Best AI for Ecommerce Sellers? ChatGPT vs Gemini vs Grok vs Claude 

No single AI assistant wins every e-commerce job. ChatGPT drafts creative work well, Gemini connects to Gmail and Google Workspace, Grok reads live conversation on X, and Claude handles large data files and builds connected workflows. The right pick depends on the project, so this guide puts all four to work on four e-commerce jobs for North Bank, a made-up water bottle brand. The numbers behind the examples come from an actual account. 

The comparison comes from Leo Sgovio and Jo Lambadjieva, AI and ecommerce experts who teach the Helium 10 eCommerce AI Bootcamp. Their view is that a one-line answer to “which AI should I use?” usually means the person has missed the nuances, because the job should choose the tool. 

Which AI assistant is best for ecommerce? 

None of the four wins every job. Each assistant is strong in a different place, and the task decides the choice. 

The sections below show each assistant on its job. 

How does ChatGPT handle creative work such as video hooks? 

ChatGPT works well for creative drafting, which makes it a good fit for short-form video hooks. A hook has about one and a half to two seconds to stop a viewer from scrolling, so producing many options quickly matters. 

Leo’s prompt gave ChatGPT the product, a 32-ounce insulated stainless steel bottle that keeps drinks cold for 24 hours, has a screw top seal, and fits a standard car cup holder. It described the customer, a commuter or gym-goer between 25 and 45 who already bought a cheap bottle that leaked in their bag and ruined something. Then it set the criteria. He asked for 10 hooks, each under 12 words, spoken straight to the camera in the first second and a half, built around the leaking bag instead of the product, with no exclamation marks. 

Jo’s read on the prompt was that it works because it names the audience, supplies enough product information, sets the criteria, and limits the count. Ten options gave Leo plenty to sort through. Several repeated each other, so he kept about three and picked number 10, “Nobody thinks about bottle leaks until their bag is drenched.” ChatGPT then turned that hook into a full script ready to hand to a creator, and the same script can be reworked into four or five variations for A/B testing. 

Can Gemini find a supplier quote in your inbox? 

Yes, once Gmail is connected. Leo asked Gemini to look through his email for a supplier quote, and it returned the supplier’s latest quote and price right away. 

Doing that by hand means scrolling through a year or more of supplier emails and opening attachments if the quote lives in one. Ten to fifteen minutes per lookup is small on its own and adds up across suppliers and quotes. A follow-up question, such as whether a supplier’s quotes changed over the past year, takes the same search one step further. Jo called the result a clear summary that saves a lot of time. 

Gemini’s strength is its connection to Google Workspace, so it suits brand owners who already work in Gmail, Google Sheets, and Google Docs. 

What can Grok tell you about customer complaints on Amazon? 

Grok pulls data directly from X, where current customer sentiment lives, which makes it useful for category research. Leo asked what people were complaining about with insulated water bottles on X in the last 30 days, and he asked for the complaints grouped by theme with a rough count for each. Grok returned four themes. 

• Mold buildup. Shoppers report mold growing inside the bottle. Leo said he would not feel comfortable selling something that could make customers sick, so he would check his own product and talk to his supplier. 

• Odor retention. Silicone bottles tend to hold odors. His own bottle avoids this one. 

• Leaking. The problem the North Bank bottle is built to solve. 

• Fake or poor insulation. Complaints about bottles that fall short of the claims in their listings. Leo ties this to AI-written listings that people reuse without reading the details. 

Pulling and analyzing a large set of Amazon reviews is another route, as Jo points out. Grok gives you the same kind of insight in a few seconds. Leo also uses it for coding. 

How does Claude find wasted Amazon ad spend? 

Claude handles large files and builds on them step by step, which suits PPC analysis. Leo gave it a 30-day sponsored product search term report with a few thousand rows and asked where money was going to waste. His prompt held five requirements. 

1. A target ACoS of 25%. 

2. Every search term with 10 or more clicks and zero orders. Roughly every 10 clicks should produce an order, so a term with none is probably the wrong term. 

3. The total number of those terms, the spend on each, and the share of total spend they represent. 

4. The 15 worst keywords by spend. 

5. A rule to use only the data in the file and estimate nothing else. Claude draws on memory from earlier chats, and Leo did not want that history to skew this analysis. 

Claude found 63 qualifying search terms with $1,570 in total spend. The worst offenders included “stainless steel shaker bottle,” which took 157 clicks and $97, and a competitor’s brand name, which took 163 clicks. Leo said these would have been hard to find by hand across all of his campaigns. Jo pointed out that an agency or a multi-brand owner multiplies that saving across every account. 

Next, Leo asked for those terms as a negative keyword file, exact match, one per row, formatted for the Amazon bulk upload. Claude returned a CSV ready to download and upload to the ad account. Claude also creates Excel and PowerPoint files, so the output can match whatever format the next step needs. 

Then Leo grouped the 63 terms by type and asked where the money was going. Competitor brand searches took the largest share, with $543 and 1,279 clicks spent on Owala-related keywords alone. 

The last step moved from a downloaded file to live data. Leo asked what shoppers search for in the insulated water bottle category and how much volume sits behind each term, and Claude pulled the answer through the Helium 10 MCP. That replaced a CSV he had downloaded that morning, which was already out of date. With live data he can query daily to see whether his ACoS improves after his changes, and new keywords were already appearing in the results. A CSV still works well for a reliable one-time analysis. 

Claude’s skills, projects, and file-working features let Leo build dashboards on the same data after starting with a simple CSV. Jo’s advice is to begin with small tasks, build more sophisticated workflows as you learn, and keep experimenting. 

Do you have to switch assistants? 

No. Leo and Jo chose Claude for the rest of the bootcamp and admit some bias, but all four assistants are strong. If you already pay for one, stay with it. The principles, the process, and the thinking carry across assistants, so a different one works the same way. 

Model names matter even less. New models and new names arrive within weeks, and the ones named here will look old soon. Learn the technique and test which model fits your job. 

Which Claude plan should you choose? 

Start with Pro and move to Max once you begin hitting usage limits. Jo is on Max. The more sophisticated your workflows become, the more you automate, and the larger your context windows grow, the sooner Max pays for itself. 

Which Claude model fits which job for Amazon brands? 

Picture the models as a ladder. Each step up adds sophistication and reasoning and gives up speed and cost, so match the model to the task. 

At the time of recording, the versions were Haiku 4.5, Sonnet 5, Opus 5, and Fable 5. Jo finds Opus 4.8 and 4.6 better than 5 for some work, so test the other versions in the menu. For newcomers, she recommends staying with Haiku, Sonnet, and Opus, which cover everything in the bootcamp. 

For a full system you want to build and let run, start with Opus. Move to Fable only if the build is very complex, and watch the cost. Leo gives one example of Fable earning its price. His server was flagged for sending spam, and Fable was the only model that found the open port the attackers were using. 

What is an MCP? 

MCP stands for Model Context Protocol. It is a way for Claude and other AI assistants to connect safely to tools you already use, such as Helium 10, Google, or Notion, or to live data sources, which makes the output richer. 

In Claude, the Customize tab holds both Skills and Connectors. Skills lists the skills you have built and lets you add more. Connectors is where you add an MCP, such as the Helium 10 MCP. MCPs bring data in and can also take data out, so connect only MCPs from developers you trust.