Ecommerce prompt discovery
How to Find the Prompts Your Customers Use in ChatGPT
You cannot open a reliable public report of every ChatGPT product search. You can build a defensible prompt set from your products, customer problems, buying constraints, comparisons, and competitors—then track those exact questions instead of guessing from one broad keyword.
Prompt seed
Commuter water bottle
Product
insulated water bottle
Use case
daily bike commute
Problem
leaks inside a backpack
Constraint
under $50 · 750 ml
Alternative
Hydro Flask
Start with evidence you own
A useful prompt is a realistic buying question, not a keyword with “ChatGPT” attached. Product catalogs supply attributes. Search Console, onsite search, support tickets, reviews, returns, and sales conversations reveal the language and friction around them. Combine both.
For a Shopify bottle store, “water bottle” is only a category. “What is a leakproof insulated bottle under $50 for commuting by bike?” contains a product, a problem, a budget, and a use case. Each part is testable against the catalog.
Five intent groups
Do not treat every prompt as a recommendation search
Discovery prompts explore a category. Comparison prompts weigh alternatives. Recommendation prompts ask for a shortlist. Problem-solving prompts begin with friction. Purchase prompts look for a product or seller that meets defined constraints.
Track them separately. A store can be absent from broad discovery but present for a precise problem it solves well. Mixing the groups into one score hides that difference.
Build the raw material
Six inputs create a useful prompt portfolio
Use concrete nouns and constraints. Avoid dozens of cosmetic rewrites that test the same decision.
Products
type, category, material, size, compatibility, price
Use cases
commuting, gifting, travel, training, small spaces
Problems
leaking, overheating, irritation, poor fit, wasted time
Comparisons
product A vs B, material A vs B, premium vs budget
Buying intent
best, recommended, under a budget, where to buy
Competitors
your category against named alternatives customers know
Measurement boundary
Estimated opportunity is not observed demand
An opportunity prompt is a reasoned hypothesis: customers have the problem, your product fits, and the wording reflects a buying decision. It is not proof that thousands of people type that exact sentence into ChatGPT.
A tracked prompt is more concrete. You know the exact wording, when it ran, which answer appeared, and which brands or sources were present. But that still measures your defined test—not private platform-wide search volume or every personalized response.
Estimated opportunity
A prompt worth testing
- Built from catalog and customer evidence
- Intent and commercial value are inferred
- No claimed ChatGPT search volume
Tracked observation
A prompt actually executed
- Exact wording and run date saved
- Answers, mentions, citations, and competitors recorded
- Still not population-wide demand data
From research to monitoring
Keep the prompt wording stable
Dropstore can generate a natural buyer question from the connected Shopify catalog, organize saved prompts into groups, and run them daily across supported AI models. The result history stores answers, mentions, citations, position, sentiment, and competitors.
Generation gives you a candidate, not customer-volume evidence. Review it, add missing commercial angles, and keep the chosen wording unchanged so comparisons over time remain meaningful.
Start the 7-day trialPrompt workspace
Sample dataPrompt discovery framework
Build a prompt set you can defend
- 1Choose one priority product or collection—not the entire store.
- 2Extract factual attributes, use cases, constraints, and problems from catalog and support evidence.
- 3Create one prompt for each intent: discovery, comparison, recommendation, problem-solving, and purchase.
- 4Add only constraints the product can genuinely satisfy, such as price, size, material, or compatibility.
- 5Include competitor prompts where shoppers plausibly compare named alternatives.
- 6Remove duplicates that would produce the same decision and keep the exact wording stable.
- 7Label untested prompts as opportunities; do not attach invented search-volume numbers.
- 8Track the final prompt set repeatedly and review answers, mentions, citations, and competitors over time.
Reusable template
“What is the best [product type] for [use case] that solves [problem], includes [must-have attribute], and costs [budget]?”
Use only fields that influence a real purchase. Omit anything the catalog cannot support.