverse contexts and settings. Nevertheless, the central message of the idea remains unaltered. Li (2009) posits that the business ecosystem bears resemblance to the biological ecosystem, wherein a multitude of intricate relationships exist among resources that coexist within a specific environment. Similar to its biological counterpart, the business ecosystem serves the purpose of achieving self-sufficiency and sustaining a state of equilibrium (de Langen, Sonn-Friese, & Hallworth, 2020; Harmaakorpi & Rinkinen, n.d.).   The ecosystem functions as a mechanism for facilitating intercommunication among these communities of living organisms, with the primary objective of guaranteeing, as Li (2009) aptly noted, that the system will furnish all the essential resources required for the triumph of any astute enterprise. Koepke (2015) posits that in this particular business model, significant actors engage with one another within the ecosystem through the exchange of data, which serves as a crucial player within the ecosystem. 
Strengths and weaknesses of Data Driven Smart Business
Businesses have employed machine learning to gain a cutting-edge competitive advantage over traditional business models, as they require large quantities of data (Garca-Muia et al., 2020). According to Zeng (2018), computing power and digital data are indispensable to machine learning. According to him, this is directly proportional to the algorithm's number of iterations. Digital data is the foundation for business decision-making, and Alibaba utilised this strategy to better position the business and become one of the most prominent companies in recent years, competing closely with Walmart, Amazon, and eBay (Hanninen, Mitronen, and Kwan, 2019; Lu et al., 2022). Smart businesses have generally been driven by data, and the data volume is neither a disadvantage nor a limitation for the model, as iterations are performed to answer generated questions precisely and at the speed of light. The microloan business of Ant financial services, as presented by Zeng, is a typical example of a business within the Alibaba ecosystem that employs the data-driven smart business model. Major decisions, such as how much a customer is eligible to borrow and, of course, at what interest rate, are now made immediately, and the best part is that these decisions are made with precision and not less accuracy. However, customer privacy concerns are a significant concern and drawback of this model. Using the instant messaging application wangwang, Taobao, a domestic retailing arm of the Alibaba group, collects live data naturally from consumers (Zeng, 2018). How secure is the customer?
Recommendations 
Smart businesses operating in emerging markets face a variety of obstacles. As a result of the implementation of machine learning methods in the decision-making process, these challenges are typically privacy concerns and other deficiencies (Li, 2018). To overcome these obstacles, businesses must adopt cutting-edge, optimised supply chain strategies and utilise and adopt a network if they are to achieve their objective of surviving the emerging market's competition. This responsibility falls on the shoulders of the leaders, whose roles must be to motivate employees and all ecosystem participants, including customers (Mabaso et al., 2021). These recommendations are not rigid because even the business environment is fluid, necessitating that leaders and their businesses adapt to remain competitive and afloat.
Conclusion 
The Chinese model of Alibaba has taught us that data and technology can drive intelligent business decisions. Alibaba has become more productive and competitive by utilising machine learning and vast quantities of digital data to better comprehend customer requirements.









Now, Give me all the details i would need for a chart for "A timeline of notable milestones in the growth and development of Alibaba and its various businesses, such as the introduction of machine-learning algorithms, the creation of Ant Financial, and its record-breaking IPO."