Product upselling and cross-selling on the Amazon E-commerce platform is one of this retailer’s major success stories, accounting for an impressive 35% of total revenues.

What technology is powering this mode of conversion?

Amazon’s product recommendation technology is powered primarily by artificial intelligence (AI).

Aside from product recommendations, online retailers are using artificial intelligence in the eCommerce industry to provide chatbot services, analyze customer comments, and provide personalized services to online shoppers.

According to a 2019 Ubisend study, one in every five consumers is willing to buy goods or services from a chatbot, and 40 percent of online shoppers are looking for great offers and shopping deals from chatbots.

While global e-commerce sales are expected to reach $4.8 billion by 2021, Gartner predicts that by 2020, around 80% of all customer interactions will be managed by AI technologies (without the use of a human agent).

So, how is artificial intelligence in e-commerce changing the shopping experience in 2019?

Let’s look at some of the most important applications of artificial intelligence in eCommerce, as well as some real-world industry examples, in this article.

How is Artificial Intelligence changing the shopping experience?

The use of artificial intelligence in online shopping is transforming the E-commerce industry by predicting shopping patterns based on the products purchased and when they are purchased.

For example, if online shoppers frequently buy a specific brand of rice every week, the online retailer could send these customers a personalized offer for this product, or even use a machine learning-enabled recommendation for a supplementary product that goes well with rice dishes.

AI-enabled digital assistants, such as the Google Duplex tool, are developing capabilities such as creating grocery lists (from the shopper’s natural voice) and even placing online shopping orders for them.

4 Major AI Applications in E-commerce

While there are numerous benefits to using artificial intelligence in eCommerce, here are four major AI applications for eCommerce that are currently dominating the industry.

Chatbots and other forms of virtual assistance

Chatbots or digital assistants are increasingly being used by e-commerce retailers to provide 24×7 support to their online customers.

Chatbots, which are built with AI technologies, are becoming more intuitive and enabling a better customer experience.

Chatbots, in addition to providing good customer service, is increasing the impact of AI in eCommerce through capabilities such as natural language processing (or NLP), which can interpret voice-based interactions with consumers.

• Providing deeper insights to consumers in order to meet their needs.
• They have self-learning abilities that allow them to improve over time.
• Customers should be given personalised or targeted offers.

Product Recommendations That Are Intelligent

Personalized product recommendations for online shoppers are increasing conversion rates by 915 percent and average order values by 3 percent, according to one of the major applications of artificial intelligence in eCommerce.

AI in eCommerce is influencing customer choices through the use of big data, thanks to its knowledge of previous purchases, searched products, and online browsing habits.

Product recommendations provide numerous advantages to eCommerce retailers, including:

• a greater number of repeat customers
• Customer retention and sales have improved.
• Online shoppers can enjoy a more personalised shopping experience.
• Allow a personalised business email campaign to run.

Ecommerce AI Personalization

Personalization, which is regarded as one of the most effective modes, is at the heart of AI in Ecommerce marketing.

AI and machine learning in Ecommerce are deriving important user insights from generated customer data based on specific data gathered from each online user.

For example, the AI-enabled tool Boomtrain can analyze customer data from multiple touchpoints (including mobile apps, email campaigns, and websites) to determine how they interact online.

These insights allow eCommerce retailers to make appropriate product recommendations while also providing a consistent user experience across all devices.

Inventory Control

Efficient inventory management is all about keeping the right amount of inventory on hand to meet market demand while not adding to idle stock.

While traditional inventory management was limited to current stock levels, AI-enabled inventory management allows for stock maintenance based on data related to:

Sales trends in previous years

Changes in product demand that are projected or anticipated

Possible supply-related issues that could have an impact on inventory levels

Aside from inventory management, AI is enabling warehouse management with the emergence of automated robots, which is predicted to be the future of artificial intelligence in eCommerce.

Unlike human employees, AI robots can be used to store or retrieve stocks 24 hours a day, seven days a week, as well as immediately dispatch ordered items following online orders.

AI in the B2B Ecommerce sector is driving a slew of innovative solutions in addition to transforming the E-commerce industry in a variety of ways.

Let’s take a look at some of the most recent industry case studies on artificial intelligence and how it’s affecting this industry.

Smart AI-Enabled Solutions for the Ecommerce Industry

AI-powered technologies are introducing online shoppers to a variety of products they had no idea existed on the market.

Sentient Technologies, for example, is developing virtual digital shoppers that can recommend new products to online shoppers based on their personal purchasing patterns and data insights.

With the success of the Amazon Alexa device, this E-commerce behemoth is introducing Alexa Voice Shopping, which allows you to review the best of Amazon’s daily deals and place online shopping orders with just your voice.

And there’s more.

Amazon Alexa can also give you wardrobe advice, such as the best fashion combinations and a comparison of outfits to see which one would look better on you.

AI is reducing the number of returned goods purchased through online sales in the Fashion eCommerce industry.

Zara, for example, is utilizing AI capabilities to suggest the appropriate apparel size (based on the shopper’s measurement) as well as their style preferences (loose or tight clothing).

This can assist the fashion brand in reducing product returns and increasing repeat purchases.

Aside from these advancements, AI-powered solutions are reshaping the E-commerce industry in the following areas:

• AI-powered email marketing that sends out marketing emails for products (or services) that the recipient is interested in. Aside from reading more humanly than automatedly, these email marketing tools conduct intelligent user analysis based on their response and are more tailored to individual customer needs.
• AI-enabled Supply Chain Automation enables effective supply chain management for e-commerce platforms. Other advantages include the ability to make business decisions about vendors, delivery schedules, and market needs.
• AI-powered data analytics tools for the e-commerce sector that offer a variety of advantages such as business intelligence, customer profiles, and online sale analysis.

Omnichannel AI solutions that provide a consistent and seamless customer experience across online and physical retail locations.

For example, Sephora’s AI-powered omnichannel solutions use a combination of AI and machine learning, natural language processing, and computer vision to bridge the gap between in-store and online customer experiences.

Conclusion

As discussed in this article, artificial intelligence is playing a key role in driving innovative solutions and customer experiences in eCommerce.

Some of the most prominent applications of artificial intelligence in eCommerce are personalized shopping, product recommendations, and inventory management.

Are you thinking about how to implement a working model of artificial intelligence for your business as an online retailer?

Content is a well-established data analytics provider that provides solutions centered on product analytics and E-commerce KPIs for AI in eCommerce startups.

Artificial intelligence makes use of computers and technology to simulate the human mind’s problem-solving and decision-making capabilities.

What is the definition of Artificial Intelligence(AI)?

While there have been numerous definitions of artificial intelligence (AI) throughout the previous few decades, John McCarthy provides the following description in this 2004 study (PDF, 106 KB),

“It is the science and engineering behind the development of intelligent machines, most notably intelligent computer programmers.

It is comparable to the analogous goal of utilizing computers to comprehend human intellect, but AI is not limited to physiologically observable methods.”

However, decades before this description, Alan Turing’s key work, “Computing Machinery and Intelligence” (PDF, 89.8 KB, was published in 1950.

Turing, frequently referred to be the “father of computer science,” poses the following issue in this paper: “Can machines think?”

From there, he proposes a test, now dubbed the “Turing Test,” in which a human interrogator attempts to discern between a computer-generated and a human-generated written response.

While this test has been subjected to considerable examination since its publication, it remains an integral element of the history of artificial intelligence as well as an ongoing philosophical notion due to its use of linguistic concepts.

Stuart Russell and Peter Norvig then published Artificial Intelligence: A Modern Approach, which quickly became one of the main textbooks on the subject.

They go into four distinct AI goals or definitions, distinguishing computer systems based on their logic and ability to think vs. their ability to act:

Humane method:

1.Human-like computer systems

2.Systems that behave similarly to humans

The optimal strategy is as follows:

1.Systems capable of rational thought

2.Systems that make rational decisions

Alan Turing’s notion would have been classified as “systems that behave like people.”

Artificial intelligence, in its simplest form, is a field that combines computer science with large datasets to facilitate problem-solving.

Additionally, it comprises the subfields of machine learning and deep learning, which are typically associated with artificial intelligence.

These fields are comprised of artificial intelligence algorithms aimed at developing expert systems capable of making predictions or classifications based on input data.

Today, there is still a lot of hype surrounding AI development, which is to be anticipated of any new emergent technology.

According to Gartner’s hype cycle, product innovations such as self-driving cars and personal assistants follow “a typical progression of innovation, from initial enthusiasm to disillusionment and finally to an understanding of the innovation’s relevance and role in a market or domain.”

As Lex Fridman observes here in his 2019 MIT speech, we are approaching the zenith of inflated expectations and the trough of disillusionment.

As discussions about the ethics of AI begin to emerge, we can witness the first signs of the trough of disillusionment.

Artificial intelligence classifications—weak AI vs. strong AI

Weak AI, also known as Narrow AI or Artificial Narrow Intelligence (ANI), is artificial intelligence that has been trained and focused on performing specific tasks.

Weak AI is responsible for the majority of the AI that surrounds us today.

‘Narrow’ may be a more true definition for this sort of AI, as it supports some quite strong applications, such as Apple’s Siri, Amazon’s Alexa, IBM Watson, and self-driving cars.

Artificial General Intelligence (AGI) and Artificial Super Intelligence are the two components of strong AI (ASI).

Artificial general intelligence (AGI), or general AI, is a speculative kind of artificial intelligence in which a machine possesses an intelligence equivalent to that of humans; it possesses a self-aware awareness capable of problem solving, learning, and planning for the future.

Artificial Super Intelligence (ASI) — often known as super intelligence — would outperform the human brain’s intelligence and capability.

While strong AI is yet purely theoretical with no practical applications, this does not mean that AI researchers are not investigating its development.

Meanwhile, the best instances of ASI may come from science fiction, such as HAL, 2001: A Space Odyssey’s superhuman, rogue computer aide.

MACHINE LEARNING VS. DEEP LEARNING

Because deep learning and machine learning are frequently used interchangeably, it’s important to understand the distinctions between the two.

As previously stated, both deep learning and machine learning are subfields of artificial intelligence; in fact, deep learning is a subfield of machine learning.

A visual representation of the relationship between AI, machine learning, and deep learning

Deep learning is composed of neural networks.

The term “deep” in deep learning refers to a neural network with more than three layers—which includes the inputs and outputs.

This is often depicted by the diagram below:

The distinction between deep learning and machine learning lies in the manner in which each algorithm learns.

Deep learning automates a major portion of the feature extraction process, removing the need for manual human involvement and enabling the usage of bigger data sets.

Consider deep learning to be “scalable machine learning,” as Lex Fridman highlighted in the same MIT presentation mentioned above.

Machine learning that is more conventional, or “non-deep,” is more reliant on human involvement to learn.

Human specialists establish a hierarchy of features to comprehend the distinctions between data inputs, which typically requires more organised data to learn.

While “deep” machine learning can benefit from labelled datasets, commonly known as supervised learning, it does not require a labelled dataset.

It is capable of ingesting unstructured data in its raw form (e.g., text, photos) and automatically determining the hierarchy of features that differentiate distinct types of data.

Unlike machine learning, it does not require human assistance to interpret data, allowing for more innovative approaches to scale machine learning.

Applications of artificial intelligence

Today, AI systems have a plethora of real-world applications.

The following are some of the more frequent examples:

Speech Recognition: is often referred to as automatic voice recognition (ASR), computer speech recognition, or speech-to-text. It is a capability that utilises natural language processing (NLP) to convert human speech to text.

Numerous mobile devices incorporate speech recognition into their systems to enable voice search—for example, Siri—or to increase messaging accessibility.

Customer service: Throughout the customer journey, online chatbots are displacing human agents.

They respond to commonly asked questions (FAQs) regarding issues such as shipping or offer personalised advise, such as cross-selling products or recommending appropriate sizes for users, fundamentally altering how we think about client involvement across websites and social media platforms.

Message bots on e-commerce sites with virtual agents, messaging programmes such as Slack and Facebook Messenger, and duties typically performed by virtual assistants and voice assistants are all examples.

Computer Vision: This artificial intelligence technology enables computers and systems to extract meaningful information from digital photos, videos, and other visual inputs and to take appropriate action based on that information.

This suggestion capability distinguishes it from image recognition tasks.

Computer vision, which is based on convolutional neural networks, has applications in social media photo tagging, radiological imaging in healthcare, and self-driving automobiles in the automotive industry.

Recommendation Engines: By analysing historical data on consumer behaviour, AI algorithms can assist identify data trends that can be leveraged to design more effective cross-selling techniques.

This is utilised by online businesses to give relevant add-on recommendations to customers throughout the checkout process.

Automated stock trading: Designed to optimise stock portfolios, AI-powered high-frequency trading platforms execute hundreds, if not millions, of trades daily without human interaction.

The History of Artificial Intelligence: Significant Dates and Persons

The concept of a ‘thinking machine’ extends back to ancient Greece.

However, significant events and milestones in the evolution of artificial intelligence since the introduction of electronic computing (and concerning several of the subjects mentioned in this article) include the following:

1950: Computing Machinery and Intelligence is published by Alan Turing in Turing—famous for cracking the Nazis’ ENIGMA code during WWII—proposes in the article to address the topic ‘can machines think?’ and introduces the Turing Test to assess whether a computer can display the same intelligence (or the equivalent intelligence) as a person.

Since then, the Turing test’s utility has been contested.

1956: John McCarthy coined the phrase ‘artificial intelligence’ at Dartmouth College’s inaugural AI conference.

(McCarthy would later design the Lisp programming language.)

Later that year, Allen Newell, J.C. Shaw, and Herbert Simon develop the Logic Theorist, the world’s first functioning artificial intelligence computer programme.

Frank Rosenblatt creates the Mark 1 Perceptron, the world’s first computer built on a neural network that ‘learned’ via trial and error.

Only a year later, Marvin Minsky and Seymour Papert publish Perceptrons, which becomes both a seminal work on neural networks and, for a while, an argument against further neural network research.

1980s: Backpropagation neural networks, which train themselves using a backpropagation algorithm, become widely employed in artificial intelligence applications.

1997: IBM’s Deep Blue defeats Garry Kasparov, the global chess champion at the time, in a chess match (and rematch).

2011: IBM Watson defeats Jeopardy! champions Ken Jennings and Brad Rutter

2015: Baidu’s Minwa supercomputer utilises a type of deep neural network called a convolutional neural network to detect and classify images more accurately than the average person.

2016: DeepMind’s AlphaGo programme defeats Lee Sodol, the world champion Go player, in a five-game match powered by a deep neural network.

The victory is important in light of the game’s enormous number of possible plays (nearly 14.5 trillion after only four moves!).

Google later acquired DeepMind for an estimated $400 million.

Artificial Intelligence has been evolving at a breakneck speed due to numerous factors including India’s foremost place in internet ranking in internet adoption. Currently, it is one of the booming technologies everyone wants to pursue.

Huge technological advancements in the tech space over the decade created lakhs of jobs in India. As per the recent reports from Accenture, it is estimated that the growth of AI can contribute US $957 billion to India’s economy in the next 15 years.

AI has the potential to bring massive incremental value to various businesses, for example, manufacturing, Agriculture, energy, healthcare, and education. It does play an imperative role in generating jobs in all these businesses.

However, Indian IT companies have been following the maneuver that is an essential factor to be successful in technology adoption and advancement. The right skilling by Indian tech start-ups has a massive contribution to creating 2 lakh job opportunities in the tech space in 2022.

Indian tech start-ups experienced a 108% growth in 2018. However, the studies also disclosed that Artificial intelligence witnessed the fastest adoption compared to other distinct technologies.

It is anticipated that with the growing youth population in India, most businesses will be switching to this groundbreaking technology AI. Many hi-tech cities like Bangalore, Hyderabad, Delhi, and Mumbai are banking on Artificial intelligence because of its potential to reach a larger audience base.

A career in AI in 2023 & beyond

India has been lagging in terms of healthcare facilities, and less number of doctors are available. It’s an arduous task for the government to assure quality healthcare to its citizens.

However, AI is forecasted to aid India to a greater extent in facilitating better healthcare shortly.

Reputed tech giants like Microsoft are collaborating with startups to facilitate AI-based tools such as the 3Nethra that can detect eye issues. This is just an example, there have been so many projects under the pipeline for the healthcare sector. Hence, AI has the potential to transform the healthcare sector.

Artificial Intelligence to transform the Education sector

India is becoming the hub of Science, Math, Technology, and Engineering graduates. Tech giants such as Google, Microsoft, and Intel have been providing short-term education plans like internships, classroom training, and other initiatives to assist learners in AI programming.

Artificial Intelligence intends to enhance the Financial system

The world of finance and banking has transformed a lot with the significant adoption of Artificial Intelligence.

The future of finance completely relies on AI. This technology enables individuals to not only manage the flow of money, but they can also ameliorate the cash flow, and profitability, optimize compliance, and more.

Artificial Infrastructure to bring advancement in Infrastructure and Mobility

The ever-increasing population of hi-tech cities leading to a great challenge for India. AI is the ray of hope that could offer Smart infrastructure and mobility with civic tool systems. Further, the tools could help in assisting customer grievances, and appropriate repair, therefore enhancing the quality of living of migrators.

Artificial Intelligence to counter cyber threats

As technological advancements are increasing day by day, cyber threats have become more usual.

AI is already competing with ever-emerging malware. The significant advancement in the IT field makes AI inevitable to handle cyber threats. India is already working on Artificial Intelligence based cybersecurity tools at a rapid pace. Hence, this enables massive opportunities for aspirants who want to build their careers in AI.

Collectively, skilled AI engineers are in high demand, specifically, in India. The groundbreaking technology seems to be imperative when it comes to providing jobs for the future generation of India.

NearLearn is one of the popular Artificial Intelligence and Machine Learning training institute in Bangalore with 4.8 Google ratings. The institute has the best faculty, so if you’re the one who is looking out to pursue a career in AI, then NearLearn is the top choice.