I made a free game that teaches you SALES

Hacker News

The site's pretty basic: astro, tailwind, typescript, netlify. Here's the bottom line up front: It's really just a spreadsheet that I was using as a checklist. … But I turned it into a little game with a cool retro vibe, because people told me the spreadsheet was so super useful for them to “learn sales.” I was inspired because I had a screenshot I posted on slack that become SUPER popular even though the text was really small & blurry – people kept asking for better quality. I couldn't find my original doc, so I used an AI tool to OCR the old image, transform to csv, then threw it on the web & everybody said it was like the best Sales Training ever — even for devs & designers! Eventually I turned it into a modern spreadsheet & then into a game, to make it more interactive and fun. #1 on Product Hunt today: ‣ https://producthunt.com/posts/sales-likelihood-calculator “Sales Likelihood Calculator” I think the moral of the story is: ① Look for what your users / audience actually want. They kept asking me for a better version of a blurry screen shot. ② Take it all the way and turn it into a product, even if the product is simple. ③ Make your first (or newest) product Free if you can. Just make a simple product before you leverage that into a larger paid product. You'll grow your audience, position yourself as an expert, and learn a ton along the way. Here are some famous HN articles about Sales: ‣ Ask HN: What is your advice for a technical founder learning sales? – https://news.ycombinator.com/item?id=18241160 ‣ Sales mistakes that software engineers make – https://news.ycombinator.com/item?id=18351891 Sincerely, Rowe Morehouse PS: After you play the game, you can interpret your Score here: https://objections.dog/sales-pipeline-review – It shows you tactics & exactly what to do next when you are trying to get your sales prospects to BUY. --

Visit product View on Hacker NewsJul 13, 2024

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Indie HackersFits the IH revenue-focused audience · Strong signals: mistakes · 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: slack, user, new · Missing: mac, agents, macos

72%72% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

66%66% 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: users, way · Missing: mobile apps, ios, personal

56%56% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

AppSumoMay struggle as an AppSumo deal · Strong signals: users · Missing: plus, platform, intuitive

27%27% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

15%15% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Strong signals: paid · 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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