AI in eCommerce: How to make it work for you (with expert tips)

Why it’s worth using AI in eCommerce

AI initiatives have been around for a while in in-store and online retailers like self-checkout cash registers. Successful eCommerce companies like Amazon and Shopify, for instance, have implemented and developed artificial intelligence in many areas. AI software like Amazon Lex, AWS, and Shopify Magic improve customer experience, online user targeting, and product selection.  

By analyzing big data and using machine learning, artificial intelligence can assist eCommerce businesses in increasing profits while improving the online shopping experience.

The area is in constant growth. These are some of the main benefits of artificial intelligence in eCommerce: 

• Machine learning.
  ML is the computing field dedicated to developing systems able to adapt without requiring explicit instructions and additional programming.

These systems use algorithms and statistical models that analyze big data and generate patterns. ML systems have broad applications, including in the eCommerce industry.

• Seamless automation.
  One of artificial intelligence’s primary goals and benefits is to perform tasks with minimal human intervention with the same or improved quality.

For example, routine tasks for an online business. Having automated, low-level customer support, product recommendations, or sending emails can free up time to dedicate to other tasks.  

• Efficient targeting and advertising.
  AI improves customer data analysis, including purchase history, and can personalize and target advertising better. As a result, customers will receive advertisements that resonate with their needs, with a consequent improvement in revenue.
• More effective sales process.
  Besides analyzing customer preferences and online browsing habits, AI can be associated with email marketing and channels such as chatbots to produce automated communication and customer interactions.

These include follow-up emails, invites to participate in research, or abandoned carts. This increases data gathering, improves sales processes, and gives you a competitive advantage.

• Customer retention and satisfaction.
  As targeted marketing and personalized offers become more precise, the customer base may react positively by purchasing products that solve their problems.  

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10 ways to use artificial intelligence in eCommerce

Let’s look over ten of the most common and promising AI use cases in eCommerce. Some are still in development and need time to expand to small businesses. 

Mostly, products and services combine more than one AI technology to deliver the best functionality. 

Due to the constant evolution and experimentation of AI tools, these examples may change quickly.

Although we strive to keep our blog current, we recommend using sites like FutureTools to access an extensive collection of AI services.  Enter a term in the search bar, select the relevant boxes, and if you’re looking for free, freemium, or paid tools. FutureTools also lists collabs with Google.  

1. Pricing optimization

Dynamic pricing strategies use artificial intelligence to analyze data and change prices according to real-time consumer demand and supply changes. 

AI tools select products and predict when to offer and how much to discount, thus improving sales processes based on historical data.

Many large retail businesses utilize the technology for pricing optimization in online shopping. However, finding affordable solutions for companies just starting may be challenging.  

Companies offering dynamic pricing technology include: 

• Accenture – pricing per consultation. 
• Omnia Retail – free trial, pricing under consultation.
• Stackline – pricing under consultation.

2. Chatbots and virtual assistants

Chatbots and virtual assistants use natural language processing (NLP) to interpret online shoppers’ questions.   

Unlike human employees, AI robots have limits to answering client queries. However, technology is constantly improving, and chatbots are often the quickest way to answer customers’ questions.  

Robots alone aren’t the best way to retain customers. They should be considered to improve your existing support services, freeing staff from common questions that don’t require human intervention. 

An omnichannel marketing approach with several communication outlets offers the best customer service method. 

There are plenty of paid solutions that specialize in AI-powered support like SnapCall ( from $249 per month). 

However, freemium tools like ChatHub may give eCommerce business owners more control over the platform and connect them with chatbots, including Google Bard, Bing, Claude, or Chat GPT.

3. Product recommendations

Product recommendations based on customer behavior are one of the most common eCommerce features using artificial intelligence. It’s seen everywhere, from Amazon, eBay, and Etsy to Netflix and Starbucks.

Website builders offer plugins and functionalities that recommend items to potential customers based on previous interactions and similar product descriptions. However, these programs’ success varies, depending on the quality of machine learning. 

The main goal of this software is to provide a seamless customer experience while improving the efficiency and accuracy of marketing campaigns. 

Owners of shops in marketplaces like Amazon and eBay benefit from the platforms’ resources, but standalone online stores also have plenty of options.  

Some of the best Shopify plugins for product recommendations using AI include: 

• Dialogue AI Personalization – from $99 per month.
• Visely Product Recommendations – from $149 per month. 
• Personalized Recommendations by Glood.ai – free plan available. 
• Similar Upsell by Simile – free plan available.

WooCommerce store owners can benefit from many AI plugins with free plans or requiring only one payment. Some of the best-rated options include: 

• The official WooCommerce Product Recommendations – from $8.25 per month.
• Leo Product Recommendations – from $79 per year, paid in the developer’s website.
• Related Products for WooCommerce by WebToffee – from $89 per year, paid in the developer’s website.
• AI Product Recommendations for WooCommerce by divcoderPlugins – one payment of $21.

Other sites, such as Perzonalization, follow different pricing methods besides traditional plans (which start from $9.99 per month), like charging per monthly page views.

4. Fraud detection and prevention

Online interactions are subject to fraud, leading to $41b in losses worldwide in 2022 – this figure is expected to grow further to 48 billion U.S. dollars by 2023. Fraudsters constantly change their techniques, making detection a constant challenge.  

Machine learning algorithms are the ideal counter-agent to fraud stratagems. They can significantly reduce financial losses by monitoring and analyzing millions of real-time transactions and adapting to different strategies.

Avoiding fraud requires a fast response. ML quickly detects patterns and anomalies that humans would take a while to notice, like attempts to enter invalid postal addresses – which may indicate identity theft and re-routing of orders to steal personal data. 

AI can react promptly to phishing attacks and payment frauds and helps as a preventative. For example, machine learning can help tackle fake reviews, detect document forging, and even ad fraud. 

Companies like Amazon use their own built-in systems to avoid fake reviews, but there are also options for small businesses. Tools like Originality.ai detect AI-generated text,  including reviews – with alleged 99% accuracy. Free Chrome extensions by Fakespot specialize in detecting fake reviews and scams in a number of marketplaces.

Some AI-powered tools for fraud prevention include Nethone, Integral Ad Science, Shield, or ZenGRC by RiskOptics. 

Some of these systems might be expensive for small businesses. Nethone’s pricing, for instance, starts from $300 per month, while most other companies only offer prices under consulting. However, companies intending to grow must invest in a full-scale solution to avoid future financial losses.

5. Customer segmentation

Customer segmentation involves organizing individuals into groups according to their characteristics, like preferences, demographics, or behavior.  

AI systems using machine learning can optimize customer segmentation as they receive more consumer data, such as purchase history or customer engagement. Algorithms use classification and clustering techniques to predict customer data based on their interactions with brands and identify trends.

Complex systems can even incorporate other AI methods, such as natural language processing and sentiment analysis, to refine grouping and adapt recommendations and messages according to the segments.  

Ultimately, companies with this valuable data can deliver a personalized shopping experience.

Google Analytics and Trends are the most popular tools that provide advanced insights. Lifesight (former Moda) offers a complete marketing suite that includes customer segmentation and advanced analytics. ZigPoll captures customer insights inside a platform that organizes surveys and analyzes responses.

6. Inventory management

Inventory and warehouse management requires keeping stock levels high enough to meet market demand without accumulating an idle supply. 

Traditional inventory management involved tracking stock levels by employees, who retrieved and dispatched products while updating inventory data. This process is timely and can be interrupted or halted due to work hours.

AI-enabled inventory management uses smart logistics techniques, such as keeping inventory according to sale trends and projected demand changes, including potential issues in the supply chain. 

Moreover, warehouse automation with robots makes it possible to retrieve and dispatch products and update inventory 24/7.

Amazon sellers from Australia, New Zealand, and the United States can use Intellify’s AWS Solutions, which provides consulting services.

There are solutions for all budgets, including: 

• QuickBooks – subscriptions from $15 per month.
• TallyPrime – one-time payment of $630.
• Fishbowl Inventory  – from $329 per month.
• Megaventory – from $135 per month.

7. Supply chain optimization

Supply chain optimization is complementary to inventory management and another application for smart logistics. 

A supply chain is a network of entities producing and delivering goods for a company. Supply chain management is critical for business operations and requires dealing with complex variables like customer demand vs capacity constraints. 

Forecasting the variables is the main challenge for optimizing a supply chain properly. If there’s enough data, artificial intelligence can be trained through machine learning to excel in identifying trends and accurately forecast potential issues. 

The same platforms for warehouse and inventory management provide supply chain optimization, including Streamline and Fishbowl. 

8. Visual search and image recognition

Visual search is an AI-driven technology developed by companies like Amazon, Google, Microsoft, and Pinterest. This allows users to input images in search engines instead of keywords. 

The technology simplifies the search if users don’t know the name or style of something they’re looking for. Also, it lets shoppers with a clear goal find the most fitting product. 

In some cases, users scrolling through social media find images they like, take screenshots, and look for similar products with visual search engines. 

Developers feed image recognition engines with thousands of visuals of objects in different styles and configurations. Then, artificial intelligence processes the pixels, searching for similarly designed objects. 

Visual search relies heavily on the quality of its data and is improving its performance over time.  

Some of the most popular visual search engines are Google Lens,  Bing Visual Search, and Pinterest Lens. eCommerce retailers, including Amazon’s StyleSnap and ASOS StyleMatch, also use this technology. 

9. Sentiment analysis

Sentiment analysis, emotion AI, or opinion mining rely on artificial intelligence to identify and quantify subjective information, such as feelings, from written materials.  

Basic sentiment analysis in eCommerce uses natural language processing to recognize if an opinion in a review, survey, email, or social post is positive, neutral, or negative.  

The main application for eCommerce sentiment analysis is understanding the customers’ purchasing decisions. Also, learn how to keep their satisfaction levels high and identify customers’ emotions related to the brand, products, and services.

AI systems can help prevent customer loss and identify poor service quality. The same technology can analyze similar metrics in competitors’ eCommerce websites and social networks. 

Tools like MonkeyLearn can connect with customer support software like Zendesk, helping tag messages and organize structured data. 

Sentiment analysis may assist eCommerce retailers in making financial decisions and adopting specific marketing strategies by predicting customer behavior. 

In addition to MonkeyLearn, many companies and services offer sentiment analysis, including BrandMentions, Brandwatch, Clickworker, and Innodata. Pricing varies greatly and most services offer some sort of free trial. 

Some tools, like Social Searcher, scan social networks for specific terms and the sentiment associated with them, including many functions for free.

10. Voice commerce

vCommerce has a different application from support chatbots and assistants but shares similar technologies. 

In vCommerce, a virtual assistant such as Alexa, Siri, Bixby, Cortana, or Google voice assistant interprets online shoppers’ commands using NLP and assists in making purchases. Voice assistants can communicate with humans via AI-generated voices and other synthetic content. 

It’s still in an early stage of development compared to other AI initiatives. It requires the usage of different areas of AI, from NLP to synthetic media and natural language generation. 

They’ve received mixed user feedback depending on how smoothly they integrate with native systems – Siri, for instance, has a wider acceptance than Bixby.

Some companies like Starbucks, Mercedes Benz, and The North Face successfully adopted voice assistants, while others worldwide have been experimenting with the tech with different success.

vCommerce works in two steps. First, the assistant converts human voice to text. After that, NLP interprets the command and tries to match the search intent. 

Complex assistants are well-integrated into larger systems and mobile apps, using different AI-driven technologies to interpret user data like purchase history and offer custom product recommendations. 

vCommerce can appeal to customers by allowing them to purchase without using their hands. This may be convenient for some, but it is a necessity for people with disabilities.

The technology also has security applications. Similarly to other biometrics, voice prints are unique to individuals and can be used to validate purchases on an eCommerce website.

However, voice recognition is limited, given that a sore throat could prevent someone from accessing their voice-locked device.   

A controversial use of vCommerce involves gathering customer data. eCommerce retailers use voice analytics to collect data from people speaking close to their devices. Despite the useful data this method can gather, it has serious privacy concerns. 

Companies like Mondo offer AI voice assistants with pricing under consultation.

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