Artificial Intelligence (AI) in eCommerce: Revolutionizing B2B and Consumer Shopping Experiences

Digital commerce was a brand-new way of doing business not long ago. Today, we use more profound technologies to understand customer behavior, enhance their shopping experience, and increase our revenue. All these processes have become easier thanks to the rise of the new AI technologies in tech ecommerce.

From the profound internal analysis to making small actions more convenient and accurate (here we mean virtual assistance and private recommendations), AI tools have entered modern ecommerce and are to develop. Let's see these technologies and how to use them to make your business thrive. 

1. HOW BIG IS THE AI INDUSTRY: STATISTICS
2. WHAT IS AI IN ECOMMERCE?
3. HOW IS AI USED IN ECOMMERCE?
4. EXAMPLES AND USE CASES OF AI IN ECOMMERCE
5. IMPACT OF AI IN ECOMMERCE
6. CHALLENGES AND PROBLEMS OF AI IN ECOMMERCE
7. CHALLENGES AND OPPORTUNITIES OF AI IN ECOMMERCE WITH VIRTO COMMERCE
8. CONCLUSION

According to Tractica research, the AI software market shows rapid growth. In 2018, its volume was $10.1 billion; in 2020, it will reach 22.6 billion US dollars. By 2025, it will reach $126.0 billion, showing at least five-fold growth.   

The impact of using AI technology on the global economy will also be significant.

AI Contribution to GDP (in 2030) Forecast 

• China — 26.1% 
• USA — 14.5% 
• UAE — 13.6%  

As for ecommerce AI use, back in 2022, it was valued at around USD 624.51 million and is forecasted to reach USD 2,530.89 million by 2032.   

Gartner believes that AI will come into maturity within 2-5 years, which means its impact on AI ecommerce will continuously grow.   

Generally, artificial intelligence technologies refer to the systems and machines that can perform the tasks usually completed by people and require intellect. As opposed to automatization (where the machine does the routine tasks per the corresponding rules), AI tries to replicate and simulate human-like cognitive functions to let the machine solve problems, provide reasoning, understand languages, and make decisions.

Key components of AI are as follows:

• Machine learning (ML)
• Natural language processing (NLP) 
• Neural networks  
• Computer vision 
• Robotics. 

The first three components are used in digital AI-powered commerce. 

ML, NLP, and neural networks can make the customers' shopping experience better by understanding their behavior patterns and offering them personalized solutions.

Generally, artificial intelligence ecommerce can be used to enhance either storefront features or back-office capabilities:

1. At the Storefront part, it is about helping customers get a better CX: searching for the right product, assisting in the correct product exchange, and finding similar products—all about personalization.
2. At the Backend part, it is about optimizing processes and making internal work easier. Here, we mean processing documents, optimizing routines via machine learning, and more. Also, all analytics processes provided by AI work in the back end as well: ML and NLP help to analyze customer behavior patterns and better understand what they need. The backend part includes intelligent client segmentation, product processing, and more. Other important ways of using AI at the back end are forecasting, trend tracking, insights, and product clustering.

Thinking of an example to illustrate the successful use of these technologies, the Starbucks app comes to mind. In the States, you can download the Starbucks Reorder skill in the Amazon Skill Store and order a drink with the Starbucks app.

Moreover, Alexa keeps track of your previous orders, and can order your favorite drink or check your Starbucks card balance for you. In September 2019, Starbucks in China launched voice ordering and delivery capabilities within Alibaba’s smart speaker, Tmall Genie, allowing consumers to use voice to order their favorite drinks and food for delivery within 30 minutes. 

The efficient management of internal and external business processes can now also be facilitated with the use of ecommerce AI and B2B procurement marketplaces. For example, one of those applications concerns inventory management, particularly maintaining the right inventory level to meet the market demand. AI analyzes the sales trends of recent years, projects the anticipated changes in product demand, and considers potential supply-related issues.

Enriching data using AI can bring certain benefits, from obtaining clean and consistent data to yielding deeper catalog insights. Imagine a large online retailer like Taobao with hundreds of millions of products that are impossible to track or tag manually. Automated product tagging tailored by AI increases business operation efficiency and ensures a better shopping experience. 

Another AI-based productivity tool is an intelligent image search or even image processing. For example, when a user searches for a particular item of clothing, say, an empire waist dress, which she cannot fully describe but if she sees it, she knows it. By analyzing the images the user looks through, AI can identify exactly what she might be looking for. The technology behind image searches is based on neuroscience. Deep Neural Networks enable AI to see images the way humans do. One of the most famous examples of image processing at work is Pinterest.

Social networks utilize image processing AI to prevent abuse, distribution of pornography, and so forth.

Real estate marketplaces use image processing algorithms to sort millions of images of properties and identify the best pictures that could potentially be sold. For example, AI places a living room picture instead of a bathroom image as the main image for that property. 

Machine translation and website localization are yet other instruments in the AI toolbox. For example, eBay applies automatic machine translation depending on a user’s location by translating a user’s request and responding with relevant inventory from other countries. 

Another way online retailers can benefit from AI is by utilizing dynamic pricing. Instead of looking at your competitor's pricing or other external factors that may influence market prices to determine the best price point, you can now apply a combination of machine learning algorithms to predict prices and establish optimal prices for your products. This way, you have the ability to leverage prices based on data from both internal and external sources. For example, when a competitor's stocks are running low, you can increase prices and vice versa. According to Business Insider, Amazon changes its product prices 2.5 million times daily, making an average product cost change every ten minutes. 

Last but not least, there are customer service tools, utilizing chatbots and AI for ecommerce businesses, and some other instruments that improve the customer experience consistently and continually while reducing the workload and overheads associated with managing customer service personnel. 

AI technology is heavily leveraged in semantic search, ensuring faster query responses. A good example is when the user tries to find a service supplier on the website that does NOT have a fully-fledged catalog.  ‌ 

Another very useful ecommerce application of AI for ecommerce technology is document sorting and "reading" from different emails, excels, PDFs, etc. When the user attaches the docs of the order, AI helps to create a quote automatically by "reading" and sorting out info from the attachments. 

AI technology helps extensively with product recommendations. We can categorize AI records into three clusters:

• Lookalike products 
• Replacing additional cross-selling products 
• Personalized recommendations (history, social profile, etc.) which are sometimes called “might interest you” products. 

Discover Weekly from Spotify is an interesting use case of such a personalized experience. In 2015, Spotify introduced a customized playlist of tracks that a given user will most likely want to listen to. It is carried out with the help of an algorithm that determines a user's taste based on their listening behavior and the most popular playlists among the entire Spotify audience. This is how it works: a user listens to tracks and saves songs, allowing the engine to develop the user's taste profile; meanwhile, billions of other users create their own playlists, which the engine keeps track of and identifies those that might suit the user's taste profile but which they have not yet listened to. Moreover, if the user fast-forwards a song within the first 30 seconds, the engine interprets such action negatively, allowing it to learn and adjust in the future. 

One of the use cases of utilizing AI to enhance the consumer shopping experience is that of the Zara online store, where to minimize customer returns, an algorithm suggests the right clothing size based on a user's measurements and style preferences. 

Most of the huge market players use AI for ecommerce in their sales. Let’s look at some examples:

1. Its cashier-less stores, Amazon Go, uses computer vision and sensors for a seamless shopping experience. 
2. Alibaba's ecommerce platform uses AI for personalized shopping recommendations and chatbots for customer support. 
3. eBay utilizes AI for search and product recommendations, helping users discover relevant items more easily. 
4. Walmart uses AI for demand forecasting, optimizing inventory levels, and improving the supply chain. 
5. Sephora's Virtual Artist app employs AI to enable customers to try on makeup products virtually.

With AI's insights and capabilities, modern businesses can reach their ecommerce AI excellence pretty fast. Here are only a few examples of how AI can positively reshape ecommerce and lead it to market leadership:

1. Shorter and more automized processes lead to faster operations and more sales.
2. Less human errors provide more productive time, better customer experience, and almost no idle time.
3. The most important thing is that AI helps to shorten the time people spend on a particular platform. With the new opportunities, people can spend more time completing other important tasks rather than struggling with purchases.
4. AI improves the LTV of buyers as it helps to understand real client needs and provide perfect offerings, enhancing their loyalty. 

Forbes, Gartner, and other industry analytics leaders claim that AI will reshape the ecommerce market in the future. The rise of AI will lead to more impressive results in client experience, faster operations, and rapid growth of the new technologically savvy companies.

More and more market leaders hire people who know how to work with AI. These facts show that the market disruptors will soon be those companies that started with digitalization and AI implementation early. 

There is a common misconception that associates artificial intelligence with software engineering. The association is inherently incorrect. While AI certainly draws upon computer science, information engineering, and mathematics, it is also based on statistics, economics, psychology, linguistics, and philosophy, among others. The AI subfields based on technical considerations and tools include machine learning and artificial neural networks. At the core of the field lies data science rather than software engineering. The lack of being able to differentiate between the two can be off-putting or even lead to a choice that does not yield the maximum benefit to the business.

All challenges, however, come with new opportunities and solutions. To help your team change the way they think about AI and its adoption, you need to educate them: start with yourself, and once you have some knowledge, it will be easier for you to manage the expectations of others and help people learn more about AI.

Because AI is inherently different from software engineering, so AI adoption requires a set of other competencies, management skills, and qualified teams. This can be solved with a clear strategic approach with measurable objectives, KPIs, and ROI.   

After determining your business needs and goals, you need to consider other variables such as data storage, data infrastructure, labeling, and ways of feeding the data into the systems. Then, there is research, model training, testing, data sampling, feedback loop creation, and performance assessment. Then, even after successful integration, you still have to train people to use the model and interpret the results. Not surprisingly, all the above requirements make business owners wary of investing in AI in order not to fail.

Those issues can be addressed by finding a reliable AI vendor and extensible commerce platform or hiring an experienced data science team. By working closely with the experts and having a clear-cut strategic approach to AI integration and implementation, as well as having reasonable expectations, the risk of failure is mitigated. 

While large enterprises struggle to find field specialists, small businesses struggle with the inadequacy or scarcity of available data. Even huge corporations like Facebook, Apple, or Microsoft compete for top talent, not to mention other businesses, where management usually lacks the technical know-how necessary to assess the expertise of the hired personnel. This can be addressed either by outsourcing a data team with a solid portfolio and relevant experience, by outstaffing, or by hiring professional recruiters specializing in top tech talent.

It is common knowledge that the built system is only as good as the data provided. AI systems require massive training datasets. The biggest question for small businesses is where to get that data. When faced with that problem, you need to understand what data you already have and what else the model requires. The missing part might be publicly available, or you may have to buy it from a third party. However, some data might still be hard to obtain or unavailable. If that is the case, there is still something you can do – use synthetic data. Synthetic data is created artificially based on real data. It is especially useful when there is not enough real data to train the model. Other solutions include using RPA robots to scrape publicly available data or a Google dataset search. 

Though this is one of the most important concerns about artificial intelligence in artificial intelligence ecommerce (frankly, like all AI concerns), it is hard to imagine that AI will be able to fully replace people in ecommerce. This is a perfect tool to enhance processes and make them easier, but people are still a core part of the whole process.

While people more and more often prefer self-service, there is still a need for human interactions during customer support, product processing, and control, work with documents, etc.

All in all, the current level of AI development is all about helping people but not replacing them. 

All in all, AI for ecommerce is the next native step after automation and digitalization, bringing many benefits of artificial intelligence in ecommerce. With the implementation of modern solutions like Virto Commerce, businesses inevitably will need to move further in their technological growth. The next step is choosing the best B2B ecommerce platform and AI implementation, and the Virto Commerce B2B ecommerce platform is fully ready to assist with this.

Virto offers two main modules and examples of AI:

• One is available publicly and allows to send POs via email. You can check out the code here: https://github.com/VirtoCommerce/vc-module-ai-document-parser#readme  
• Another is a private repository that allows for creating Generative AI to generate summaries and descriptions for a product in multiple languages.

Virto can also work on additional sample use cases:

• Product data attributes harmonization (when data from multiple suppliers comes with different values, the system can harmonize it)  
• Extraction of missing attributes from the product description

Virto currently offers integrations with Azure AI Document Intelligence Service, Open AI ChatGTP, Azure AI, and any model from HuggingFace AI. In other words, Virto Commerce solutions can integrate with any third-party AI technology via API, which means that whatever innovation you need, you will always have it thanks to Virto’s API-first and Virto Atomic Architecture ™ approach. 

As AI integration naturally comes after digitalization, businesses have to discover the topic now and plan for their future integrations. To enhance your ecommerce with AI and reduce any implementation risks, it is better to start by analyzing your needs and choosing the solution that will support any AI integrations in the future.

Choosing Virto Commerce is a perfect step in this way. If you want to learn more about the solution or find out what your first steps should be in the AI adaptation journey, reach out to our teams or request a free demo.