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Dealora – What we learned starting up

Hacker News

Dealora – What we learned starting up

My experiences with how we just launched Dealora [1]. In June 2013, my wife and I decided to start a business together. We brought in 2 close friends of ours to help with sales and dev. The following month, launched a landing page [2] by sending out 2000 postcards to local businesses. We did see a small bump in traffic for the first few days but email sign ups for the pre-launch was disappointing. Cold-calling and Adwords - major disappointment. We met with a few local businesses in person but with little to show, we couldn't even get a handful to sign up for the launch. The data showed little interest. We shouldn't move forward. Somewhat defeated, we pushed forward anyway. The opposite of what a start up should do. Months later, we launch. No more cold-calling or reaching out by email. We're meeting face-to-face, sitting down with the business owners, being hands-on with on-boarding -- They appreciate the attentiveness. We're now getting sign ups daily but operating at a loss. Not a failure or success, but a slow start. The next few months will be the hardest in a crowded market. My big takeaways learned: - Not being able to validate your idea doesn't may not mean the idea isn't good, it could be you aren't conveying it in a meaningful way. We didn't. - Know the language you need to use when pitching. We used technical terms to pitch something to potential customers who are normally not very technical. - Cold-calling restaurants is bad idea. - Building the product is not the hard part, its selling it. - You can sell your product by listening first. Business owners love to talk about their business. If you listen closely, you can find ways for your product to solve their needs. Would be really helpful to get feedback from the HN community on things we can improve and experiences others have had selling to SMB. [1] https://dealora.com/ [2] https://news.ycombinator.com/item?id=6012998

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Indie HackersFits the IH revenue-focused audience · Strong signals: wife · 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.
Product HuntOn track for Day 1 leaderboard · Strong signals: new, email · Missing: mac, agents, macos
71%71% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
TrustMRRFits verified-revenue profile · Strong signals: month, way · Missing: mobile apps, ios, personal
51%51% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: ide, 000 · Missing: https docs, excited, just released
44%44% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive
21%21% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue
19%19% 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.

Correct prediction on native model

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