Since the popularization of AI (artificial intelligence), many people have started to use them frequently and productively in their daily lives, especially in the commercial environment.
If you have a business, it is very likely that you have already asked yourself: how can I use artificial intelligence in commerce?
The possibilities are diverse: marketing, content creation, automation, security, data analysis, among others. However, one of the most promising applications with the potential for lasting impact is undoubtedly data-driven personalization.
By using behavioral data and consumer preferences, artificial intelligence in commerce makes it possible to significantly improve customer service, products and services offered in a personalized way for each customer.
Those who already adopt this approach report significant gains in customer experience, especially in retail. Companies that invest in personalization with AI are able to adapt their offers to the individual needs of each customer, which results in greater satisfaction, loyalty and increased sales. In this article, you will discover how artificial intelligence in commerce, applied to data-driven personalization, is revolutionizing the way companies connect with their consumers and how your business can also benefit from this transformation.
What is artificial intelligence in commerce, applied to data-driven personalization?
Data-driven personalization represents the union between the scale of mass production and individual customization. This approach allows companies to offer unique experiences, shaped from real consumer data. With the support of artificial intelligence in commerce, it is possible to automatically analyze large volumes of information, such as browsing behavior, preferences, purchase history and previous interactions, to personalize everything from customer service to the products and services that will be offered.
Benefits:
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Increased Customer Satisfaction: Artificial intelligence allows companies to better understand the behavior and preferences of each consumer.
With this data, it is possible to offer personalized recommendations, more relevant communications and tailored shopping experiences. This makes the customer feel valued and, consequently, more satisfied. A practical example: e-commerce sites that show suggestions based on browsing history tend to have higher conversion rates and lower cart abandonment. -
Greater Loyalty: When consumers realize that a brand understands their needs and offers exactly what they are looking for, the relationship of trust is strengthened.
Artificial intelligence helps to create this bond by adapting products, services and even after-sales service to each customer’s profile. This creates a positive cycle: the more personalized the service, the greater the chance of repeat purchases and referrals to new customers.
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Operational Efficiency: In addition to improving the customer experience, artificial intelligence in commerce also optimizes internal processes.
AI-based tools can automate everything from customer service (such as chatbots that resolve common queries) to inventory control, demand forecasting and marketing campaign management. This reduces rework, minimizes human error and lowers operational costs, allowing the team to focus on more strategic tasks.
Practical Applications of Artificial Intelligence in Commerce
1. Product Recommendation
AI systems analyze previous browsing and purchasing behavior to suggest relevant products, increasing the chances of conversion.
2. Personalized Marketing
Email campaigns and ads are tailored based on customer preferences and history, making communication more effective.
3. Product Customization
Companies offer options for customers to customize products, such as choosing colors, sizes, or adding names, to suit individual preferences.
In addition to the practical applications of AI in commerce, it’s important to consider how technological innovations are shaping the future across a variety of industries. For a more in-depth look at emerging trends and the impact of technology on society, check out our article on technology and innovation shaping the future.
Implementing Artificial Intelligence Personalization in Commerce
Essential Steps:
1. Data Collection: Use tools to gather information about customer behavior and preferences.
How to Collect Data: What to Retain and Which Tools to Use
Personalization starts with relevant, well-organized data. Here’s what you need to retain and how to do it.
🎯 What data is important:
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Browsing data: pages accessed, time on each page, clicks.
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Purchase data: items purchased, average ticket, frequency.
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Declared preferences: filters used, favorited products, completed questionnaires.
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Interactions with customer service: chats, emails, complaints.
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Data demographics: location, age, gender, device used.
Collection tools:
| Tool | What it does | Free? |
|---|---|---|
| Google Analytics 4 | Tracks navigation, clicks, and conversions | Yes |
| Hotjar | Heatmaps, session recordings | Yes |
| Typeform or Tally.so | Creates interactive forms for preferences | Free & Paid |
| Microsoft Clarity | AI-powered navigation analysis (heatmaps + sessions) | Yes |
| Meta Pixel / TikTok Pixel | Tracks user behavior for ad campaigns | Yes |
🔎 Expert tip: Use UTMs in your URLs to track where each click is coming from and which campaign is generating the most interest.
2. Data Analysis with AI: Employ machine learning algorithms to interpret the data collected.
AI Tools for Custom Data Analysis
Data analysis with AI turns numbers into intelligent decisions and automations.
🧠 What to analyze:
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Audience segments with similar behavior.
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Most clicked products by segment.
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Cart abandonment patterns.
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Best times to buy.
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Products frequently bought together.
🤖 Tools that do this:
| Tool/AI | Type | Free? |
|---|---|---|
| ChatGPT (with Advanced Data) | Structured data analysis | Yes (with border) |
| Microsoft Power BI | Dashboard with predictive insights | Yes |
| Looker Studio (Google) | Connects with GA4, BigQuery etc | Yes |
| Piwik PRO | Alternative to Google Analytics | Free and Paid |
| Segment + Twilio Engage | Segmentation + automatic campaigns | Paid |
💡 Real example: You can import Hotjar data + your e-commerce sales into Looker Studio and see patterns like “users who click on the filter X(example) buy Y(example) 30% more often”.
3. Integration with Sales Systems: Adapt sales systems to use the information analyzed to personalize the customer experience.
Integration with Sales Systems: Platforms and Advantages
Integration allows you to transform analysis into action, such as showing personalized products, sending specific offers or reordering the digital storefront for each user.
🛒 Main e-commerce platforms with support for personalization:
| Platform | Main advantages | Free? |
|---|---|---|
| Shopify + Apps | AI apps for recommendations (e.g. Wiser, LimeSpot) | Paid |
| Nuvemshop + Autonix | Automates personalized emails based on profile | Paid |
| WooCommerce + Jetpack AI | Total flexibility with plugins | Free + plugins |
| Tray Commerce | Brazilian support, CRM integration and basic AI | Paid |
| VTEX (Enterprise) | Scalability and robust AI integrated | On request |
💡 Example: In Shopify, you can use the “LimeSpot Personalizer” app to change the order of displayed products based on a user’s browsing history — without writing a single line of code.
4. Continuous Feedback: Implement mechanisms to collect customer feedback and adjust strategies as needed.
Continuous Feedback Mechanisms
Listening to your customers helps you fine-tune your personalization strategies.
How to get actionable feedback:
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Smart forms.
After the purchase, or service, the customer receives a form asking, for example: “Did you like our product/service?” -
Ratings with conditional questions.
Assessments with conditional questions are feedback forms that adapt to the user’s answers, that is, the next questions change based on what the person answered previously. Usually the answers have the options YES, or NO, or something objective. -
Heatmaps and session recordings (via Clarity or Hotjar).
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Monitoring of chats and most used words in support (via Manychat, JivoChat, Intercom).
Useful tools:
| Tool | Specific utility | Free? |
|---|---|---|
| Survicate | Feedback with conditional logic | Yes (limited) |
| JivoChat with CRM | Monitors recurring complaints and questions | Yes |
| AI Intercom | Support with sentiment analysis | Paid |
| Trustpilot / Verified Reviews | Ratings with feedback detailed | Free and Paid |
Ethical Challenges and Considerations
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Data Privacy: It is essential to ensure security and consent in the collection and use of customer data.
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Transparency: Clearly informing how data will be used increases consumer trust.
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Avoid Algorithmic Discrimination: Ensure that algorithms do not reproduce bias or exclude specific groups.
FAQs on Artificial Intelligence powered personalization in commerce:
Yes, with affordable AI and data analytics tools, small businesses can implement effective personalization strategies.
By adopting transparent collection practices, obtaining explicit consent and using robust security measures to protect information.
Industries such as healthcare, education, and financial services can also apply personalization to improve the customer experience.
Not necessarily. It complements human service, allowing professionals to focus on more assertive and meaningful interactions.
Conclusion on the application and use of artificial intelligence in commerce:
Artificial intelligence in commerce is not just a trend, it is a strategic transformation that is already shaping the present and future of commerce.
By using AI to interpret massive volumes of data about consumer behavior and preferences, companies can deliver highly relevant, almost tailored experiences at scale. This means that each customer is treated uniquely, with personalized offers, recommendations and communications, which increase satisfaction and connection with the brand.
In addition, businesses that invest in artificial intelligence gain agility and precision in internal processes, are able to make faster and more informed decisions, reduce waste and increase operational efficiency. This generates a significant competitive advantage, especially in an increasingly competitive and volatile market.
In short, AI applied to commerce allows companies to stop just selling and, in fact, understand and serve their customers better. Those who implement this technology with strategy and purpose not only follow market changes, but lead them and take advantage of all the opportunities that transform the world and markets.
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