Find the selling points in a product photo
Turns a product image into a ranked list of visual selling points, with copy angles and the photos you are still missing.
The prompt
Look at the product photo I've attached and pull out the selling points a buyer would notice.
Product: [WHAT IT IS - e.g. "insulated stainless water bottle"]
Target buyer: [WHO - e.g. "commuters who want something leak-proof for a bag"]
Where this will be used: [product page / marketplace listing / ad / social post]
Main competitors or alternatives: [OPTIONAL - what buyers compare it to]
Facts I can confirm: [OPTIONAL - materials, sizes, certifications]
Steps:
1. Describe only what is actually visible: shape, materials, finish, color, details, scale cues, included items.
2. Translate each visible feature into a buyer benefit for my target buyer ("wide mouth" -> "fits ice cubes, easy to clean").
3. Rank the selling points by how much they would matter to that buyer.
4. Flag anything that looks like a weakness or could raise doubts (looks cheap, unclear size, missing angle).
Return:
- A table: Visible feature | Buyer benefit | Strength (high/med/low) | How to show it better.
- The top 3 selling points written as short benefit lines for [WHERE THIS WILL BE USED].
- A list of claims I should NOT make from the photo alone (e.g. "waterproof", "BPA-free") marked [CONFIRM] until I verify them.
- 3-5 extra shots I should add (angles, close-ups, scale or in-use photos).
Never state specs, certifications, or performance you can't see; ask me or mark them [CONFIRM].
Highlighted [brackets] are blanks to replace with your own details.
Fill in the blanks and use this promptMore Marketing & Brand prompts
What to fill in
- WHAT IT IS - e.g. "insulated stainless water bottle"
- product page / marketplace listing / ad / social post
- OPTIONAL - what buyers compare it to
- OPTIONAL - materials, sizes, certifications
- WHERE THIS WILL BE USED
- CONFIRM
How this prompt is built
This prompt follows the Few-Shot structure (Few-shot prompting). A few-shot prompt includes one or more worked examples of the input and the output you want, so the model can copy the pattern instead of guessing it.
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