Generative AI in eCommerce: Common Use Cases and Challenges

The rapid growth of GENAI-powered solutions has significantly impacted productivity and increased revenue across various industries, including banking, high-tech, and life sciences. Generative AI tools like ChatGPT, DALL-E, and Midjourney can be applied to generate marketing and sales content, draft responsive codes, and support automated customer interactions.

According to Precedence Research, the e-commerce industry’s revenue reached a staggering $5.9 trillion in 2023 and is expected to hit $6.5 trillion in 2024 due to the integration of Generative AI models. Moreover, an anticipated 18.25% increase in eCommerce’s annual growth rate from 2023 to 2032 has triggered related businesses to intensify the use of AI for robust scalability.

Generative AI in eCommerce showcases its applications in various forms and for different purposes. This article will clarify the importance of AI solutions and introduce the top 10 trending approaches in integrating them into the work system to increase productivity for eCommerce businesses.

Benefits of Generative AI in eCommerce

Generative AI in eCommerce has reshaped the customer buying journey with a more immersive online experience. Most e-commerce businesses have applied AI technology to their online stores to automate routine tasks and shorten customers’ time for researching, considering, and ordering products.

As analyzing customer data into insightful reports, implementing generative AI solutions has transformed digital shopping habits in the e-commerce industry with the following benefits:

• Personalize customer experience: Generative AI algorithms result in higher customer satisfaction while enabling eCommerce shops to display recommended products and support virtual try-ons for faster purchasing decisions.
• Manage inventory accurately: Based on historical customer data, Generative AI models can predict demand patterns and capture ongoing trends to manage inventory levels properly.
• Enhance performance efficiency: Generative AI tools release marketers from manual intervention in creating content and images. Consequently, they can reduce the workload and have more time to focus on long-term strategic tasks like optimizing paid ads, adjusting the price structure, and preparing product launching plans.
• Improve conversion rates: By analyzing customer preferences based on historical data and demand prediction, AI allows e-commerce businesses to approach their target segment with relevant campaigns and products. Therefore, the sales reports will see an increase in customer engagement, add-to-cart, and order clicks, generating more revenue for businesses.

9 Generative AI Applications in eCommerce

According to Accenture, 75% of C-suite executives believe the rigorous competition in Generative AI integration can wipe out multiple businesses from the market in the next five years. Despite the risk of Generative AI’s impact, e-commerce companies possess more ways to approach customers with a streamlined experience enhanced by AI technology. Leveraging and combining the following trending Generative AI applications can assist e-commerce online stores in grabbing most of the market shares to surpass competitors.

Generate Targeted Marketing Campaigns

1. Content and Image Generation

Due to the development of Generative AI algorithms, prompting is becoming a more critical skill in content creation. Generative AI tools like ChatGPT and DALL-E use advanced machine learning models to generate relevant content, images, and videos for social media posts based on input prompts.

The diverse data basement gathered through years of experience and knowledge consolidates the power of integrating Generative AI systems into content and image generation. Reducing the workload for manual research, brainstorming, and writing allows marketers to focus more time on optimizing, testing, and modifying content to enhance customer expectations.

2. Advertisement Strategies

Generative AI in eCommerce analyzes users’ relevant data on their purchasing behavior across touchpoints for valuable insights. Based on a detailed understanding of customers, the AI tool enables businesses to create relevant advertising messages that align with target segments and campaign objectives.

Besides, AI solutions can track competitors’ ads/banners to identify the gap in your creatives and messages that affect performance. The tracking results can suggest optimized practices to enhance the ROI metric.

3. Price Strategy Optimization

An unattractive offer reduces the chances of converting your insightful messages into orders. Businesses can leverage Generative AI to optimize the pricing strategy by analyzing inventory management and predicting market demands without mitigating customers’ received value.

Practically, eCommerce businesses apply AI-driven pricing to personalize prices for individual customers, fostering customer loyalty, conversion rates, and profitability. Besides, Generative AI solutions can quickly adapt to market demand changes by conducting A/B testing to define effective pricing strategies for each customer segment.

Personalize Customer Experience

4. Providing Actionable Insights

An AI system enables e-commerce enterprises to personalize user experiences with actionable insights immersively. Indeed, marketers and planners have full access to customer personas through detailed data-driven reports to gather relevant data for better decisions. Shoppers can feel the impact of this approach through personalized product recommendations that display items and advertisements related to their preferences and historical orders.

For example, a digital brand named Myntra applies Generative AI to create personalized app experiences and tailored social commerce content, enhancing customer engagement. Meanwhile, Shopify enables sellers the flexibility to personalize their customers’ shopping experience through decent technology integrated into Shopify Magic, a free AI-enabled feature.

5. Enhancing Lead Engagements and Conversion Rates

Generative AI solutions trigger customers’ purchasing decisions quicker when delivering tailored messages and products according to their preferences and demands. Therefore, the e-commerce store sales reports can spot an improvement in conversion rates with higher revenue and ROI metrics. Mckinsey has proven this statement by showing a 3-15% and 10-20% increase in revenue and ROI, respectively.

Support Conversational Artificial Intelligence

6. Using Chatbots

Conversational artificial intelligence stimulates all human conversation scenarios to provide instant customer solutions. Every e-commerce platform displays a responsive chatbot to respond to customers quickly.

Chatbots answer customer queries and navigate them to appropriate product pages on the website. Artificial conversations can be humanized with the support of Natural Language Processing (NLP). Businesses can experience the beneficial features of chatbots through popular AI tools like Amazon Lex, Ada, or ChatGPT.

Learn more: Chatbot Pricing: How Much Does a Chatbot Cost?

7. Supporting Other AI Virtual Assistant Types

Besides chatbots, AI virtual assistants provide a wide range of applications relating to processing voice-activated, task-oriented, and predictive tasks:

• Voice assistants: Popular voice assistants like Google and Siri support audio responses to verbal queries.
• AI avatars: Giant technology companies like NVIDIA have developed human-lookalike 3D models to entertain customer experiences as virtual assistants.

Predict Future Demands

8. Integrating Machine Learning Models

Generative AI machine learning models encourage long-term sustainable development for e-commerce businesses. The high-end machine learning algorithms can analyze market trends and plan strategies for stable business operations. It includes marketing mix models, visual search, and voice search.

9. Analyzing Historical Data

Analyzing historical sales data assists e-commerce enterprises in planning optimized inventory and supply chain management generative strategies. Different types of Generative AI analytics, focusing on extracting valuable insights based on customer feedback, product attributes, and sales performance, include:

• Descriptive Analytics: generate insights about real-time product preferences, customer behavior, and market trends.
• Diagnostic Analytics: diagnose patterns that affect the campaigns’ performance.
• Predictive Analytics: predict the market demand, future trends, and consumer behavior to optimize the marketing campaigns and manage stocks.

How To Leverage Generative AI in your E-commerce business?

The decision to implement Generation AI into your e-commerce businesses evaluates multiple considerations regarding:

• Business objectives and outcomes with Generative AI uses: Determine AI solution choices that match operational goals, such as achieving expected ROI or improving internal team productivity.
• Required skillset and expertise to implement an AI project: Evaluate the current team quality to conduct people strategies for upskilling, training, or working with Generative AI consulting services for eCommerce to repurpose the existing resources.
• Timeline to successfully deploy an AI project: Depending on the complexity and scope of the business case, an AI project integration will last between 3 and 36 months.

After defining all internal factors affecting the success of AI adoption, here are the essential steps for e-commerce businesses to turn the AI roadmap into practical implementation:

1. Choose the Right Model: When choosing AI models, consider the business use, infrastructure limit, and data quality.
2. Train the AI Model: The training algorithm identifies the optimal model architecture after data collection and preprocessing. It constantly evaluates the performance and modifies the model to ensure accurate results.
3. Deploy and Monitor: Integrate e-commerce businesses’ knowledge databases and documents into the AI models and monitor the performance aligning with expectations. 
4. Maintain and Improve: Maintain and Improve: Empower and update the AI-based models with quality data patterns and retrain the model when necessary.

Learn more: Training AI model process

What Are the Challenges of Implementing Generative AI in eCommerce?

E-commerce businesses encounter various barriers to approaching Generative AI, including technical, financial, and human resource issues.

Key challenges that prevent companies from transforming operational approaches with AI solutions:

• Data privacy and security: Ensure the data management complies with GDPR and CCPA regulations to avoid leaking customer data.
• High-quality data: Accessing a high-quality database requires various efforts to collect and evaluate data to generate accurate recommendations and predictions.
• High initial costs: Implementing AI solutions requires businesses to purchase hardware, hire data engineers, and train the models.
• Skill deficiency: Adapting to the new AI technology development requires internal staff to update their knowledge and skills to deal with complicated requirements when operating AI-driven processes.
• Intangible ROI results measurement: It’s challenging to measure the return on investment of Generative AI implementations because it takes a long time to see their effectiveness in generating business revenue.

Generative AI in eCommerce Case Studies

Shopee and Amazon have expanded the limit of their e-commerce platforms to personalize customer experiences and convert more sales with innovative AI-driven features.

Case Study 1: Shopee

• Overview: As the leading e-commerce business in Taiwan and Southeast Asia, Shopee leveraged Generative AI engines to drive customer engagement with better personalization.
• Problems solved by GenAI: AI algorithms access customer behavior, preferences, age, gender, and historical purchase data to personalize their product recommendations and navigate them to find the needed items faster
• Results: Creating a personalized shopping experience enables Shopee to deliver more value to its users, brands, sellers, and partners. The 9.9 Super Shopping Day in 2019 resulted in 113 deals offered on September 9, with a peak of 187,606 items sold in a minute.

Case Study 2: Amazon

• Overview: The world’s leading e-commerce business has pioneered testing and applying the newest AI technology to remain on top.
• Problems solved by GenAI: Amazon uses Generative AI to summarize customer reviews in a single paragraph. This AI model shortens the time customers spend researching the product without reading all the reviews.
• Results: The AI model in the review summaries improves customer experience by providing relevant information and generating more sales conversions.

Future of Generative AI in eCommerce

Generative AI in eCommerce has paved the way for various high-end approaches to personalizing customer experiences and generating more sales. Based on high-end technologies, Generative AI technology enhances service quality with responsive customer support and less manual labor. The ongoing development of artificial intelligence by top-notch market leaders like Google, Nvidia, Open AI, and AWS will cause e-commerce businesses to continue chasing generative AI in the coming years.

However, overreliance on AI algorithms can lead to risks in maintaining customer trust, enhancing data security, and protecting users’ privacy. Practically, e-commerce enterprises can differentiate themselves with human-focused methods to deliver a friendly image to customers.

Learn more: The Impact of Generative AI Technology in the Retail Industry

FAQs:

1. How can Generative AI assist customer support for e-commerce businesses?

Gen AI chatbot assistant enhances the speed and efficiency of post-purchased support. Faster responses to queries around order status or product troubleshooting can satisfy customers’ online shopping experience and increase their lifetime value. Consequently, your e-commerce business will build better customer relationships and achieve higher ROI.

2. How can Generative AI optimize an e-commerce website?

GenAI models analyze performance metrics and create A/B testing to adjust design elements for optimal conversion rates. Besides, AI-driven technology can support generating SEO-friendly content and technical factors such as website structure, schema markup, and internal linking to boost online store visibility on search engines.

3. What are key AI models leveraged in e-commerce?

Generative AI supports e-commerce businesses with diverse applications across various modalities:

• AI text-generation model: Used for content production, chatbots, search results, and product analysis.
• AI image-generation model: Generate images for social posts and ads.
• AI audio-generation model: Support voice search, voice-activated shopping carts, and soundtrack for marketing purposes.
• AI 3D-representation model: Support designing 3D product models for different purposes.