Optimizing Product Management, Search And Customer Experience
Manish Mittal is the Founder & CEO of OpenSource Technologies, providing AI-driven software consulting and custom web & mobile app solutions
Online shopping has consistently grown since after Covid-19, and more customers are shopping online than ever before. E-commerce accounts for 20% of global retail sales and generates $4.12 trillion in revenue globally. This space is changing, and generative AI is one of the major contributors to this change.
According to Precedence Research, e-commerce AI efforts will generate upwards of $54 billion by 2032.
How Is Generative AI Transforming E-commerce Operations?
There are a lot of unique ways generative AI is transforming e-commerce operations across the internet, including:
Optimization And Personalization Of Content Generation
One of the most relevant uses for generative AI in e-commerce is content generation. Beyond random content generation, generative AI is used by e-commerce brands to generate content that is tailored to individual clients. These contents include marketing emails, website copy, product descriptions and more.
Generative AI’s capabilities have even extended further, allowing for customization of some product designs and virtual try-ons. Combining both these capabilities, customers can now fully experience a form of practical testing of these products to meet their needs before making a buying decision.
Improved Customer Support
Generative AI is also having a big influence on customer service. Chatbots driven by AI have progressed from basic answer generators to advanced virtual assistants that can handle intricate customer inquiries. Generative AI allows these chatbots to comprehend the conversational environment and deliver responses that are more precise and tailored to the user’s needs. Consequently, consumers get better service all around thanks to faster and more informative support agents.
Fraud Detection And Security
Beyond the customer-facing aspects of e-commerce businesses, generative AI is also finding applications in improving security for e-commerce platforms. AI-generated fraud detection algorithms are growing increasingly advanced, allowing for more efficient identification and prevention of fraudulent transactions. AI systems can better analyze transaction patterns to identify suspicious actions as they happen and take appropriate actions to safeguard e-commerce platforms and customers.
Customer Segmentation And Predictive Analytics
Since generative AI can help brands deal with large amounts of data, they are often used in segmenting audiences and can accurately aid businesses in forecasting the next actions of various customers based on their previous data. With this understanding, brands can position content to address particular pain points of customers at certain points in their buying process and also improve the whole buying process for these customers. This data can also help businesses target specific customer demographics with customized experiences that fit their profiles rather than offering generic solutions to all customers.
Major fashion brands like H&M use generative AI to afford customers customized clothes design functionalities, which puts their customers at the center of the buying process, highly increasing the chances of a customer making a purchase.
Visual Search And Product Discovery
This is another key way generative AI is being used on e-commerce platforms. The provision of a visual search that allows users to take pictures of samples of the products they are looking for, with AI helping to make the connections and provide accurate results, is a game changer for many e-commerce platforms.
Services like OpenSource Technologies take this AI function a step further with processes like its natural language processing capabilities. This allows AI to better identify and interpret common search phrases like “3-legged table” to give more accurate results. Furthermore, there is the improved contextual feature allowing AI to correct spelling mistakes, and identify synonyms for certain searches improving results for users. To cap its visual search function, OpenSource Technologies provides a voice search optimization function that allows hands-free searches with great accuracy.
Challenges Of Implementing Generative AI In Operations
The benefits of adding generative AI tools to e-commerce operations are easily derived from their multiple use cases. These use cases by various businesses across the globe do not always work out smoothly, and these challenges sometimes require out-of-the-box solutions. Some of these challenges include:
Seamless Integration With Existing Software
Generally, older legacy customer relationship management (CRM), enterprise resource planning (ERP), inventory management systems and other existing software for e-commerce operations may not integrate well with new AI tools that can improve their operations. Finding possible bottlenecks and thoroughly mapping the current systems are necessary for seamless integration. Making sure that data flows smoothly between platforms is essential for keeping operations running. Working with IT professionals and AI experts to create custom solutions that connect old and new systems can be a viable way forward in resolving this challenge.
Source Data Quality
Generative AI tools and models are often trained on a wide range of data, and the quality of the data they are trained on determines the quality of their output. A problem could arise when building custom tools for specific e-commerce operations. The quality of data available to e-commerce platforms will determine the quality of their output and the success of deploying the AI tool. The easy fix is to get high-quality input, but doing so may involve obtaining difficult-to-get permits, which would increase expenses and time.
Keeping Up With AI Technologies That Change Quickly
The improvement of AI technologies has been rapid from the very first popularized generative AI models to the likes of DeepSeek, GPT4. Ordinarily, this comes with its advantages but it also has its drawbacks. With each new iteration of AI models, some tools built on previous models become obsolete. Keeping up with these changes can be expensive and time-consuming, but missing out on these changes can leave profitable e-commerce operations struggling for relevance.
Getting Customers To Trust AI
This is a case where a modern solution comes with its problems. Although AI adoption has been widespread among businesses and e-commerce platforms, many customers are still skeptical about its safety, privacy and accuracy. This is a hurdle that can only be overcome with time and education for customers.
Conclusion
Generative AI is changing e-commerce operations in real time for numerous brands. When employed strategically, it can improve customer experience, inventory management and decision-making.
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