Fernando Academia Jr., CDMP, PCM

Full Stack Marketer | Digital Marketing Instructor | Speaker/Trainer

I solve marketing technology challenges for mid-market companies ($5M-$50M revenue) across FinTech, HealthTech, and E-commerce.

Build and optimize marketing technology ecosystems driving measurable outcomes:

→22x-31x ROI improvements through platform optimization
→100% client retention across $50K-$66K MRR portfolios
→12-40% performance gains in customer acquisition and retention
→Email engagement improvements from 12% to 40% through AI-powered automation

📌 My MarTech Expertise

Platform Strategy & Implementation
Marketing automation (Netcore Cloud, MoEngage, Salesforce, CleverTap), CRM systems (HubSpot, Odoo, Pipedrive), analytics platforms (Tableau, Looker Studio, Power BI)

AI-Powered Optimization
Customer segmentation, predictive analytics, campaign automation, personalization at scale

Integration Architecture
Connect marketing platforms with CRM, data lakes, e-commerce systems, and enterprise applications

Team Development
Build marketing technology capabilities through training, process design, and change management

📌 Experience Highlights

Previous Leadership:

• Strategic advisor managing portfolios for top 3 digital banks, largest telemedicine platform, international e-commerce brands, and 90M+ user digital wallet
• Marketing strategist for US-based growth consultancy serving 5-10 clients
• Instructor at CIIT Philippines, teaching digital marketing to 80%+ student satisfaction

📌 Education & Executive Training
Executive MBA, Asian Institute of Management (2025)

Executive Education:

‣ Digital Transformation (Global Network for Advanced Management, Dublin)
‣ Customer-Centric Innovation & Metrics (FGV EAESP, São Paulo)
‣ CMO Leadership Program (Asian Institute of Management)

🤝Fractional CMO Services
Available for select engagements helping companies:

→Audit and optimize marketing technology stacks
→Implement AI-powered customer engagement platforms
→Build customer data and segmentation strategies
→Develop marketing performance measurement frameworks
→Train teams on marketing automation and analytics

🚀 Current Venture
Building SukiNomics, a networked, gamification-powered loyalty platform transforming customer engagement for Philippine MSMEs.

Open to: Fractional CMO roles, advisory positions, and strategic partnerships with growth-stage companies needing marketing technology leadership.

Not available for: Direct competitors to telecommunications industry.

Member

Fernando Academia Jr., CDMP, PCM, CSSWB

I asked an AI to draft a campaign brief last January.
It took me 20 minutes to fix.

I asked it again last week.
It took me 90 seconds.

Same tool.
Same me.
Different result.

The AI learned my format.

My tone. My structure. The sections I always reorder. The data I always want upfront.

It didn't just get better at writing briefs.
It got better at writing MY briefs.
This is the shift most marketers are missing.

The conversation isn't "Will AI take my job?"
The conversation is "Am I training my AI to do the work I shouldn't be doing manually?"

The marketers who figure this out first will operate at 3x the speed of those who don't.

I wrote about this shift in detail.

Fernando Academia Jr, EMBA, CDMP, PCM

Marketing Technology and Digital Transformation Leader | Building SukiNomics | 15+ Years helping eCommerce/SaaS/FinTech scale with MarTech & Strategy

View newsletter: Future-Forward Marketing

Series: Strategic Horizon 2026 | Article 1 of 5 Your job isn't to run campaigns anymore. Your job is to design the system that runs them for you. Let 

Open article: The Digital Marketer's Job Title Just Changed (And Nobody Sent a Memo) by Fernando Academia Jr, EMBA, CDMP, PCM • 5 min read

The Digital Marketer's Job Title Just Changed (And Nobody Sent a Memo)

I’m 3weeks into building ad optimization engines.

I’m particularly stoked about the engine I’m bootstrapping.

Aside from the usual ad metrics, it reads daily search trends from the web and TikTok. It pulls ad performance from Meta and Google.

Impressions, spend, conversions. Then it looks for patterns across all of them.

The underlying logic is basic economics.

> Impressions are supply. Available attention in the market at a given price.

> Search trends are demand signals. What people are actively looking for right now.

> Conversions tell you where supply and demand are meeting efficiently.

When search demand spikes for a category on Google and TikTok but CPMs on Meta for that same category stay flat, that is a pricing inefficiency.

You are buying attention at yesterday’s price for tomorrow’s demand.

When conversions climb on one platform but cost per impression rises with it, demand has already been priced in.

The arbitrage window is closing.

Most advertisers look at these numbers inside one platform at a time.

They optimize spend against their own campaign history.

That is like setting prices for your store by only looking at your own sales data and ignoring what every other store in the market is charging.

This engine reads across the market.

The goal is not to compete with big tech algorithms. They have more data than I ever will.

The goal is to build a correlation layer on top of their signals.

Meta optimizes for Meta.
Google optimizes for Google.
TikTok optimizes for TikTok.

Nobody optimizes across all three at the same time.

That is where the opportunity sits.

But this is not just about budget allocation.

It also suggests what to do with creatives.
- Which formats to try.
- Which angles to explore.
- What to change based on how platforms like Meta are evolving their ad ranking systems, including how Andromeda now retrieves from a much larger pool of candidate ads before scoring relevance.

The creative strategy has one rule: no rules.

No fixed color palette.
No predetermined tone of voice.
No brand guidelines restricting what gets tested. 

Maximum variation across every dimension.

Most marketers start with assumptions about what their audience wants to see.

Then they test within those assumptions.
Then they optimize within those tests.

Every decision after the first assumption is constrained by it.

I am doing the opposite.

No starting assumptions.
No premature hypotheses.

Let the platforms receive the widest possible range of creative inputs.

Watch what the data surfaces.
Then form conclusions.

Observation first.
Hypothesis second.

I’ll be running this in low budget experiments for a few friends’ businesses.
- No ROI expectations.
- No controlled variables.
- Raw experimentation.

The best way to learn how ad platforms actually behave is to stop assuming you already know and start watching what happens when you remove every constraint.

I will share what I find.

The military has a concept called "commander's intent."

It means: tell your team what success looks like. Don't tell them how to get there.

This is now the most valuable skill in marketing.

Not copywriting.
Not media buying.
Not dashboard building.

The ability to clearly articulate what you want — so an AI system can execute it for you.

In 2026:
75% of marketers are increasing programmatic budgets.
33% are running AI across creative, media, and measurement at the same time.
56% still don't trust the systems enough to let go.

The gap between those who architect and those who execute is widening fast.

Your title might still say "Marketing Manager."
Your actual job is now "Marketing Architect."

I wrote about what this means for your career and your daily workflow.

Full article here: https://lnkd.in/gAsUQGUE

Juji, Inc.

Stakeholder

QIDS Venture Partners

Tech Advisor

L9 Analytics, LLC

Managing Partner & Co-Founder

Graphen

Led the research and development teams on developing an advanced data processing and
computing framework for large-scale high-performance graph computing and machine
learning, and building solutions for applications in the financial industry.

Led the development team to build a non-performing loan solution
for one of the largest banks in the world based on graph computing and machine learning,
and a cybersecurity monitoring system for an international bank in US using behavioral modeling and unsupervised anomaly detection techniques.

CTO & Co-Founder

Research Staff Member

15+ years of research experiences using machine learning, graph computing, data mining, statistical
modeling and text analytics algorithms to address both research challenges and business needs in the
areas of information retrieval, human-computer interaction, graph and network science.
7 years of experiences in client engagement with multiple large corporations on developing industry
and domain specific applications (e.g. anti-money laundering, anomaly detection, cybersecurity monitoring) that incorporate cutting-edge graph analytics, behavioral analytics and machine
learning technologies to solve key business problems

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