eCommerce artificial intelligence is a term that's taken on new life, thanks to chatGPT and the rush of generative AI, all ready to transform the world of online retail.
“There are two streams for businesses when it comes to AI - one, solve existing problems with emerging technologies like generative AI, and two, innovation - leveraging AI to explore possible new avenues of growth,” says Soundararajan Velu, CPO of AI tech company, Argoid.
Most businesses across the spectrum are drawing on emerging AI to help solve problems and ecommerce is no exception. Business owners want to differentiate, and eCommerce AI seems like the logical next step, as it offers a (much-needed) competitive edge.
There's enough data to back up the role of AI in eCommerce:
- The global artificial intelligence (AI) software market will reach 126 billion U.S. dollars by 2025:
- By 2025, 95% of all customer interactions will be powered by AI.
- By 2030, AI-powered eCommerce solutions will be worth USD$16.8 billion.
- 78% of brands claim to have already implemented or are planning to implement artificial intelligence and virtual reality.
- 40% of online buyers don't mind seeking discounts and shopping deals from chatbots.
On that note, let’s decode eCommerce AI, with help from experts, and dive deeper into:
- What is AI in eCommerce and why it is important
- How AI has changed eCommerce
- How AI is Used in eCommerce
What Is eCommerce in AI— And Why Is It Important?
AI and eCommerce are a powerful combination that is transforming the eCommerce industry, from personalizing the shopping experience to optimizing supply chain management. AI-powered tools like recommendation engines, chatbots, and predictive analytics allow retailers to create a more seamless and efficient shopping experience for customers while driving business growth.
AI is the 'smart brain' working behind the scenes to help online retailers “eliminate ‘junk work’ or repetitive website tasks, freeing humans to focus more on business creativity. AI holds the promise of enabling site designers to present curated experiences to shoppers as individuals,” states James Tenser, Retail Tech Marketing Strategist and President, VSN Media LLC.
For customers, AI is the digital equivalent of a super-smart shopping assistant—one who can help users find the perfect product, suggest complementary items, and more.
Retailers are leveraging AI to:
- Understand their customers better by analyzing the user's browsing and purchase history
- Empower customers to leverage intelligent, personalized search for eCommerce
- Drive personalized product recommendations based on data
- Make informed sales and marketing decisions
- Roll out targeted marketing campaigns
- Optimize the pricing and supply chain strategy, and manage inventory more efficiently
- Predict when a customer is likely to churn or abandon their shopping cart
- Sell more products and drive revenue
Furthermore, VR & AR are emerging areas that can transform ecommerce when blended with generative AI tech.
But how exactly does ecommerce AI work?
Within ecommerce, AI generally involves machine learning, natural language processing (NLP), computer vision, data analytics, and deep learning. Here’s a brief look at what they mean.
- AI algorithms in ecommerce rely heavily on data analytics. This involves collecting and analyzing customer data, such as purchase history, browsing behavior, and demographics, to understand their needs and preferences. This data is then used to personalize product recommendations and marketing campaigns.
- Machine Learning (ML) involves training a computer program to learn patterns in data by using statistical models. These models then enable the program to make predictions or decisions based on new data.
- Natural Language Processing (NLP), a subfield of ML, focuses on analyzing and understanding human language. In ecommerce, NLP is used for content curation and cleansing, to build chatbots that can communicate with customers and answer their questions. It’s also used in smart search engines to improve queries and results.
- Computer vision, another subfield of AI, focuses on analyzing images and videos. In ecommerce, It is used to recognize products in images, which can help with product attribute recognition, inventory management and better customer experience.
- Deep learning algorithms can learn complex patterns in data and are used for tasks such as image recognition and natural language processing. In ecommerce, deep learning is used to build recommendation engines that can provide highly personalized recommendations to customers.
The 360-Degree Benefits of eCommerce AI
Not all digital retailers are sold on the idea of eCommerce AI, however, as not everyone understands its potential, and there are quite a few instances of AI gone wrong.
Mr. Tenser believes the AI hype still precedes its practical application but doesn’t think it will be very long before “we see a spread of AI-enabled personalization – of assortment curation, digital offers, and shopping assistance.”
Dan Grey, CEO of Vendry, is convinced that there are multi-faceted benefits in using AI for eCommerce. He says “AI will have massive implications for ecommerce, especially for retailers with large numbers of SKUs. This technology will make it easier to:
- Develop unique content
- Create more sophisticated search algorithms
- Recommend products based on large swaths of complex data”
Digging deeper, there are several more key benefits of AI in ecommerce to be found. Let’s take a look:
1. Hyper-Targeted Marketing
AI can help eCommerce players establish a 1:1 relationship as consumers want personalized and relevant messaging. While “hyper-personalization has tremendous potential to impact the customer experience and conversion rates, it’s not a one-size-fits-all solution. Different demographics respond differently to a brand or manufacturer 'knowing them' on a granular level. Also, younger consumers expect more in return for their data collection,” according to Sanford Stein, Forbes contributor and founder of Retail Speak on LinkedIn.
2. Personalized Customer Journey
eCommerce AI uses machine learning algorithms to analyze customer data and behavior to personalize their shopping experience. It can recommend products, suggest sizes, and even predict what customers may want to buy next. Says Mr. Tenser, “Personalization at scale is possibly the biggest idea in retailing. The mass marketing pendulum – CPM-based advertising and 'stack-em-high-watch-em-fly' merchandising – has swung back toward one-to-one shopper interactions.”
3. Brand Reputation
With AI, eCommerce businesses can look at large data sets and acquire USP/value propositions in the form of smart customer service, tech-savviness, personalization, and more. This improves brand reputation, and inspires word-of-mouth marketing.
5. Seamless Automation
AI can orchestrate eCommerce workflows and make decisions that conform to business policies and internal guidelines. It can automate end-to-end processes and cut down dependence on human resources as well as reduce human error. However, Mr. Tenser cautions against assuming AI is a magic way to eliminate headcount and save money. Instead, be ready for the transformation in processes, and invest in training the workforce, he says.
AI in eCommerce: Popular Use-Cases
Interestingly, one of the first AI applications that came to most of our experts’ minds was customer service chatbots. Though they have some way to go in terms of finesse, they are the most visible and customer-facing AI application at present. According to Mr Tenser, chatbots are “pretty crude so far – scarcely better than FAQs – but they will likely improve as the systems ‘learn’.”
Another most commonly quoted AI application is the virtual store experience with try before you buy features. Jon Morgan, CEO of Venture Smarter talks about how “AI-powered virtual try-on technology can help customers try on clothes virtually, reducing the need for physical try-ons and returns. In the food industry, AI-powered recommendations can help customers find new and exciting recipes to try.”
Adam Stewart, digital marketing consultant at Digital Bond Marketing calls out three more interesting applications:
- Dynamic pricing: AI algorithms that can adjust prices in real-time based on market trends and demand.
- Visual search: AI-powered image recognition that enables customers to search for products using images.
- Fraud detection: AI that can identify and prevent fraudulent transactions by analyzing patterns in customer behavior.
Let’s examine a few of these in detail.
Use-Case #1: Smart Product Recommendations
AI can make smart product recommendations, either on the website or through marketing channels, and deploy strategies like upselling, cross-selling, and down-selling to increase revenue.
Amazon's recommendation engine is worth looking into. The platform leverages AI algorithms to recommend products based on a customer's purchase history, browsing behavior, and other relevant data.
“Curated digital experiences will very rapidly become the norm, as digital retailers become adept at using AI to tailor assortment presentations on the fly, based on first-party and zero-party data volunteered by shoppers,” says Mr Tenser.
Use-Case #2: Dynamic Pricing
Personalized pricing – in the form of promotional offers – are already beginning to eclipse mass consumer promotions. AI can monitor buyer behavior and use smart pricing models to implement dynamic pricing to improve revenue potential. It can also encourage users to increase the average order value for benefits (such as free delivery or new user discounts) depending on the spending capacity of the buyer - that's artificial intelligence at its best!
The simplest instance of this is identifying first-time visitors to drive new purchases:
Use-Case #3: Fraud Prevention
The eCommerce sector is a major target for cybercrimes. AI can be an excellent tool to verify shopper identity and prevent fraud by detecting anomalous patterns and flagging suspicious activities.
Dhaval Sarvaiya, Co-Founder of Intelivita, says,
"By leveraging machine learning algorithms and predictive analytics, to analyze vast amounts of data in real-time, retailers can quickly identify and prevent fraudulent activity, saving both time and money. As fraudsters become more sophisticated, AI-powered fraud prevention systems provide a critical line of defense to protect both retailers and their customers from financial loss and reputational damage."
Use-Case #4: Virtual Assistants for Customer Support
Virtual assistants such as AI-powered chatbots can conduct end-to-end buyer interaction, starting with recommending products based on user inputs to accepting payments, to offering customer support.
Nordstrom, like a number of other retailers, uses a chatbot to provide instant, useful assistance to customers:
Gokul Muralidharan, CEO, Argoid, believes customer service chatbots are going to make a significant difference by solving customer queries efficiently. He is confident “we’ll soon reach a point where one cannot tell the difference between a human or a bot responding!”
Use-Case #5: Sales, Demand, and Revenue Forecasting
By crunching multiple variables (think: historical sales and demand data, market trends, etc.) to work out the expected peaks and valleys, AI tools can help eCommerce businesses make accurate sales, demand, and revenue forecasts.
For example, ASOS may stock up its ‘crochet’ clothing inventory if the demand for it is higher in Singapore during the summers:
Use-Case #6: Intelligent Inventory Management
When it comes to inventory, the offline aspect of retail takes on great significance. AI-enhanced processes make it more accurate, easier and faster. Mr. Tenser describes how AI is empowering field service representatives to be much more effective at stock replenishment merchandising:
“Upon arrival at a retail location – say a convenience store – the merchandiser takes several digital photos of an existing display, transmits them, and receives “next-best-action” instructions within a minute or two. After completing their re-stocking tasks with products from their van, they upload additional images to confirm completion and trigger an automated reordering process for the next visit. The AI-augmented process improves accuracy and efficiency, reduces stock-outs, and enables less-experienced field reps to perform at a higher level.”
AI-driven inventory management tools are in because:
- They can optimize stock depending on anticipated demand - and do it 24x7.
- They can immediately register product requisitions when the need arises.
- They can optimize supply chain management and help businesses better plan their inventory and reduce waste.
Use-Case #7: Synthetic Media Creation
The use of AI in synthetic media creation is a unique opportunity for eCommerce businesses to expedite time to market. From writing product description copy to generating hyper-realistic product images or optimizing existing content assets, AI can double up as a useful tool.
One real-life example of this is the furniture retailer Wayfair:
Wayfair has implemented an AI-powered image generation system that creates hyper-realistic product images from 3D models. This system allows the brand to create product images more quickly and efficiently than traditional photography methods, which can be time-consuming and costly.
The AI system is also able to generate multiple images of a product from different angles and in different settings, allowing customers to see the product in more detail and in a variety of contexts. The generated images are then optimized for use on various platforms, such as the Wayfair website and social media channels.
Possible Risks & Downsides of Using AI in eCommerce
While AI provides significant benefits for eCommerce businesses, Mr. Tenser observes that AI has potential to go off the rails if handled poorly. “There will be some excruciating (meme-worthy) mistakes. But on the whole it should lead to a better total experience for consumers.”
Mr. Muralidharan, on the other hand, believes there are very few AI products that actually work. “Many tech companies claim to be AI, to ride on the hype, but under the hood it turns out to be a simple analytics system or a statistical model,” he clarifies.
Like every new technology, there are potential risks and limitations. From data security to implementation costs, there are a number of aspects to think about. According to Mr. Stewart, there are three main areas of concern:
Data privacy concerns: Collecting and processing customer data may raise privacy concerns and require compliance with data protection regulations.
Implementation costs: Integrating AI solutions can be expensive, especially for small businesses with limited resources.
Over-reliance on automation: Relying too heavily on AI could result in a loss of human touch, which may negatively impact customer relationships.
Mr. Stein adds, “Predictive analytics can begin to anticipate what’s next in a customer’s buying patterns, but it has the capacity to ‘weird’ some people out.”
Let's examine some of these a little more:
Risk #1: Data Security
AI relies heavily on data, which means that there is a risk of data breaches, cyber-attacks, and unauthorized access to sensitive information. This can result in significant financial losses, reputational damage, and legal liabilities.
Risk #2: Accuracy Errors
AI algorithms are only as good as the data they are trained on. If the data is biased or incomplete, the AI system may produce inaccurate results, leading to poor decision-making and a negative impact on the customer experience.
Risk #3: Capital Investment
AI technology can be expensive to develop and implement, which may be a significant barrier to entry for smaller businesses. This can create a competitive disadvantage for companies that cannot afford to invest in AI technology, potentially limiting their ability to compete with larger, more established players in the eCommerce market.
Adoption of commercial AI technology in ecommerce has long been a challenge. Mr. Muralidharan elaborates, “Large companies like Amazon can build this in-house, but small and medium businesses struggle with a two-fold problem - to first find the right AI technology and then adapt because there’s minimal awareness of how AI works or is supposed to work.”
How, then, do we go about implementing it?
Implementing AI in eCommerce - How to take the Plunge
According to Mr. Stein, the ‘how' has everything to do with the retailer’s specific customer service needs, existing tech infrastructure, and identifying the primary areas of friction.
Most experts agree that starting small via pilot or small projects is the way to go - possibly by first identifying specific tasks or processes, and then exploring possible AI solutions or tools.
Mr. Stewart puts this into three steps:
- Assess your business needs: Determine the specific areas where AI can add value to your operations.
- Choose the right technology: Evaluate different AI solutions to find the best fit for your business requirements.
- Prepare your data: Ensure that you have clean, well-structured data to train and optimize AI algorithms.
Mr. Tenser advocates taking the plunge but don’t build your own, he says. “AI platforms will provide all the foundational technology you need. Focus on mastering how to use the tools, not on how to fabricate the tools.”
Daniel Chabert, CEO & Founder - PurpleFire, on the other hand, suggests a more inward looking strategy to implement AI in e-commerce:
- Understand the limitations: It is important to recognize that AI technology cannot provide solutions to all your problems. To be successful, it is essential to understand the capabilities and limitations of AI, and determine which areas can benefit from its application.
- Invest in data science: Investing in data science capabilities such as predictive analytics and machine learning will enable businesses to make better decisions and improve customer experience.
- Monitor performance: Companies should monitor the performance of their AI systems regularly to ensure they are delivering the desired results.
- Stay up to date: AI technology is evolving rapidly, so it is important that companies stay abreast of the latest developments and trends in order to remain competitive. In addition, they should also update their models periodically in order to take advantage of new breakthroughs in the field.
Ecommerce AI: What’s next?
Mr. Sarvaiya, talks about the next best thing in online retail - shopping in the metaverse. He says:
"The metaverse has the potential to completely transform the way we shop, blurring the lines between physical and digital retail experiences.
Imagine browsing a virtual storefront, trying on virtual clothes, and interacting with virtual sales associates—all from the comfort of your own home. The metaverse opens up a world of possibilities for retailers to create immersive, personalized shopping experiences that transcend physical limitations.
However, as the metaverse evolves, retailers will need to stay ahead of the curve and continually innovate to meet changing customer needs and expectations.”
On similar lines, but outside the metaverse, Mr. Muralidharan envisions a mind-blowing futuristic experience with personalization, especially in fashion. “Imagine you’re browsing an online shop looking to buy a T-shirt. Every product page will show ‘you’ as the model, wearing the T-shirt, in your size, in colors and styles you like, giving you a virtual try-on experience in real-time.”
Whether hype or reality, as we get caught up in the generative AI frenzy, Mr. Stein adds a note of caution, “ChatGPT is technology’s current “shiny object.” It’s in its earliest stages and is highly “opaque” which is to say it’s very difficult to determine its efficacy. There are also societal implications of generative AI that can’t begin to be measured.”
Mr. Tenser advises against making AI a headcount-reduction tool. “AI technology is a new type of tool that people control. It is not a substitute for strategic or analytical thinking – in fact it requires a new type of strategic thinking that most of us are barely ready for,” he maintains.
That understood, eCommerce businesses that leverage AI can reap a multitude of benefits to significantly improve operations and increase revenue.
As we’ve seen, AI technologies can:
- Provide personalized shopping experiences
- Streamline inventory, logistics and supply chain management
- Automate customer service
- Optimize pricing strategies
- Help businesses gain valuable insights into consumer behavior, improve marketing campaigns, and make data-driven decisions to stay ahead of the competition.
Plus, AI technologies are becoming increasingly accessible and affordable, which means that eCommerce businesses of all sizes can benefit from them. From chatbots to recommendation engines, there are various AI tools available that can help eCommerce businesses meet their unique needs and goals.
AI has the potential to improve the shopping experience for customers, and create meaningful business outcomes for retailers. It is important to ensure that the technology is accurate, reliable, and able to adapt to the unique needs of each store.
Q. How can customers benefit from AI in eCommerce?
Customers can benefit from eCommerce AI in numerous ways:
- Personalized recommendations: AI algorithms can analyze a customer's purchase history, browsing behavior, and other relevant data to suggest products that they are most likely to purchase.
- Improved customer service: AI-powered chatbots can provide 24/7 customer support, answer common questions, and resolve issues quickly.
- Enhanced shopping experience: AI technology can help customers find products quickly and easily, making their shopping experience more enjoyable and convenient.
- Fraud detection: AI can be used to identify fraudulent activities and protect customers from financial loss.
- Voice-activated shopping: AI-powered voice assistants can help customers search for products, add items to their cart, and place orders using voice commands.
- Price optimization: AI algorithms can help eCommerce businesses optimize their pricing strategies by analyzing customer behavior, market trends, and competitor prices.
Q. How is AI used in eCommerce?
AI finds many applications in eCommerce:
- Product recommendations: AI algorithms analyze customer data to provide personalized product recommendations.
- Chatbots: AI-powered chatbots provide instant customer support, answering questions and resolving issues 24/7.
- Image recognition: AI can be used to identify product images and automatically tag them with relevant keywords, making them easier to find in search results.
- Voice-activated shopping: AI-powered voice assistants can help customers search for products, add items to their cart, and place orders using voice commands.
Q. What are some examples of AI in eCommerce?
Here are a few examples of AI in eCommerce:
- Argoid's product recommendations: Argoid uses AI algorithms to analyze customer data and recommend products that they are most likely to purchase.
- Sephora's Virtual Artist: Sephora's Virtual Artist uses AI-powered technology to allow customers to try on makeup virtually, helping them find the perfect products.
- eBay's image recognition: eBay uses AI to automatically tag product images with relevant keywords, making them easier to find in search results.
- Alibaba's fraud detection: Alibaba uses AI algorithms to detect and prevent fraud, protecting customers from financial loss.