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Artificial Intelligence in E-Commerce: A Business Process Analysis
Citation:
Kalia, P. (2021). Artificial Intelligence in E-Commerce: A Business Process Analysis. In C.
Bhargava & P. K. Sharma (Eds.), Artificial Intelligence: Fundamentals and Applications
(pp. 9–19). Florida, United States: CRC Press, Taylor & Francis Group. Retrieved from
https://www.taylorfrancis.com/chapters/edit/10.1201/9781003095910-2/artificial-
intelligence-commerce-prateek-kalia
Abstract
New internet-based technologies have changed the way business enterprises handle marketing,
discovery, transaction processing, and product and customer service processes. One such
significant technology is artificial intelligence (AI) which has partially or completely substituted
humans for the performance of tasks. In a technology-driven industry such as e-commerce, the
applications of AI are immense. To explicate the impact of AI as a technological phenomenon on
the companies engaged in e-commerce, this chapter will detail the role of AI under each e-
commerce business process like market research, market stimulation, terms negotiation, order
selection and priority, order receipt, order billing/payment management, order scheduling/
fulfillment delivery and customer service and support.
Keywords Artificial intelligence, e-commerce, business process analysis, marketing, transaction
processing, service, support
1. Introduction
Researchers argue that the fourth industrial revolution will be powered by information and
communication technology, machine learning, digitization, robotics, and artificial intelligence
(AI). Machines will be utilized to make decisions, creating a profound impact on business
marketing practices and society (Syam and Sharma 2018; Dwivedi et al. 2019). In the next twenty
years, the AI revolution will have an even greater impact than the Industrial and digital revolutions
combined (Makridakis 2017). Studies confirmed that the emergence of intelligent products and
services is not just hyped as these possess the capability to transform the world (Sonia et al. 2020).
Researchers are convinced with the genesis of AI which has already crossed two “hype cycles” i.e.
first hype cycle in 1950-83, a resurgence in 1983-2010, second hype cycle in 2011-17 and now AI
is going to be the future of brains, minds, and machines (2018-35) (Aggarwal 2018; Simon 2019).
In a recent survey study, Frey and Osborne (2017) found that 47 percent of US jobs could be
automated by 2033, as AI will have a significant impact on sales, marketing, and customer service.
Experts have predicted the substantial effect of AI in three industries i.e. retail, education,
and health care (Ostrom et al. 2018). The retail industry with a high proportion of human work and
concurrent low-profit margins is a natural fit for AI applications, especially e-commerce (Weber
and Schütte 2019). Commonly, e-commerce companies are using AI for personalizing websites
and product recommendations (Netflix). Tech and retail giants such as Amazon.com are investing
heavily in research and development for advancing AI applications like recommendation engine
(Alexa), voice-powered assistants (Echo), the Prime Air drone initiative, etc. Amazon is offering
AI and machine learning capabilities to other companies through its cloud platform (AWS) (Weber
and Schütte 2019).
To understand the role and importance of AI in e-commerce, this chapter will be discussing
AI and its application in various business processes involved in an e-commerce business. But
before starting the discussion we should first understand the concept of AI which is detailed in the
proceeding section.
2. Artificial Intelligence
Before discussing artificial intelligence, we should first understand “intelligence”, which is the
ability of a person to learn, understand or deal with new situations, think abstractly, and use
knowledge to manipulate one's environment (Merriam-Webster.com 2020). In general terms,
intelligence can be defined as the ability to acquire and apply memory, knowledge, experience,
understanding, reasoning, imagination, judgment, opinions, facts, skills, calculations, information,
language to calculate, classify, generalize, perceive relationships, solve problems, plan, think
abstractly, comprehend complex ideas, learn quickly, overcome obstacles and adapt efficiently to
new situations, either by changing oneself or the environment (Legg and Hutter 2006; Paschen et
al. 2019).
2.1.AI mimicking human intelligence
The concept of AI originated from the considerations involving the extent to which the machine
can partially or completely replace humans in the performance of tasks (Weber and Schütte 2019).
Therefore, marketing literature defines AI in terms of human intelligence. For example,
researchers define AI as machines that exhibit aspects of human intelligence (Huang and Rust
2018), mimic intelligent human behavior (Syam and Sharma 2018), or as non-biological
intelligence (Tegmark 2017). Similarly, McCarthy (2007) defines AI as “the science and
engineering of making intelligent machines, especially intelligent computer programs. It is related
to the similar task of using computers to understand human intelligence, but AI does not have to
confine itself to methods that are biologically observable”. These definitions make AI contingent
on human intelligence (Bock et al. 2020).
2.2.AI Exceeding Human Intelligence
At times, humans indulge in behaviors that may not lead to the best outcome (Kahneman and
Tversky 1979), because of bounded rationality arising due to limited information, cognitive
abilities, and finite time to make decisions (Dawid 1999). Some scientists believe that machines
can exhibit human-like intelligence in two ways: acting intelligently (performing processes like
memorizing, learning, reasoning, perception, and problem-solving towards goal-directed
behavior) and rationality (achieving “right thing” under uncertainty) (Paschen et al. 2019).
AI, with the help of big data and deep learning can identify inclinations, intentions, and
patterns which are beyond the intellectual capacity of a human brain. The human brain can interpret
and conclude from limited data; however, machines can interpret billions of data points. AI has
advanced through “four intelligences” (i.e. from analytical to emotional) (Huang and Rust 2018)
to acquire advanced capabilities like reasoning, planning, conceptual learning, creativity, common
sense, cross-domain thinking, and even self-awareness (Bock et al. 2020). In this scenario, the
definition of AI proposed by Kaplan and Haenlein (2019) sounds more appropriate, i.e. AI “as a
system’s ability to interpret external data correctly, to learn from such data and to use those
learnings to achieve specific goals and tasks through flexible adaptation.”
3. E-commerce Business Processes and Artificial Intelligence
There are various business processes like marketing, buying, selling, and servicing of products and
services performed by the companies involved in e-commerce (Figure 1). These businesses
completely depend on e-commerce applications and internet-based technologies to carry out
marketing, discovery, transaction processing, and product and customer service. E-commerce
websites carry out interactive marketing, ordering, payment, and customer support processes on
the World Wide Web. E-commerce also includes processes related to e-business, where suppliers
and customers access inventory databases through extranet (transaction processing), or sales and
customer service representatives access customer relationship management (CRM) systems via
the internet (service and support) or customers collaborate in product development through email
and social media (marketing/discovery) (O’Brien and Marakas 2011).
Researchers believe that AI can improve business performance because AI solutions are
faster, cheaper, and less prone to human mistakes (Huang and Rust 2018; Canhoto and Clear 2020).
Therefore, in the next sections, we will try to understand how AI is contributing to various e-
commerce business processes.
Figure 1. E-commerce business processes supporting the electronic selling of goods and services.
3.1.Marketing
3.1.1. Market Research
Till the third industrial revolution, businesses used information technology for data processing and
communication only. However, the fourth industrial revolution will allow computers to make
appropriate and reliable decisions (Syam and Sharma 2018). Researchers believe that large
businesses failing to deploy the latest technologies such as AI will be swept away in the face of
competition (Stone et al. 2020). The primary focus of market research is to identify accurate
segments of customer groups. Selected segments are targeted with suitable products, offered at
appropriate prices, supported with reasonable promotional and communication strategies to deliver
products to customers through appropriate distribution strategy (Syam and Sharma 2018). Earlier,
segmentation was generally based on ‘traditional’ techniques like cluster analysis (for clustering),
chi-squared automatic interaction detection (CHAID) (for classification), or more recent
segmentation techniques like hidden Markov models, support vector machines, artificial neural
networks (ANN), classification and regression trees (CART) and genetic algorithms. Machine
learning tools of the new century have increased the efficiency of these segmenting algorithms.
For example, AI empowered ANN models can find solutions for marketing problems faced by
B2B e-commerce companies (Wilson and Bettis-outland 2020). Marketers have enormous
statistics in their hands to process massive unstructured data (Big Data) for segmentation with the
help of unsupervised neural networks (Hruschka and Natter 1999). Now profitability or customer
lifetime value (CLV) segments can be identified based on machine learning-powered decision
trees (Florez-lopez and Ramon-jeronimo 2009). AI can process a huge amount of written and non-
written user-generated content available on social media platforms to reveal user needs,
preferences, attitudes, and behaviors. For example, the IBM Watson AI system can identify
psychographic characteristics expressed in a piece of text to give valuable insights to marketers
for new product development or innovation (IBM 2020). AI can identify themes and patterns in
users’ posts to interpret user experience and the information can be used for creating strategies to
enhance user experience. It also helps in gathering, sorting, and analyzing external market
knowledge i.e. intelligence about external market forces and stakeholders which may influence
customer preferences and behaviors. For example, AI systems empowered by machine learning
and natural language processing algorithms can identify fake news from huge amount of content
published on blogs, social media etc. (Berthon and Pitt 2018). Similarly, competitive intelligence
can be developed by identifying themes or keywords from unstructured data (news, social media,
website content etc.) (Paschen et al. 2019).
3.1.2. Market Stimulation
Market stimulation is concurrent to marketing which is, “the activity, set of institutions, and
processes for creating, communicating, delivering, and exchanging offerings that have value for
customers, clients, partners, and society at large” (American Marketing Association 2017).
Typically, it encompasses four separable but interlinked components i.e. product. price, place, and
promotion (McCarthy 1960). However, the concept of marketing has arrived at an evolutionary
point where adaptation to technology is imperative and the impact of AI under each marketing mix
component is obvious i.e. for the product (hyper-personalization, new product development,
automatic recommendations, etc.), price (price management, personalized dynamic pricing), place
(convenience, speed, simple sales process, 24/7 chatbot support, etc.) and promotion (personalized
communication, unique user experience, creating wow factor, minimized disappointment, etc.)
(Jarek et al. 2019; Dumitriu and Popescu 2020). Out of five AI areas i.e. image recognition, text
recognition, decision-making, voice recognition, and autonomous robots and vehicles, the first
three are used quite extensively in marketing. Marketers are cautiously implementing AI
applications because of the cost and uncertainty attached to them. However, large tech companies
like Google, Amazon, Microsoft, and Apple are investing in AI areas like voice recognition and
autonomous robots and vehicles solutions (Jarek et al. 2019). As a result, there is a significant
effect of AI on contemporary marketing practices, for example, routine, time consuming and
repeatable jobs have been automated (data collection, analysis, image search, processing), strategic
and creative activities to build competitive advantage are emphasized and business enterprises are
designing innovative ways to deliver customer value. It has further created a marketing ecosystem
where entities offering AI solutions are in demand (Jarek et al. 2019).
3.2.Transaction Processing
3.2.1. Terms Negotiation
Negotiation can be defined as art (more than science) to get what you want from bargaining or
person-to-person interaction. Most of the interactions in the case of e-commerce transactions are
electronic i.e. through email, social media, text chat, or phone. In this setting, AI can be applied as
functional science to give an advantage in the negotiation process (Mckendrick 2019). Negotiation
is like a persuasion dialogue where series of arguments are proposed by a proponent and an
opponent iteratively, and both issues counter-arguments to defeat each other’s argument (Huang
and Lin 2005). Based on the components of negotiations i.e. negotiation set, a protocol, a collection
of strategies, and a rule of a deal, scientists are training their chatbots and virtual assistants to plan
several steps and assess how saying different things could change the outcome of the negotiation
(Reynolds 2017). These AI negotiation agents can operate 24/7 on behalf of e-retailer to locate
customers and automatically negotiate to best term as per parameters set by the administrator or
even market conditions (Krasadakis 2017). It is quite difficult for online stores to engage in
communication through graphical user interfaces (websites) to acquire, serve, and retain
customers. Similarly, there is no chance for the customers to negotiate for a better deal (Huang
and Lin 2005). But deploying a natural language interface for human-computer interaction can
effectively solve this problem (Jusoh 2018).
E-commerce is a highly dynamic market and prices change rapidly. E-commerce
companies are using AI for dynamic pricing of their product and services i.e. adjusting prices as
per market conditions (demand-supply) on a real-time basis (Kephart et al. 2000).
3.2.2. Order Selection and Priority
Recently, Alibaba launched Fashion AI technology to boost sales. Customers can upload pictures
of a product they would like to buy to its Taobao e-commerce site and the website automatically
searches that item for sale similar to the photo (Simon 2019). Similarly, AI can collect real-time
data by tracking the online activity of the customer on their or competitor’s website to decide and
offer a price discount or search through the company’s database to check if those shoppers have
rejected or accepted previous product recommendations (Canhoto and Clear 2020). Another
important application of AI is replenishment optimization. AI can reduce the inventory costs by
determining the right time and quantity, for placing an order to the central warehouse and the
suppliers (Stone et al. 2020). This can resolve several issues such as reduction in the number or
amount of the unsold goods, optimization of the shelf space in the warehouse, and increase in cash
flow. AI algorithms can optimize individual order and delivery (personalization) (Zanker et al.
2019) and simplify complex tasks like same-day delivery (Kawa et al. 2018).
3.2.3. Order Receipt
With the help of predictive systems, AI can evaluate prospects (customers) on their propensity to
buy (high-quality leads) (Järvinen and Taiminen 2016), answer common questions and overcome
objections of customers by using emotion AI (Paschen et al. 2019) and automate and speed up the
checkout process (Campbell et al. 2020). Front-runner e-commerce companies like Amazon have
introduced language-assisted ordering (Amazon Echo) (Holmqvist et al. 2017). Complex AI
models are used for sales forecasting (Dwivedi et al. 2019), store assortments (Shankar 2018), and
personalizing the searches, recommendations, prices, and promotions (Montgomery and Smith
2009). AI can automate service encounters and give personalized and relevant information on a
variety of devices (Bock et al. 2020).
3.2.4. Order Billing/Payment Management
AI can help e-commerce companies in three key areas: invoicing, payment optimization, and fraud
detection (Mejia 2019). Billing and invoice processing are the most burdensome and complicated
tasks in business. However, AI applications can help businesses in matching customer invoices
with received payments (Dwivedi et al. 2019). Manual and semi-automated billing processes
cannot handle a huge number of customer payments, but AI-enabled billing systems support
features like invoice segregation, data extraction, invoice generation, etc., and can handle huge
amounts of data to avoid any anomalies, inconsistencies, and disparities within the invoices (Bajpai
2020). Online transaction is one of the most convenient ways to perform payment with the help of
a computer and the internet. Customer can use their e-wallets, credit card, debit card, online
banking credentials for an online transaction (Kalia 2016; Kalia et al. 2017a). Despite various
security measures risk in terms of fraud is associated with every online transaction (Papadopoulos
and Brooks 2011). AI-powered billing software can prevent fraud from occurring in the first place
by activating an automatic decision-oriented and sophisticated fraud detection system (Khattri and
Singh 2018). For example, Fraugster uses historical data related to the transaction, billing, shipping
addresses, and IP connection to detect payment fraud (Canhoto and Clear 2020).
3.3.Service and Support
3.3.1. Order Scheduling/Fulfillment Delivery
Order scheduling and fulfillment include tasks related to the pick-up or delivery of products and
services at the right place and time in the right quantity and quality (Weber and Schütte 2019).
Due to irregular order patterns, limited time for order processing, and short-term delivery
schedules, the e-commerce industry require extremely efficient fulfillment processes (Leung et al.
2018). Here, AI systems can actively monitor and optimize these processes by considering order
demand factors and product characteristics to automate perfect logistics strategy (Lam et al. 2015;
Paschen et al. 2019). Researchers argue that sustainable supply chains and reverse logistics are
predominant themes for the present as well as future researches in AI (Dhamija and Bag 2019).
Therefore, leading e-commerce companies like Amazon.com are investing in robotics and space-
age fulfillment technologies (drones for order delivery) (Dirican 2015). Apart from fulfillment,
reverse logistics is another challenge. Products are generally returned without their original
packing plus seasonally changing collections and product similarity can complicate the process.
AI-powered automatic image recognition can compare these returns with catalog images to sort
the products (Kumar et al. 2014).
3.3.2. Customer Service and Support
In the e-commerce industry, service quality can be a game-changer (Kalia et al. 2016, 2017b; Kalia
2017a, b). AI can play a leading role in customer service and support by enhancing satisfaction,
improving relationships, personalizing support, and providing recovery in case of service failures.
Researchers call it “service AI”, and define it as, “configuration of technology to provide value in
the internal and external service environments through flexible adaptation enabled by sensing,
learning, decision-making and actions” (Bock et al. 2020). Therefore, Service AI is not just about
applying pre-programmed decisions but it has learning ability as well (Makridakis 2017). AI can
affect customer satisfaction because AI-based service is more reliable, high quality, consistent,
continuously available (24/7), and less susceptible to human errors arising due to fatigue and
bounded rationality (Huang and Rust 2018). Similarly, it is easier for e-commerce companies to
undertake marketing activities to establish, develop and maintain relationships with customers (Lo
and Campos 2018). For instance, virtual assistants can notify millions of users or analyze their
purchases, returns, or loyalty card information and provide services that are beyond human ability.
AI-enabled systems can create comprehensive profiles of current or potential customers by using
structured and unstructured data related to their psychographic, demographic, webographic
characteristics and online buying behavior (frequency, recency, type, and size of past purchases)
and process it with the help of machine learning and predictive algorithms to strengthen customer
relationship efforts and prospecting of potential customers (Lo and Campos 2018; Paschen et al.
2019). AI can provide a high-quality personalized experience to customers by learning to speak in
multiple languages, identify customers’ emotional states, or retrieve information for them. A
dissatisfied customer can spread negative word of mouth. However, firms can mine high-quality
customer feedbacks to develop service recovery strategies (Lo and Campos 2018).
4. Concluding Remarks
Artificial Intelligence holds an elaborate and key role in various segments/ processes of e-
commerce business enterprises. To achieve this objective role of artificial intelligence under each
e-commerce business process like market research, market stimulation, terms negotiation, order
receipt, order selection and priority, order billing/payment management, order scheduling/
fulfillment delivery and customer service and support has been detailed. We noticed that possible
applications of AI for marketing, transaction processing, and service and support for e-commerce
businesses are numerous. At the pace of current technological developments, AI will graduate
from merely a tool for data and information processing (Weak AI) to an independent system
capable of taking human decisions (Strong AI).
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