10-20 targeted meetings per month for early stage VC fund from US

Sharing the case study from our team lead Amir Abdelrahman: 10-20 qualified meetings per month!

For the past 3 months, we’ve been helping a US-focused early-stage VC fund by sourcing targeted company databases, crafting AI-personalized outreach, and engaging on Twitter, email, and LinkedIn.
I’ll share insights on building effective VC prospect lists and how our AI engine drives higher conversions.

1️⃣ Company Search Initial data sources: LinkedIn, Crunchbase, Apollo, axel directories, Reddit. Here are some datapoints we pay attention to when collecting companies, besides the trivial industry and geo types.
- Date or availability of last round.
- Growth in the number of employees.
- Hiring in specific departments.
- Growth in the number of website visits
- Availability of public case studies
- Identify specific investors who have entered the project.

Founders and C-level: We vet founders and key employees. We do background checks on everyone, for example: previous exits or tier-1 companies in previous work experience.
Among the interesting things: our algorithm allows us to go from funds and collect companies that shortlisted funds have entered. Or collect companies that were founded ex-FAANG, + dozens of other filters.

AI helps to collect data, rank companies by relevance, semantic analysis of texts: product descriptions, press releases, news.
But no matter how good AI is and no matter what kind of prompts we write, we do a manual check. We can't get rid of hallucinations, and we keep data accuracy for our clients at 100%, without compromise.

2️⃣ AI-enabled message personalization After collecting a base of companies, we gather founder and C-level profiles.
AI identifies meaningful product launches, partnerships, successful cases and collects data on awards and ratings to personalize messages.
An example of one of the 5 versions of personalization, followed by a pitch inviting you to discuss collaboration:

Hi [Name], Impressive background at [Google, AI department].
I know [Name] from [VC fund Name], which invested in [Company Name].
[PITCH]

3️⃣ Search emails and tweets
- Find employee contact information via waterfall using 5 providers - this allows us to cover 95% of the base.
- Verify emails using verifiers for bounce rate less than 1%.

4️⃣ Email delivery and LinkedIn/Twitter messages
- Sequence: 3 short relevant emails + 3 link messages + 3 Twitter messages
- We write on behalf of the fund analyst and bounce calls with him, manually warming up when answering.

5️⃣ Results: 10-20 calls per month with up to 30% conversion to cold contact calls
- Interestingly, conversions are 3-5 times higher than in B2B leadgen, but here we are offering to put money, not selling.
- Every contact falls within the investment criteria of the fund.
- Personalized approaches provide 10-30% in calls. We set up 10-20 eligible calls with founders per month. Conversions vary because different segments and geo.