Tweetscrape

How Tweetscrape Reached $5K MRR in Just 120 Days

Founded By

Gradoo

Monthly Revenue

$5K

Founders

Profitable

Yes

Year Started

2022

Customer

B2B

Founded By
Gradoo: Monthly Revenue
$5K

Founders
1: Profitable
Yes

Year Started
2022: Customer
B2B

Who is Gradoo?

The founder of Tweetscrape, known as grad00, has a background in agency work and successfully leveraged cold emails to grow his SaaS from zero to $5k MRR in just 120 days.

What problem does Tweetscrape solve?

Tweetscrape helps businesses find high-quality leads by extracting valuable contact information from Twitter, solving the challenge of efficiently targeting potential customers.

How did Gradoo come up with the idea for Tweetscrape?

The idea for Tweetscrape was sparked by an observation that many businesses struggled with effective lead generation through social media, particularly Twitter. The founder noticed a gap in tools that could efficiently scrape valuable data from Twitter followers of niche influencers, a method he understood well from his background in the agency world. This initial observation led the founder to investigate further, comparing existing solutions and identifying shortcomings in their functionalities.

Through research and feedback, the founder refined Tweetscrape to better meet the needs of users looking for high-quality email lists for cold emailing and marketing campaigns. Realizing the potential of targeted lead lists, the founder ensured the tool could easily extract and organize data, paving the way for businesses to connect with the right audience more efficiently. Throughout this process, the founder faced challenges in ensuring the scraped data was both relevant and compliant with privacy standards, but by focusing on these aspects, he managed to develop a tool that turned a foundational insight into a practical, user-friendly solution for lead generation.

How did Gradoo build the initial version of Tweetscrape?

Tweetscrape was developed as a SaaS tool designed to transform Twitter data into high-quality leads by scraping emails from niche audiences. Initially, the product was built by leveraging existing web scraping technologies and APIs, with a primary focus on effectively parsing Twitter's vast data landscape. The development team used Python for scripting, with libraries such as Beautiful Soup and Scrapy to manage the data extraction processes. The first version of the product took approximately three months to build and was an MVP that focused on delivering core functionalities with simplicity and efficiency. During development, the major challenges included ensuring compliance with Twitter's data policies and effectively managing the scale of data required for viable lead generation.

The process involved multiple testing and iteration cycles to optimize the data collection and to refine the algorithms that accurately target relevant Twitter users, based on criteria like follower count and engagement levels. This fine-tuning was crucial to overcome challenges related to accuracy and relevance of the generated leads. As the product matured, further iterations introduced more advanced analytics capabilities and automation features, significantly enriching its value proposition to users seeking to enhance their marketing effectiveness through granular audience insights.

What was the growth strategy for Tweetscrape and how did they scale?

Cold Emails

Tweetscrape leveraged cold emails as a major channel for acquiring customers. By scraping email lists of niche audiences from Twitter using their own tool, they were able to target potential customers who closely resembled their ideal customer profile. They sent around 100 emails per day using a tool called Instantly. This strategy resulted in approximately a 1% conversion rate, translating to about one new customer each day. Why it worked: Cold emails allowed Tweetscrape to directly engage potential customers who were already interested in their product niche. The personalization and relevance of these emails were key factors in achieving a high conversion rate.

Facebook Ads

Tweetscrape used a creative approach with Facebook ads to significantly increase their monthly recurring revenue (MRR). They created lookalike audiences by scraping emails from Twitter accounts related to their competitors and complementary products. Then, by setting up multiple audience and creative combinations, they focused on the best-performing ads to maximize their return on ad spend (ROAS). With a small investment of $200, they achieved an MRR increase of $800. Why it worked: The use of lookalike audiences allowed them to precisely target users similar to their current customer base, optimizing the effectiveness of their ad spend and enhancing the likelihood of conversions.

What's the pricing strategy for Tweetscrape?

Tweetscrape offers tiered pricing starting at $29/month, with features expanding in higher plans, while also providing a free trial to attract potential users.

What were the biggest lessons learned from building Tweetscrape?

Leverage Cold Emails: Tweetscrape's significant growth came from effectively using cold emails, proving them to be a powerful tool for customer acquisition.
Creativity in Ads Pays Off: Strategic Facebook ad campaigns using lookalike audiences and creative ads turned a modest investment into substantial revenue.
Targeted Lead Generation: By scraping relevant Twitter data, Tweetscrape demonstrated the importance of precision in targeting potential leads.
Stay Adaptive: The ability to quickly adapt marketing strategies, like utilizing post-iOS 14 Facebook features, showed resilience and a keen understanding of the market landscape.

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