AI in Ecommerce

Artificial intelligence and Machine learning have become a buzzword. We can’t imagine a life without AI/ML today. Having said that, ecommerce is not back in this race. From UI/UX to customer experience, online merchants have the momentum to seize this opportunity to leverage AI/ML, as AI is the future of e-commerce.

Read this article on how you can implement AI in business in easy ways for:

  • Price optimization
  • Search result optimization
  • Product recommendation and many more…

Let’s get started.

Top 10 Machine Learning (AI/ML) Strategies for Your Businesses: AI in Ecommerce

Global e-commerce sales in 2023 amounted to nearly 5.8 trillion U.S. dollars. The worldwide AI in the e-commerce market is anticipated to foresee a substantial increase, recording an estimation of up to USD 22.60 Billion by 2032.

Retail e-commerce sales

Adopting AI in sales, marketing, and inventory management is a good idea if you want to make your business cost-effective and efficient.

There are many more such use cases of AI in ecommerce. Let’s see them one by one.

How does machine learning work?

If we define machine learning, then we can say that it’s an algorithm designed to learn from a certain set of data. It can predict trends and outcomes based on that data.

With recent developments, it has been able to match user intent. Let’s consider a simple AI use cases in ecommerce. Suppose a user is searching for a product on an e-commerce site, such as Amazon, with a typo. Still, the search returns the intended product. How is this possible? Because of ML technology. Behind the scenes, AI algorithms are trained on massive data and understand the context. So even when we type with typos, the search query is adjusted to match the most relevant products.

ML Process Flowchart

ML Process Flowchart

Similarly, AI can create complex representations of products, capturing their features, categories, and relationships.

That means AI is powerful. But are there any limitations? Yes! Though AI-powered search has impressive capabilities, it still may sometimes produce inaccurate and biased results and struggle with complex or nuanced searches.

Now that we have a basic understanding of its working mechanism, let’s learn about the strategic implications of AI in ecommerce.

Can we make a robust strategy to enhance overall e-commerce operations? Which are the major areas these algorithms can impact, and how?

Let’s dive in. 

1. AI Commerce Optimizes Pricing for Maximum Impact

shoppers in the United States

Pricing is the first thing people care about. 90% of customers look for the best online deals before making a purchase. It means you must offer competitive pricing.

E-commerce is all about data. You need to play around with it. Be it customer data like purchase history, buying patterns, etc.; ML analyzes all these to predict and adjust the price of the product.

There are many other factors that influence pricing, such as the browsing habits of customers, their response to promotional offers, etc. The goal should be to identify the sweet spot price that maximizes your profit while maintaining a good customer experience.

2. Segmenting, Personalization, and Targeting Customers

In the traditional brick-and-mortar world, segmentation was performed by salesmen who came directly to where their customers were. They learned about their target audience, their pain points, and their concerns through observation and asking a few questions to gather enough information on demographics along with how they think (non-verbal cues).

You could now use this to tackle any concerns around the products, reiterating the words and phrases used by the customer, upselling instantly, and understanding their purchase intent.

How AI Is Changing eCommerce?

When the customer is in a primarily digital environment, all of this must be recreated — and automated. Segmentation must be behavior-based and measurable. Searching on-site is a good place to start. Shoppers search using natural language, which can provide hints on their background and native language. An algorithm can identify what the user is looking for and tailor more relevant results based on the clicks. This boosts their chances of buying something.

This could then be consistently accompanied by lists of cross- and upsell offers, placed based on what other customers in the store previously bought using this search. Demographic information can be provided when someone first signs up. You can track what they do on your site — which content they read, what materials they download, how often they come back, and shop for more. You can even see what emails they are opening and when in the day, week, or year they are most likely to buy.

There is a massive amount of data trapped in all these interactions — and it is sensible that AI should be the buzzer to evaluate behaviors that can unearth buying patterns. AI can transform e-commerce by understanding patterns in data sets to build more effective marketing decisions. For example, you can design even the most ultra-targeted campaigns with messages that are so relevant to your target audience to convert by serving them exactly what they are looking for.

Search Results Optimization

Of course, offering search results by matching keywords is only the simplest and most primitive step in on-site search. That only scratches the surface of what you might be searching for to get shoppers the best experience possible. On a big data scale, the result may provide you with what is more useful for people from different locations, how to improve e-commerce search results filters, and even which products suit them better based on previous behavior.

Based on such data, both these items go together. You can also recommend other products that might be similar and even cross-sell those frequently bought by your users. This additionally assists cutting-edge data science — machine learning, as it can be used to identify the trends and patterns that are necessary for automatically determining this. Doing this results in a better click-through rate on the results page, improved conversion rates, and higher average order value. Notably, faceted search filtering in real-time based on user inputs is the only known methodology for returning accurate results with AI suggestions.

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3. Product Recommendations

We already know that automatic product recommendations work well — just watch Netflix if you want to try it out yourself. McKinsey says 75 percent of what people watch on the streaming service is recommended by an algorithm based on their viewing habits. (The number is 35 percent for purchases on Amazon as well.)

It can sound like a straightforward task — like, okay, everyone who finishes watching Futurama should just be recommended Rick and Morty because what else is there?

However, considering the demographic, people in non-English speaking countries might be more eager to use it for a movie or series in their mother language. Young audiences will prefer one thing, and older prefer another; the tastes of those living in Asian and American continents will differ.

You will need an algorithm — in particular, machine learning for ecommerce — to do this, even if you have hundreds of products against which comparisons could be made.

4. Predictions About Your Customers

Machine learning has a lot to tell you about the people who visit your site and buy something — from how likely they are to return to what else they might wish to purchase.

Discover what machine learning can infer.

Predictive Analytics for Customer Lifetime Value

To customize your communication and messages, you must know what the customer is expected to spend in your store during a certain period.

Being able to determine something like a lifetime value for any given individual only makes your marketing cheaper yet more effective.

Or you can highlight who your top customers are and how they should be treated.

Will a customer buy this?

Let’s say you sell office supplies online. You have a customer who purchases ink cartridges from you every six months for the same number of cartridges.

Let us imagine that in five months’ time, this customer will go to your site and not complete the order. This, of course, would be very logical, and we could assume that they are simply checking prices to determine their budget for the next order or comparing your pricing with your competitors.

You will probably never know that they logged in and simply did this as a store owner.

But AI will detect it.

It can also realize that perfect moment to sprinkle in some point-based motivation regarding Average Order Value (AOV) — the customer is likely returning but still on the fence over a specific product, so what better time to kick off an automated message providing them a personalized discount for their next order using your store that thanks and rewards them for being continued loyal.

Predicting Customer Return (And Sales!)

If a customer is inclined to come back to your store, for example, by their behavior, you could even use an entirely different marketing message that will be way more resonant with them.

Thanks to algorithms that can be used to start longer workflows with messages aimed at loyalty and brand reinforcement.

Customer Churn Prediction

One of the most crucial tasks is retaining your already existing customers so that you can save some dollars from your marketing budget hemorrhage. Buying a new customer is highly expensive.

This list can last long, but a machine learning algorithm will identify the pattern, for example, which shoppers are the most likely to abandon your site.

Naturally, when the AI learns this as well — it triggers workflows to keep shoppers and give them more reasons not to leave.

Client Size Prediction

An algorithm that estimates the size of your client relies on their AOV (Average Order Value) and purchase frequency, along with other data like the number of employees or company type.

This provides you with some very clear signals for identifying who might be that more special customer. So, you can offer them more customized deals to give them cheaper, long-term offers.

Therefore, Statistical Learning really tells your future by predicting what your customers will or won’t do.

There is another application of machine learning. AI in ecommerce is as much about the future as it is about understanding its past, and it has its own importance.

5. Site Search Autocomplete

A good autocomplete should be able to learn instead of classifying all kinds of product attributes and descriptions.

Instead of the usual technical or machine-like phrases of database entries, it needs to be able to comprehend how users express themselves in natural language.

E-commerce best practices searches suggest that online stores must use an AI-based autocomplete as it makes the overall shopping journey smoother and more comforting for shoppers who now take this for granted.

Thus, natural language processing and machine learning need to be aware of what terms your customers will use and how often they do so, whether those queries get resolved, which phrases as well as spelling errors are common, and then also the correct way to spell.

This way, the user would feel like: “oh wow this search engine is really here to help,” and engage more without becoming annoyed. And that’s the real meaning of interpreting AI in B2B ecommerce, too.

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6. A/B Tests Using AI

A/B testing is an easy tool to use online in marketing. However, it can be complicated. For example, you might want to run an A/B test for a product page.

So, What do you change:

  • The display of pricing?
  • Location of your CTAs?
  • The background colors?

You see, if you change more than one thing, you can no longer be sure what causes the next positive or negative change. But if you change just one thing, it may be unnoticeable. The use of AI in ecommerce can help tackle this problem.

Another question you may ask is: What KPIs to track? Obviously, you can start by tracking conversion and purchase rates. However, time spent on a page and the number of clicks, if they come back — are important as well.

It becomes easier to test with machine learning and AI. Based on the history, determine what elements we should be testing and build variants automatically. It can automatically modify page elements depending on the results of a test. Like showing pages differently to different age groups or locations. AI/ML can decide faster which versions are the best, as it can consider many variables and see connections between small changes.

7. Generative AI in ecommerce: Automated Customer Support Chatbots

When it comes to support and best practices, there is usually no silver bullet. Also, today, when people are overwhelmed with ecommerce technology or other technological developments and rapid products, the burden on customer support teams is obviously increasing. If we implement manpower to solve these massive issues for customers, it will be expensive and time-consuming as well.

The answer is ecommerce AI chatbot support. However, you cannot fully automate support because so many issues will require human intervention, and your customers will get frustrated quickly if they have no way to contact a person.

One of the ways to address this issue is by creating a machine learning-powered chatbot (or so-called generative AI developed through LLM or large language models).

They converse with customers via these chatbots. By way of not just the use of predefined answers but also through AI — they learn each conversation and get to know the customer and their issues through natural language.

Indeed, the chatbot will have to learn about the products and services, the type of customer interaction, and their preferred method of communication.

It will never be able to clear a Turing test, but it might someday start recognizing upsell opportunities and generating personalized coupons or new tickets for human customer-support staff to pick up.

Keep in mind, however…

A professional custom chatbot built for businesses can range from $ 10,000 to $300,000 (plus ongoing maintenance costs), depending on its functionality.

When it comes to a good user experience, this is pricey, but it is money well spent.

How Artificial Intelligence Can Benefit E-Commerce Business?

Benefits of AI in Ecommerce

Artificial Intelligence has cracked a new way of sales: merging e-commerce stores with chatbot support and messaging apps in online shopping. This new way of AI shopping — conversational commerce or AI ecommerce business — not only helps with customer experience but also can drastically improve conversion rates and thereby increase online sales overall.

8. Inventory Management

AI/ML can be used for efficient order management. It’s like how the smart fridge tells you that your milk is empty, and it just adds itself to the shopping list.

ML does this for E-commerce.

Inventory management and logistics are not a piece of cake when talking about complexity.

When you run an e-commerce store, you need to track the balance of inventory items, place purchase orders in time, determine ecommerce trends in demand ahead of time, and adjust your online shop smoothly.

Demand Forecasting:

  • Historical Sales Analysis of the Process: ML algorithms analyze old sales data to establish trends and seasonality in demand for various products. This helps forecast future sales and prevents understocking or overstocking.
  • External Data Integration: ML is also capable of integrating considerable input data like upcoming holidays, weather information, and economic patterns to make more precise predictions in demand forecasting. For instance, an increase in purchases of sunscreen ahead of summer.
  • Analytics Data: Machine Learning is more than increasing sales numbers. It can track customer behavior such as abandoned carts, browsing history, and product reviews, which allows online merchants to get a pretty good idea of optimal buying patterns, the demand that will need fulfillment, and so much more relevant information.

Inventory Optimization:

  • Lead Time Management: ML also considers lead times – the time it takes to receive new stock -ensuring that products are reordered before they run out. This can help prevent stockouts, which cause you to lose potential sales and customers.
  • Safety Stock Optimization: Machine learning in ecommerce can help identify the correct level of safety stock that needs to be held with minimal storage costs.
  • Better Product Lifecycle: ML considers the product lifecycle stage. New products may need different inventory management strategies than well-established bestsellers.

The Benefits of ML-enabled Inventory Management

  • Lower Stockouts: Increased accuracy in demand forecasting reduces the chances of being out of stock, thereby increasing customer satisfaction and sales.
  • Reduced Inventory Costs: With the ability to forecast demand accurately, ML can potentially save millions for retailers by not needing excessive inventory and freeing up capital that is tied to storage costs. This is certainly a good example of AI in Retail and E-Commerce.
  • Enhanced Cash Flow: The inventory optimization strategy executed by ML enables you to have the exact amount of inventory to meet market demand, which, in turn, improves cash flow management.
  • Predictive Modeling: Has there ever been an enterprise that hasn’t wanted to know exactly what their customers are going to buy, when they’re going to make a purchase, and how much inventory they should have on hand?

The benefits of AI in ecommerce are not limited to those mentioned above. Remember, AI is still in its early stages and under development. There is much more to come as it reaches its zenith.

9. Boosting Omnichannel Marketing with Machine Learning

It goes without saying that omnichannel marketing improves both retention and conversion rates, helping you boost revenue—but only if you use available channels correctly.

Alternatively, you could hire a CX solutions team to manage it for you.

But what better way is there?

Recently, ecommerce AI has become particularly critical due to the volume and variety of data your customers generate when engaging with your activities on those channels.

By leveraging customer behavior, such as successful ads, popular content, and email open rates, pattern recognition algorithms can analyze your messages to ensure each customer gets the right message.

We all know Digital media has become more relevant than print media today—in part because of the ability to track results and optimize campaigns. ML is the next iteration in that evolution.

10. Computer Vision, Image Processing, and Recognition

Image recognition can be a great tool for an online store with thousands of products in inventory. Ideally, all it takes from a customer is snapping a photo of a product they have at home or in a store and uploading it. The system then processes it on the store’s servers and sends back information on availability, current price, or order-to-ship details, giving the shopper up-to-date information to make a purchase. Most of the time, this results in a sale because it is the easiest shopping experience.

A 2024 study by Smart Insights revealed that one-third of consumers (36%) have used visual search to find products online. Visual search also reduces return rates and enhances mobile browser engagement. Image processing can also provide extremely targeted recommendations, such as taking a picture of the customer and virtually trying on clothes available in the store, giving them an idea of how the dresses would look on them.

Pro Tip: Fraud Protection

Ecommerce is all about trust and security. Users fill in their personal information, sensitive data like card details, etc., on ecommerce sites. So, online merchants should prioritize safeguarding customers’ details.

Despite these guardrails, we came across the news that Fraudsters are finding new ways to exploit these systems. It makes sense for good e-commerce practices to include effective means of assessing transaction risk and identifying fraud. Just adding a CAPTCHA is insufficient. You need to analyze user behavior on your site as well.

Deep learning can help expose patterns of behavior that do not conform to human activity, such as rapid form-filling, extremely quick clicks (often 30 or more-page loads for a split second each), numerous sets of information entered in short timeframes, and so on.


AI can automate tasks, analyze data for insights, and improve customer experience.

AI is used in ecommerce marketing to help businesses make smarter decisions, personalize experiences, and gain a competitive edge.

AI plays a crucial role in e‑commerce. Here are some of the ecommerce AI tools: Octane AI, Synthesia, Landbot, Lavender, SearchIQ, etc.

AI store builders like Shopify or can help design, populate, and personalize your ecommerce store.

Ecommerce giants like Amazon, Alibaba, and eBay leverage AI for tasks like recommendations and fraud detection.


One might assume that bringing AI for ecommerce activities hints at something futuristic or extremely intricate, but it is practical. Most human-developed functional applications and services are already prepared to serve you in various ways. While it is never cheap, it is worth the investment.

AI in ecommerce will help you understand your customers and audience better, boost your sales and average order value (AOV), cut unnecessary work, and provide insights beyond human capability.

Of course, you do not have to hand over everything at once to the machine. Start small. Deploy a sophisticated on-site search solution, begin with machine learning-based recommendations, and expand as you become better equipped to focus. But hurry up, as AI is moving fast and will impact the bottom line of e-commerce soon.

Are you curious about how AI and ML can transform your business? Contact us today!

Build Future-proof Customer Experiences

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