The e-commerce industry has witnessed tremendous growth in recent years, with artificial intelligence (AI) playing a pivotal role in its evolution. As AI technology continues to advance, businesses are finding innovative ways to harness its potential, leading to significant shifts in the e-commerce landscape. In this blog post, we’ll explore some of the key ways AI is reshaping e-commerce, from marketing automation to social media and beyond.
OK, time to come clean. The above paragraph (and some of this blog post) was written by ChatGPT, so if you’re interested in AI for eCommerce, you have probably read 10 articles with similar introductions. AI has been implemented in eCommerce already, and many of its use cases have been well articulated. This article has two new goals:
- Recapping the traditional AI use-cases and predicting how big of an impact they will have. I’ll let GPT-4 summarize the mainstream point of view on the technology, and then I’ll provide my own commentary.
- Predicting future use cases, not yet implemented, that could impact the future of eCommerce in a profound way. GPT-4 is less adept at predicting future technologies that have not yet been created (or well documented), so I’ll provide my own commentary without GPT-4’s assistance.
Current and near-term AI Applications for eCommerce
AI-Driven Personalization and Enhanced Customer Experience
GPT’s POV: One of the most significant changes brought about by AI in e-commerce is the increased emphasis on personalization. Online retailers now have the capability to create highly customized shopping experiences for individual users, thanks to AI algorithms that analyze customer data, preferences, and behavior. This allows businesses to recommend products, offer targeted promotions, and create a more engaging shopping experience.
Editor’s POV: Yeah. Personalizing the shopping experience has been a focus in eCommerce for multiple decades now (originally pioneered by Amazon). The most obvious way this manifest’s is via “recommended products”… which is obviously a very important feature of the PDP for most brands. Intelligence both on the PDP and the PLP is critical to raising conversion rates and AOV’s. Brands should always have intelligence around what products (and how) they are selling to consumers.
AI-Powered Customer Support for Ecommerce
GPT’s POV: The advent of AI-driven chatbots has revolutionized customer support and marketing automation in e-commerce. These virtual assistants can handle a wide range of tasks, from answering frequently asked questions to helping customers navigate a website or complete a purchase. Chatbots not only provide faster and more efficient support but also free up human customer service representatives to focus on more complex issues. Gartner predicts that by 2027, 25% of organizations will use chatbots as the primary customer service channel.
Editor’s POV: There have been significant improvements in AI-driven chatbots over the last 6 months. This is a big opportunity to vastly reduce customer service costs and improve the customer service experience. I’m skeptical it will have a big impact on revenue, but it’s a low-hanging fruit for businesses and it is already expected by consumers. Don’t lose business over antiquated customer service.
Social Media and Influencer Marketing Strategies
GPT’s POV: AI is transforming the way businesses approach social media and influencer marketing in e-commerce. Advanced algorithms can analyze data from social platforms, such as user preferences and interactions, to identify potential influencers and creators who align with a brand’s values and target audience. This enables businesses to build more effective ambassador programs and leverage the power of creator marketing.
Editor’s POV: I’ll write a longer blog post about the impact on AI on influencer marketing. But I agree with GPT-4 that there are opportunities for identifying influencers, identifying ambassadors, and selecting influencer content to white label.
Dynamic eCommerce Pricing Strategies with AI
GPT’s POV: AI-driven dynamic pricing strategies have become increasingly popular in e-commerce. These strategies involve adjusting prices in real-time based on factors such as customer demand, competitor pricing, and inventory levels. By using AI to analyze vast amounts of data, businesses can ensure that they are offering competitive prices while still maintaining healthy profit margins.
Editor’s POV: Admittedly, I’m not a pricing expert. However, I believe this is an example where a large dependency on AI can create problems for SOME businesses. I believe AI needs slightly better reasoning capabilities and business intelligence before it gets control over pricing. Which really means I think most brands need to wait until eCommerce focused AI pricing solutions are launched. Until those eCommerce enablement products are created, there are opportunities for dynamic discounting strategies and products that are priced in real-time (like utilities). However, pricing for most businesses involves too much margin (finance), supply chain, and marketing coordination to easily automate with existing tools. Larger brands with enough bandwidth could accomplish that coordination, but small brands should wait.
A study by McKinsey shows that brands can improve revenue by 2-5% and margins from 5-10% with dynamic pricing strategies. While that’s exciting, with all of the other pricing contingencies that doesn’t feel like the lowest hanging AI fruit.
Enhanced Visual Search Powered by AI
GPT’s POV: AI-enhanced visual search technology allows customers to search for products using images rather than text. This can be particularly useful for industries such as fashion, where customers may be looking for a specific style or pattern. By analyzing the visual content of an image, AI can recommend similar products and help customers find exactly what they’re looking for.
Editor’s POV: I have not seen this implemented on any eCommerce sites yet (or search engines), but this is really cool. I think computer vision models are almost ready for primetime in this capacity, so expect some neat eCommerce enablement products to come out in the next 6 months.
Timeline: Six months
AI in Logistics and Supply Chain Optimization
GPT’s POV: AI is playing an increasingly important role in optimizing logistics and supply chain operations. Machine learning algorithms can analyze data from various sources, such as GPS tracking and weather information, to optimize delivery routes and reduce shipping times. AI can also help businesses make data-driven decisions when it comes to selecting suppliers and managing inventory levels.
Editor’s POV: eCommerce enablement tools in the supply chain space have already leveraged AI in exciting ways, and I would continue to expect these tools to allow brands to drive down operational costs. Fortunately for small brands, I think most of these improvements can simply be managed by selecting effective eCommerce enablement solutions. For larger brands, AI expertise will be valuable to drive down operational costs.
Long-term AI Applications for eCommerce
Super Marketing Automation / Personalization
Editor’s POV: I believe there is a relatively large gap between marketing automation / personalization products we currently see in the market, and what’s currently possible with large language models and machine learning.
There is already enough data available on users, and now there is certainly enough generative capability, to deliver personalized ads to everyone in almost every context. Brands could effectively make every ad feature an influencer that consumer follows, with personalized copy as if it were an email. I like to visualize this is the scene in Minority Report with the billboards that speak to individuals that walk by… but instead of billboards these are digital ads. There’s no technological gap in achieving this level of personalization right now.
Timeline: 6-12 months to see products highly personalized ad products, probably another 12 months to see widespread adoption if the market accepts these ads as not overly “creepy”
Personal Shoppers (the virtual sales-person)
Editor’s POV: With the success of video in eCommerce and the rapid enhancements in generative AI, the next iteration will be a true “personal shopper” or “virtual sales person”. Think a digital agent that gives feedbacks on products, answers questions, and provides inspiration. There’s a reason that retail stores have had sales representatives for hundreds of years, and we now have the generative capability to create digital, autonomous versions of the physical sales rep.
Timeline: Early versions are here, but expect them to reach mainstream in 18 months
AI-powered Virtual changing rooms for eCommerce
Editor’s POV: Virtual changing rooms have been speculated about for more than a decade. It makes sense since one of the greatest risks of shopping online is mismatched expectations with how products will look / fit when they arrive. We started seeing companies selling virtual fitting rooms in the last 5 years, but they haven’t really reached mainstream yet.
With generative AI and 3D modeling, we now should have enough technical capability to create higher quality virtual changing rooms that delight customers and drive more demand.
Timeline: Early versions are here, but expect them to reach mainstream in 18 months
AI Agent Optimization (SEO for AI agents)
SEO and organic search are important pillars of any brand’s marketing strategy. Google is the single largest channel for discovering products currently. However, many are now speculating that Google’s core business of search is in danger from large language models. That will have a downstream impact on brands. If people discover products through their AI bots and not via Google, the brand now needs to optimize its “AI discoverability” instead of their search engine discoverability.
The current ChatGPT interface is not conducive to shopping since it’s responses are primarily text without links, but it’s not hard to imagine in the very near future a better shopping interface could be created using GPT as a backbone. Perhaps a Google Chrome plugin that focuses its interface on displaying series of images, prices, reviews, and links and has more advanced reasoning than a basic Google search.
This product does not exist yet, but once people start shopping with AI agents, brands will have to experiment with this channel as a source of revenue. Given search’s current power in the market, it will be many years before people unlearn Google / search as a habit and begin asking bots for feedback on products. But that day may be coming in the distant future.
Timeline: 1-2 years to be an experimental channel. +5 years to replace search engine optimization as a primary organic channel.
Brands have made considerable progress with AI (whether or not we noticed) for the last decade. Many machine learning models are already built into platforms like Shopify and eCommerce enablement solutions. However, the rate of progress with AI is about to expand by orders of magnitude. It will become a requirement in the very near future for any brand to adopt AI functionality. Simply put, cost savings and marketing efficiencies will be too great for companies to stay competitive without leveraging AI tools.
In my opinion, the most important factors in a brand’s success will not change: you still need to build a great product and then get customers excited about that. But AI will become a core competency of any business, and it will also drive increased competition. Brands that do not keep up will not be able to stay competitive. The good news for brands is that most of this work will be done for them by eCommerce enablement partners and the actual eCommerce tools themselves, so the key will be understanding and implementing the correct solutions.
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About the Author(s)
- Gary Garofalo is a marketing-focused technologist and the CEO of LoudCrowd. He’s spent his career focused on analytics, strategic consulting, and building technology companies. When he’s not writing about social media, he spends his free time reading, lamenting over the risks of climate change and artificial intelligence, and playing pickleball with the LoudCrowd team.
- Chat GPT-4 “is a sibling model to InstructGPT, which is trained to follow an instruction in a prompt and provide a detailed response.