Should I Build It Should I Build It
29 Jul 2025
E-Commerce

Should i sell this? You are e-commerce seller, you make a photo of the ...

...product upload this and we show you, should you sell this or not, how much money you could earn

Confidence
Engagement
Net use signal
Net buy signal

Idea type: Swamp

The market has seen several mediocre solutions that nobody loves. Unless you can offer something fundamentally different, you’ll likely struggle to stand out or make money.

Should You Build It?

Don't build it.

Your are here

Based on your idea, which involves analyzing product photos to determine their e-commerce selling potential, you're entering a crowded space. Our analysis shows 18 similar products, so you're squarely in the 'Swamp' category, meaning there are several existing solutions, but none that have truly captured the market's heart. The average number of comments on similar products is just 2, indicating low engagement. This suggests that while many are trying to solve this problem, no one has created a massively successful solution yet. Since there's no net use or buy signal, that means that the user base neither loves nor hates this product. But you have to be aware that it will be really hard to make money in this space since there are many competitors and nobody loves the existing solutions.

Recommendations

  1. Given the crowded market, start by deeply researching why existing image-based product assessment tools haven't taken off. Are they inaccurate? Too slow? Not integrated well with existing e-commerce platforms? Understanding their shortcomings is crucial before moving forward.
  2. If you proceed, identify a very specific niche of e-commerce sellers to serve. For example, instead of 'all e-commerce sellers,' focus on 'vintage clothing resellers on Depop' or 'independent jewelry makers on Etsy.' This specialization will allow you to tailor your analysis and recommendations for maximum impact.
  3. Consider creating tools or services that integrate with existing e-commerce platforms or seller tools, rather than trying to replace them. For example, you could build a Chrome extension that overlays your 'sell/don't sell' analysis directly onto product photos within Etsy's interface.
  4. Given the difficulty of predicting sales potential, explore related problems that might be more solvable. For example, could you help sellers optimize their product photos for higher click-through rates or improve their product descriptions using AI?
  5. Before investing significant time and resources, validate your assumptions by manually analyzing product photos for a small group of sellers and tracking their actual sales results. This will provide valuable data to refine your algorithm and value proposition.
  6. Since comments on similar products revealed concerns about ease of use and integration, prioritize a seamless user experience. Focus on a clean interface, clear explanations of your analysis, and easy integration with popular e-commerce platforms. Address the reported bugs and login issues, and make sure the core functionality of the product is stable.

Questions

  1. What specific, unique data points or algorithms will your tool use to predict sales potential that existing solutions don't offer?
  2. How will you acquire a sufficient volume of high-quality product photos to train and continuously improve your analysis algorithms, especially given the low engagement of similar products?
  3. What is your go-to-market strategy for reaching your target niche of e-commerce sellers, and how will you differentiate your tool from the many existing competitors in the 'Swamp' category?

Your are here

Based on your idea, which involves analyzing product photos to determine their e-commerce selling potential, you're entering a crowded space. Our analysis shows 18 similar products, so you're squarely in the 'Swamp' category, meaning there are several existing solutions, but none that have truly captured the market's heart. The average number of comments on similar products is just 2, indicating low engagement. This suggests that while many are trying to solve this problem, no one has created a massively successful solution yet. Since there's no net use or buy signal, that means that the user base neither loves nor hates this product. But you have to be aware that it will be really hard to make money in this space since there are many competitors and nobody loves the existing solutions.

Recommendations

  1. Given the crowded market, start by deeply researching why existing image-based product assessment tools haven't taken off. Are they inaccurate? Too slow? Not integrated well with existing e-commerce platforms? Understanding their shortcomings is crucial before moving forward.
  2. If you proceed, identify a very specific niche of e-commerce sellers to serve. For example, instead of 'all e-commerce sellers,' focus on 'vintage clothing resellers on Depop' or 'independent jewelry makers on Etsy.' This specialization will allow you to tailor your analysis and recommendations for maximum impact.
  3. Consider creating tools or services that integrate with existing e-commerce platforms or seller tools, rather than trying to replace them. For example, you could build a Chrome extension that overlays your 'sell/don't sell' analysis directly onto product photos within Etsy's interface.
  4. Given the difficulty of predicting sales potential, explore related problems that might be more solvable. For example, could you help sellers optimize their product photos for higher click-through rates or improve their product descriptions using AI?
  5. Before investing significant time and resources, validate your assumptions by manually analyzing product photos for a small group of sellers and tracking their actual sales results. This will provide valuable data to refine your algorithm and value proposition.
  6. Since comments on similar products revealed concerns about ease of use and integration, prioritize a seamless user experience. Focus on a clean interface, clear explanations of your analysis, and easy integration with popular e-commerce platforms. Address the reported bugs and login issues, and make sure the core functionality of the product is stable.

Questions

  1. What specific, unique data points or algorithms will your tool use to predict sales potential that existing solutions don't offer?
  2. How will you acquire a sufficient volume of high-quality product photos to train and continuously improve your analysis algorithms, especially given the low engagement of similar products?
  3. What is your go-to-market strategy for reaching your target niche of e-commerce sellers, and how will you differentiate your tool from the many existing competitors in the 'Swamp' category?

  • Confidence: High
    • Number of similar products: 18
  • Engagement: Low
    • Average number of comments: 2
  • Net use signal: 10.0%
    • Positive use signal: 10.0%
    • Negative use signal: 0.0%
  • Net buy signal: 0.0%
    • Positive buy signal: 0.0%
    • Negative buy signal: 0.0%

This chart summarizes all the similar products we found for your idea in a single plot.

The x-axis represents the overall feedback each product received. This is calculated from the net use and buy signals that were expressed in the comments. The maximum is +1, which means all comments (across all similar products) were positive, expressed a willingness to use & buy said product. The minimum is -1 and it means the exact opposite.

The y-axis captures the strength of the signal, i.e. how many people commented and how does this rank against other products in this category. The maximum is +1, which means these products were the most liked, upvoted and talked about launches recently. The minimum is 0, meaning zero engagement or feedback was received.

The sizes of the product dots are determined by the relevance to your idea, where 10 is the maximum.

Your idea is the big blueish dot, which should lie somewhere in the polygon defined by these products. It can be off-center because we use custom weighting to summarize these metrics.

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