01 Jul 2025
Hiring

i would like to start a support candidates with hiring processes and ...

...careers, and get their data (education, experience, skills) within the web app to start building a global talent pool.

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

You're stepping into a crowded space. The "Swamp" category isn't encouraging: many have tried to create similar talent platforms, but few have truly broken through. The high number of similar products (n_matches = 13) confirms this, indicating significant competition. The average engagement (n_comments = 2) across these similar products is low, suggesting that these solutions generally struggle to capture user interest or solve the problem in a compelling way. The neutral buy/use signals reinforce this lack of strong positive feedback, so you will need to rethink if this idea can stand up to scrutiny. Your idea of building a global talent pool has been tried many times before, and it is going to be an uphill battle to change things.

Recommendations

  1. First, deeply analyze why existing talent platforms haven't fully met market needs. Review failed ventures and identify their shortcomings. This involves understanding why users haven't embraced these solutions and what unmet needs still exist. Dive into the criticism summaries of similar products, looking for consistent pain points or areas of dissatisfaction.
  2. If you decide to proceed, narrow your focus to a specific niche within the talent market. General talent platforms often struggle to gain traction, whereas tailored solutions for specific industries, skill sets, or experience levels can resonate better. For example, instead of 'global talent pool', how about 'global talent pool for sustainable energy startups'?
  3. Consider providing tools or services that integrate with existing platforms rather than trying to replace them. Instead of building a competing talent pool, build features or integrations for existing talent platforms. This could involve developing AI-powered screening tools that integrate with LinkedIn or creating specialized assessment modules for specific industries.
  4. Explore adjacent problems in the hiring space that may be more promising. Rather than directly competing with talent platforms, consider focusing on areas like skills gap analysis, personalized career development, or employer branding. These adjacent areas may have less competition and offer a clearer path to value creation.
  5. Given the challenges in this space, it might be best to focus your efforts on a different problem. The high competition and low engagement suggest that breaking through will be extremely difficult. Don't be afraid to pivot to a different idea that offers a more compelling value proposition or a clearer path to success.
  6. Focus on the ethical implications of your platform, especially when it comes to data privacy. Given the criticisms around data scraping, be transparent about how you collect and use candidate data. Prioritize user consent and data security to build trust and avoid legal or ethical concerns.

Questions

  1. What specific, unique value will your platform offer that existing platforms like LinkedIn, Indeed, or niche job boards don't already provide? How will you achieve significantly higher engagement and satisfaction among both candidates and employers?
  2. How will you ensure your AI-powered candidate screening process is fair, unbiased, and culturally sensitive, given the concerns raised about AI's ability to handle cultural nuances and soft skills? What measures will you put in place to audit and validate the AI's performance across diverse candidate pools?
  3. What is your plan to balance the desire to monetize candidate data with the increasing privacy concerns raised by users, and what specific data privacy and protection measures will you implement to build user trust?

Your are here

You're stepping into a crowded space. The "Swamp" category isn't encouraging: many have tried to create similar talent platforms, but few have truly broken through. The high number of similar products (n_matches = 13) confirms this, indicating significant competition. The average engagement (n_comments = 2) across these similar products is low, suggesting that these solutions generally struggle to capture user interest or solve the problem in a compelling way. The neutral buy/use signals reinforce this lack of strong positive feedback, so you will need to rethink if this idea can stand up to scrutiny. Your idea of building a global talent pool has been tried many times before, and it is going to be an uphill battle to change things.

Recommendations

  1. First, deeply analyze why existing talent platforms haven't fully met market needs. Review failed ventures and identify their shortcomings. This involves understanding why users haven't embraced these solutions and what unmet needs still exist. Dive into the criticism summaries of similar products, looking for consistent pain points or areas of dissatisfaction.
  2. If you decide to proceed, narrow your focus to a specific niche within the talent market. General talent platforms often struggle to gain traction, whereas tailored solutions for specific industries, skill sets, or experience levels can resonate better. For example, instead of 'global talent pool', how about 'global talent pool for sustainable energy startups'?
  3. Consider providing tools or services that integrate with existing platforms rather than trying to replace them. Instead of building a competing talent pool, build features or integrations for existing talent platforms. This could involve developing AI-powered screening tools that integrate with LinkedIn or creating specialized assessment modules for specific industries.
  4. Explore adjacent problems in the hiring space that may be more promising. Rather than directly competing with talent platforms, consider focusing on areas like skills gap analysis, personalized career development, or employer branding. These adjacent areas may have less competition and offer a clearer path to value creation.
  5. Given the challenges in this space, it might be best to focus your efforts on a different problem. The high competition and low engagement suggest that breaking through will be extremely difficult. Don't be afraid to pivot to a different idea that offers a more compelling value proposition or a clearer path to success.
  6. Focus on the ethical implications of your platform, especially when it comes to data privacy. Given the criticisms around data scraping, be transparent about how you collect and use candidate data. Prioritize user consent and data security to build trust and avoid legal or ethical concerns.

Questions

  1. What specific, unique value will your platform offer that existing platforms like LinkedIn, Indeed, or niche job boards don't already provide? How will you achieve significantly higher engagement and satisfaction among both candidates and employers?
  2. How will you ensure your AI-powered candidate screening process is fair, unbiased, and culturally sensitive, given the concerns raised about AI's ability to handle cultural nuances and soft skills? What measures will you put in place to audit and validate the AI's performance across diverse candidate pools?
  3. What is your plan to balance the desire to monetize candidate data with the increasing privacy concerns raised by users, and what specific data privacy and protection measures will you implement to build user trust?

  • Confidence: High
    • Number of similar products: 13
  • Engagement: Low
    • Average number of comments: 2
  • Net use signal: 6.7%
    • Positive use signal: 10.4%
    • Negative use signal: 3.8%
  • Net buy signal: -3.8%
    • Positive buy signal: 0.0%
    • Negative buy signal: 3.8%

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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