🦾Eric Nowoslawski

Growth Engine X

Founder

Clay

Certified Expert

Smartlead

Instantly.ai

REITIUM

Advisor

Gated

Advisor to an exciting Startup called Gated. Gated challenges unknown senders to donate to charity in order to reach you - so everything in your inbox is worth your attention.

It's like a postage stamp for email. 

As a huge email sender, I'm most excited for the ability of our messages to cut through the noise, prove to our prospects that we are being intentional and we want to solve their problems, instead of relying on fancy copywriting and sending 10s of thousands of emails.

Gateway Regional Chamber of Commerce

Vice President

Help Chamber Members grow their business
Facilitated Connecting Members through observed Synergies
Public Policy Research and Advocacy
Negotiate discounts on behalf of the Chamber
Engaged with Local Municipalities in Economic Development and Business Policy

The Gateway Regional Chamber of Commerce is the largest business association in the New York Metropolitan Area with about 1,200 members. We hold approximately 150 events per year from large 600-person dinners to small 10-person breakfasts, and everything in between, with a great emphasis on networking and business promotion.

The Gateway Chamber has a long history dating back to 1911 when it was founded as the Elizabeth Chamber of Commerce by John D. Rockefeller and other New Jersey industrialists.

Today, the Gateway Chamber serves as an umbrella organization for a number of regional and local chambers and networking groups including: the Union County Chamber of Commerce; the Central Jersey Chamber of Commerce; the Route 22 Chamber of Commerce; the Kenilworth Chamber of Commerce; the Roselle Park Chamber of Commerce; the Clark Chamber of Commerce; the Linden Chamber of Commerce, the Somerset Hills Business Network; the Black Business Alliance; the Gateway Warehouse Association; the Gateway Hospitality Alliance; and The Irish Business Association. When you join the Gateway Chamber you automatically become a member of ALL these groups at no additional cost.

The Gateway Chamber is a strong advocate for its members. It works at the federal, state and local levels on both legislative and regulatory issues.

We consider it to be core to our mission to help our members grow their businesses, and as a result our retention rate has averaged 95% over the last five years. 

Members of the Gateway Chamber run the gamut from large corporations like Merck, Comcast, Verizon and Continental Airlines, to small and medium-sized business, to a very active home-based business group.

Director of Special Projects

CCFGiving

Operations Associate

Eastern National Academy

Instructed, refined, and improved skills for gymnasts of all levels
• Earned a 100% retention rate for all Recreational classes 3 months after starting
• Created Ninja Warrior Program that retained 180% of students over 9 months
• Manage employees and planning for Birthday parties and special events

Men's Gymnastics Coach

I am a direct response outbound lead generation expert. And by that jumble of words, I mean that I leverage cold email to build predictable revenue pipelines for my clients. 

I leverage over 20 databases to merge Firmographic, Technographic, and Persona data to build highly targeted playbooks with my clients. 

I believe we need to arm sales teams with the knowledge of their total Target Addressable Market and teach them how to score that Market for which companies have the most pain and need their solution. 

The best way to do this is to look at public indicators such as open jobs data, employee growth/attrition, social media growth, web traffic, new product launches, employee sentiment, individual employee tech investment, company tech stacks, etc.

With this data, not only are you only messaging with people that you know have a problem that you can solve, you also are armed  with powerful messaging that cuts through the noise. 

I teach sales teams to embrace technology and multiply what is possible per employee.

Instantly.ai just released something I've even gone as far to build for myself. But it's way more powerful because of their scale.

They just released their upgraded email verification enrichment for catch all/risky email addresses. 

This is huge for a company like them to be doing because they are using their scale to be able to verify the email addresses with real sending instead of an SMTP check or other methods of catch all verification. 

Now users will be able to check if anyone else on the platform has sent an email to the email address you uploaded and see if it went through successfully in the past. 

Allowing you to contact people that usually don't get as many emails because platforms have a tough time with catch all verification. 

We tried to build this based on our own sending data too but it just didn't have the scale necessary to be useful. With the amount of emails Instantly sends on a daily basis, it's incredibly valuable data for those serious about keeping their bounce rates down. 

I don't think this was publicly announced either but I've noticed that if you upload a lead list, they are automatically taking out leads that have a history of bouncing 100% of the time and I haven't even been charged for that. 

Very interesting development for them to keep pushing features for the cold email community.

Founder Growth Engine X | Clay Enterprise Partner

I don't see enough people in marketing talking about Claude Skills and I think it's wildly impactful for knowledge work. I'll give you one of my favorites in this post. 

Claude Skills is basically a really easy way to create training for an AI model. 

Explain what you want to accomplish, give it data to train off of what you would do manually, and it creates a markdown text file to keep doing the skill. 

For months, I've been trying to build an email copywriting bot. It worked great for some customers, but inconsistent for others. Frustrating as hell.

Then Claude Skills dropped.

We trained a Claude Skill on our internal campaign strategy docs, my favorite writers, and our best-performing campaigns.

Now here's what it does:

You give it a website URL as input.

It scans for signals that indicate the company has a problem we can solve.

Then it ranks campaigns:
- Best signal (most exclusive, highest intent)
- Second best signal
- Third best signal
- Fallback campaign (for everyone in your ICP without specific signals)

It scores each campaign and gives you a mini-analysis on how to approach the copywriting.

It also includes the first draft of the email for each signal as well as follow up email ideas. 

This is what I mean when I say Claude Skills is a powerful replacement for knowledge work.

It's not just generating generic outputs. It's making strategic decisions based on your specific methodology.

Comment "skills" if you want me to send you the campaign strategy file to see how it works. If you don't like the way my copywriting is trained, upload your own copy to Claude and retrain it yourself!

What knowledge work are you still doing manually that could be trained into a skill?

I've been completely wrong about AI prompting, and it's cost me thousands of dollars and it might be costing you money too. 

For months, I've been telling my team (and all of you) to structure prompts like this:

1. Tell the AI what you want it to do
2. Give it the data to analyze and reference
3. Provide examples of good outputs

I didn't realize that this approach wastes tokens. Especially when you're running outbound campaigns at scale.

I know this may seem obvious to technical people out there but I didn't even consider making a change. 

I had a call with my enterprise rep from OpenAI last week and they asked me why I wasn't using cached inputs based on my use case. 

I didn't even consider it because we use the Clay OpenAI integration.

Cached inputs save 50% on input tokens for GPT-4o mini, and 90% on GPT-4 when the input stays the same.

For those of us using AI for outbound messaging, this is exactly what we're doing. Running the same prompt structure over and over with different prospect data.

But the prompt needs to stay the same for as long as possible for the inputs to cache.

My old approach was mixing up the prompt by putting variable data (company descriptions, prospect info) in the middle.

The correct way:

Put everything that changes at the very bottom of your prompt.

Keep the instructions, examples, and framework identical at the top.

Only vary the prospect-specific data at the end.

I met with Jeff Barg on the Clay team directly about this and you shouldn't have to change all your integrations. The prompt will cache automatically with how they have the integration set up. 

This might seem obvious to technical people, but this is the ignorance debt you pay when you're not technical.

I'm sorry I made the wrong suggestion for so long.

If you're running AI-powered outbound campaigns and not using cached inputs correctly, you're burning money.

We have 8 customers sending over 25k emails per day (one of them sending over 100k per day). Here's how we keep their campaigns full of leads completely automatically with Clay and Supabase.

And if you clicked learn more because of the volume, yes the numbers are good. That sales team sending 100k per day gets about 125ish leads per day and their TAM has 6M people in it. They're fine. 

In order to send this much volume, you can't rely on CSVs. We tried and epically failed. 

So here's what we do instead. 

1. We get the whole company TAM in Clay. 

We break a part the searches to stay under the 50k threshold by changing filters that don't matter. For instance, everyone targets the US and we can filter by headcount and states to make the list smaller.

2. We stage that company list in Supabase.

3. Using some code we made for an edge function in Supabase, every day at 2 am, we process more leads from Supabase back into Clay to find contacts. The tables auto delete so we stay under the 50k row limit. 

People sleep on the fact that on whatever plan you have, people searching is unlimited in Clay. Auto deleting rows is an enterprise feature but you could get around it if you need.

4. After contacts are found they are pushed to another table for enrichment, qualification, AI message writing and finally, being dropped into a Smartlead campaign.

5. We manage it all from Clickup with analytics on how much of their TAM they have left and forecasts on when we need to find more companies so we can try to hit their TAM with 1 email every 3 months. 

This way we can have it on repeat every day. 

Sometimes, I even get a sale on email credits or something and we process a big batch in one go. I'm processing 1.8M leads for a client right now actually. 

With tools like Cursor and Clay, doing repeated things manually is just wasted time.

revgenius

clay-run

gateway-regional-chamber-of-commerce

pitchindustries

growth-engine-x

soku-swap1

innovate-self

seltzer-revolutions-inc2

beyondyetwithin

sales-technicians

Talks about #tech, #sales, #startups, #growthhacking, and #demandgeneration

Built a workflow this weekend that completely changed how we process leads for our customers..

The problem: Downloading CSVs from databases and manually uploading them into Clay

We process data from 3 main places. 

→ We collect company and contact data in Clay
→ Bulk Data in Apollo as well
→ Custom Scraping with RapidAPI scrapers

Every week, we process about 20M rows of CSV data in Clay.

What I built on Black Friday with Cursor automates the following

→ One form submission kicks off the workflow from Clickup
→ Finds all qualifying companies from our internal database OR RapidAPI scrapers → saves to Supabase
→ Drip companies into contact enrichment every day at 2 AM (we use Clay and BlitzAPI ($500 per month for 10,000,000 contacts tell them I sent you)
→ Check our email cache (23M+ emails) to avoid re-buying data. Revalidate if over 30 days old. 
→ Works with Apollo, Clay, OR custom Rapid API/Apify Scrapers

Over the weekend I successfully processed lead refills for 8 of our customers and now we will roll it out to more of them! 

 it even looks up daily how many contacts we have in total and how many we have left to process, so we know when we have to get more or restart the list 

The magic: Our database tracks every campaign, daily lead quotas, and Clay webhooks.

Every morning at 2am, it auto-sends contacts to refuel campaigns.

No more CSVs. No more manual uploads. All hosted on Railway and built by Cursor.

Here's everything I built with Cursor over Thanksgiving weekend. Some things were done in one prompt, some things took me a whole day. 

1. Automated our entire company/contact workflow
One form → Companies to Supabase → Contacts on-demand → Campaign auto-refill at 2am

2. Overhauled analytics

We already had pretty good analytics, but everything would run at midnight, and I wouldn't get snapshots during the day. So, I built this. 

- Snapshots of sending at 12pm, 3pm, 6pm EST
- Warnings if anything looks wrong
- Faster action on issues

3. Built our own email lookup API

One endpoint: first name + last name + domain
Checks our database cache first
Then LeadMagic → Prospeo.io → Icypeas → MillionVerifier guess → No duplicate paying for emails

4. Souped up our Google Maps scraper
- Now finds contacts mid-scrape
- Maps + contact data in one run

5. Automated campaign setup
- Settings done automatically
- Reads copywriting
- Suggests how to structure the Clay table

6. Batch API + cached inputs for scoring
- 50% off a request so we can make the prompts longer with more detail.
- 90% off GPT-5 token costs when you use a cached input on top of the batch API discount. 
- Company scoring at scale

7.  AI Lead Categorization 

- Uses the Smartlead API to pull all unassigned category leads. 
- We use the same prompt with GPT-5 Nano, GPT-40 Mini, and GPT-4.1 Nano. As long as they all agree on what category it automatically uploads the category. 

And now we only have to manually categorize what it doesn't agree on. This was a way harder issue than I thought, which is why I kind of hammered it with the three APIs, and we only have to filter 30-50 replies a day manually.

8. Bulk Contact Enrichment Flow for our biggest senders. 

We have 6 customers sending 50k+ emails per day (1 sending 120k emails per day) that just need a ton of data. 

I'm right this second processing 14M leads to be enriched, qualified, and distributed to the right campaigns. 

Can't wait to see what I can get done this week in Cursor.

The tests are in and we are not allowing generic AI personalizations based on company descriptions as first lines anymore. I could prove this raised reply rates but recently, in most markets, it falls flat.

We try to always lead with some kind of signal or pain point that we research but if we can't do that, we would fall back to a company description based personalization. 

Something like, "I saw you help fitness coaches retain clients and I wanted to get connected."

We used to see increased reply rates when we personalized first lines with AI-generated content scraped from company websites.

Now the data is pointing the opposite direction.

Sophisticated buyers recognize generic AI personalization instantly which hurts your message:
- "Hey, I saw how you helped fitness enthusiasts breathe better..."
- "I noticed you're focused on helping local businesses..."
- Generic references to their company description

They've seen it a thousand times.

Here's what we do instead:

If we have a real signal: We use it. Something specific, custom-scraped, that ties directly to their problem and our value prop.

**If we don't have a signal:** We treat cold email like a banner ad.

Put the offer in the preview text.
Make it punchy.
No fake personalization.

Think about it: When someone's scrolling on their phone or web browser, they see the subject line + preview text together.

That's your click opportunity.

Stop wasting time generating first lines from company descriptions.

Unless you have custom research that goes well beyond what's publicly available on their homepage, foregoing AI personalization and writing punchy, split-tested first lines performs better.

We've seen this deteriorate over the past six months across our customer base and as someone that would suggest it based on the data, I want to correct the record and say we're out of it now.

Here's how you can train a Claude Skill to replace your knowledge work in less than an hour.

Most people haven't tried this and it's far easier than you think. 

Step 1: Record tons of context on what you want

I create a Loom video or voice memo (usually while walking my dog) explaining:

- What I want the skill to do
- What decisions it needs to make
- What outputs I'm looking for

Takes 10-15 minutes max.

Step 2: Give it real training data

This is the key. Don't create hypothetical examples.

Use actual work you've already done that represents good outputs.

For my cold email reviewer skill, I used:
- Real prompts we've created for customers
- Actual Clay tables we've built
- Campaign copy that's performed well

Step 3: Test and iterate

If you don't like the outputs, just edit the Claude Skill. Again, using something like Wispr Flow to speak directly to the model so you don't waste time typing. 

Add more training data.
Adjust your instructions.
Don't overthink it.

Step 4: Replace yourself

Now you have a skill that makes decisions the way you would, trained on your actual methodology.

I've made Claude Skills for:
- Campaign strategy generation
- Apollo List filter building
- Suggesting Clay AI prompts based on use cases
- A skill to help write LinkedIn posts

You don't need to be technical. Just talk to Claude about what you want, give it real examples, and iterate.

What knowledge work are you going to train a skill for?

Wispr Flow

We just finished up some testing, and this cold email is outperforming 6 other tests on behalf of one of our customers. 

This customer is one of the largest cleaning equipment companies by market share in the United States. 

They want to target hotels to of course sell cleaning equipment but there's one hang up. 

Most people who have cleaning equipment don't feel that it needs to be replaced for a couple years. So many of the other emails are getting objections that they're good right now. 

I think there are a couple of reasons why this one breaks through the noise. 

Subject line: starts off with something it's possible a customer could have sent them. 

Preview text/first line all about a real google review we scraped with a Rapid API endpoint. We search for the top reviews that mentioned cleaning type things and go about 40 reviews back. 

If we don't find anything related to cleaning (which we almost always do), we just have a fallback referencing another good review. 

Then we ask who's in charge instead of a direct pick because there's really no way to be able to tell if they are the ones that make the purchasing decision. 

Leaning into the objection by calling it out and handling it. 

Some of the biggest issues for hotels is the churn of their cleaning staff. So we leaned into that and how truly the equipment can get them done with rooms faster so they don't need as many people. 

This company is genuinely partnered with every Hilton in the country. So we find the closest one and get it's address to get a nice social proof point with a  location they might know for each recipient. 

Easy CTA again hammering on the efficiency of the equipment. Not if they need something new.

There's only 2 rules you need to follow to be able to save anywhere from 50%-90% on your OpenAI input tokens by using cached inputs. 

I made a post recently about how you could save on your OpenAI calls with cached inputs and I did some testing to make sure it works.

Jeff Barg explained to me that the integration in Clay with OpenAI is ready to do cached inputs. We were prompting wrong (and so is the Clay metaprompter). 

The 2 rules you need to follow to have the highest chance of hitting cached inputs is to 

1. Make sure your prompt is over ~1,024 tokens. You can just ask chatgpt how many tokens a prompt is and it'll tell you. This is nothing for most of our prompts. 

2. As much of your prompt needs to be the same from the beginning of the prompt to whenever you make a change. 

That's what the caching of the input is. They already computed the beginning of the prompt and stored it. They recognize that it's been computed before and pass that saving to you. 

We were messing it up with putting custom parts of the prompt in the second or third sentence. 

Put all that custom stuff at the end and you will have a really high chance of hitting cached inputs. 

It feels like a black box because the Clay API at the point of this post doesn't tell you if you used cached inputs but they'll add that in soon!

Check out this video to see my experiment and proof that it's working with these two rules. 

https://lnkd.in/eFgRrhVK

One of my favorite Black Friday grabs with this Google AI mode scraper from OpenWeb Ninja. 200k requests for $75 a month. Here's what we are using it for. 

They are really accurately pulling the result that comes up when you google search something and the AI mode takes over. 

Some use cases I already validated with it 
- Local business contact search
- research on companies
- research on competitors
- research on company news
- google shopping results 

Really nice and super cheap (especially with the Black Friday sale) 

I just found the company through Rapid API and am not affiliated at all. I just thought it was a really effective API for a great price and they have a whole suite of APIs to use too.

Made this video explaining how you can use Cursor to scrape Google Maps and stop messing with custom coding it yourself. Here's the overall guideline to get this done below. 

Why Scrape Google Maps?

LinkedIn isn’t enough: Some businesses, especially local ones, lack a LinkedIn presence.

Google Maps is universal: Almost every legitimate business is listed here.

This method is ideal for local businesses or industries with limited LinkedIn activity.

Tools You'll Need:
Cursor: An AI-powered coding assistant and development environment.

Google Maps Extractor API:
RapidAPI version: Affordable and reliable.
Serper.dev, Outscraper, or similar APIs are also compatible.

CSV of ZIP codes or locations: Optional, but useful for targeted searching.

Clay or CRM: For storing and using your scraped data in email workflows.

Step-by-Step Instructions
1. Get Your API Access Set Up
Visit RapidAPI.com's Google Maps Extractor page (or your preferred API provider).

Subscribe to a suitable plan (e.g., $100/mo for 500,000 requests).

Copy your API key from the provider.

Note: One API request = 1 Google Maps page of results.

2. Open Cursor and Start a Project

Launch Cursor and create a new workspace.
Use natural language prompts like:
"Help me set up a workflow to search Google Maps for businesses in a CSV of ZIP codes. Use the RapidAPI Google Maps Extractor endpoint."
Cursor will write the necessary code for you (this might be Python, JavaScript, etc.).

3. Teach Cursor the API Endpoint
In Cursor, paste in the API documentation from RapidAPI (e.g., the search or locate endpoint).

Show it the input parameters and expected response format.

This improves Cursor’s ability to generate valid code.

4. Build Your Scraping Workflow
Provide prompts like:
"Loop through this list of ZIP codes, perform a Google Maps search for 'restaurants', and save the business name, address, category, and phone number to a CSV."

Modify as needed: e.g., "Go 3–5 pages deep" or "also fetch review data".

Let Cursor adjust the code and test locally within the tool.

5. Once you’ve scraped your data:

Export it into a clean CSV.
Import it into Clay to find owner email and contact information.

Here's how we use Cursor to know which domains are healthy vs. unhealthy and remove them automatically and replace them live in our campaigns.

Most people wait until their cold email campaigns tank to check deliverability. By then, it's too late.

We built an automated health scoring system that flags problems before they kill reply rates.

Here's how it works:

The Smartlead API pulls our metrics:
- Sends last 2 weeks
- Sends last week  
- Sends this week
- Reply rate last 2 weeks
- Reply rate last week
- Reply rate this week

Cursor uses Python to:
- Compare reply rates to our baseline
- Flag domains as "suggested cancel" if they're consistently underperforming
- Flag "warning" when reply rate drops sharply week-over-week

Then we have a human review checkpoint.

Once approved, Cursor updates Supabase with domain status:
- Active (healthy and running)
- Insurance (backup domains ready to swap in)
- Canceled (removed from rotation)

The insurance logic is key:

We keep at least 50% of our active volume in insurance domains. Goal is 100%.

Cursor even suggests how many inboxes to buy based on current health trends.

The outcome: fully automated health scoring with team-wide visibility in Supabase.

No more guessing which domains are working. No more manual spreadsheet hell.

We see the health of every domain in real-time and act before campaigns suffer.

What's your current process for monitoring domain health?

Cursor

Supabase

This campaign was split tested against 4 other tests and easily took the cake (while still sending images in Email 1). Here's how we made it happen. 

We were working with a company that can help impact-driven brands showcase the benefits of every purchase from their customers. 

We used Claygent to find a couple of things.

- Do they have a sustainability/mission page? Any company that we couldn't find one of these pages for were not allowed in the campaign.

- If yes, what's the mission and what's the link?

- We used screenshot(dot)net to then take a screenshot of the page to prove we were there. 

We then made the subject line all about the mission of their company, and that also translated into the first line/preview text. 

We also use Claygent to find actual products, so that we can name drop the actual product names into the middle of the email, highlighted below. 

And between these customizations and the image, we pretty much just had to give social proof and a light CTA in order to get the response. 

The other campaigns that we split-tested were:

AI-only campaigns without images
Look-alike campaigns
New in roll campaigns

But nothing really did as well as showing the image. 

 I know then I'll get a lot of questions about if you can have an image in an email, but what I like to tell people is even if the image hurts deliverability, but it raises responses, it's worth sending the image. 

Everything in marketing is about testing, and so if it's worth sending the image, send the image.

We've recently been running free tests for people that want to work with us to prove that our system can generate leads for them. This free test was a fun one. 

We helped a custom shelf design company book sales calls with Interior Designers. 

Here's the process we used to launch everything in under 15 minutes. 

First, the copywriting

__________________

Hi {{first_name}} - saw {{company_name}}'s {{googe rating}} star Google review for Interior Designs and wanted to reach out. 

We’re the U.S. team for a custom-engineered modular closet system called ____________- designers use us to get a non–cookie-cutter look, but “custom without being custom.”

I saw that project you did with {{case study project}} and thought you might have other {{type of project}} projects that could use custom closets. 

Would it be worth a chat to see how we can partner on projects together? 

%signature%
_______________________

We used a RapidAPI endpoint to do the Google Map Scraping for the reviews. 

Once we had the list of companies, we used Claygent to find the contact of the owner or just the owner's name. 

Then we used Claygent to find a recent project on their site to put the rest of the email together. 

This whole campaign was launched from a 15 minute call and then maybe 20 minutes of Clay table building. 

This allows us to give people a chance to see what a campaign ROI could possibly look like before they pay us a dime. 

If you have a TAM of over 50k contacts, have an LTV of $5,000, and want to try some outbound, connect with 🎯 DK Kim to set up a test with us on the house!

VP of Sales @Growth Engine X | Content on outbound tactics for startups & growth strategies for IRL communities

This company didn't pay us a dime and we generated 8 leads for them with a campaign we launched with less than an hour of my team's time. 

🎯 DK Kim fully led the charge on this one. 

He needed to launch a campaign that caught the attention of ecommerce founders with over 30 employees in the US. If you do any outbound, you know this isn't an easy task. 

The offer was to help the companies get into large retailers. Again, a tough task with somewhat an unbelievable offer. People think a lot about creating offers that are so good, you can't say no but would you ever say yes to someone that promised to give you $1,000 for every $100 you gave them? 

Most definitely not, so sometimes we need to tone down the offer. 

This person worked especially well with supplement brands which can't be filtered from a regular database so he used Open AI to clean the list we got from storeleads. they have category fitlers but you can always use a little double check. 

He used Clay to find the e-commerce companies main product names to put directly in the first line. 

Then he toned down the offer a bit to a connection call with a promise to go over how they could do it themselves if they so chose. 

As you can see from the screenshot, it worked pretty well. So well, this customer is now working with us in a full engagement. 

If you have a TAM of 75,000 contacts, an LTV of over $5,000, and want to try some outbound with us, connect with 🎯 DK Kim to get some of his magic for yourself.

Here's how we custom coded a process to make sure all of our cold email campaigns are full of leads and get warnings when they aren't.

For years, it was the same manual process: download CSVs, upload into Clay, repeat.

So we built a system using Cursor, Supabase, Clay, and Railway.

Here are the inputs:

- Campaign ID
- Link to TAM list
- Webhook to Clay

Supabase stores:
- Campaign record
- Job record
- Scraped contacts

Cursor handles the scraping:
- Scrapes all contacts from the provided link
- Stores them in Supabase

The nightly worker (runs on Railway at 2AM ET):

Checks all campaigns + jobs.

Decides how many leads to send to Clay webhook.

Batch size options: 500 / 5,000 / 50,000 per day. really anything that you want

Tracks whether each contact was processed or used before.

Clay staging → processing:

Staging in Clay ensures the contact has never been used for that client.

Approved contacts move to the processing table (cleaning + enrichment).

Auto-delete on the Clay table keeps the system running autonomously.

Smartlead:

Leads flow into active campaigns automatically.

The alerts:

Supabase sends a Slack message when all contacts are processed: "All contacts processed" warning.

This tells us it's time to add more leads or the campaign will run dry.

Optionally for you, we use ClickUp analytics to verify leads are being added.

The outcome: we never have to manually upload CSVs again, and we get proactive warnings before campaigns run out of leads.

How are you currently managing lead intake for your cold email campaigns?

Another fun campaign from the GEX archives. this one helped land a $300k deal. 

This customer was one of the first to make AI phone support possible for ecommerce brands and this was the winning campaign. 

We started off using Claygent to find:

If they had a customer support phone number
What the phone number was
Did they list their customer support hours or were they 24/7

So we could send an email like below. 

The other shout out to make here is leaning into the language that is relevant to the prospect.

Most people don't know this but WISMO stands for "Where is my order?"

As you can imagine, this is what most customer support teams deal with over the phone even though the tracking numbers are so easy to use and takes a ton of the team's bandwidth. 

Being able to handle these was a huge value prop for these brands. 

Ending with a P.S. about how we could customize it for them also turned into a nice touch. 

We get that information actually just from the home page and scraping with ZenRows!

How we ensure we're always using healthy domains in all of our campaigns  automatically with Cursor.

Here's the problem: domains get flagged as unhealthy (from the audit in my last post), but they're still sitting in active campaigns.

Manually removing them and swapping in new ones? That's hours of work across dozens of campaigns.

So we automated it with Cursor.

Here's the workflow:

First, we run the deliverability audit (I covered this in my previous post).

This gives us three domain statuses:
- Active (healthy, ready to use)
- Insurance (backup domains)
- Canceled (unhealthy, needs removal)

Cursor hits the Smartlead API to:
- Pull all active campaigns
- Pull all domains/inboxes attached to each campaign

Then Cursor checks Supabase to:
- Get the status of each domain (Active/Insurance/Canceled)
- Find unused Active domains available for swapping

The swap logic:

Cursor removes any domains marked Insurance or Canceled from campaigns.

Counts how many were removed.

Replaces them with Active-unused domains.

If there aren't enough Active-unused domains, Cursor:
- Redistributes domains from other campaigns
- Or flags that new inboxes must be purchased

We have a human approval step before any changes go live.

Then Cursor writes updates back to:
- Smartlead (campaign domain assignments)
- Supabase (notes + final state)

The result: every campaign is always running on healthy inboxes without manual swaps.

How do you currently handle inbox rotation across multiple campaigns?

taplio

🦾Eric Nowoslawski's Linkedin Analytics