ContactOut

Most startups fail because they build before validating. But you don’t need to spend months building something nobody wants. The trick is to start by generating data and pages programmatically. Here's how we did it at ContactOut to create millions of pages that drive significant traffic and revenue. Step one: Get the data. We bought pre-scraped data sets from services like CoreSignal and Kaggle. These datasets contain LinkedIn profiles, Crunchbase company data, and even flight tracking information. You can purchase entire scrapes of popular directories for a few thousand dollars up to six figures, depending on the source. Next, we used this data to create programmatic SEO pages, targeting high-value keywords—like famous people or companies. For example, we built pages for profiles like Bill Gates and other notable names that attract lots of searches. These pages drive massive traffic without needing to manually curate each one. If you’re just starting, you can scrape data yourself or hire experts through services like Upwork. But for us, it was more time-effective to let a professional scraping company handle it. By leveraging available data, you can quickly test whether your product or idea resonates with your target audience—without wasting months on development. Keep your experiments fast, gather insights, and pivot based on what you learn. Ready to get started? The key is to stop overbuilding and start validating with real data. #athena_vc

Here's a little trick to get you a head start in Q4

when you bump into a prospect that went with competition

Two years ago, when I joined ContactOut, everyone warned me about Q3. "It's the worst," they said. Veteran reps would claim no one’s hiring, no one’s buying, no leads—just vacations and empty offices. It was a season to simply survive, wait for Q4, and hang on for renewals. I accepted that at first. Q3's tough for sales; budgets are spent, and projects stall. But what I learned was this: For Mavericks, Q3 isn’t a waiting game—it’s a winning one. With one month left in Q3, we’re already at 108% team quota attainment, and 60% of the team has hit their individual targets. And there’s still a full month ahead! Here’s the twist: This success wasn’t by chance. It’s the result of relentless hard work. We’ve shifted our focus to enterprise deals, built pipeline while closing inbound leads, and kept renewals strong—all year long. Yes, it's been demanding. But I’ve been with the team every step of the way—prospecting, strategizing, making calls, customizing proposals, closing deals. And it’s paying off!! The past two months have been incredible, and I can't wait to see what we achieve in September. The rumor? We might just hit 200% quota and close some historic deals for ContactOut. 💚 I couldn’t be prouder of the team, and a massive thanks to Rob Liu 🐸 and Morny Zhang for their leadership. Let’s keep pushing. The grind doesn’t stop! P.S. The team as follows Sandra Efremov Ersun Kaya Nikos Douroukis 🐸 Irem Gokdeniz Ben Kljajic P.P.S. If you're seasoned and independent SaaS AE looking for a change of environment - ping me.

POV: You come to the office last day of the Q only lost deals in the building 🏫😞

Ever Wondered What Data Scraping Is? Let's break it down. Data scraping is basically when a program pulls information from another program's output. The most common example is web scraping, where tools sift through websites to collect data. 💡 Why Do We Scrape Data from Websites? Think about it—companies often lock down their content, making it tough to access through APIs or other straightforward means. But web scraping tools are designed to get around these restrictions and collect the data anyway. This creates a never-ending game between those trying to protect their content and those trying to extract it. 🛠️ So, How Does Web Scraping Actually Work? The process is pretty simple on the surface: 1. Request: A scraper bot sends a request to the target website. 2. Parse: It then digs through the website's HTML to find the specific data it wants. 3. Extract: Finally, the bot converts this data into a format that can be easily used. Even though these steps seem straightforward, pulling it off can be quite tricky. But here’s the real question: Should the use of data be controlled by law, or is scraping just part of the game? What do you think? Should there be more control over data scraping, or is it just an inevitable part of our digital world? #DataScraping #WebScraping #TechTalk #DataEthics #DigitalRegulation

notorious job standoff

Televisa presents... oh wait... LinkedIn... The drama unfolding in the comments ----- #MayTheQuotaBeWithYou Feel free to follow or connect with me for more insights on big data and lead generation. ( ͡❛ ͜ʖ ͡❛)✊