This document discusses how artificial intelligence (AI) is transforming the ecommerce industry. It begins with an overview of the growth of ecommerce and how AI technologies like machine learning (ML) are being adopted by major online retailers and tech companies to improve business processes and customer experiences. The document then explores several applications of ML and AI in ecommerce, including personalization, price optimization, fraud detection, product recommendations, self-service support, demand prediction, AI chatbots, customer relationship management, inventory management, and more. It concludes by comparing the roles of ML engineers versus AI engineers in ecommerce companies.

1. THE COMPLETE
GUIDE TO USING
AI IN ECOMMERCE

2. Despite the downturn in the global market, the eCommerce
industry keeps growing at a fast pace.
According to a Statista study, eCommerce sales accounted for 14.1
percent of total global retail sales, and the upward trend is set to
reach 22 percent in 2023.
As the number of digital shoppers exceeds 2 billion, market
analysts predict that by the end of 2020, cumulative eCommerce
sales volume will rise to $4.13 trillion.
99 Firms, in its Ecommerce Statistics for 2020, states that 95
percent of retail customers will conduct purchase transactions via
eCommerce by 2040.
While the e-retail market is booming worldwide, online retailers
must remain aware of and focused on the latest stats and trends
in the eCommerce industry. Staying up-to-date will enable them
to make more informed strategic decisions that drive business
growth.
INTRODUCTION
eCommerce

3. The Global Consumer Survey Report 2019 recommends that
e-commerce companies should not only measure the Return
on Investment (ROI), but they should also assess the Return on
Experience (ROX) to determine how user experience impacts their
business.
If an online retail business is to survive in the fiercely competitive
e-commerce market, it must make customer
experience a top priority, all the more so when
studies show that the percentage of shopping carts
abandoned is shockingly high at 68.8 percent.
Abandonment of shopping carts is a critical
problem in the eCommerce industry, which stems
from a variety of issues related to the website’s
design, slow-loading pages, a complicated
shopping process, payment gateway errors,
long response time, and unsatisfactory
customer interactions.
The Baymard Institute, an independent web
usability research organization, pinpointed
39 areas where a majority of eCommerce
companies need improvement to enhance user
experiences, in terms of speed, convenience,
accuracy, knowledge of customer preferences,
and 24/7 availability.
To address the difficulties end-users face, many
online retail brands are adopting AI in eCommerce
customer journey stages. AI in e-commerce enables
retailers to collect and analyze data in near real-time,
facilitating competence and efficiency in driving revenues and
enhancing personalized experiences.
In this eBook, we will delve into the use of AI in the ecommerce
industry, its applications, and the job opportunities opening up
due to AI’s growing prevalence in ecommerce.

4. AN OVERVIEW OF
AI IN ECOMMERCE
Artificial Intelligence is working ultra-fast to transform the global
retail game.
Many experts predict that AI will soon go into a turbo-drive,
with big boys such as Microsoft, Amazon, and Google investing
heavily in innovative AI technologies.
Other tech leaders, including Facebook, IBM, and Yahoo, publicly
stated that they would use AI to improve business processes.
In the eCommerce domain, major players like eBay, Amazon Go,
Home Depot, and Walmart are integrating AI solutions into their
services to make the buyer’s journey easier and seamless.
A subset of AI, Machine Learning (ML), is also gaining popularity
within the eCommerce industry for its capability to provide
computer systems with the unique ability to be in a continuous
‘self-learning-mode’ without explicit programming.
ML algorithms enable computer systems to learn automatically,
change, and improve on their own, using both past experiences
and new data, with no need for any manual intervention.
Machine Learning techniques help identify patterns and trends
in data, enable rapid analysis, real-time prediction, improved
decision-making, process optimization, etc.

5. APPLICATIONS OF
MACHINE LEARNING
IN ECOMMERCE
Personalization
In a brick-and-mortar store, when a customer has doubts,
concerns, or questions, the salesperson addresses the customer’s
problem, offers the necessary information, and helps the customer
complete the purchase.
However, in an online environment, it is difficult to convince a
customer to buy a product because the customer is not in contact
with a human salesperson.
ML technologies learn from data and enable online retailers to
provide customers with the same customized “offline” experience
in real-time.
Price Optimization
Nowadays, customers have multiple options to compare products
and prices from various websites with only a few clicks.
Therefore, it is critical for online stores to set the right price to win
a deal.

6. Machine-Learning techniques can dynamically adjust product
prices, taking into account a number of factors, including
competitors’ prices, the product’s demand, customer profile, time
of day, or peak/off-season.
Fraudulent Transaction
Detection and Protection
The eCommerce sector is highly vulnerable to cybercrime
and fraud.
One fraudulent transaction can inflict an irreparable damage
to the reputation of a brand.
Machine-Learning technologies process tedious, repetitive data at
lightning speed, detecting and preventing fraud before it occurs.
Personalized Product
Recommendations
Generating 35 percent of sales, Amazon’s Recommendation
Engine is proof that personalized product recommendations work.
But identifying the shopping trends of millions of customers and
providing them with personalized product recommendations is no
joke.
Machine-Learning tools and methodologies simplify this task.
Machine-Learning algorithms produce instant product
recommendations by analyzing customer trends and buying
behavior at a frenetic pace.

7. Self-Service Support
Providing a high level of customer service at scale can be
challenging.
An ML-powered, self-learning chatbot can not only automate the
entire customer service process, but it can also improve the user
experience by providing relevant information, smart suggestions,
and 24/7 support.
Demand and Supply Prediction
Now that companies have access to vast amounts of data, many
eCommerce enterprises are using Machine-Learning to predict
demand and supply.
Machine-Learning tools perform super-fast data analysis and
predictive analytics to extract insights that facilitate accurate and
deeper predictions.

8. APPLICATIONS
OF AI IN ECOMMERCE
AI Chatbot
It is now possible for most eCommerce websites to provide 24/7
customer support because of AI chatbots.
In earlier times, chatbots used to provide customary responses,
but now with the advent of AI and ML technologies, they have
transformed into intelligent beings with near-human intelligence.
Top eCommerce platforms are not only using AI chatbots to
deliver customer service, but they are also deploying speech-
and-text-based chatbots to assist people in making intelligent
shopping choices.
Customer Relationship Management
Human resources are important, but it is no longer the backbone
of CRM (Customer Relationship Management), as it was a couple
of years ago.
Now, Artificial Intelligence can screen large amounts of data to
help companies create a graphical story of a customer’s journey
map.

9. A visual representation of shopping patterns and interactions
enables retailers to direct their marketing activities in a correct
manner.
In addition, AI-driven CRM facilitates predictions with heightened
precision, freeing marketing teams from the tedious role of
analyzing figures, allowing them to focus on developing long-
lasting relationships with consumers.
AI and Sales Goals
eCommerce is predominantly about revenue generation and sales.
From helping brands to identify potential clients to providing
after-sales support, Artificial Intelligence can streamline sales
channels by optimizing resource allocation, analyzing team
performance, and offering clear insights into shipment trends.
Improved Decision-Making
From transactional data to campaign reports, online surveys to
social media data, and website traffic, there are multiple data
points for collecting information today.
Comprehensive and effective analysis of massive datasets to
extract actionable insights that enable informed decision-making
is not possible without applying AI algorithms.
By analyzing complex patterns in datasets, identifying shopping
trends, and predicting customer behavior, Artificial Intelligence
accelerates business decision-making with pinpoint accuracy.

10. Image Search
Major eCommerce enterprises, to augment user experiences, are
incorporating the image-search feature with the help of AI.
The feature allows buyers to search for an item by simply pointing
the camera of their smartphone at the item.
AI algorithms instantly identify the item and enable shoppers to
find it via eCommerce mobile applications, eliminating keyword-
based search.
Inventory Management
Proper management of inventory is a key area in the online retail
business, given the fact that eCommerce companies have to deal
with innumerable products.
Monitoring tens of thousands of products, tracking their current
stock and future requirements, can be a labor-intensive and time-
consuming process.
AI applications and systems help enterprises efficiently manage
their inventories, striking an accurate correlation between the
need and demand for each product category.
Customer Service
In today’s fast-paced and competitive eCommerce ecosystem,
providing first-rate customer service is crucial to ensure success.
Artificial Intelligence allows online merchants to focus on the
vital aspect of eCommerce - boosting user experience, which
eventually translates into higher conversion rates.
As personal communication is on the decline, the relevance of AI
in eCommerce will soar in the coming years.

11. Today, top eCommerce enterprises are recruiting professionals
who are proficient in both ML and AI. While both Machine
Learning and Artificial Intelligence engineers perform their roles
under the banner of AI, their job responsibilities differ because of
the different end outcomes they
Job Description -
ML Engineer v/s AI Engineer
ML puts more emphasis on making systems learn from data, AI,
on the other hand, uses the data to automate decision-making
and labor-intensive, tedious tasks, ensuring high performance, and
improved decisions.
AI engineers must have a solid understanding of programming
languages such as C++ and Java, while ML engineers should
be familiar with algorithms and data processing skills involving
TensorFlow and H2O tools.
AI JOBS
IN ECOMMERCE

12. Below are the common Machine Learning and Artificial
Intelligence skills that online retail businesses are seeking:
In-depth knowledge of programming languages, including R,
Python, Java, and C++
A clear conception of Matrix Multiplication, Matrices, and
Vectors
Strong grasp of statistical concepts, such as Gaussian
Distributions, Mean, and Standard Deviations
Expertise in Signal Processing methodologies and Advanced
Signal Processing Algorithms, including Bandlets, Curvelets,
Shearlets, and Wavelets
The idea of probability theories for writing algorithms like
Gaussian Mixture Models, Hidden Markov Models, and Naive
BayesA good foundation in subjects like Summations, Partial
Differential equation, Quadratic Programming, Lagrange,
Convex Optimization, and Gradient Descent
Deep understanding of Neural Network for Speech
Recognition, Image Classification, and Translation tasks
Excellent communication and problem-solving skill

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