I just got my fourth paying b2b client

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I have a SAAS site where small food producers can create nutrition facts labels online (http://www.menutail.com). They have to subscribe to a monthly plan to use the service. However after that first lead, I ran into a problem immediately on how to get more clients. Farmer's Markets - In Person: The first thing I did was to hit the ground and visit all of the farmer's markets around Southern California. Luckily there are quite a few here because of the great weather. But as you can probably see, this yields to a scalability problem where if it is just me, there are only so many leads I can pursue. From visiting about 5 farmer's markets, I did yield one new client. But what made the trips worthwhile is I talked with a vendor who won some food award which brought me to their website. Amazingly, the website had a list of all of the vendors who applied with their e-mail address. Data scraping: The next thing I proceeded to do is to scrape all of the food award websites which had e-mails on it. With that I generated my first list of contact e-mails. I sent e-mails soliciting my product to them, and continued scraping different lists and sending e-mails. From there I acquired my third and fourth paying clients with pretty minimal work just by sending the e-mails and fielding some questions.

Visit product View on Hacker NewsJul 12, 2013

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting

94%94% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

best fitHighest predicted score across all platforms for this description.

AppSumoStrong fit for a featured deal · Missing: plus, platform, intuitive

57%57% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Hacker NewsStrong engagement from HN community · Strong signals: io · Missing: https docs, excited, just released

55%55% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

TrustMRRFits verified-revenue profile · Strong signals: month, monthly · Missing: mobile apps, ios, personal

54%54% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Product HuntUnlikely to reach the leaderboard · Strong signals: new · Missing: mac, agents, macos

43%43% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Strong signals: saas · Missing: arr, mrr, revenue

21%21% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto

0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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