You're 100% right if you think Mobile Commerce needs AI

You're 100% right if you think Mobile Commerce needs AI

Last updated on 15th January 2022

You're 100% right if you think Mobile Commerce needs AI

You're 100% right if you think Mobile Commerce needs AI

Last updated on 15th January 2022

A study by Statista stated that Americans spend 5 to 6 hours on their mobile phones every day, excluding work-related smartphone use. This significant amount of mobile usage directly impacts the rapid escalation of Mobile Commerce or M-Commerce.

In this guide, we will delve into the concept of mobile commerce, its significance in today's consumer landscape, and discover how retailers can optimize their mobile shopping app or store by adopting artificial intelligence to improve customer experience and boost sales.

What is M-Commerce?

The term mobile commerce was originally coined in 1997 by Kevin Duffey at the launch of the Global Mobile Commerce Forum, to mean "the delivery of electronic commerce capabilities directly into the consumer’s hand, anywhere, via wireless technology."

— Wikipedia

With M-commerce, you can make commercial transactions on your mobile phone or tablet instead of only depending on your computer or laptop to access online eCommerce websites and marketplaces.

Mobile commerce allows users to

Buy and sell goods,

Pay bills,

Use digital wallets

Make in-app purchases,

Book travel tickets,

Use mobile banking services,

Find nearby stores and products, and more

Mobile shopping makes it easy for consumers to shop for product anywhere and at anytime. All they need to do is to look for a product to make that purchase. On the other hand, mobile commerce makes selling easy too. You just need to have a mobile-friendly eCommerce store or a mobile app to be discoverable.

This is why businesses are heavily competing to provide the most convenient, accessible, and user-friendly mobile shopping experience to consumers.

Mobile Commerce Market Share & Size

M-commerce market size is predicted to hit $510 billion by the end of 2023, and reach approximately $710 billion by 2025.

The M-commerce market share in the US amounted to almost 42% of total ecommerce sales in 2022, and will grow to approximately 43% of total ecommerce sales in 2023, and 44% by 2025, slowly equaling out the mobile commerce vs ecommerce ratio.


Artificial Intelligence in Mobile Commerce

The future of AI arrived earlier than anyone expected. Artificial intelligence is a lot more common in mobile commerce than you’d think.

Be it an eCommerce website, a mobile shopping app, or a chatbot, solutions are offered based on personal preferences and buying history, making the whole shopping experience highly personalized to the customer.

Many eCommerce companies now use personalized product recommendation engines to offer targeted product search assistance to customers searching for products.

Amazon’s product recommendation uses AI and machine learning to understand user interests and recommend products that they like.


Businesses now use AI chatbots or virtual shopping assistants, where customer engagement and support can be provided to customers round the clock. This saves the company on staff hiring costs.

AI can converse with multiple people at a given time, thus saving valuable staff time that would otherwise be spent on each customer.

Amazon's Alexa Voice Shopping service lets you shop for the day's best deals with a voice search. It can also give you wardrobe tips and compares outfits to recommend what would look best on you.

Sentient Technologies recommends new products for online shoppers based on their personal buying patterns and data insights.

While there are many benefits of artificial intelligence in eCommerce, here are 15 major AI applications dominating the eCommerce industry for mobile devices today.

15 Powerful ways you can use Artificial Intelligence (AI) in Mobile Commerce

Artificial Intelligence (AI) in mobile commerce undoubtedly gives users a rich and hyper-personalized mobile shopping experience.

75% of executives fear going out of business in five years if they don't scale AI. And this is simply because AI makes selling and managing eCommerce more efficient in every way.



Get personalized recommendations using Artificial Intelligence in Mobile Apps

Mobile apps that use artificial intelligence allow customers to get more personalized shopping experiences, while businesses get leads that are more likely to convert.

Imagine an eCommerce journey that goes beyond mere recommendations and becomes interactive. Picture yourself engaging in conversation with a virtual shopping assistant, sharing your needs, preferences, budget, and all the factors influencing your decision-making process. In response, this knowledgeable helper guides you through product catalogs, provides personalized recommendations, and offers additional suggestions and relevant product content. AI-driven recommendation systems analyze your browsing and purchase history, delivering recommendations that match your preferences.

A few examples of mobile apps that use artificial intelligence brilliantly.

Example 1: Starbucks uses AI and predictive analytics

By collecting data, AI can use mobile app intelligence to generate more personalized product recommendations.

With more than 20 million regular users on its US app, Starbucks collects a lot of user data every day.


This means more personalization leading to more user convenience and thus bringing in higher sales.


Here are a few things the Starbucks mobile app can do:

Get personalized recommendations from the menu as part of the Starbucks Rewards program

You can customize and place your order on the app

Find nearby stores, get directions, know visiting hours, and view store amenities

Pick up your order from a nearby store without waiting in line at the physical store!

This is how Starbucks very smartly engages, entices, and rewards customers for using their app.

Intelligent mobile applications also offer data-driven predictions and suggestions that can benefit users and improve their health.

Example 2: Urbandroid smart alarm clock and sleep guide

The smart wake-up feature of the app awakes the user at an ‘optimal time’ based on the individual’s sleep cycle.

It also provides a sleep tracker that monitors sleep patterns, heart rate, breathing, etc.


Its advanced AI-powered sound classification also detects snoring, sleep talking, sleep movements, etc and provides daily insights.

Users can improve their sleep quality using the suggestion provided by the app.

Watch the video here to understand how artificial intelligence understands sleep patterns, generates data-driven solutions.


Conversational AI

If you think you’ve never interacted with conversational AI, think again. Google Assistant, Siri, Alexa are all audio chatbots that have helped you in a way or another.

Conversational Artificial Intelligence (AI) refers to technologies, like chatbots or virtual agents, which users can talk to.

They use large volumes of data, machine learning, and natural language processing to help imitate human interactions, recognizing speech and text inputs and translating their meanings across various languages.

Conversational AI could be a chatbot on an app or website, a voice assistant on a phone, or any other voice-enabled device.

Techniques and algorithms used in conversational AI

Machine Learning

Machine learning (ML) is a type of artificial intelligence (AI) that allows software applications to become more accurate at predicting outcomes without being explicitly programmed to do so.

Machine learning algorithms use historical data as input to predict new output values. Recommendation engines are a common use case for machine learning.

Natural Language Processing

Natural language processing (NLP) is the ability of a computer program to understand human language as it is spoken and written -- referred to as natural language.

Natural Language Understanding (NLU)

NLU is a subset of NLP. While NLP attempts to analyze and understand the text of a given document, NLU makes it possible to carry out a dialog with a computer using natural language.

AI can comprehend the intention of the user even amidst grammatical errors or shortcuts.

Natural Language Generation (NLG)

AI can generate human text and speech generating the responses of chatbots and voice assistants such as Google's Alexa and Apple's Siri;

Natural language generation (NLG) is the use of artificial intelligence (AI) programming to produce written or spoken narratives from a data set.

Conversational AI uses machine learning to:

Analyze large quantities of data,

Interpret text in multiple languages, and

Mimic human interaction using speech and text input.

Artificial intelligence in chatbots reduces customer service costs and offers 24/7 customer support. It also provides a faster response time, a scalable infrastructure.

AI Chatbots

A chatbot, or chatterbot, is a computer program that simulates human conversation through text or voice interactions over the Internet.

Chatbots are projected to generate 454.8 million dollars in revenue by 2027, up from 40.9 million dollars in 2018.


Around 40% of U.S.-based consumers state that they have used chatbots to engage with the retail industry.


Chatbots will result in saving $8 billion in business expenses by 2022.


Chatbots can have varying levels of complexity based on the natural language processing (NLP) engine it uses. For instance, an AI voice assistant bot will use a speech-recognition engine.

Another important thing to consider when you look for a chatbot is to choose if you want structured or unstructured conversations.


Security and privacy concerns are the main challenges of implementing conversational AI. Artificial intelligence and chatbots can’t understand emotions, communicate in native languages or dialects.

Chatbot virtual assistants are used to handle simple tasks to search and retrieve data in both business-to-consumer (B2C) and business-to-business (B2B) eCommerce industries.

With AI chatbots, virtual assistants, and conversational AI, businesses improve customer service.

A conversational chatbot can retrieve essential information on order status, payment status etc and so, can be very helpful for human customer service executives in answering repetitive requests as well.

An AI-powered eCommerce platform like ewiz commerce can offer a structured conversation chatbot that is often used by medium to small eCommerce companies.

Example 1: Tommy Hilfiger chatbot

A good example of what AI chatbots can do is the Tommy Hilfiger eCommerce mobile app:

They employed a chatbot, named TMY. The eCommerce AI chatbot provides product recommendations and advice on style besides providing customer support.

The conversational AI chatbot asks a series of questions about customers' preferences to gather information about them. It then makes outfit suggestions based on the data gathered, which are constantly refined.

You can watch a video of the review for the Tommy Hilfiger chatbot here.

Example 2: IBM Watson® Assistant

IBM Watson Assistant is a cloud-based AI chatbot that automates customer interactions using a natural language interface. It also transfers clients to human agents when needed.


Customer service chatbots are low-cost chatbots used by eCommerce retailers. Here, the AI takes care of routine tasks like tracking orders, answering customer queries, etc while retail employees can focus on other important tasks.

You can watch a video of how it works here.

Automated chatbots or AI chatbots help reduce customer service costs to the company, while customers get 24/7 customer support and a faster response time.


Product matching using Visual or Image Search

Visual search involves using images from the real world (screenshots, images from the Internet, or photos) as the input source for searches instead of text.

With visual search, you can take a photo of a product you’re interested in. An AI tool will then identify and match it to similar products. Visual search AI helps find items based on size, shape, color, fabric, and even brand logos or designs.

Consumers are no longer required to go shopping to see items they want to buy. For instance, they may be inspired by a friend's new dress or a work colleague's new laptop bag. With an image, AI makes it possible for consumers to find similar items through online stores.

While some retailers have created their own visual recognition tools, Google Lens and Pinterest open up a world of possibilities to customers snapping, searching, and buying anything, anywhere.

Example 1: Google Lens

Google Lens is an image recognition technology developed by Google. It maps images to information present online.

With it, you can get more detailed information about objects in your images, get insights and reviews on destinations, restaurants, menu highlights, scan images to translate text, and more. You can also get fashion ideas based on screenshots or pictures of clothing that you spot.

It is basically an image search variant to the text-based google search we have been accustomed to using.

If you have a phone with a camera and good internet speed, you can use google lens to find more information about anything and everything that you see around you.

It’s a great app for curious kids (and adults alike) who often ask a lot of questions about their surroundings.

Example 2: Pinterest

Pinterest is a visual discovery engine for finding creative ideas for cooking, DIY, graphic designs, illustrations, web designs, education, home and style inspiration, and more. You can find images for anything and everything on Pinterest.

They introduced the concept of ‘pins’ that can be images, videos or products. If you like something on the app, you can just pin it and come back for it later.

The app also lets you use your camera to search for inspiration on Pinterest if you’re on your mobile device. You can find ideas related to your photos or what’s right in front of you in real life. You can also shop for products on the app.

Example 3: Farfetch’s visual search feature

Farfetch is an online fashion retailer whose mobile commerce app has great visual search functionality.


Here’s how Farfetch uses artificial intelligence in its mobile fashion app:

Visual search: The ‘see it, snap it, shop it' feature lets you upload a picture of a clothing item you are looking for. You can then buy it on the app instantly.

A personalized feed: The app lets you find the latest pieces and your favorite designers in one place, completely tailored to meet your taste.


Find an eCommerce product using voice search on a mobile device

Voice search enables eCommerce customers to look for information by speaking into a microphone.


Around half of the world's web traffic comes from mobile devices. In the first quarter of 2021, mobile devices accounted for 54.8 percent of global website traffic.

— Statista

Voice search uses AI in speech recognition and can be 3x faster than typing.

Computer vision and linguistics is used to recognize the voice command and distinguish each word based on phonemes.


44.2% of all internet users in the U.S. use voice search. This equals approximately 128 million people or 38.5% of the population in the U.S.

— eMarketer

Voice search can help the elderly, visually impaired, or even people in a hurry who don’t have the time to type out a query.

The way we engage with most technology is tactile and visual. These are challenges for someone losing their sight or motor skills. With voice technology, the user interface is now manageable and easy to adopt.

— Matt Smith, CEO of Speak 2 Software

Speak 2 Software offers voice-enabled smart speakers to assisted living centers for seniors.

The ability to use voice search can benefit seniors who may have difficulty navigating a computer or using a mouse, indicating that voice search is capable of bridging the digital divide.


64% of users of voice technology ages 55+ search products online using voice, compared to only 47% of users aged 18-34 and 63% of users aged 34 to 54.

— The Manifest

By optimizing your mobile commerce website for voice search, you can get better visibility online. And since many people find typing on a mobile device uncomfortable, using a voice search is a great way to optimize your eCommerce business for voice search.


Siri and Google Assistant lead the global market for voice assistants, holding a 36% share across devices.

— SEMrush

Example 1: Siri, the AI-powered virtual assistant

Siri is a built-in, voice-controlled personal assistant available for Apple users.

Siri uses Artificial Intelligence and Natural Language Processing to function. You can use Siri to make calls, send text messages, answer questions, and offer recommendations based on voice search or by using buttons.

The 3 main components include a conversational interface, personal context awareness, and service delegation. It delegates requests to several Internet services, moreover, Siri can adapt to users’ language, searches, and preferences.

You can activate the voice assistant by saying “Hey Siri” into your Apple device.

If you have a phone with a camera and good internet speed, you can use google lens to find more information about anything and everything that you see around you.

It’s a great app for curious kids (and adults alike) who often ask a lot of questions about their surroundings.

Example 2: Google Assistant

Google’s Voice assistant, called Google Assistant, lets you search for your query on Google using your mobile phone or computer with a voice command.

Unlike Siri, Google offers voice commands, voice searching, and voice-activated device control that works on any Android device and smart speakers. You just need to select the microphone icon in the Google bar and speak out loud to get the results.

The system not only understands 60 different languages but can also deliver localized search results based on the language you speak.

To activate Google Assistant, you can say "OK Google" or "Hey Google" into your smart device.


Amazon's Alexa is the third most popular voice search assistant (after Siri and Google). It accounts for 25% of the market, followed by Microsoft’s Cortana with a 19% share.

— SEMrush

Example 3: Amazon Voice Search - Alexa

A good example of a company using voice search is the eCommerce giant Amazon:

Amazon Voice Search uses speech recognition to understand and answer voice search queries. This technology also converts speech to text using deep learning for accurate results.

By leveraging content on the website like product descriptions and customer reviews, Amazon is also using AI to answer customer queries with voice search.


AI-based product recommendations

AI can generate suggestions based on past purchases and searches, which means buyers can find products quickly and easily using AI-based recommendations.

How do AI-based product recommendations work?

Enterprise data collected from the company and the customer is fed into an ML algorithm that identifies the information within it and establishes accurate correlations.

The system can then match the product listings to the customer information to generate intelligent recommendations based on the user’s search query.


How does personalizing product recommendations with AI help?

Based on a user's browsing history and interactions with a website, personalized product recommendations deliver content that corresponds to the customer’s interests.

Personalize local recommendations

The AI takes into account a user’s GPS location to suggest products based on the current weather, time, or even the route to work.

For instance, a person traveling to Alaska will receive recommendations for snowsuits, whereas a person in Miami may receive recommendations for beachwear.

Send behavior-based recommendation

Based on a user's browsing history and interactions with a website, behavior-based recommendations can deliver content that corresponds to the customer’s interests.

For instance, a person who enjoys drinking artisan coffee would get recommendations about a new cold-brew place that has opened up near his house.

Improve product discovery

AI algorithms can make recommendations based on a user’s search history and cookies to improve product discovery on multiple channels, which ultimately increases sales.

For instance, if a person searches for leather boots, they will get similar ads for boots on their browsers, social media platforms, or even gaming apps.

Example 1: Amazon’s product recommendation engine

Amazon’s AI-powered product recommendation is an effective sales and marketing strategy. It has shown proven results in engaging customers and increasing revenue.


Amazon’s AI recommendation engine fuels 35% of customer purchases or an estimated $50 billion in incremental sales

— McKinsey

Amazon uses item-to-item collaborative filtering, which can match and interpret massive datasets, providing highly relevant recommendations in real-time.

Each customer's homepage is customized to reflect their interests and previous purchases. Amazon also uses AI to optimize its product pages and checkout page.

Amazon produces hyper-personalized product recommendations offering on-site suggestions to visitors like “Your recommendation”, “frequently bought together”, “bestsellers”, “curated for you”, “products you might like,” “frequently bought together,” or “customers also bought”, and so on.

It also provides offsite recommendations as a follow-up activity after customers purchase a product based on their shopping behaviors, shopping history, preferences, and so on.

Amazon’s AI recommendation engine fuels 35% of customer purchases, or an estimated $50 billion in incremental sales.

As a result of the Amazon recommendation algorithm, each customer receives a customized shopping experience, helping Amazon increase gross revenue from each order.

Example 2: Alibaba’s AI ecosystem

The biggest tech company in China, Jack Ma's Alibaba is the world's largest e-commerce platform that sells more than Amazon and eBay combined.

Here’s how Alibaba uses artificial intelligence and machine learning to boost eCommerce sales. China is currently beating the USA with its machine learning and deep learning technologies.

Tmall Smart Selection: Backed by deep learning and NLP algorithms, Tmall recommends products to online shoppers. It then asks retailers to increase inventory, helping them keep up with the demand.

Tmall app: As soon as you open the Alibaba app, it provides content tailored to your online shopping experience based on intelligent recommendation algorithms.

Here’s how AI product recommendations work on Tmall.

A few incredible ways Alibaba uses artificial intelligence and machine learning

Apart from AI-powered product recommendation engines, Alibaba uses AI and deep learning in much bigger projects, around both eCommerce in China.

It is the main contributor to China’s leadership in AI across the globe, helping China beat the USA in the field as well.

Tingwu AI

On October 22, 2021, Alibaba Cloud, introduced a voice AI for business meetings, alongside a new version of its cloud computer.

The AI-powered meeting assistant, named Tingwu, can convert speech to text in real-time, create meeting summaries and post-conference to-do lists in real-time with up to 98%* accuracy.

It can distinguish up to 10 voices and identify speakers in the transcript. It understands English, Mandarin, and 14 other Chinese dialects. Tingwu AI can also autocorrect or refine notes based on the context of a meeting.

Yitian 710

This year on October 19, 2021, Alibaba released a server chip to boost data centers’ performance and contend against US competitors like Amazon.

Its 60 billion transistors contribute to a 20% increase in performance rate and 50% boost in energy efficiency. This is higher than any other server processor in the market in 2021.

Dian Xiaomi

This AI-powered chatbot can understand more than 90 percent of customers’ queries according to Alibaba and serves more than 3.5 million users a day.

Robots to pack and drones to deliver

More than 200 robots in automated warehouses can process 1 million shipments each day.

Alibaba uses smart logistics and cloud computing to optimize its supply chain, build products and drive personalized recommendations. It has also turned many of its physical shops into "smart stores” (video).

The Alibaba M6

On June 25, 2021, Alibaba DAMO Academy (the R&D branch of Alibaba) announced M6. Alibaba claims it to be better and bigger than the best AI training models present in the world, beating Google and Microsoft.

M6 is the first 10-trillion-parameter large language model — 50x GPT-3’s size, which serves as the standard to measure the rate of progress for large AI models.

According to the academy, M6 has achieved ultimate low carbon, high efficiency in AI models using 512 graphic processing units (GPU) to train 10 trillion parameter neural networks within ten days.

Alibaba M6 has cognition and creativity beyond traditional AI, is good at drawing, writing, question, and answer, and has broad application prospects in many fields such as eCommerce, manufacturing, literature, and art.

— InfoQ, a popular Chinese tech magazine

So it is safe to say that AI-driven product recommendations are not just a luxury but a necessity for eCommerce companies of all shapes and sizes in this day and age.


AI in Inventory Management

Real-time inventory management systems can help you track and manage customer orders and transactions. You can use demand forecasting algorithms and automate inventory management processes at your online and offline stores.

Using AI in inventory management, you can avoid lost sales due to out-of-stock inventory, overstocking, and clear out old stock that takes up additional space and company capital. Artificial intelligence also eliminates the need to manually update your database, leading to fewer errors.


47% of retailers believe that AI can significantly improve inventory management by effectively managing costs and buyers' needs.

— Statista


AI-based forecasting reduces errors by 30-50% in supply chain networks, leading to 65% of lost sales reduction, which was mainly due to inventory being out-of-stock, and, also, warehousing costs decreasing by 10-40%.

— Mckinsey Digital

Any good B2B or B2B eCommerce company, at its core, is a company that sells high-quality products (that are not damaged) that are safely delivered to the customer on time.

And so, good inventory management can help improve customer satisfaction, customer loyalty, and ROI.

Now let's look at a few examples to understand how Coca-Cola and Shell used AI-powered inventory planning to deliver a seamless experience to their customers.

Example 1: Coca-Cola’s AI-driven inventory placement

Coca-Cola used Salesforce to develop an app that increased inventory efficiency.

Instead of having a large staff checking stock levels of coolers, Salesforce's AI technology, called Einstein, can see the stock level by taking a picture.

Here’s how Coca-Cola began implementing AI suggestions:

Coca-Cola used Salesforce to develop an app that increased inventory efficiency.

Instead of having a large staff checking stock levels of coolers, Salesforce's AI technology, called Einstein, can see the stock level by taking a picture.

Inventory management using AI

AI helped them calculate how a certain product will perform at a specific location. The technology can:

Take an inventory of the bottles,

Check how many Coca-Cola products are there in the cooler

Recommend the needed inventory based on location and user patterns

The company places less inventory of its Monster energy drink in vending machines at hospitals with emergency rooms since its AI tool recognizes that people rarely buy energy drinks there.

The AI tool also allocated two rows of Minute Maid lemonade beverages for the vending machine of a sports and entertainment stadium in Sacramento where visitors typically drink a lot of lemonade.

In addition to a 15% increase in vending machine transactions, Coca-Cola saw an 18% decline in restocking visits because it stocked the right products in the right locations.

App-based ordering

Using the Freestyle mobile app, consumers can order their drinks ahead of time, pay via their app, and then collect their drinks at a nearby fountain. This provides Coca-Cola with valuable insight into the preferences of their consumers.

AI for app-based marketing

The Coca-Cola company introduced AI-powered touchscreen soda fountains in 2009 that allowed consumers to mix and match flavors from more than 100 different beverages called Freestyle.

Consumer insights are also used to generate geo-targeted marketing campaigns.

Social media marketing

In order to use the mobile app, consumers need to register with their social media account.

Coca-Cola then analyzes their social media content to generate insights about its products' popularity in different areas based on the demographic and consumer behavior data collected by the app.

AI helps them analyze the social media content of consumers via mobile app interactions, creating insights on where, when, and how their products are consumed.

AI Chatbot platform

Through Facebook Messenger, Facebook users can chat with the "vending bot". Based on location data, tone of the conversation, and the consumer's Facebook activity, the bot customizes its dialect and attitude based on the user's personal preferences.

The chatbot adapts its dialect and attitude to each user based on location data, creating a personalized interactive experience.

Example 2: Shell’s immaculate inventory planning

Shell is a leader in oil exploration and production in the United States. But had an inefficient inventory planning system, resulting in a lack of flexibility in its operations.

When they switched to AI for inventory management, BestPracticeAI evaluates the results as follows:

More than 3,000 types of materials are being effectively analyzed using a predictive model in 50 locations.

Inventories analyzed and forecast in 45 minutes rather than 48 hours, a 32X improvement

Millions of dollars in annual cost savings

If you want to improve inventory management in your eCommerce business, here are a few AI-based inventory and warehouse management systems you can use.

Top 3 Inventory Management Tools that use Artificial intelligence


Intellify's AI-Powered Inventory Management AWS Solutions Consulting Offer

Amazon’s Intellify helps improve your inventory health by taking the guesswork out of your inventory management system.

It offers API or batch integration to simplify interaction with your existing enterprise resource management (ERP), business intelligence (BI) systems, and inventory systems.


Pluto7 by Google

Pluto7 claims to accurately forecast demand weeks and months in advance.

It is a software as a service (SaaS) that uses machine learning to provide the most accurate inventory forecasts for small and midsize businesses.

The drive toward Google Cloud Platform was to get beyond performance bottlenecks and leverage Google machine learning on a cloud platform that scales and is cost-effective.

— Salil Amonkar, COO and AI/ML Professional Services Leader, Pluto7


IBM Supply Chain Control Tower

IBM Sterling Inventory Control Tower is an AI-powered inventory control system that tracks and monitors your end-to-end supply chain network.

It provides insights on inventory location, identifies impact, predicts disruptions, and recommends actionable workflows to mitigate the effects. It also offers smart integrations to connect existing inventory solutions and ERP systems.

Some companies allow you to access your inventory and order management software from your mobile devices.

While most functionalities would be limited to desktop users, you would still be able to track orders, view inventory, or make changes using your dashboard.


Improve search accuracy with AI


Nearly 40% of customers go straight to the search bar when they land on the site, so this is their first impression.

— Forrester

When a customer searches for something on your search bar it means that they know exactly what they want. This means they might as well buy that product if they find it on your online store!

Would you let them go or help them find it ASAP?

Online retailers must optimize their internal site search if they don’t want to lose customers.

Artificial intelligence can help you optimize your site search function to offer your online customers more accurate and faster search results.

Personalization of search results in eCommerce


Online eCommerce stores cannot simply stop at offering a site search bar. Online shoppers want a high-quality search experience, or you will risk losing them.

68% of shoppers would not return to a site that provided a poor search experience

Forrester Research

80% of consumers are more likely to make a purchase when brands offer personalized experiences

Epsilon Research

77% of consumers have chosen, recommended, or paid more for a brand that provides a personalized service or experience.

Forrester Research

On average, 71% of consumers feel frustrated when their shopping experience is impersonal.

Segment Research

Every user would see unique search results when they use an AI-powered Mobile Commerce platform

Optimizing your site search can give you a competitive edge compared to other eCommerce websites. Even giant B2B eCommerce companies like Alibaba use AI in eCommerce to better understand customer behavior and use different AI algorithms to better their customer experience.


Improved search accuracy is nothing but an improvement in the customer journey. When you do this well, it can result in a better customer experience, higher conversion rates, and organic brand promotion.


Facial recognition

Face recognition identifies or verifies a person's identity based on their face. There are dozens of applications of facial recognition in our daily lives.

Through facial recognition, we can unlock smartphones, order food from Cali Burger, or use a point-of-sale (POS) machine equipped with cameras to make payments. These POS machines link an image of a face to a digital payment system or bank account.

4 Distinctive uses of implementing Facial Recognition in Mobile Commerce


Reduced cart abandonment rates

Cart abandonment rates across all industries average 69.57% and mobile abandonment rates are even higher at 85.65%.

Any interruption in the shopping process leads to a potential buyer rethinking their decision. However, face payment technology reduces the chances of these transactions falling through due to interruptions and delays.


Streamline online transactions

Many eCommerce sites have already adopted facial recognition technology. Over 500+ stores in China allow customers to pay using software like 'Smile-to-Pay'.

This software by Alipay is a financial label by Alibaba, and is about the size of an Apple iPad!

Wedome bakery based in Beijing, China incorporates this specialized facial recognition point of sale system to run a seamless transaction process.

I don’t even have to bring a mobile phone with me, I can go out and do shopping without taking anything.

— Bo Hu, chief information officer of Wedome bakery

Facial recognition technology allows eCommerce stores to accept online transactions, eliminating the need for cash, wallets, and even mobile phones.


Maximize payment security

While most eCommerce sites offer secure payment options, scammers still find ways to defraud the payment system somehow. Facial recognition technology also helps retailers spot shoplifters in a physical store.

Using facial payment technology, however, eCommerce sites can secure payments.

When using real-time identity verification when users make payments, facial recognition reduces the probability of a fraudulent transaction.


Prevent Security Issues On Websites

The majority of online accounts require users to keep track of their passwords. Face recognition technology, however, makes it possible to access online accounts without remembering passwords.

Clearly, there are various implementations of facial recognition to enhance customer experiences. This shows that using facial recognition is always a good idea.

Example 1: MasterCard identity-check

A MasterCard Identity Check Mobile app uses fingerprints and facial recognition to verify online payments, circumventing the need for passwords.

A U.S. trial found that 86% of respondents found the app easier to use than passwords. According to MasterCard, the app is available in 14 countries and has recently expanded to Mexico and Brazil.

Example 2: Ubamarket app’s smart facial recognition

Ubamarket is a UK-based tech company that provides shopping apps for retail stores. It allows users to scan items as they shop, earn loyalty points automatically, and check out without standing in a queue.

The app's most recent addition is facial recognition, which detects the age of shoppers when they pay for products like alcohol, which has a legal age limit.

However, people aren't always comfortable with this technology as it raises a lot of privacy concerns.

So if you’re planning to implement this into your mobile commerce app or eCommerce website, you may have to wait or provide alternate options like a fingerprint scanner or the plain-old cash and card payment options as well.


Emotion recognition

Emotion detection and recognition technology is similar to facial recognition but is more intense. Emotion AI combines the knowledge of artificial intelligence (AI) and psychology to detect emotions.

Illustration showing emotion AI

Why use AI emotion detection?

An advanced M-Commerce site or app that uses AI for emotion detection can understand customer behavior and feed it to an algorithm that detects how you interact with a product.

Emotional AI allows businesses to capture the emotional reactions of customers in real-time by:

Analyzing facial expressions,

Analyzing voice patterns,

Tracking eye movements, and

Quantifying neurological immersion levels.

This ultimately leads to an improved understanding of what customers like. You can then use it to get a sense of whether or not you must recommend similar products or move on to something else.

Hundreds of firms around the world are working on emotion-decoding technology, in an effort to teach computers how to predict human behavior.

American tech giants including Amazon, Microsoft, and Google all offer basic emotion analysis, while smaller companies such as Affectiva and HireVue tailor it for specific sectors such as automotive, advertisers and recruiters.

— Financial Times

Marketing depends heavily on emotions to influence customer behavior. Customers are more likely to remain loyal to brands if they relate them to positive emotions rather than negative ones.

Using a system that incorporates cognitive and emotive reasoning has helped eCommerce businesses in improving customer satisfaction.

Example 1: Walgreens’ cooler screens

In order to track shopper behavior, Walgreens uses digital cooler screens. This system uses cameras, motion sensors, and face recognition software to target advertisements on the screens in real-time.

In order to customize your ads, it uses information such as your age and gender, as well as the current weather conditions.

Example 2: Kellogg’s emotionally impactful ads

Global food manufacturer Kellogg's is using emotion recognition technology to brand and advertise its food products.

When multiple versions of an advertisement are shown to viewers, face recognition software is used to analyze their emotional state. Based on these insights, the company then designs an advertisement that garners the desired level of engagement.

Example 3: Humana’s emotion-based customer service

The health insurance company, Humana, is using AI in emotional recognition (analyzing voice patterns) to help their call-center agents to deliver a better emotional experience to customers.

Agents receive real-time voice emotion analysis in the middle of a call, enabling them to determine if the customer is feeling frustrated, sad, or happy at any given moment.

Furthermore, it offers useful suggestions such as altering their tone of voice, speaking faster, and displaying empathy if needed, in order to turn the call around and provide a better customer experience.

Example 4: Amazon’s customizable computer vision (CV) service

Amazon Rekognition API is a simple, easy-to-use API that can quickly analyze any image or video file that’s stored in Amazon S3. It can identify objects, people, text, scenes, and activities and also detects any inappropriate content as well.

The challenges of using Emotion AI

However, emotional AI is prone to bias as it is subjective; in addition, AI is unaware of cultural differences, which makes it more difficult to forecast emotions accurately.

Various technologies associated with emotion, voice, biofeedback, and neuroscience raise ethical issues relating to privacy. In addition, the question to what extent public places are protected, as opposed to private ones, is speculated under current privacy laws.

And the moral dilemma is whether machines should determine how humans will react, especially without our permission.


Marketing and sales automation

AI in sales helps meet customer expectations and increase sales. Don’t believe me? Here are some statistics that show how AI impacts eCommerce businesses:

79% of marketing and sales teams have seen revenue growth due to the adoption of AI, according to the McKinsey State of AI in 2020 report.


As a result, early AI adopters have seen higher customer satisfaction and an increase of up to 10% in potential sales.


53% of B2B companies use marketing automation technology today, and 37% plan to use it in the near future.


Most eCommerce businesses already use sales and marketing automation. But now they are moving to a mobile-first approach. This means delivering contextual, personalized, interactive messages on mobile devices.

Mobile marketing and sales automation tools have evolved in response to the evolution of businesses, consumer behavior, and competition.


Ideally, mobile sales and marketing automation platforms will have the same features that other automated platforms offer, but they will be tailored to mobile environments. Ultimately, this can boost productivity and increase resource utilization.

Your online store probably already includes sales and marketing tools. The only thing left is to integrate it with a mobile app on your smartphone.

Example 1: Cisco’s automation success

Cisco implemented marketing and sales automation to:

Dedicate employees to driving engagement and results instead of working on customer cases

Intelligent routing of cases and automated processes to match cases with the right agents

Reduced the need for human agents

The automation resulted in:

Increased customer engagement by reallocating 60% of back-office staff to customer outcomes

Reduced the number of alt tabs per day by more than 1,000 by consolidating customer service tools into one pane

88% of orders are fully or partially automated so that no human interaction is necessary

Example 2: Kellogg’s emotionally impactful ads

Despite generating a massive number of leads from its marketing efforts, McAfee's sales team was concerned that the leads weren't of high enough quality.

Using marketing automation, McAfee implemented a scoring system and created a nurturing program for prospects that delivered the right information at the right time. As a result, the lead quality passed on to the sales team was greatly enhanced.

Through this new automated system:

Despite a reduction of 35% in leads, the overall quality of leads improved.

Conversion of leads into opportunities increased four-fold.

There was a significant improvement in the alignment between sales and marketing.


Dynamic pricing

The concept of dynamic pricing involves selling the same product at different prices based on the current or real-time market conditions.

You can use AI to personalize your pricing based on the current users on your website and their behavior. If your competitor’s store has run out of stock, you could even increase prices on your website.

On the other hand, if your sales have been low, you can improve conversions with lower pricing or discount models.

Dynamic pricing leads to higher eCommerce revenues as their products can be priced based on market trends, sales volume, and competition.

AI-powered eCommerce platforms allow pricing to be dynamically adjusted with the most appropriate prices.

Example 1: Amazon

Amazon's pricing model reflects this as it continuously changes based on market trends, competitor pricing, and customer behavior.

If there’s a sale there would be a huge discount on products, right? But consider product prices on non-sale days, even there Amazon has different prices based on demand or season.

By doing so, Amazon is able to sell more products at a higher profit on its website and app.

Example 2: Uber’s dynamic pricing

Because of Uber's dynamic pricing algorithm, when you request the same trip today the price might be different from what you paid a few days ago.

Its algorithm takes into account factors such as the number of requests for rides, the time of day, events, etc. If the online behavior shows that particular places and times are preferred by consumers, they may ask for more at those times. This leads to higher prices in peak hours.

Using ML, Uber generates a forward-looking forecast of a variety of market conditions and uses a system that is highly sensitive to external factors, including global news events, weather, historic data, holidays, traffic, etc.

The LSTM (long short-term memory) network lets Uber predict future prices based on past data. A deep learning model is used to make predictions before unaccounted events and future market conditions take place.


Retargeting potential customers


In the United States, 71% of mobile sales happen in-app, and advertisers with a shopping app generate 68% of transactions on mobile devices.

— Criteo

Users often abandon apps after initial installations, making it difficult to keep them engaged. App retargeting, on the other hand, enables you to get people to download or engage with your app again by reaching them across multiple channels.

By analyzing past purchases, machine learning can predict future buying patterns and optimize remarketing campaigns.


90% of the users switch between screens to complete a task.

— Google

Let's consider a scenario where Jane uses your application to look for brown leather motorcycle jackets.

When Jane browses the app, her intent is captured. When she becomes distracted, she starts playing a game.

An ad is generated in real-time to target Jane based on her purchasing intent determined by cookies from her search history.

On the other hand, Jane can now see ads for brown leather motorcycle jackets and other similar products on her gaming app too. If she clicks on the ad, she will be taken straight to the app to complete her purchase.

Dynamic pricing leads to higher eCommerce revenues as their products can be priced based on market trends, sales volume, and competition.

AI-powered retargeting platforms analyze real-time user behavior to find out which users are most likely to convert. Based on this information, marketers can target their marketing efforts at the most valuable customers.

Your users engage with your app multiple times until they complete a purchase. And then retargeting ensures that they come and buy from you again.


Omnichannel personalization


Companies that have been able to personalize their customers' experiences across multiple channels have reported revenue increases of 5-15%.

— Mckinsey

Omnichannel engagement takes many forms. One way to think about it is to put yourself in the customer’s shoes.

Take, for instance, you have a problem with a mobile device that you pre-ordered from a brand before its official launch date. This means, only the company can solve your problem right now, and nobody else.

So what do you do?

You might send out an email to the customer service. But that would take time.

So you log on to the website on a phone or a laptop or you’ve used voice search.

When you’re on the website, a chatbot greets you. You explain your situation.

You may want to talk to a human support staff member who asks you to visit the nearest store with your defective product.

Again, at the outlet, you have to explain yourself to the staff and finally get your device repaired. But after a few days, you see that the same problem persists. Now you have to go back to the chatbot on the website, and explain the whole story again - and the cycle repeats every time you face an issue.

Now if I were to ask you about your experience, what would you say? Did you absolutely love it?

Of course not!

What if you didn't have to explain your problem so many times, to so many people on so many different platforms? This is where omnichannel engagement could have helped you as a customer.

Omnichannel personalization provides a consistent and relevant customer experience across all online platforms where your customer exists.

Using an omnichannel AI approach, every touchpoint of your customer journey across different communication channels would be tracked and saved, including your past purchases and all the problems you have faced so far.

Today, a few companies use virtual assistants to build holistic customer experiences across channels so that they don't have to repeat their stories to different representatives of the company.


Example 1: Pega's intelligent virtual assistant

Pega is an AI system provider that lets you seamlessly move customer conversations across channels and devices without losing context.

Pega Intelligent Virtual Assistant is an artificial intelligence (AI) powered bot that creates an omnichannel dialogue with your customers. It can easily turn applications into smart assistants on any channel – from SMS and email, to Facebook, Alexa and more.

Customers and employees can use it as a banking bot, a mobile service provider bot and the company's internal IT helpdesk bot.

You can watch this video to know more about Pega here.

Example 2: Disney’s omnichannel approach

Disney gives customers the same magical experience online and in the real world with its seamless omnichannel approach to everything.

Be it your pre-booking engagement, your arrival at the airport, check-in at your hotel, or your experience at the Disney theme park, everything seems to be in perfect alignment.

With the ‘My Disney Experience’ website or app, you can book and plan your vacation, select restaurants, select visits to attractions, view a map of all the places you want to go, and more.

The My Disney Experience app helps you find and navigate to attractions using GPS and know the approximate wait times for rides. Your mobile device can also act as a ticket to any planned fast-pass attractions.

Disney's strongest unique selling point is its ‘Magic Wristband’, which is both a fast pass and a photo storage device. Every family gets one band which can be used in so many ways.

This waterproof band can be your hotel room key, your ticket to the parks, or as payment at DisneyWorld. You can also order a custom made version to personalize the design.

The wristband also links to the app, so when you take a picture of any attraction or with a Disney character, the picture will appear on your phone.

Through the integration of multiple channels, such as a website, mobile app, and smart wristband into one unified and integrated experience, Disney demonstrates how you can create a memorable seamless experience.

Ultimately, omnichannel personalization helps you create a seamless customer journey at multiple touchpoints. This allows you to provide a unique experience that can drive more growth and create more customer engagement.


Virtual shopping assistant

Virtual reality (VR) and augmented reality (AR) simulate the experience of shopping in a physical store through a virtual environment.

When customers browse through your mobile shopping app or site, a virtual shopping assistant helps these customers through the shopping process without needing any human assistance.


Approximately 8 billion digital voice assistants will be in use worldwide by 2024 (roughly the population of the world).

— Statista

Apple’s Siri was the first virtual assistant to be both highly advanced and commercially available. Following Apple's success, other tech giants like Google (Google Assistant), Amazon (Alexa), and Microsoft (Cortana) created their own virtual assistants.

Virtual assistants are increasingly being used in homes, cars, and in conjunction with numerous smart devices, including smartphones and smart speakers. Today, any eCommerce business can create an effective virtual assistant with the help of a good eCommerce platform.

A virtual shopping assistant is a good option for online retailers because they:

Reduce manual labor on repetitive tasks by solving customer queries

Engage visitors with personalized content, recommendations, and offers

Encourage customers to come back and buy more with retargeting

Detect customer buying patterns, collects data, and personalize the experience

Set you apart from the competition by giving your store a unique personality

Allow you to seamlessly sell and operate on multiple platforms like mobile, website, tablet, or a smart device

Available 24/7 to navigate, help, and direct customers through their eCommerce journey

Virtual shopping assistants are like diligent salespeople, always eager to assist customers to decide on buying a product, hear out their problems, and recommend solutions. They can provide information on orders, answer questions, and provide product recommendations.

Example 1: Sephora’s virtual shopping assistant

Using Sephora's virtual assistant, customers can find the right products while taking their preferences into account. Once the correct product has been identified, the assistant redirects the customer to Sephora's website so the customer can complete their purchase.

In addition to the Sephora Virtual Assistant, Sephora offers virtual assistants that can help customers book appointments with a Sephora beauty specialist and experiment with different makeup color combinations using augmented reality.

Example 2: LEGO’s virtual assistant, Ralph

During the Christmas holiday season in 2017, LEGO discovered that customers were overwhelmed with too many choices when buying gifts. They also feared giving the wrong gifts to kids.

LEGO, with its creative partners at Edelman, developed and rolled out its first bot for Messenger campaign. To assist and enhance the digital shopping experience, LEGO built a virtual assistant called Ralph for Facebook Messenger.


In addition to Ralph's LEGO movie-inspired voice, he used playful GIFs and emojis to make the stressful shopping experience seem simple, entertaining, and relaxed.

Ralph also asks users a series of questions about the recipient's age, personality, interests, and budget before making accurate suggestions based on the answers. It then offers up gift recommendations, where you’d be transferred to the shopping cart on Lego’s shopping site.

Furthermore, a custom API was developed to provide localized stock updates from the LEGO store in real-time, and customers received a free gift and a free shipping code for each order. Then, a single tap would take the customer to checkout on the website.

Three weeks after Ralph's launch, the company reached more than 2.69 million people with over 50,000 virtual conversations across the UK, US, Canada, Germany, France, and Poland.

We are continuously searching for new and fun ways to engage with our consumers and shoppers. Chatbots are increasingly being used by brands to engage with consumers in the digital space. The Lego Group is one of the first in the toy industry to embrace this concept.

— James Poulter, senior manager, digital consumer engagement at LEGO

LEGO’s approach to Voice and Conversational AI had a great impact on sales and marketing:

1.2 million post engagements, with an engagement rate of over 45%

8.4x higher conversion rate and a 65% lower cost per purchase than other conversion-based ad formats

3.4x higher return on ad spend for click-to-Messenger ads compared to ads that linked to the LEGO website

71% lower cost per purchase when clicking through to the Messenger experience compared to ads optimized for clicks

1.9x higher value for website purchases made from click-to-Messenger ads

Ralph then become a permanent chatbot for LEGO


Enrich the shopping experience with VR and AR

Agumented Reality (AR) along with Artificial Intelligence (AI) opens a whole new world of possibilities

AR is a technology that superimposes computer-generated objects (image, text, video) on a user’s view of the real world, thus providing a composite view.

It’s different from Virtual Reality (VR) in which everything that a user sees is computer generated. In Augmented Reality, the technology uses the real world and adds computer-generated graphics to enhance the experience.

AR is expected to become the new human-machine interface, connecting the digital and physical worlds.


In eCommerce and mobile commerce, AR offers many promising applications such as:

Sample digital products before purchasing,

A personalized shopping experience,

User manuals with interactive features and

A better shopping experience, based on better information

As a result, there are fewer returns and cart abandonment rates.

When customers browse through your mobile shopping app or site, a virtual shopping assistant helps these customers through the shopping process without needing any human assistance.

Example 1: L’Oreal’s augmented reality makeup

With AR and live streaming technology, L'Oréal Paris USA brings professional makeup experience into homes with its virtual makeup simulation app.

Makeup Genius by L'Oreal lets you apply preset looks or try specific L'Oreal products in different shades. Each change is applied instantly to your image.

The company also lets customers book a live-streaming appointment with a beauty assistant and go for a digital makeup session. By doing so, they can receive the same personalized service that they would receive in-store.

Additionally, each session's data is recorded and stored to improve future interactions. You can watch it here.

Example 2: IKEA App

The IKEA Place app lets customers visualize furnishings in their space and customize them in real-time. By giving customers this power, retailers can experience a reduction in returns and save on logistics costs.


Example 3: Dulux Visualizer app

With its app, Dulux has attempted to solve a problem that has plagued painting companies for decades - paint testers

The Dulux Visualizer app lets users view individual colors on a wall by pointing their cameras at it. The app reads the edges of walls and applies any color desired by the user.

In addition, Dulux has an in-built tool for ordering paint, with AR's immediacy a nudge to boost sales. You can watch it here.

Current market penetration of M-Commerce

Here’s how mobile commerce is killing it today:

Global M-Commerce revenues are expected to reach $3.56 trillion by 2021 — with a growth rate of 33.8% year over year.

Business Wire

In 2021, sales from mobile retail commerce via smartphones are expected to reach 221.2 billion U.S. dollars in the United States.


Mobile devices accounted for 71% of retail site visits and 56% of online orders in the third quarter of 2020.


In 2024, smartphone retail commerce sales are expected to surpass 400 billion US dollars, nearly double what was forecast for 2021.


Approximately one in five Americans reported using retail apps at least once a week, while most use them at least once a day.


On average, Amazon has 14 million daily active users as of December 2020, making it the most popular shopping app for iPhone in the United States.


As of December 2020, Walmart had almost 7 million daily active users among iPhone owners.


The graph shows US retail revenue from mobile commerce from 2013 to 2020.

US retail revenue from mobile commerce has multiplied over 12 times from $41.71 billion USD in 2013 to $338.03 billion USD in 2020.

Use of mobile shopping apps

49% of U.S. mobile users used an app to compare prices while in-store.


Mobile consumers redeeming coupons came in second place with 40%, while 30% used mobile shopping apps for extra information on products.

— Statista

Use of artificial intelligence in mobile commerce

AI use cases in consumer goods and retail industry worldwide as of 2020

Globally, AI is being used for customer care, quality control, and inventory management as the three most popular applications. m-commerce respondents from the consumer goods and retail industries say that artificial intelligence can help improve customer care.


48% of respondents from the consumer goods and retail industries say that artificial intelligence can help improve customer care.

— Statista

Predictions for the future of M-Commerce

Here’s what the future of mobile commerce holds:

The mobile commerce market is expected to grow 25.5% CAGR from 2019 to reach $488.0 billion by 2024, or 44% of eCommerce.

Business Insider

72.6% of internet users worldwide will only access websites with mobile devices before 2025.


U.S. mobile payment transaction users are projected to reach 80.1 million by 2023.


Approximately 187.5 million U.S. users will have purchased at least one item using a mobile device's browser or app by 2024, up from 167.8 million in 2020.


U.S. smartphone retail e-commerce sales will surpass $432 billion by 2022, up from $148 billion in 2018.


How Kohl’s is killing it with AI in mobile commerce!

Kohl’s is one of the largest department store retail chains in America. To achieve its goal of becoming the ‘most engaging retailer in America,’ Kohl's has one clear objective: to make shopping at Kohl's as easy as possible, whether in-store or online.

Kohl’s is committed to providing our customers with an easy, convenient shopping experience in a way that is personalized and engaging – no matter how each customer prefers to shop.

— Kevin Mansell, Kohl’s chairman, CEO, and president

In September 2019, the company relaunched its mobile commerce site, aiming to streamline the buying process for online as well as in-store customers. And from that time onwards they have seen steady growth in digital sales year over year.

The new consumer-focused initiatives include:

BOPUS (Buy Online, Pick Up in Store) functionality


A better mobile payment experience (including Apple Pay)

Online Visa Checkout

Delivery on the same day in certain areas

Savings wallet to track Kohl’s cash (shopping reward points)

Barcode scanning functionality

Geo-targeted push notifications for in-store customers

Complete WiFi access in store to gather concrete data about consumer behavior

This is how it works:

Consumers will receive exclusive content and offers in exchange for connecting to the Kohl's network. The retailer will use this data to gather insights into consumer behavior and preferences, which will help it improve in-store experiences and products in the future.

The company did not disclose its exact figures, but overall online traffic, conversion rate, and average order value increased after the digital relaunch.

Kohl’s total revenue

$2.43 billion in Q1 2020

$3.89 billion in Q1 2021 (60.1% increase from 2020)

Online sales acceleration after relaunching the mobile app-

24% in Q1 2019

60% when Covid-19 started

Overall revenue generated through online sales

24% in 2019

Order fulfillment through curbside pick-up

35% in 2019

40% in Q1 2020 (Overall 15% of online purchases)

Sales completed on mobile devices


50% of which

70% on smartphones (16 million app users)

30% on other devices

Highest digital sales in the department of

Home goods by 50%

Kohl's shopping app accounted for more than a third of its digital sales, which it attributed to growing app users by 21% year over year in Q1 and improving app conversion rates.

A net income of $14.0 million was reported in Q1 2021, as opposed to a $541 million loss in Q1 2020.

Kohl's has effectively demonstrated that mobile commerce is the future. Such rapid revenue growth year after year proves that mobile commerce isn't just a fad, but an industry that's here to stay.

Advantages of M-Commerce

Your customer is on multiple platforms and uses multiple devices. And if you want their attention, you have to make a legit effort.

Why is Mobile Commerce extremely important

Mobile commerce is where you have to start. There are many advantages of M-Commerce like:


A faster buyer’s journey

By providing a responsive website, many businesses believe they can give their customers an experience similar to mobile apps. However, this is not the case.

Mobile applications load data and search results 1.5 times faster than desktop applications. Consequently, they help customers browse and purchase products more quickly on their mobile devices.

In fact, mobile app users make twice as many purchases as mobile website users


Improved accessibility and reach to customers

A study found that 97% of all Americans own a personal cell phone. This provides businesses with the perfect opportunity to directly connect with consumers, whether or not they have a physical store.

This approach to selling allows companies to deliver constantly updated content, products, and services directly to mobile users. Buyers can purchase goods and services directly from their mobile devices from anywhere, at any time. Additionally, mobile devices account for over 40% of B2B eCommerce sales.


Delivering the right message at the right time

M-Commerce applications allow businesses to reach customers with the right message at the right time. 61% of smartphone users like personalized offers.

So, if you send them a push notification letting them know when an out-of-stock item is being restocked, they will enjoy this. Sending your customers personalized promotions like discount vouchers when they use your M-Commerce app can act as the last nudge to convince them to complete a purchase as well.


Easier marketing and remarketing opportunities

80% of B2B buyers use mobile devices at work, proving how mobile media can influence a buyer's decision-making process, hence the value of developing a mobile-first marketing strategy.

Mobile commerce allows you to reach your target audience directly through your app and in turn, reduces your marketing costs. By integrating your app with social media platforms, you can inspire users to spread the word about your brand and share your products with their networks, thereby reducing your marketing campaign costs.


Valuable customer data

Consumer data can be tracked from the moment of product discovery to the moment of purchase using mobile analytics. While you're at it, you can also learn more about the habits and preferences of your audience and gain valuable insights into their purchase intent. Using this data, you can create a personalized customer journey with targeted offers and products.


Better customer experience

It has never been more important for businesses to provide a unique customer experience because of such fierce competition. As a result, mobile app development is increasingly focused on customer experiences. High conversion rates and revenue are a direct result of superb customer experiences.

The Boston Consulting Group reports that 90% of B2B buyers who said they had a superior mobile experience said they would buy from the same vendor again and only about 50% of those who reported a poor mobile experience said the same.

Basically, having a simple, intuitive, and effective mobile e-commerce application will enhance the overall purchasing process.

Disadvantages of M-Commerce

Let's be honest. When we think of B2B sales, most of us still think of traditional ways to do it where sales reps physically go to the customers to convince them to buy a product.

So it is understandable that implementing eCommerce or M-Commerce is a big step for both the seller and the buyer. And for this reason, it is important to understand and work on the disadvantages that come with implementing new technology.

Just like any other technology, trend, or innovation that comes into the market, M-Commerce comes with its own sets of disadvantages too. Some of these include:


Access to technology is required

M-Commerce will not be available to customers without a mobile device. Regardless of whether the device is owned, it must be capable of retrieving and displaying company data, providing product and service information, and submitting an order to demonstrate its usefulness. Using an app will also require users to download and upgrade it in order to gain access to information.


It requires faith in the product

In spite of the fact that augmented reality technologies are now hitting the M-Commerce scene, consumers cannot try out most products before purchasing them.

When consumers receive products and realize that they are not right for them, they may not have a way to return them for a meaningful refund or replacement item. Some products and services will see lower returns than they do with in-person sales unless there is a clear, concise, and valuable return option (or a try-before-you-buy option) available.


There is fierce competition in this market

With M-Commerce, you have a global market where your products and services are up against a huge amount of competition. It's very likely that you'll be competing with a number of businesses around the world that do your exact job.

Even if you are the only supplier in your community, it can take time for sales from mobile commerce to build up. Consumers must find your quality through the white noise of everyone else saying they can do the same thing.


Security Concerns

M-Commerce still faces doubts regarding its security because users worry about their personal information being exposed. There are always concerns about a data breach where their payment information like bank details or credit card numbers will be exposed. And B2B users really cannot risk that. Imagine making a purchase for $100,000 and having your bank details leaked.

To overcome this, your mobile apps need to have accreditations of cybersecurity and payment security from third-party apps facilitating this. For instance, Mcafee, Verisign, TRUSTe, etc. You also need to have payment security accreditations from third-party providers like Visa, AmericanExpress, PayPal, etc.


The mobile payment process is different for B2B

B2B transactions have one thing in common: they're complex. There are large orders, so it's not surprising that transactions are expensive. What's more problematic is that each product needs to be customized for each customer in addition to its price based on the quantity ordered.

As a result of all these complexities, the payment process becomes more complex. Often, B2B buyers request quotes instead of paying upfront, making it crucial to offer them a variety of payment options.

With all of these variables at play, B2B sellers must offer customized choices to their customers so that they can have a seamless purchase journey.

This includes:

Customized pricing catalogs

Enable bulk discounts and other offers

Order reviews before confirming purchases

One-click purchase options for routine options

Multiple payment gateway integrations

Easy and trackable shipping

Basically, the whole point of B2B mobile commerce is to make the customer purchase journey easier and faster. By providing your customers with all of these options, you can do just that!

It's 100% true. Mobile Commerce is simply so much better with AI.

With the rise of artificial intelligence in mobile commerce, more and more businesses are adopting this trend. In this competitive environment, AI in eCommerce and M-Commerce has become vital to creating a personalized experience.

Artificial intelligence is already used extensively by businesses for optimization and suggestion systems. But businesses are increasingly relying on AI for machine learning, chatbots, and many other applications that can create real value for their companies.

And this is possible because the cost of developing and integrating AI technology is decreasing so that even a small business can afford it.

Here are the primary benefits of AI to companies:


The capabilities and applications of AI in online stores are unprecedented and smart eCommerce platform providers can help you leverage this in your own mobile optimized eCommerce stores effectively.

So yes, it's 100% true that Mobile Commerce is simply so much better with AI.

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Jovita Mendonca
Content Marketing Manager, ewiz commerce

Jovita Elveera heads content marketing at ewiz commerce, an AI-powered eCommerce platform. As a software engineer, marketer, editor, and writer, she is responsible for communicating the insights and research around the emerging trends in artificial intelligence in eCommerce and frequently writes about it.