Blue Water Marketing

Chief Executive Officer

Managing Partner

I have 15 years of experience building marketing plans, purchasing media and analyzing successes for companies including Match.com, Nat Geo, Lending Tree, Harvard Business School & EMC. I founded BWM in 2017 and currently oversee all marketing activities at the company as Chief Marketing Officer. 

I currently lead a team of professionals who cover all aspects of integrated marketing and communications, including product marketing, market research, branding, social media, PPC, SEO & lead generation.

My diverse experience has enabled me to manage all aspects of a BWM's profile—from developing marketing plans to client-relations. 

Also, as part of my role at BWM, I've had the privilege of interviewing a number of business owners on our Podcast highlighting the life of a business owner, discussing the growing pains, planning & successes of various business owners.

Chief Marketing Officer

TCSM Holdings

TCSM Holdings is the parent company of direct-to-consumer eCommerce stores in the United States,  committed to providing excellent products for all their customers.

Principal Owner

https://chrismarrano.com/

Business Coach

Harvard Business School

On a daily basis, I was in charge of purchasing, maintaining and tracking all digital and traditional media placements to ensure all programs and courses were up to date and filled.   

Additionally, I updated course websites with new faculty, dates and program subject matter.   

I researched, secured and managed all events and promotions for Business School courses and programs.  

Worked closely with web team and Social Media team to plan and establish campaigns for future programs and events.  

Helped create Instagram, Facebook and Twitter posts that aligned with both existing and future programs.

Marketing Account Executive

Coordinated the implementation of a variety of global online, print, mobile, and sponsorship initiatives, as well managed marketing operations and deliverables with our ad agency.

Worked with media buying agency and creative agency/in-house creative services to coordinate advertisements with various online publishers, print publications, and outdoor venues. 
Additionally, I worked closely with internal teams to create messaging and custom content with publishers. Furthermore, established various online strategy plans for custom brand content with thought leadership and web teams.

I assisted with the planning and implementation of various sponsorship initiatives that involved brand activation of sponsorships for MLB, PGA, NFL, and F1. Within those sponsorships I managed all deliverables required for activation, logistics and tracking. I also created Excel and Powerpoint for documentation of sponsorship benefits and metrics which included proof of placements, impressions and various other results for clients.

Associate Program Manager

Mullen

I worked on planning all offline media for four national brands. I strategically implemented ads in magazines, newspapers, radio and television, while also working hand-in-hand with the digital media teams for the brands. 

On a daily basis, I worked with our Brands' account and creative teams in making sure our clients were happy with everything within their plans. Additionally, I worked with outside sales representatives across all mediums to make sure that the clients received the best positioning and timing for their media buying.

Assistant Media Planner

BarCast Network, LLC

Assisted with sales and promotion initiatives for digital advertising network displaying broadcasts on a proprietary channel in bars and restaurants in major cities in the US. 
Generated weekly activity reports to CEO and partners. 
Participated in partnership strategy meetings with major accounts such as Anheuser-Busch and Diageo and provided demographic input and feedback for programs.

Intern

Founder @Blue Water Marketing | eCommerce & Digital Marketing Nerd

Hey, I’m Chris Marrano.

I’ve spent the last 15 years in the trenches of digital marketing, starting on Madison Avenue-style agency floors at Mullen (Boston) working with brands like Match.com and LendingTree, then going client-side at Harvard Business School before launching Blue Water Marketing from the ground up.

Today, I run an eCommerce growth agency that helps Shopify brands scale from 7 to 8 figures—without bleeding budget.

We do this by combining Meta Ads, Google Ads, Klaviyo, and content at scale with deep financial visibility (think MER, aMER, CAC, and contribution margin). We don't just run ads—we integrate into your brand’s systems and decision-making so growth isn’t just fast—it’s profitable.

I'm also building ADIQ.AI, an AI-powered SaaS platform that helps brands analyze creative performance in real time—because in this market, "creative testing" without data is guesswork.

Outside of business, I live in South Florida with my wife Taylor and our two boys. I’m a family-first operator. Weekends usually involve beach days, t-ball games, and building something in the garage (usually with a goldendoodle supervising).

If you're a DTC founder looking to:
✅ Turn creative chaos into profitable customer acquisition
✅ Build predictable growth systems backed by numbers
✅ Actually understand what's driving your MER

Let’s connect.

Shopify brands, has your agency implemented AI into their systems yet?
Probably not like how we are doing it.

We have built a custom automation stack in N8N that changes how we buy media on Meta.

Here is how it works:
1. Automated data pulls
 We connect directly to the Meta Graph API to extract ad-level data in controlled loops. This prevents oversampling and gives us fresh performance data on 250–300 ads at a time.

2. Benchmarking system
Every ad is parsed for cost per click, CPM, CTR, ROAS, conversion rates, creative type, and copy length.
Benchmarks are recalculated continuously so we always know what is a good ad versus a bad ad in real time.

3. Creative categorization
Ads are grouped and compared by format: image vs video, short vs long copy, different hooks and structures.
This tells us exactly which creative categories are carrying performance and which ones are wasting spend.

4. Automated reporting
Every three days, the system generates visualizations and delivers reports directly to our media buying team.
We know which ads to scale, which to kill, and which creative categories to double down on.

The result is a testing and scaling system that is faster, more accurate, and less emotional than the way most brands run Meta ads today.

AI is not the future of media buying.

It is already here, and only getting better. 

📩 Comment “WORKFLOW” and I will send you a free breakdown of the 3 exact workflows we use for Meta automation.

Chris Marrano

Scaling 7 & 8 Figure DTC Brands Profitably | Building AI-enhanced systems | Founder@BlueWaterMarketing | Founder@ADIQ.AI

In the last two weeks, I have spoken with 17 eCommerce founders running 7 and low 8-figure brands.

There is one thing that has been abundantly clear. Most of them do not know their break-even CPA or ROAS.

They can tell me their AOV.
They can tell me their monthly ad budget.

But when I ask, “What is your exact break-even point?”
The answer is usually silence.

Without knowing these numbers, scaling is just guessing. Guessing is expensive.

That is why I built the Meta Ads Profitability Calculator.
It is the same tool we use at Blue Water Marketing before scaling any account, whether it is spending 10K or 500K a month.

In two minutes, it will show you:
 ✅ Your exact break-even CPA and ROAS
 ✅ How your margins change with different offers or AOVs
 ✅ The impact of budget allocation between cold and retargeting
 ✅ Scenario testing for ad angles, audience segments, and costs

We have used it to help brands avoid wasting over 20K in unprofitable spend and to scale into their most profitable months.

I am giving it away free. No catch.

If you want the calculator, comment “Calculator” and I'll send it over

Shopify brands running Meta, stop guessing every morning.

I am giving away a plug-and-play n8n JSON that sends a Daily Performance Digest to Slack.

What it does every morning at 8:30:
 ✅ Pulls the last 7 days of Meta performance
 ✅ Calculates CTR, CPC, ROAS, CPA, and CPM
 ✅ Compares yesterday to the 7-day average for quick context
 ✅ Ships a clean Slack message to your team that auto refreshes daily

Why this matters:
You make decisions before Ads Manager even loads
Creatives that slip on CTR or spike on CPA are obvious
Your team aligns on one source of truth without screenshots

What the Slack digest looks like:
 • Account: Brand XYZ
 • Window: Yesterday vs 7-day
 • CTR: 1.42% vs 1.67%
 • CPC: $1.12 vs $0.98
 • ROAS: 3.4 vs 3.1
 • CPA: $42.10 vs $38.50
 • CPM: $12.70 vs $11.20
 • Note: “Flagged 2 creatives for rising CPA and falling CTR”

What you get:
The .json to import directly into n8n
A short setup guide for Meta Graph API and Slack webhook
Optional add-ons for anomaly alerts and creative fatigue tags
Install time: about 10 minutes if you already have API access.

I have audited 93 accounts this year. The brands that win do not check dashboards at noon. They get signal at breakfast and act.

Want the JSON? Comment DIGEST and I will send it.

Scaling ads is not about finding winners.

 It is about knowing exactly when those winners stop being profitable, and replacing them before they drain your budget.

I have audited 93 Meta ad accounts this year alone. Across those reviews, the most common problems I saw were:
- No financial guardrails to define profitable scaling limits
- Creative testing that lacked structure and consistency
- AI tools being used as optional add-ons instead of built into daily operations

Here is the process we use to solve this:
Step 1: Establish financial clarity before scaling
We calculate break-even CPA and ROAS for every account before increasing spend. This number becomes the performance threshold. Scaling decisions are then based on protecting and increasing profit, not gut instinct.

Step 2: Implement a structured creative testing process
All creatives are tied to themes and personas. New ads go into testing campaigns with clear benchmarks. Winning creatives move into scaling campaigns. Underperformers are replaced within 72 hours to prevent wasted spend on fatigued ads.

Step 3: Integrate AI into daily operations
We deploy AI agents that monitor creative fatigue, CAC changes, and inventory velocity around the clock. When a metric crosses a set threshold, the system sends an instant alert to Slack so we can act before performance drops.

The impact of this system across multiple brands:
- MER increased by 22 percent from rotating creatives earlier
- CAC decreased by 18 percent while tripling ad spend
- Weekly clarity on why performance changes, which improves decision-making speed

Scaling without these three components is like playing poker without knowing the odds.

Scaling with them turns growth into a predictable, repeatable process.

Let me know if you have any questions down in the comments!

If your Meta creative testing is failing, this is probably why

This year I have audited 93 Meta ad accounts.
The same frustration comes up again and again:
- Testing campaigns overspend on one ad that is not hitting KPIs
- Other ads get pennies of spend and never get a fair chance

Teams spend hours creating new creatives only to watch them die in the first 24 hours

Here is what is really happening.

Meta optimizes for what it believes will perform based on its audience signals. It knows who stops scrolling, who clicks, and who converts. If the system decides one ad looks promising, it can take the majority of the budget and starve everything else.

That is fine for scale campaigns, but for testing it is a disaster.

Here is how we fix it at Blue Water Marketing:
1. Protect evergreen first - 80% of the budget stays in proven campaigns that are hitting targets.

2. Isolate each test creative in its own ad set - Instead of dropping multiple creatives into one ad set, we duplicate the ad set for each creative. This means every ad gets its own budget and a real chance to prove itself.

3. Set a clear kill target - For example, if the goal is $40 CPA, we let each ad spend up to $40 over two days.

 If it delivers no purchase and poor click-through rates, we turn it off.

If it hits KPIs or shows strong leading indicators, it moves into scale campaigns.
This is not the fastest way to build a campaign, but it gives you back control.

Every creative gets equal spend, you find winners faster, and you stop wasting time on tests that never had a chance to succeed.

Scaling is not just about the algorithm. It is about how you run the algorithm.

Watch the full campaign build and walkthrough in the comments.

Most “product launches” fall flat.

Here’s what happens when you execute a true product drop the right way.

📉 July (no drop)
Revenue: $356,497
MER: 8.84
CAC: $34.72
Conversion rate: 2.19%
Orders: 2,537

📈 August (drop executed, month-to-date)
Revenue: $495,031
MER: 11.85
CAC: $26.63
Conversion rate: 2.82%
Orders: 2,930

That’s a $138,534 revenue lift with lower CAC and higher efficiency.

Here’s what made the difference:
 ✅ Anticipation was built weeks before the drop with segmented Klaviyo flows (VIPs, win-backs, high-engagement non-purchasers)
 ✅ Meta ads were structured with creative mapped to urgency and exclusivity, aligned with email + SMS touchpoints
 ✅ Inventory was intentionally limited to drive urgency and faster decision-making
 ✅ Messaging framed the drop as an event, not just a new SKU quietly added to the store

The result:
 More sessions.
 More conversions.
 A sharper MER than any “always-on” month.

This is why I tell every founder:
A product drop is not a launch.
It is a structured revenue event that, when done right, can define your biggest months of the year.

This is going to change how you run Facebook ads.

Here is a quick behind-the-scenes look at just one of the 12 systems inside AdIQ.

Every 6 hours, the platform pulls your real Facebook Ads data.
 It then analyzes:
 ✅ What ads are working
 ✅ Why they are working
 ✅ What to do next

Instead of waiting days (or wasting spend) to figure out which creatives are carrying your account, AdIQ gives you a continuous feedback loop that gets smarter every cycle.

This is not guesswork. It is structured learning built directly on your data.

If you run an ecommerce brand and rely on Meta to scale, this is the edge you have been missing.

$180K in 30 days. 36% growth.
Here is what actually drove it.

This brand is spending about $1,500 per day on Meta.

In the last 30 days, revenue hit $180,913 from 3,140 orders with a conversion rate increase of 11%.

Here is what mattered:

1. Financial guardrails first
We mapped break-even CPA and ROAS before scaling. This gave us the exact threshold where growth remained profitable.

2. Structured creative testing
Twenty percent of budget was dedicated to testing. Each new creative was placed in its own ad set, which forced Meta to give it a fair budget. This allowed us to find winners faster and shut down losers before they wasted spend.

3. Evergreen stability
Eighty percent of spend went into proven scaling campaigns. Once new creatives passed testing benchmarks, they were rotated into evergreen campaigns. This kept MER steady while still driving new growth.

4. A continuous optimization loop
AI agents monitored creative fatigue, CAC movement, and conversion rate shifts. Alerts were pushed into Slack in real time, so changes were made within hours instead of days.

The result:
📈 Sales up 36 percent month over month
📈 Orders up 35 percent
📈 Conversion rate up 11 percent

This is the difference between running ads and running a growth system.

Scaling is not about luck.
It is about giving every dollar a job and every creative a chance to win.

graphical user interface, chart, line chart

Taplio

Chris Marrano's Linkedin Analytics

's Best Posts (last 30 days)

Want to drive more opportunities from LinkedIn?

Taplio

.css-1jgreyo{background-image:linear-gradient(284deg, rgba(39,43,72,1) 0%, rgba(33,47,183,1) 100%);color:transparent;-webkit-background-clip:text;background-clip:text;}Chris Marrano's Linkedin Analytics

's Best Posts .css-qqfgvy{font-size:var(--chakra-fontSizes-md);}(last 30 days)

Want to drive more opportunities from LinkedIn?

Chris Marrano's Linkedin Analytics

's Best Posts (last 30 days)