MBA633 Case Study: Data-Driven Decision Making for Alibaba's Success

This case study analyzes Alibaba, a leading e-commerce giant, focusing on how data-driven decision-making can enhance its operations. It explores Alibaba's current business models, including B2B and B2C, and highlights the importance of leveraging big data analytics for strategic advantages. The study identifies potential inefficiencies in Alibaba's current practices, such as inefficient customer engagement and a lagging customer base, and proposes a data-driven decision-making framework. This framework includes setting goals, formulating key questions, data collection from various sources, data analysis to clean and categorize data, deployment of analytical tools, and implementation of strategies. Ethical considerations, such as customer privacy and data accuracy, are also addressed. The document emphasizes the importance of tracking data throughout the project lifecycle to improve service delivery and resource utilization. This case study offers a comprehensive understanding of how data analytics can drive decision-making in the e-commerce industry, particularly for a large organization like Alibaba.

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Digital Analytics, Big Data and Technologies
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Introduction
As the technological innovation is experienced in the e-commerce industry, businesses
search for various strategies that can help them to determine challenges, utilize the opportunities
and make strategic decisions to enhance their efficiency and enable them to survive in the
competitive environment. Studies have it that many business organizations are now adopting
data analytics (Blazquez, Domenech, Gil and Pont, 2018, pp.1-19). The organizations are driven
by the fact that businesses that rely on data analytics for decision making expect a better
financial performance (Djerdjouri and Mehailia, 2017, pp. 57-67). This is in the sense that
customers have been spending most of their time and resources online. Additionally, the spread
of smart phones among other mobile devices has led to a dramatic increase of online shoppers.
As a consequence, there have been a considerable rate of growth in e-commerce sector
which has subsequently lead to the phenomenon known as “big data”. Big data is a term that is
used to describe the massive data that are collected from various users online for example in the
e-commerce and search engines among other online platforms (Tonidandel, King and Cortina,
2018, pp.525-547). The big data is mainly accessed by organizations that operate online like e-
commerce organizations such as Amazon, e-Bay and Alibaba among other e-commerce
organizations. Alibaba is the largest Chinese e-commerce organization having a considerable big
data that can be harnessed to help in providing information for decision making (Liu, Jiang and
Wu, 2018, pp. 23-31). In this document, we articulate the steps that are essential for data driven
decision making that can be utilized in Alibaba organization.
The current mode of operation in Alibaba

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Alibaba is the e-commerce titan in China. The organization operates through various
kinds of business models including business to business B2B business to consumer B2C among
others. It has two major branches including Taobao and Tmall each with over ten million active
customers. Like any other e-business, Alibaba has an online platform where its customers can
buy and sell products during which they collect the customers’ data. Though Alibaba has
released the benefits of data analytics like its counterparts including Amazon; the business uses
big data analytics in tracking fake products and fraud detection as reported by Chen, Tao, Wang
and Chen (2015, pp. 1-10), Alibaba’s mode of operation majorly rely on the enterprise resource
planning ERP. The big data analytics for data driven decision making is rarely applied in making
decisions in the organization (Wang, and Zhao, 2018, pp. 120-124).
The possible inefficiencies
Ineffective utilization of data analytics by the organization in the current technology
pervaded environment could suggest various inefficiencies in the business operation. Some of
these inefficiencies are captured below:
Inefficient customer engagement: the customer engagement is one of the critical aspect of
online business that determine the business success. Organizations have been utilizing real-time-
data to provide solutions and personalized services to their customers. Alibaba can use the
millions of data it has to provide a customized loyalty to its consumers. These data can help in
generating actionable insights that could assist in enhancing loyalty of consumers which is
essential for the organization’s strategic goals.
Customer base: lagging to adopt the data driven decision making would lead to inefficient
customer base which would pose a considerable negative impacts on the organization’s strategic

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goals. Organizations are currently adopting big data analytics to improve customer experience
and consequently increase the customer base that is essential to achieve strategic goals.
The available data and how the data can be used by the organization to provide the
efficiencies
Alibaba Company boasts a multitudinous statistics including its users’ data that could be
harnessed to improve efficiencies in the aforementioned areas. The organization account for
more than a half of the e-businesses in China alone apart from other countries where it has
extended its operations like India and Australia among others. Moreover, Alibaba has over 570
million active users, a population that every online business would like to access its data.
In such rich data environment, Alibaba Corporate can access a wealth of data about its
consumers that are essential for big data analytics induced decision making for strategic reasons
(Mikalef, Pappas, Krogstie and Giannakos, 2018, pp.547-578). The data, however may have
limited use as it can even lead to a considerable effects on the business if the organization
decision makers does not have a clear understanding of the advantages and the disadvantages of
the data to the business and the data types that are relevant for decision making as well as how
they can use the data to appropriately make decisions. There are many decision steps/frameworks
for decision making out there. A team of scholars including Jeble, Kumari and Patil (2018,
pp.36-44) suggest a conceptual framework for decision making which involves five major steps
including development of data sources, data mining, data analysis, analytics and decision making
which are not explored into details. The decision making framework covered in this report aims
to offer a comprehensive picture of data driven decision making process in e-commerce industry,
Alibaba corporate in particular.

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E-commerce data driven decision making framework
The data driven decision making as mentioned previously involve the process of making
decisions on basis of the available data. Since data systems and technology have become
prevalent in the current technological innovations, it has become essential to utilize the data in
decision making in the e-commerce organizations (Suciu et al. 2018, pp. 821-828; Akter et al.
2019, pp.85-95). The data driven decision making process proposed in this document can be
used in improving the organization’s efficiency in operation as well as customer base and many
others areas that ensure strategic planning for the business success. The following shows the
steps for data driven decision making that would be used in the e-commerce.
1. Setting goals
This is the first step of data driven decision making process. The process should be done
even before data collection starts. It involves formulating objectives that the organization need to
achieve (Young, McNamara, Brown and O’Hara, 2018, pp.133-158). This process is very
instrumental in the process. This is in the sense that in order to analyze the performance know
whether the organization will be successful or not, it is essential to identify the objectives of the
business. As such, it can be noted that goal setting is a very critical step when it comes to data
driven decision making.
2. The formulation of the key question
This is the second step that should be considered in data driven decision making. It
involves matters to do with resources, customers and capacities that determines the cause of
action. The following questions should be asked during this process:

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 How many active customers do we have in the current system?
 From which region do we have the largest number of customers?
 How long does the customers spend in the organization’s system? Among others.
For the business to address these questions, it is essential to develop a conceptual
understanding in order to explain how services, programs as well as business activities are done
so as to determine the accurate and relevant performance as well as progress measures (Matheus,
Janssen and Maheshwari, 2018, pp. 14-19).
3. Data collection
This is the third stage in the data analytics process. Once a clear goal and the cause of
action is determined, the next step should be compiling that data that will be relevant for the
process (Edmondson, McCollum, Chantre, and Campbell, 2019, pp.4). The organization here
should collect the customer data from various sources. The data should be of as large quantity as
possible basing on the fact that the larger the quantity of the data the organization have about its
consumers the easier it becomes to understand the customer behavior and the easier it become to
attain the business goals.
The data that are collected should be relevant with the business goals, else it will be very
difficult to work with the data accordingly. As an example, the data that should be collected
should be tracked first for instance the number of clicks by a customer on a certain product and
how the customers move around the website. Tracking tools should be used while collecting data
in order to help in this process.
4. Data analysis

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The data analysis is the fourth and another important stage in data driven decision
making. It is not a must that all of the data that will be collected should be used here, the main
thing which is important here is cleaning the data for efficiency; the decision makers here need to
keep only the data that are significant and important (Faber, Glas and Visscher, 2018, pp.43-63).
This is in the sense that using the data that are not relevant may yield the results which are
meaningless which may lead to confusion in the process.
This stage also involves categorizing the data sets. The data should ensure the best
accuracy possible. The major factor in achieving the goals here should be how well the data is
used; this defines whether the goals will be achieved thus the questions: how can the data be
modelled? And how can the data be used to achieve the goals? The data are then segregated on
basis of the answers to the questions stated.
5. Deployment of tools
After the above stages for identifying and preparing the data are done, the next stage will
be the deployment of tools. This stage involves the application of all of the stages mentioned
above. The business will utilize the statistical and analytical strategies in this stage in order to
gain an understanding towards the business (Li, Chi, Hao and Yu, 2018, pp.113-128). The
technological innovation have come with a wide variety of tools that can help in this process. As
such, it is upon the business to select the tool which is the most appropriate for the business to
complete this process. An understanding on the business operation should determine the model
that is suitable for the business.
6. Implementation

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Implementation is the last stage in the big data analytics induced decision making
process. This process involves the deployment of all the strategies that have been developed up
to this end. Here, the business goals come to reality depending on how the organization
transforms its understandings and focus on accuracy. Else, the business may fail to meet its
objectives. This step involves a continuous process as it keeps repeating itself due to the changes
that may be experienced in the data. As such, it will demand that the organization keep making
improvements on the off chance that changes are experienced.
Tracking the data throughout the project life cycle can help the business to gain some
insight into the efficiency of service delivery, the underlying causes and setbacks if any (Ziegler
et al. 2018, pp.1357-1367). The data analytics along with a well-articulated key performance
index supports the business productivity as well as the optimal utilization of the business
resources.
The process will involve various audience including the key stakeholders, managers and
funders among others. The managers will always require customer information to help them
make decisions and manage the business daily operations. The data will also be essential for
middle managers of the corporate in determining the business operation costs. The organization
stake holders would be interested in knowing how the data driven decision making impact the
day in day out workflow among other business activities.
Ethical considerations
There various ethical considerations that should be considered during the process. Some
of them include the following:

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 Ethical standards in data collection process require that the data collection process should
not impede on any customer’s privacy. The organization should ensure that the customers
provide their consents on how their data will be used in the process.
 According to professional ethics, it is required that the data analytics process provide
relevant data and the best accuracy possible during data analysis.
 During implementation, the implantation related ethical standards demand that the
customers know the shortcomings of the initiatives.
Conclusion
At a glance, this document has provided a brief overview on the data analytics process
that would be useful in e-commerce industry for decision making. In doing so, we have analyzed
the current state of arts in the industry, we selected Alibaba corporate for the case study and
analyzed its mode of operation as well as significant inefficiencies it may experience. We also
looked at the available data and how the data can be utilized in data driven decision making. This
enable us to gain an understating to develop a framework for data driven decision making that
would be used in the e-commerce industry to improve efficiency in its operation and achieve
business goals. The document has also analyzed the relevant ethical considerations for the
process. It can be concluded that data driven decision making is a critical aspect that every
business should consider.

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Reference list
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