Open AI’s ChatGPT has created a buzz about the current “AI Revolution,” but it isn’t a revolution for everyone. This is a time of innovators slowly handing off AI applications to early adopters. The early majority is still waiting on the sidelines, waiting for that AI Revolution to reach their industry or market.
It’s only begun to permeate ecommerce, where we see businesses using artificial intelligence and machine learning to streamline operations, personalize marketing and enhance the shopping experience.
As those early adopters start integrating them into their systems, here is how you can apply them to your ecommerce business:
1. ChatGPT and AI-Generated Content
The most obvious application of AI is using tools like ChatGPT to generate strategic copy and content. ChatGPT is particularly astounding to us all because it responds in a way that we all understand, with no code or programming knowledge required. As a language model, its skill is navigating human language, pulling from vast libraries of information, giving you exactly what you asked.
That means you don’t need to rely on human writers to dig through research to create search engine-optimized product descriptions. Businesses are already using ChatGPT to identify those keywords and use them to optimize their copywriting. Shopify even now offers AI generated descriptions based on keywords merchants input.
Though these AI models are impressive, they aren’t infallible. They still make errors: hallucinations — or information gaps that have been creatively filled in by the AI to give a complete answer. These confabulations can manifest in lies or wrong information, citing sources that don’t exist.
The data sources that AI pulls from are limited in scope and variety… and slightly controversial. Copyright claims are a concern when AI generates from other sources, and more companies, such as Reddit, want to make more money from the data they provide.
But text is only half the battle. On Amazon, the title and image are priority number one. The first image of a product on a white background is essential. Then you need lifestyle shots, bullet point overlays and an example of product scale. You always miss a few images that you need during a photo shoot. Photoshoots are expensive, and AI could bridge that gap. Sort of.
Image generation isn’t quite there yet. Levi’s, the denim company, recently had a campaign using AI from Lalaland.ai wearing their clothes. The models have a slightly “off” look to them, as most AI-generated images do, but it shows off the clothes without having to hire an actual model to put them on. This technology works well with clothes, but we have yet to see a tool that uses models interacting with more complicated 3-D objects.
Related: The Dark Side of ChatGPT: Employees & Businesses Need to Prepare Now
2. Chatbots and customer interactions
More and more customers are interacting with chatbots and are enjoying the process. They’re available 24/7 and generally converse naturally, personalizing the experience. They also can upsell in the moment of interaction.
Chatbots also speed up the customer support process. A survey of executives with companies using chatbots found that 90% had “measurable improvements in the speed of complaint resolution.” The less time people wait on the phone for a customer service agent, the happier they are.
They do have limits, though. Chatbot company, Tidio, found that people prefer a human assistant when it comes to returning a product, troubleshooting and complaining about a service or product. Other companies offer chatbot integration for online businesses as it becomes more common to interact with these chatbots during an online customer journey. It’s possible to have one custom-built for your company, but also more expensive.
3. Advertising targeting and personalization
Catching potential customers in the consideration phase is getting easier, as AI-targeted ads intercept them during their shopping process. Online buyers will research for the product that best fits their needs, and as they hone in on their searches, an ad might pop up, giving them exactly what they need.
Online furniture retailer Wayfair is an example of a company that uses AI to determine which customers are most likely to be influenced by the ads and, using their browsing histories, choose products they might actually buy.
AI algorithms analyze vast amounts of data about customer behavior, demographics, purchase history and interests. More businesses are specifically using AI to distill this info for audience targeting and segmentation, avoiding bombarding consumers with irrelevant content. Higher engagement rates turn into more conversions.
Another important aspect of creating targeted ads is through keyword harvesting — finding the best keyword match for your product. Automatic campaigns can be set to mine keywords, transfer keywords between campaigns and boost bids depending on peak and off-peak hours. It’s an optimized ongoing process that either you or an employee would otherwise have to do constantly.
Marketing personalization gets even more advanced with AI-generated customer personas. Companies like Delve.ai use millions of data points from internal and external sources to create ideal customer personas, competitor personas, and social personas. Some AI tools use collected psychographic data and qualitative psychological factors to create more accurate personas than ones made with just demographic and behavior metrics.
Related: 5 AI Marketing Tools Every Startup Should Know About
4. Sentiment farming and fraud prevention
Sentiment analysis is a newer tool to mine opinion data from reviews, surveys, web articles and social media. Language models are used to sift through the noise online to pull out what customers say about your products.
You’re left with actionable insight into how consumers feel about your brand, your products and their customer journey. Opinions are measured by the adjectives used in conjunction with the product or service being reviewed. These adjectives are rated, and a score is revealed to rank the opinions. These opinions are sometimes skewed by paid reviewers making fake positive or negative reviews, which mislead customers. Sentiment analysis has been found to help prevent fraud by using language models to find spam reviews.
Related: How AI and Machine Learning Are Improving Fraud Detection in Fintech
5. Supply chain planning
By analyzing customer behavior and demand data, AI-powered tools can help businesses optimize their inventory levels, reduce waste, and improve the efficiency of their supply chain.
Forecasting customer demand and capacity constraints is necessary for supply chain management. AI tools can ensure that warehouse facilities have the correct flow of inventory in and out to protect against under- or overstocking. Amazon offers AI-powered inventory management through Intellify, building demand forecasts that allow your teams to act on inventory purchase recommendations.
These AI supply chain solutions will not make the decisions or purchases for you, though. AI isn’t advanced enough yet to be trusted to make independent solutions. Complicated loop systems are being developed to reduce human interactions, giving AI like ChatGPT the ability to make iterative decisions based on the task given to them.
The AI Revolution is upon us, but don’t expect an imminent Terminator apocalypse. The ecommerce tools offered by many AI services can help you streamline your business but won’t take you out of the equation yet.