Artificial Intelligence in eCommerce: how can it really help us?

Premise (if you're short on time, skip straight to the next chapter!)

As we were saying, since Alan Turing, a series of discoveries and innovations have made AI a ubiquitous presence in our homes and workplaces, radically transforming habits and behaviors.

Now, imagine yourself in your favorite store, browsing through items that perfectly match your tastes, trying them on, mixing and matching, and then deciding what to purchase. A traditional experience, you might say, but with one fundamental difference: you're comfortably seated on your couch at home, and your favorite store is entirely virtual. Scenarios like this, which are no longer so futuristic, are changing the way we shop. The same eCommerce that twenty years ago seemed like science fiction can now feel old and outdated if we don't find the latest AI technologies supporting the shopping flow.

A series of considerations are leading all merchants to raise the bar of their attention threshold. Shalini Kurapati, CEO of Clearbox AI, as well as a researcher and lecturer at the Polytechnic University of Turin, predicts that "the global AI market in this sector will reach 31.1 billion dollars by 2028, offering enormous economic potential that must be harnessed responsibly."

Therefore, the advantages of using AI in eCommerce are numerous and radically transform the way we buy and sell online. To give you a clearer picture, we will mix theoretical examples with practical situations drawn directly from our projects.

AI in the Drop Ecosystem for eCommerce

Personalization of the Customer Experience

AI-powered recommendation systems are powerful tools that analyze user behavior, preferences, and past purchases to suggest highly relevant products. This not only increases the likelihood of conversion but also significantly enhances customer satisfaction through highly personalized shopping experiences. By leveraging collected data, which is cross-referenced and processed by AI, companies can anticipate their customers' needs, offering products that meet their real desires. Additionally, the reduction in returns is facilitated by technologies like virtual try-on, allowing customers to try products virtually before purchasing, reducing uncertainties, and increasing confidence in online shopping.

Optimization of Pricing Processes

AI allows retailers to dynamically adjust prices based on demand and supply, historical data, and market trends. This approach, known as dynamic pricing, can maximize profits and improve market competitiveness.

Real-Time Big Data Management

AI systems can analyze Big Data to identify hidden patterns and trends, enabling merchants to make informed and responsive decisions. This can influence logistics and inventory management as well as marketing and customer retention strategies.

Marketing Automation

One of the main advantages of AI-based marketing automation is the ability to personalize content across different channels such as email, social media, and website interactions. Knowing the customer's individual preferences and behaviors helps these systems deliver highly targeted content that resonates with the audience, leading to greater engagement, higher conversion rates, and overall marketing effectiveness.

Moreover, companies can use their advertising budgets more efficiently by identifying the correct channels and automating A/B tests and campaigns. This saves time and resources while ensuring that marketing experts allocate their budgets more strategically.

CRM

Customer Relationship Management (CRM) is a fundamental tool for companies as it allows them to compile customer data in one place, streamline communications, and improve the overall customer experience. However, with AI integration, CRM has evolved to the point of completely changing how companies manage customer relationships.

This evolution is based on AI's ability to process large amounts of information and identify patterns or even create previously impossible insights. Consequently, using machine learning algorithms, CRM systems can automatically segment customers, thus personalizing communication more effectively and making more accurate predictions about consumer behavior.

The ability to provide super personalized experiences to customers is one of the most important advantages of AI-enhanced CRM. Marketing campaigns, product recommendations, and customer support can be customized based on customer interaction history, browsing history, and purchase patterns. This level of personalization fosters better customer satisfaction and increases loyalty.

CDP/SCV

AI is transforming Customer Data Platforms (CDP) and Single Customer Views (SCV), essential tools for companies that want to understand and interact effectively with their customers. In CDPs, AI facilitates the integration, cleansing, and enrichment of data from various sources, helping create a cohesive and comprehensive database. This allows companies to segment customers with great precision and use predictive analysis to anticipate future behaviors like purchases or cart abandonment.

Regarding SCV, AI is crucial in recognizing and unifying customer records across various systems, eliminating duplications and offering a complete and updated view of each customer. This helps companies personalize interactions in real-time based on customer context and preferences, significantly improving the overall experience.

Logistics Optimization

As previously mentioned, AI can analyze vast amounts of data to predict product demand, optimize shipping routes, and reduce warehouse costs. This results in greater operational efficiency and faster delivery times for customers.

Fraud Prevention

By analyzing user behavior patterns and transactional data, AI systems can identify suspicious transactions and block potential fraudulent activities in real time. This not only protects buyers but also helps companies reduce financial losses associated with fraud.

Content Processing

With AI, it is possible to automate the creation and management of large amounts of content, making the process much faster and more efficient. Think of generating articles, summaries, and business reports almost instantly. Additionally, AI can personalize content by analyzing user data to tailor it to their past preferences and behaviors, improving engagement and satisfaction.

Chatbots

They operate 24/7, offering immediate assistance to customers at all times, which is particularly advantageous for companies operating globally. Chatbots can also support multiple languages, thereby improving customer service on an international scale. Moreover, thanks to machine learning, they can improve by learning from previous interactions to provide increasingly accurate responses.

Recommendations

AI enhances the user experience by providing personalized suggestions based on browsing and purchasing habits. This not only helps users discover products or content they might not otherwise find but also helps companies understand which products to promote based on consumer preferences.

Our Projects

• Clerk.io for Upselling and Cross-selling: this tool, like others in the same category, automatically analyzes visitor behavior, trends, and transactions to present more relevant search results and AI-recommended products based on user behavior.
• AI on Adyen for Fraud Detection: it enables better utilization of the large amount of data absorbed to make quicker and more accurate decisions on people's creditworthiness in real time. With artificial intelligence, the Risk Engine automates transaction reviews, reducing review times and speeding up the checkout process.
• AI on Hubspot CRM: from the Content Assistant based on OpenAI GPT technology to the adaptive testing available in the Marketing Hub that helps optimize proposed content. In general, Hubspot is enriching itself with tools based on machine learning algorithms that help users refine their marketing and sales strategies.
• AI for Unit Testing in Development: as well as for generating documentation and FAQs.
• Creation of Specific GPTs (Generative Pre-trained Transformers): Both for post-processing images (resizing, background replacement, etc.) and for data analysis and translation review.

 

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— 16 May 2024