AI-Driven Digital Marketing Intelligence Consultant & Growth Engineer in Pakistan
I’m Usman Saeed, I help enterprise brands eliminate tracking data drops, protect conversion assets, and maximize digital profitability. By integrating Marketing Intelligence strategy, server-side tracking pipelines, and cloud-based E-commerce Engineering architectures, I transform fragmented tracking logs into automated, high-yield digital growth engines.
Trusted by 100+ clients across 6 markets since 2014Recognized by the world's leading digital platforms.
My Certifications
Auto-scrolls · pauses on hover
Ad, analytics and commerce platforms
MetaTikTokLinkedInShopifyMore than a marketer.
A researcher and a builder.
I started in 2014 with SEO, went full-service by 2016, and spent the next decade inside real campaigns for 100+ brands across six markets. Then I moved deeper, into AI, data science and research, because the problems worth solving had outgrown tactics. Today the same practitioner works with a deeper toolkit: models, data pipelines and AI agents.
- 2014Started with SEO
- 2016Full-service marketing
- 2026AI, data science & research
The thinking
behind the work.
Five short videos, five ideas that shape how I work: what I’m aiming for, where I came from, how I grow, what I put first, and why clients choose an intelligence partner over an agency. Each video is a few minutes long. Watch the short version here, then read the full story, with examples, on each page.
Why it matters: every recommendation is tied to a business outcome you can measure, not to activity.
Mission & Vision
Marketing decisions guided by intelligence, not reaction.
Forecast-driven, insight-led growth for every brand I serve.
12+ years, 100+ clients, 6 markets: the same practitioner, with a deeper toolkit every year.
Journey & Timeline
Every stage added a skill; none replaced the one before it.
Journey & TimelineProfessional Evolution
I grow vertically, into deeper and harder problems, not just horizontally into more channels.
Excellence Over Income
My priority order, and why income comes last by design: when the first three are right, it follows.
Why I'm Different
An intelligence partner, not an agency.
From activity
to intelligence.
Most marketing optimises for more activity: more posts, more clicks, more reports. I optimise for better decisions, made earlier, on data you own. Every engagement follows the same three moves, in the same order, so you always know what is being done, why it is being done, and how it will be measured.
Diagnose with evidence
Find the real constraint before prescribing anything.
- Data & tracking audits
- Audience & market research
Model what matters
Predict value, churn and conversion before budget moves.
- Predictive modelling
- Incrementality analysis
Build compounding growth
Systems and AI agents that keep learning, month over month.
- Engineered first-party signals
- Revenue-level measurement
The Cognitive Marketing Engine
A 4-loop system that turns data into decisions, and decisions into measurable growth. Each loop feeds the next, so every month starts smarter than the last.
Empirical Diagnostics
Understand market, audience and business with data before anything is planned.
Causal Strategy
Identify what will actually drive growth, and what only looks busy.
Programmatic Execution
Precise targeting and automation across channels.
Continuous Optimisation
Learn, adapt and compound results.
Typical start: a discovery call, an audit in the first two weeks, then a written strategy before any execution.
Engagement Process
What happens from first call to results.
You keep everything: every tool runs in your own accounts, so the data, models and setup stay yours.
Tools & Tech Stack
Five layers, and a different combination for every solution, service and SaaS replacement.
Example: the stack behind predicted LTV for Meta Ads.
Most stacks start with tools you already have; new ones are added only when a model needs them.
Explore the interactive stackThe stack behind every solution.
Pick a solution, service or SaaS replacement to see the exact technologies used, layer by layer, from data collection to reporting. Every stack is built inside your own accounts, documented and handed over, so nothing is locked in a vendor’s platform and nothing disappears if we stop working together.
Predictive Intelligence · pLTV
Score each customer's future value and feed it back to ad platforms. View page →
Organic Growth Intelligence
Intent and entity modelling for search, AEO and GEO visibility. View page →
Media Buying Intelligence
Predict creative fatigue and audience overlap before they cost you. View page →
E-Commerce Intelligence
Margin, repeat-purchase and CLV analytics for online stores. View page →
Content Marketing Intelligence
Topical gaps and content attribution, measured not guessed. View page →
Omnichannel Data Intelligence
Attribution and Bayesian MMM connecting every channel to revenue. View page →
Social Signal Engineering
Organic social managed for brand signal across platforms. View page →
E-commerce Revenue Engineering
Store growth with paid channels bundled and measured. View page →
Predictive AI
A custom value model you own, instead of a per-seat predictive SaaS. View page →
Marketing Mix Modeling
Open Bayesian MMM on your data, instead of a black-box MMM platform. View page →
Ad Attribution
Attribution you can audit, instead of a vendor's hidden logic. View page →
Business Intelligence
A warehouse and dashboards you own, instead of a BI subscription. View page →
SEO Growth
Your own SEO models and agents, instead of stacking SEO subscriptions. View page →
Paid Media
Bidding logic built on your margins, instead of a generic optimiser. View page →
Ecommerce DTC
Store analytics you own, instead of another DTC analytics app. View page →
CRM & Marketing Automation
Value-scored segments pushed into your CRM, instead of a black-box add-on. View page →
Know a customer's value before you pay for them.
Predicted Customer Lifetime Value (pLTV) tells ad platforms who is worth acquiring, so budget chases profitable customers instead of cheap clicks. It is the first solution I am building out in full, starting with Meta Ads, followed by Google Ads and TikTok Ads once the Meta version is proven.
Customer Lifetime Value (pLTV)
A model trained on your first-party data scores each new customer's future value and sends it back through Conversions API. Meta Ads comes first and is in development now; it will run as an ongoing service, retrained as your data grows.
- Runs inside your own Meta and Google accounts
- Retrained as your data grows
- Reported in revenue and profit, not clicks
How the model scores
ILLUSTRATIVEHow it works
Intelligence-led solutions for real business problems.
Each solution adds a data-science layer on top of a growth channel you already use, so decisions are based on predicted value and real margins, not on what happened last month.
Not sure where to start? The audit tells us which solution will move your numbers first.
- Seven solutions, one method
- Built inside your own accounts
- Measured in revenue, not clicks
- Audit first, so you start in the right place
Organic Growth Intelligence
Search, AEO and GEO visibility modelled on intent and entities, so your pages answer the questions buyers and AI assistants actually ask, and rank for them.
Learn morePaid Search Intelligence
Value-based bidding and budget accountability, so Google Ads spend follows revenue, not clicks, and every campaign has to earn its budget.
Learn moreMedia Buying Intelligence
Creative fatigue and audience overlap predicted before they cost you money, across Meta and TikTok.
Learn moreE-Commerce Intelligence
Margin, repeat-purchase and customer value analytics that show which products and buyers really grow the store.
Learn moreContent Marketing Intelligence
Topical gaps and content attribution, measured, so every article has a clear job and you can see if it does it.
Learn moreOmnichannel Data Intelligence
Attribution, marketing mix modeling and identity resolution that connect every channel to revenue, so budget moves to what truly works.
SaaS tools vs Cognitive Intelligence.
Off-the-shelf SaaS rents everyone the same model and tells you what already happened. Cognitive Intelligence is custom-built around your business, predicts what will happen, and you own it.
- Reports what already happened
- One generic model for every customer
- Vendor owns the logic and data
- Per-seat cost, forever
- No judgment on edge cases
- You still have to interpret it
- Predicts what will happen, and why
- Models built around your business
- You own the model and the data
- Scoped engagement, no seat tax
- Human judgment + AI on every decision
- Delivers the decision, not a dashboard
Nine categories, side by side
The SaaS tools businesses usually rent in each category, and what a custom CI build does instead.
Autonomous agents, built, not rented.
Built from data science and real marketing practice, one discipline at a time. They are not trigger automations or plug-ins: each agent reads your data, finds the gap and acts on it, with my judgment on every decision. SEO agents are live; more categories are being built.
SEO & Organic Visibility Agents
Content Optimization
Aligns every important page with search intent, entities and the questions buyers ask.
ExploreLink & Authority
Finds and prioritises the links and mentions that actually move authority and rankings.
ExploreTechnical Health
Checks crawling, rendering, indexing and speed continuously, and flags issues early.
ExplorePPC Agents
Search-term mining, budget pacing and bid checks for Google Ads, flagged before money is wasted.
Learn moreGrowth Engineering
Funnel and experiment agents that spot where users drop off and what to test next.
Learn moreE-com & Content
Product-feed, catalogue and content agents that keep listings and articles working for search.
Learn moreNot automations. Not plug-ins.
Each agent is built from data science and real marketing practice, for one discipline at a time. SEO is live; the next categories are being built.
- Reads live data, not monthly exports
- Every action reviewed by me
Your data stays yours: agents work inside your own accounts and every change can be traced.
Reads your live data from analytics, search and ad accounts.
Models the gap between where you are and where you could be.
Executes or drafts the fix, with my review before anything goes live.
Measures the result and improves the next decision.
From strategy to implementation.
Intelligence applied to the channels you already run: search, ads, social and your store. Each service has its own page with the full scope, the plans and exactly what is included, so you can see what you get, and what it costs, before we even talk. Not sure which one fits? The video explains it.
Organic Visibility Engine
SEO, AEO, GEO and LLMO as one system, so you are found in Google and cited in AI answers.
View serviceGMB & Local SEO
Google Business Profile audit, optimisation and management for the local map pack.
View serviceGoogle Ads Intelligence
Google Ads managed against revenue, not clicks, with clean conversion tracking and value-based bidding on every campaign.
View servicePaid Social Intelligence
Meta, TikTok and LinkedIn ads scaled on predicted customer value, with every creative tested before the budget moves up.
View serviceSocial Signal Engineering
Organic social managed for brand signal: branded search, saves and enquiries, not post counts.
View serviceE-commerce Revenue Engineering
Store growth with paid channels bundled in and measured end to end, from first click to repeat purchase.
View serviceFull-Business Consulting
Strategy across the whole business, not one channel, scoped on a call.
Book a callSee pricing for every service
Every service lists its plans and what each one includes. International projects are scoped and quoted after a free audit.
Brands I've worked with, and what changed.
Clients across six markets, and case studies that show the problem, the strategy and the result, with the data behind every number.
Inside every case study: the starting point, what was changed and why, and the screenshots behind each result.
Real problems, documented step by step
Each case study walks through the problem, the strategy and the result. Every number comes from the client's own Search Console, Analytics or ad account.
The full client portfolio
Every engagement, filterable by market, industry and service. Engagements without verified data are listed without numbers, never with guesses.
- Pakistan, UK, USA, UAE, Canada, Turkey
- SEO, PPC, social and consulting
- Across 15 industries
Industry intelligence, not one-size marketing.
15 industries, each with its own buying cycle, data and decision drivers. Four have dedicated playbooks; the rest are served with the same method, adapted to how that industry’s customers actually research, compare and decide before they buy, from the first search to the final enquiry.
Why industry context changes the model
The same model trained on a clinic, a store and a property developer gives three very different answers. Context comes first, then the data, then the model.
- Buying cycle and seasonality
- Which data the industry actually has
- Trust, compliance and local rules
- Where the buyers actually search
Ecommerce
Repeat purchase, margin and customer value guide acquisition, not just ROAS.
Key model · pLTVExplore industryHealth & Wellness
Trust signals, compliant targeting and long decision cycles handled with care.
Key model · Appointment predictionExplore industryReal Estate
Lead scoring that separates high volume from real buyers across long sales cycles.
Key model · Lead scoringExplore industrySaaS & B2B
Trial conversion, churn early warning and expansion signals for complex sales.
Key model · Churn predictionExplore industryResearch that moves beyond theory.
I apply data science and machine learning to real marketing problems, and write it up as research. Three papers are in peer review.
Research Papers
Academic Journey
Why I went back to study: vertical growth needs research, not just more experience. The academic work feeds directly into the models I build for clients.
In practice: methods like SHAP explanations and sequence models go straight into client pLTV and lead-scoring work.
AI & Data Science JourneyResearch Interests
Where marketing practice meets machine learning: the questions I'm working on now, and the ones clients bring me next.
Research InterestsOriginal articles and video breakdowns.
First-hand writing on predicted LTV, AI search, attribution and data science for marketers, plus a YouTube series that walks through the same ideas on screen. Fifteen articles are planned across four areas, from AI-driven SEO to performance measurement, with new pieces added as the research moves on.
pLTV for Meta Ads: Feed Predicted Customer Lifetime Value into Meta CAPI
How to score customers by future value and send that value to Meta, step by step.
Read articleWhy Your Rankings Dropped and Every Tool Says the Site is Fine
From the Computational & AI-Driven SEO series: what SEO tools miss, and how to find the real cause.
See the seriesWhy Your Meta, Google and Hyros Numbers All Disagree
From the Performance Intelligence series: why ad platforms over-count, and how to get one version of the truth you can act on.
See the seriesVideo breakdowns of the same ideas: models, tracking and AI search, explained on screen.
Data over opinion
Decisions backed by evidence.
Diagnosis first
Understand, then recommend.
Traceable measurement
Clear metrics, real accountability.
You own your accounts
Full access and transparency.
Questions clients ask first.
Straight answers to what clients ask before we start: how I work, what predicted LTV and Cognitive Intelligence mean, and how an engagement begins.
Still have a question?
Ask me directly on WhatsApp. You talk to me, not a sales team.
What does an AI-driven digital marketing intelligence consultant do?
I diagnose where growth is leaking, build predictive models and clean data pipelines, and run channels on those signals, so decisions are made on what is likely to happen, not on last month’s report.
How is this different from hiring a digital marketing agency?
You work with me directly, not a junior account team. I start with diagnosis instead of a package, optimise for predicted customer value instead of clicks, and build models and AI agents you keep.
What is the difference between SaaS tools and Cognitive Intelligence?
SaaS tools rent everyone the same generic model and mostly report what already happened. Cognitive Intelligence is a custom model built on your data, around your business, that predicts what will happen, with human judgment on every decision. You own the model and the data.
What is predicted customer lifetime value (pLTV)?
pLTV is a model that estimates how much a new customer will be worth over time, using early signals from your first-party data. That value is sent to ad platforms such as Meta through Conversions API, so they bid more for customers likely to be worth more.
Do you work with clients outside Pakistan?
Yes. I work with clients across six markets: Pakistan, the UK, USA, UAE, Canada and Turkey. International projects are scoped and quoted after an audit.
Are your AI agents automations or SaaS tools?
Neither. They are autonomous agents I build from data science and real marketing practice. They are not trigger-based automations or off-the-shelf SaaS, and the SEO agents are live now.
Which industries do you work with?
Fifteen so far, including ecommerce, health and wellness, real estate, SaaS and B2B, education, finance, hospitality and legal. Ecommerce, health and wellness, real estate and SaaS have dedicated pages.
Where can I see your pricing?
On the pricing page, which lists every service. Each service page also shows its own plans in detail.
How does an engagement start?
With a discovery call and an audit of your data, tracking and channels. The audit sets which solution or service comes first. Minimum terms and plans are listed on each service page and on the pricing page.
Start with the real problem.
A conversation about your goals, your data gaps and the decision that matters next.