These Are the 3 Most Common Mistakes I See First-Time Founders Make as an Investor | Entrepreneur
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These Are the 3 Most Common Mistakes I See First-Time Founders Make as an Investor | Entrepreneur
"Over the years, I've worked with and invested in many early-stage companies. I've seen promising startups gain traction and scale beyond expectations. Sadly, I know too many founders fall into the same predictable traps. They make simple mistakes that stall growth or even derail their businesses entirely. It's not incompetence or a lack of determination. Passion, drive and ambition are vital qualities for entrepreneurs. However, they can lead founders down a dangerous path if they go unchecked."
"They believe their idea is solid and move full steam ahead, spending money on development, marketing and hiring without validating their product with real customers. Why does this happen? Simple: It's easy to fall in love with your own idea. You think you're building something the world needs, and it feels obvious to you. But that's a dangerous place to operate from."
"Case study: Pivoting based on real users I remember a founder in our network who started a cosmetics company. When he launched the company, he thought the core audience would be women in their mid-20s, so they targeted, built for and marketed to that group. But when the sales data started coming in, it told a different story. It turned out that middle-aged and older women were the most loyal customers."
Many founders fall into predictable mistakes that stall growth or derail businesses despite passion and determination. One common error is assuming product-market fit without validation. Founders often spend on development, marketing, and hiring before putting the product in real customers' hands. Falling in love with an idea creates dangerous confirmation bias. Product-market fit requires testing with unbiased users and interpreting sales data. Early customer behavior can reveal unexpected core audiences, prompting pivots toward the most loyal segments. Validation, data-driven targeting, and willingness to change are essential to avoid premature scaling and resource waste.
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