Oribi
Iris Shure came to Oribi with hard-won experience from two successful exits. After studying architecture and co-founding a 3D modeling startup (acquired by Autodesk), then OverOps (a DevOps debugging tool), she was intimately familiar with the frustration of trying to be data-driven in marketing. She could see CTR on videos but couldn't answer the harder question: did it actually drive conversions? "It was very hard for me to answer more advanced questions," she recalls. This pain, combined with her strategic assessment of her own strengths (B2B, low-touch, high-scale products), crystallized into a conviction that the analytics space had a massive gap.
Instead of rushing to raise money, Iris spent the first year talking to people and asking herself a crucial question: "If there's a pain here, why is nobody doing it?" She hired one developer and started building. Her research revealed the core problem: to build good analytics, you need a solid data layer, but most tools require developers to set up code-based events. Developers are always busy, so the data foundation never gets built—and without it, even amazing reports and insights are useless.
She decided to start with Facebook Analytics, a smaller beachhead where the opportunity was clear: Facebook's native analytics and ads UI were "very, very basic," and advertisers were losing money because they couldn't get insights. With a tiny budget, she launched Facebook ads. "30 minutes after I started Facebook ads, I got an email from a customer saying, 'Wow, this is amazing. For the first time, I understand my audience.'" First customer, first validation, in 30 minutes—and it cost less than $50.
Two months later, flush with early traction and inbound customers, Iris started fundraising. Investors gave her the same feedback: "Facebook will change their UI. You're building a business on Facebook's incompetence. That's not a defensible business." They were right in principle, but wrong in practice—four years later, Facebook's UI hadn't changed. Iris, however, decided to kill the product. "I had something really good and I decided to kill it," she reflects. "If I had something that didn't work, it's something else. But we already had something really good." She still regrets this decision.
Rather than give up, Iris pivoted to her original, bigger idea: a no-code analytics platform for all marketing channels. Starting from scratch with just one developer again, she spent over a year building the core technology—a codeless data collection system that didn't require developers. This was the key differentiation: like WordPress vs. Wix, or Shopify vs. building from scratch, the magic wasn't in the design—it was in accessibility.
When she launched the second product, she applied hard-earned lessons. She didn't reach out to her network or portfolio companies. Instead, she immediately ran Facebook ads to strangers, simulating what growth would actually look like. This gave her real signal: would real marketers, who didn't know her brand, see an ad, sign up, and see value? She started free, then charged $79/month after raising money. But she learned that serious, budget-conscious marketers would pay far more—$300, then $500/month—if they understood the ROI. Higher prices actually improved product-market fit because serious customers invested more.
For two years, the team stayed lean at about 10 people. Iris resisted the urge to scale fast. "There's something very healthy about starting with a very small team," she says. She focused on polishing the product, improving engagement, and really nailing the value prop. Around the two-year mark, everything clicked. The team expanded from 10 people and a $2k/month marketing budget to $50k/month, launching a real go-to-market machine.
The growth curve accelerated: from 100k to 1 million ARR in roughly 8-10 months. Today, Oribi has 60+ employees, thousands of customers across e-commerce, SaaS, agencies, and finance, and has raised $28 million. Iris credits Facebook and YouTube ads as her primary channels, paired with vertical-specific landing pages and onboarding flows that made prospects feel the product was built just for them.
Iris emphasizes three counterintuitive moves: (1) Bootstrap first to understand the market without investor pressure to pivot or abandon good ideas; (2) Target real customers from day one via ads, not your network; (3) Charge more, not less, if you have a superior product, because pricing signals quality and attracts serious users. The biggest regret—killing a working product for investor feedback—taught her to trust her instincts about what's actually working.
- •The founders solved a problem they personally experienced, giving them deep intuition about customer pain points that enabled faster product-market fit discovery.
- •Immediate traction from paid ads ($50/day generated a customer in 30 minutes) validated that the problem was urgent enough for prospects to convert quickly, indicating strong product-market alignment.
- •Systematic validation through LinkedIn outreach to well-defined personas (pricing managers at comparable companies) replaced guesswork with targeted feedback, accelerating iterations toward product-market fit.
- •Willingness to invest in extended development cycles (2+ years total) before aggressive scaling allowed the product to mature enough to convert cold prospects efficiently via paid channels.
- 1.Start by identifying a specific operational problem you encounter regularly in your own work, then validate that other companies in your industry face the identical bottleneck through conversations with 5-10 people in similar roles.
- 2.Launch a minimal paid ad campaign on Facebook or YouTube with a small daily budget ($50-100) targeting your ideal customer profile, and measure how quickly prospects convert to measure genuine demand urgency.
- 3.Build a list of 50-100 prospects matching a specific job title or persona at companies similar to yours, then conduct systematic LinkedIn outreach to gather qualitative feedback on your product roadmap rather than relying on surveys.
- 4.Plan for 18-24 months of product development before expecting sustainable customer acquisition, allocating time for both an initial MVP and a full iteration based on early user feedback.
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