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Happy Saturday!

In our last issue, I showed how I used AI agents to compile a list of 19,000 leads using the Pain Point Pursuit Playbook. (new name coined by GPT4, what do you think?)

1. Pain Points: Start with your ICP's common dream or pain points.

2. Practical Moves: Brainstorm actions they'd take to address them

3. Public Artifacts: Find public artifacts of these actions

4. Paths to Pursue: Trace backwards to collect the customer and their contact

Also last week, I offered to build a hyper-targeted lead list for one business for free. Over 40 people took me up on the offer.

So today, I picked 3 of them to demonstrate how you can apply this playbook to build a high-converting list for your company.

Case Study I - Rewritewith.ai

Rewritewith is a Chrome extension that helps you draft authentic comments to engage more effectively on LinkedIn.

The founder mentioned to me he wants to target Sales professionals, because they actively use comments to engage with prospects.

But finding customers for this is challenging with a generic sales-people list, because only a tiny percentage of them actively use LinkedIn for comments. So we need to go way beyond a generic list like Apollo.

We do it by applying our 4-step playbook:

1. Pain Points: Professionals struggling to generate leads on LinkedIn.

2. Practical Moves: Regularly posting and engaging with other users on LinkedIn.

3. Public Artifacts: LinkedIn posts and comments.

4. Paths to Pursue: LinkedIn posts with numerous comments.

Putting it together, sales, LinkedIn, comments - We know a hot place is under posts from major sales influencers on LinkedIn.

The reason for this is because of a popular strategy called the "$1.80 strategy," which involves leaving your "2 cents" on 9 posts across 10 relevant niches, totaling $1.80. This is also why you often see hundreds of people promoting their products under tweets from big influencers like Marc Lou.

To execute on this idea, I started by googling "top LinkedIn sales influencers" and found this article listing 100 sales influencers:

Upon examining the profiles commenting under these influencers' posts, it's indeed evident that many individuals leave 10-20 comments daily to grow their accounts.

By compiling a list of people who comment on the recent posts of these 100 influencers, we would have a very solid starting point, because for each influencer we can find 20 × 100 people who engage with them.

To actually achieve this, you'll need a programatic way to collect LinkedIn data. For this case study I used PhantomBuster, a no-code tool for scraping various social platforms. The built-in agent is sufficient for this task without any customization. Here’s the step-by-step breakdown:

Step 1 - Collect 100 Sales Influencers

This step is easy. You can find the list in the article I mentioned above.

You can use ChatGPT to scrape the list, but the post is too long to fit into the context window all at once. Instead, break the input into smaller chunks that fit within the context window and manually merge them.

Quick tip for those of you who are developers, you can ask ChatGPT to generate a CSS selector to scrape all the links. Here's what it provided me:

const links = document.querySelectorAll('a[href^="/in/"]');
const linkArray = Array.from(links).slice(0, 100).map(link => link.href);
console.log(linkArray);

This is actually a really quick way to scrape data from most pages without building a custom scraper.

Step 2 - Scrape 20 Posts from Each Influencer

This is where the fun begins.

Using Phantombuster's LinkedIn Activity Extractor with our initial list of 100 influencers as input, we obtained a range of posts - some viral with thousands of comments and others with only a few.

Next, I filtered for posts with 30+ comments to save on compute credits in the next step, which still gives me around 1000 highly engaging posts to work with.

Step 3: Scrape All Comments Under Each Post

Similar to the step above, we use the LinkedIn Post Commenters Export to process the 1000 posts, scraping all comments along with their LinkedIn profile links.

On average each post has 50 comments, which gives us a list of 50,000 prospects.

Step 4 - (Optional) Scrape The Lead's Recent Comment Activity

This is an effective method to confirm if the person is actually heavily using comments and fits our target.

But it is also important to evaluate the ROI of each step in the list-building process. Does filtering the list cost more, or is it more expensive to contact additional people? In this case, scraping LinkedIn is costly, and our first three steps are accurate enough, so it's more cost-effective to just skip this step.

Step 5 - Combining Everything In Clay

Let's take a look at what we know at this point:

• what they commented

• when they commented

• the post they commented on

• the author of the post

All this data points can be used to craft a hyper-personalized copy. For example, I used GPT4 to summarize it to create a personalization point in the email copy. (“Saw your comment on a viral post about the exploding demand for LI exec personal branding”)

The last piece of the puzzle is just finding these people's email, because reaching people via email is far cheaper and more scalable than LinkedIn, as InMail credits are very expensive.

This can be done using Prospeo or Findymail, both available inside Clay already.

Results

The founder from Rewritewith helped me actually test this list since he already has a warmed-up mailbox. (Thank you!)

Here’s the result:

• 130 delivered

• 61 opened (need to optimize deliverability here)

• 9 replies (14.7%!!)

What’s next?

The best part of this strategy is that we only just covered people in sales. We can apply the same approach to influencers in tech, marketing, business, entrepreneurship and whatever niche you can think of.

Each niche would yield 50,000 more unique prospects.

Since this list identifies a group of people who spend a significant amount of time on comments to grow their social accounts. You can sell many things in addition to our AI comment extension based on this common pain point.

Here are some ideas on what you can sell to these leads:

• Social media management

• Ghostwriting services

• Engagement analytics platforms

• Sales community access

• VIP event access and networking opportunities

• Personal brand coaching

If you have an offer priced at $1000 or more, this is how much a list like this will make you:

• 200,000 leads

• 10,000 replies (5% conservatively)

• 5,000 meetings

• 1,000 closes

• $1M revenue

Case Study II - BoringLaunch

The second product we are featuring today is BoringLaunch.com.

This service is tailored for startup founders who have recently launched their products and need a quick SEO boost.

BoringLaunch saves you 20 hours of manual labor by listing your product on 100 different directories. These directories provide a foundational set of backlinks, helping Google start ranking your site faster.

I was one of their first users, and the results were immediate. After using their service, PressPulse AI ranked #1 for the "PressPulse AI" keyword, quite essential if you were to run a cold email campaign.

To build our lead list, there are two key concepts are at play here:

• ICP (Ideal Customer Profile): Who you are targeting.

  • Startup Founders

• ICS (Ideal Customer Situation): When you are targeting them

  • When they want to start marketing

The ideal way to hit both?

Scrape new products from launch directories like ProductHunt and BetaList.

Fortunately, this is already a solved problem (as with most things you'd want to scrape), and you can use an Apify actor to handle it for you.

Here's a sample of what the result looks like.

Since most companies are pretty new, the catchall emails you can find on the website would typically hit the founder directly.

Alternatively when the email can’t be found, the founder's maker profile often has their social links, so the ones without emails can still be reached via social media DM. (Automate it with this Phantom)

With this approach, you'll roughly see 200 new product launches a day, 100 of them are serious (have 5 upvotes) and 60 of them have valid contact info.

By automating this lead collection method and integrating it into an outbound system, you will have a reliable list of 1800 founders who are looking to advertise their freshly launched product you can reach out to each month on auto-pilot every single month.

Companies that might benefit from this list are those who cater to the startup world:

• Marketing Agencies

• Public Relations Firms

• Business Consulting Services

• Investor Relations Firms

• Technology Development Firms

• Legal and Compliance Services

• Financial and Accounting Services

• Human Resources and Recruiting Firms

• Customer Support Outsourcing

• Design Agencies

Case Study III - GetSuperPress.com

The third example we're building a list for today is SuperPress, founded by a fellow Canadian living in Japan!

SuperPress helps organizations with mission-critical WordPress sites stay online with 24/7 support and premium site management.