What is Generative AI for eCommerce?
Generative AI for eCommerce is artificial intelligence technology that can create new content or data without human input. It uses algorithms based on parameters and constraints to generate data, such as text, images, videos, and audio. Generative AI can generate realistic-looking images, create product descriptions, or suggest changes to blog posts, product, and SEO titles and descriptions.
Let’s look at a straightforward example of Generative AI for eCommerce can transform the shopping experience and improve conversions by incorporating Conversational AI for Search.
Conversational AI for Search with a Chatbot
The chatbot should be able to recognize various forms of queries, including questions, statements, and requests. Once implemented here are the type of questions your Conversational AI Chatbot should be able to answer.
- I have a date I want to impress. What outfit works well for a moderately fancy restaurant in NYC?
- I’m a full-figured woman interested in work clothes that are comfortable for the office and stylish to go out with friends for dinner. What do you recommend?
- I want the fastest quick-dry clothes for my triathlon.
- I need a cover-up for super oily skin.
Chatbots can provide a more engaging and personalized shopping experience for shoppers, making it easier for them to find the products they need and increasing the likelihood of making a purchase. By implementing an interactive chatbot that uses NLP and ML algorithms to enable conversational search, retailers can transform the shopping experience and improve conversions.
There are so many practical uses of Generative AI for image recognition, voice recognition, natural language processing, and more. It can also analyze customer behavior, sales, and other data, enabling e-commerce brands to gain insights without technical knowledge.
AI for retail can save time and money on product photography by creating custom visuals based on customer taste or onsite search words and phrases. It can also suggest changes to blog posts and product descriptions that will improve search engine rankings and create personalized product recommendations that will increase sales by providing customers with tailored product suggestions that match their search words’ natural language.
Why is Generative AI Important for Retail?
Generative AI is important for retailers because it can be used to automate manual tasks and provide insights that would otherwise be difficult to obtain. For example, AI-powered search engines use natural language processing (NLP) to process and understand the query and then use the meaning to present the best-ranking search results.
Chatbots can also guide product-related questions and answer frequently asked questions about sizing, product variants, or discounts. Chatbots can be crucial in providing customer support and enhancing the customer experience on an e-commerce website by guiding product-related questions and providing personalized recommendations based on enriched content metadata.
Retailers can use product attributes (tags) to inform their chatbot by enabling it to understand and respond to shopper queries with greater accuracy and relevancy. By incorporating rich product attributes such as silver snap buttons, ripped jeans, and formal evening wear into the chatbot’s knowledge base, it can provide more detailed and personalized responses to customers’ questions, ultimately leading to a better shopping experience. Additionally, rich product attributes can help the chatbot make more accurate product recommendations, increasing sales and customer satisfaction.
Digitile’s Generative AI can also generate branded product description copy, and great product visuals help drive product and marketplace listing performance.
What Challenges Does Generative AI Face in eCommerce?
One of the challenges that Generative AI faces in eCommerce is that it requires large amounts of computer processing power that most companies may not be able or willing to invest in. Corporate governance teams may also not be equipped yet to deal with the real-time flow of generative content generated by tools like ChatGPT or the possible liability issues it could raise from sensitive or offensive material. There are a few ways for retailers to mitigate content liability issues by requiring a workflow that includes a human approver and a methodology for automating that sensitive and offensive content be excluded from the Generative AI model from the start.
Another challenge is that there is an arms race between ChatGPT and Alphabet/Google’s Bard, a competing tool. Understanding how each model has been trained, the input variables and how to deduce which one produces the best results requires time and tests. Additionally, as generative AI tools become more widely used, they may become more expensive and difficult to access, as well as more difficult to develop. Although these tools are extremely beneficial, it takes resources to understand how to use and train them effectively. This could make it difficult for smaller eCommerce businesses to use their power.
Finally, there is still a lot of fear and suspicion around generative AI, with concerns ranging from cybersecurity, intellectual property rights, and liability exposure to ethics. As technology continues to evolve, it’s important to be aware of the potential risks it poses and ensure that it is properly regulated and monitored.
Benefits of Generative AI for eCommerce
Improve Shopper Experience
AI for retail can create a better user experience on an eCommerce website by providing more personalized product recommendations, tailored visual content, and product descriptions that dynamically match a shopper’s natural language to improve the product’s appeal that it’s what they want to buy. This can make customers feel more valued and increase the likelihood they buy and become loyal customers.
Speed Up Product Discovery
Product discovery is identifying the right products that meet a shopper’s needs. Customers rely on product information such as product titles, descriptions, attributes, and images to do this.
AI-accelerated human annotation is a process that combines the power of artificial intelligence (AI) and human expertise to label and categorize large amounts of data efficiently. The process involves using AI algorithms to automatically identify and categorize data and then having humans review and correct the results to ensure accuracy. By combining the efficiency of AI with the precision of human expertise, AI-accelerated human annotation can significantly speed up the process of categorizing data, such as product images or descriptions, and make the data more useful for various applications, including product discovery.
Product data can be inconsistent, incomplete, or contain errors, making it challenging for customers to find what they want. AI-accelerated human annotation uses a combination of artificial intelligence and human expertise to analyze and annotate product data. This process helps to remove duplicates, merge product variants, fix inconsistencies on product detail pages, and correct errors, making the data more accurate and complete.
By improving the quality and accuracy of product data, AI-accelerated human annotation can help retailers provide a better shopping experience for their customers. It can also improve search and discovery within their online stores, resulting in more conversions and higher customer satisfaction.
Research has also shown that conversion rates double with the number of product images. AI-powered image tagging and enrichening of metadata can make it easier to process and manage large volumes of product images. This can make product discovery much faster and more efficient.
Digitile scales Generative AI to enrich product catalog data to reduce costly inefficient manual work.
Personalize the Shopping Experience
Feeding customer behavior and search trends into a Generative AI, it can suggest changes to blog posts and product descriptions to improve search engine rankings. This can increase intent buyer traffic as customers are more likely to find the website when searching for a product.
Personalized product recommendations created by Generative AI can increase sales by providing customers with tailored product suggestions. This builds customer confidence and loyalty and encourages them to purchase more items from the store.
AI-powered demand forecasting uses machine learning algorithms to predict and recognize changes in consumer demand. This helps ensure that the right stock level is available, reducing the risk of lost sales due to items being out of stock.
Examples of AI in eCommerce
Amazon uses Generative AI to personalize content for its customers. This includes personalizing product recommendations, customizing the user experience, and creating personalized product descriptions. It’s no secret Amazon implemented AI-powered search tools to improve the customer experience. Generative AI helps Amazon quickly identify relevant products to customers, providing a better shopping experience.
Digitile uses Generative AI to improve the quality of its customer’s Amazon product descriptions to improve discoverability and conversion. AI-powered natural language processing technology can generate product descriptions that are more accurate and detailed than those generated by humans. This help optimizes product listing descriptions, which increases customer satisfaction and reduces returns.
Walmart Smart Store
It comes as no shock, Walmart is using Generative AI to power its Smart Store. The Smart Store uses AI to analyze customer behavior and sales data to provide personalized recommendations and product suggestions and make smarter substitutions for its grocery business. “The decision on how to substitute is complex and highly personal to each customer. If the wrong choice is made, it can negatively impact customer satisfaction and increase costs.
The tech Walmart built uses deep learning AI to consider hundreds of variables — size, type, brand, price, aggregate shopper data, individual customer preference, current inventory, and more. And Walmart knows it’s working because, since deploying the tech, customer acceptance of substitutions has increased to over 95%.
Shopify Generative AI
As Shopify continues to help its 2.5+ million brands, they recently launched Shopify Magic. “Merchants enter a few keywords to target in search results, select a tone (like “expert” or “sophisticated”), and Shopify Magic will create a product description.” The disadvantage of Shopify Magic is it needs to be deployed on a per-product basis, so although it saves time, it’s not a solution that works well for retailers with a high volume of SKUs. This is where Digitile helps the larger brands scale the process. Our solution automates writing product and SEO titles and descriptions at scale to make the process effortless.
Best Practices for Implementing Generative AI for eCommerce
Developing a Data Strategy
Developing a comprehensive data strategy is the key to successfully implementing Generative AI for eCommerce. This strategy should include data collection, storage, and analysis processes tailored to the eCommerce organization’s unique needs. Additionally, a data strategy should include developing a plan for how to use the data collected to inform decisions about product pricing, marketing campaigns, and customer segmentation.
To ensure data accuracy, organizations should develop processes for verifying and validating data before using it in Generative AI models. This process should also include methods for ensuring data security and privacy and mitigating potential biases in the data set. Finally, organizations should consider how they will store the data and ensure it is accessible to the appropriate stakeholders.
Organizations should also develop processes for monitoring and evaluating the data strategy to ensure it is meeting the needs of the business. This should include establishing metrics for tracking the accuracy of data collection and analysis processes, as well as for measuring the impact of Generative AI models on the organization’s performance.
At Digitile, here is a list of metrics we track and analyze for each client to prove the value of our service.
- Resources to manage the process
- Time to manage the process
- Organic Traffic
- Searches with CTR
- Searches with no CTR
- Onsite Conversion
- Avg items per cart
- Avg order size
- PDP conversion rates
Creating an AI Framework
Organizations should also develop an AI framework to guide the development and implementation of Generative AI models. This framework should include guidelines for developing and testing Generative AI models and monitoring and evaluating their performance.
Define goals – Retailers should define clear business goals and use cases. They should identify areas where automation can bring significant value, such as reducing manual efforts and improving product discoverability and customer experience.
Define brand voice guidelines – These guidelines help establish consistency across all brand communications, including AI-generated content. AI-generated content may not accurately reflect the brand’s personality or message without these guidelines. By setting clear brand voice guidelines, retailers can ensure that their AI-generated content is aligned with their brand and creates a cohesive customer experience.
Choose the right data – Retailers need to ensure that they have quality data that is relevant and sufficient for training the generative AI model. This data should represent their products and include various attributes.
Train and test the model – Once the data is collected, retailers must train and test the generative AI model to ensure it produces accurate and relevant product attributes and descriptions. They should also continuously monitor and improve the model’s performance to maintain accuracy.
Implement a feedback loop – Implement a feedback loop to improve the generative AI model’s accuracy and relevance.
Integrating AI with Existing Platforms
Organizations should also consider how to integrate Generative AI models with existing processes and platforms. This should include developing processes for integrating Generative AI models with existing systems, such as customer relationship management (CRM), Product Information Management (PIM), and enterprise resource planning (ERP) systems. Additionally, organizations should ensure that the data used to train and test Generative AI models is accessible to the appropriate stakeholders.
Finally, organizations should also consider how to ensure that Generative AI models comply with country laws and regulations, as well as any ethical considerations.
AI Technology Players for Retailers
Chatbots for Shopping
- Kore – Virtual assistants can act as automated shopping assistants for your customers, improve and personalize service experiences, streamline retail operations, and simplify existing workflows.
- Alphachat – A great chatbot tool for building retail chatbots that understand natural language for marketing and customer support.
- Manychat – Brings the convenience of in-store shopping right to your customer’s phone with two-way personalized conversations.
- Intercom – Enterprise-grade AI chat for service that takes Shoppers through NLP product question flows.
Marketplace Product Listing
- Digitile – Enterprise AI that auto-generates tens of thousands of marketplace listings in days. Perfect for retailers and agencies that manage high volume of SKUs.
- Jasper – A well-funded solution for marketplace listing AI tool that is perfect for retailers under 100 SKUs.
- WriteSonic – Marketplace listing templates that are perfect for retailers under 100 SKUs.
Product Image Generation
- MidJourney – A community where thousands collaborate to create fantastic characters and unique imagery from short text descriptions.
- Pixelcut – Create product photos with AI-generated backgrounds that are as easy as snapping a photo.
- Topazlabs – Improve your photo and video quality with cutting-edge image enhancement technology.
Conclusion: Unleashing the Power of Generative AI in eCommerce: An Introduction
The Potential of Generative AI for Retailers
Generative AI has the potential to revolutionize the eCommerce industry. By automating manual tasks, providing insights, and personalizing the customer experience, Generative AI can help eCommerce businesses increase their efficiency and mitigate lost revenue. Using Generative AI, eCommerce businesses can create personalized product recommendations, suggest changes to blog posts and product descriptions that will improve search engine rankings, and create custom visuals based on customer taste.
Challenges of Implementing Generative AI for Retailers
Generative AI presents challenges that eCommerce businesses must overcome, such as the need for large amounts of computer processing power. Additionally, there is fear and suspicion surrounding the use of Generative AI. It is important to be aware of its potential risks and ensure that your team properly regulates and monitors it.
Best Practices for Implementing Generative AI for Retailers
Developing a comprehensive data strategy is the key to successfully implementing Generative AI for eCommerce. This strategy should include data collection, storage, and analysis processes tailored to the eCommerce organization’s unique needs. Additionally, organizations should develop processes for verifying and validating data before using it in Generative AI models and ensure data security and privacy to mitigate potential biases in the data set.