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AI's Impact on Ecommerce: Real-life Examples Explored

With the evolution of digital technologies, we can witness unparalleled changes in the e-commerce sector. And that becomes even more clear with the implementation of AI-based solutions.

Nowadays, businesses are very eager to explore all the prospects of AI and leverage its capabilities to their advantage. It seems that the integration of AI opens doors to an unparalleled experience for everyone — buyers, sellers, shippers, and distributors. Enthusiasts see the great potential of AI in workload automation, inventory management and tracking, customer experience, and other functions. However, the efficiency of AI solutions has to be properly studied at this stage to spot their potential vulnerabilities and find efficient ways to resolve them.

This is exactly what we are trying to achieve with this article. Our team has conducted a couple of prolonged case studies to figure out whether AI can be considered a modern-time necessity for the competitors in the e-commerce industry.

Basic Solutions to Boost Customer Experience — Based on Case Studies

Our case studies were focused on how the implementation of AI-integrated tools can boost the overall customer experience in two different aspects — buying experience and technical support. For that, we’ve tracked and analyzed the performance of a basic solution applied by many online stores these days — AI-based chatbots. GPT-3 and Mistral 8X7B models were used in the studies.

In the course of the research, we explored that both models of chatbots boost efficiency in their respective sectors. They both provided quick responses to customers’ inquiries, which made users more satisfied with their overall experience, and as a result, they continued shopping.

To be more specific, buyers were able to reduce response time by up to 75%, in the case of customer support, and by 60% in the case of technical support. Thanks to their 90% and 82% success rates both GPT-3 and Mistral 8X7B managed to satisfy 80% and 78% of users respectively. In the first case, such a percentage of user satisfaction helped to increase sales by 20% through up-selling and cross-selling, which is an obvious benefit for the overall online store budget. Although the GPT-3 chatbot manages to handle twice as many conversations at once, the 1,000 simultaneous conversations from 8X7B are still a good contribution to the overall store’s performance.

Image by case study result:

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Both our case studies proved that the implementation of AI-based chatbots not only increased customer satisfaction but also decreased manual labor. In both cases, human agents’ workload was cut by 45%. For entrepreneurs, this means several important things:

  1. They can cut down excessive expenses by reducing nearly half of the support team staff.

  2. They can shift the remaining specialists to other departments where human involvement is necessary for better productivity.

  3. They can focus on more sophisticated challenges and customer inquiries, leaving routine requests to chatbots.

  4. They can increase sales without increasing investments.

But how can this robust optimization can be achieved? Such success rates of our case studies became possible because we did the following:

  • tuned both models using a vast dataset that included the e-commerce website's product catalog, FAQs, customer service dialogues, and order processing information;

  • enabled API access, customer service dialogues, and website information;

  • integrated the chatbot with the e-commerce platform's API, allowing it to access and relay real-time information about orders, deliveries, and product availability;

  • designed a user-friendly chatbot interface with features like quick reply options and intuitive navigation;

  • ensured the system can handle high volumes of queries, especially during peak times.

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Customers these days are looking for flexible and convenient ways for online shopping. AI can help in providing this. However, the focus is not only on the satisfaction of customers' needs. There are many more benefits it can bring to the e-commerce industry. Consider the following:

  • Customer segmentation tools: Special AI-driven instruments based on can track customer behavior. This helps to identify certain similarities and divide your buyers into various categories. These data can later be used to tailor your marketing practices to target specific segments of customers.

  • Smart logistics systems: When being integrated with the Internet of Things, AI solutions can boost inventory management to a higher level. With such tools, entrepreneurs can track their goods, verify the conditions of products, and optimize supply chain operations in real time.

  • Auto-generated: You cannot succeed in e-commerce if you don’t make your business visible online. Proper text content is the fastest way to boost your store’s ranking in search engines. AI solutions can greatly simplify this task by instantly generating well-structured content based on your SEO needs.

  • Market trends: AI and ML-driven solutions are able to analyze huge datasets much more efficiently than people. You can utilize this capability to study your competitors and overall market trends. This data can later be used to spot customers’ needs and develop a well-thought-out marketing strategy to engage customers in believing that your business is exactly what they are looking for.

  • Increasing conversion rates: Modern AI tools can return customers. This works very simply. For example, a customer adds a certain product to a cart but leaves your store without placing an order. AI can track customers’ activity and save data in order to contact that customer via email and remind them about adding a product to a cart. This might motivate a customer to come back and place an order, transforming traffic into sales.

 

Are there Any Limitations to AI?

While AI has made significant strides in transforming the e-commerce landscape, it is not without its limitations. Understanding these constraints is crucial for businesses aiming to harness AI effectively in the e-commerce industry.

  • Lack of human intuition: AI, despite its advanced capabilities, lacks the intuitive understanding that humans possess. This limitation becomes evident in scenarios requiring nuanced decision-making, empathy, and deep comprehension of complex human emotions, which are integral in customer interactions.

  • Data dependency: AI heavily relies on data for learning and decision-making. Limitations arise when dealing with incomplete or biased datasets, leading to skewed outcomes.

  • Ethical concerns: AI algorithms can inadvertently perpetuate biases present in training data, leading to discriminatory outcomes. E-commerce businesses must navigate ethical considerations to prevent reinforcing societal biases in areas such as product recommendations, pricing, and customer service.

  • Customer privacy concerns: Implementing AI solutions often involves the collection and analysis of vast amounts of customer data. Striking a balance between utilizing customer data for personalization and respecting privacy rights is challenging, with potential consequences for customer trust and regulatory compliance.

  • Inability to handle unforeseen scenarios: AI models are designed based on historical data and patterns, making them less adept at handling entirely novel situations or unforeseen market changes. Rapid adaptation to unforeseen events, such as global crises or sudden shifts in consumer behavior, remains a challenge.

AI in E-Commerce: Modern Tendencies

Yet, despite the limitations, it has been evident that the vast implementation of AI into the e-commerce sector is inevitable. Here are the most striking trends we can witness at the beginning of 2024:

  • Growing AI importance: At the beginning of 2022, the estimated AI market in e-commerce was around 5.81 billion USD. By 2032, it’s projected to increase up to 22.60 billion USD, facilitating the development and implementation of new AI-driven and ML-based solutions into e-commerce.

  • Increasing personalization: In 2024, AI will be mostly used in the e-commerce industry to create personalized user experiences, generate personalized advertising and content, and set tailored pricing.

  • Predictive analytics: Another important trend is that AI-driven technologies enable online retailers to forecast the behavior of their target audiences. This trend is expressed in the implementation of AI for data analyses and customer behavior projections.

On the whole, the prospects of AI in the e-commerce industry are yet to be discovered in their full prospects. This becomes especially obvious when you consider that despite striking efficiency, it still faces some severe limitations. Conducting more case studies to spot and eliminate more of such limitations is a relevant task for all the participants of the e-commerce world.

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