Gunal K
Data Analytics Consultant
I turn raw business data into decisions that make money. Specialized in payment analytics, churn prediction, and marketing attribution — for fintech, SaaS, and D2C brands globally.
I turn raw business data into decisions that make money. Specialized in payment analytics, churn prediction, and marketing attribution — for fintech, SaaS, and D2C brands globally.
Independent data consultant working with businesses across fintech, SaaS & D2C
I help businesses stop bleeding money through data.
Most companies collect data but can't answer basic questions: Which marketing channel actually works? Why are customers leaving? Where is revenue leaking?
I specialize in finding these answers through:
→ Revenue Analytics: Payment failure tracking, pricing optimization, leak detection
→ Customer Intelligence: Churn prediction, segmentation, LTV modeling
→ Performance Optimization: Marketing attribution, ROI analysis, budget allocation
Projects & verified work:
Analyzed 3.5M+ payment transactions — surfaced ₹4.84M in recoverable revenue
Built XGBoost churn model at 90.3% accuracy (AUC 0.978) on 1,700+ student records
Designed multi-touch attribution framework showing 2.3x projected ROAS uplift
Areas I'm actively researching and experimenting with
Moving beyond correlation — studying DoWhy and CausalML to answer "did this campaign actually cause the uplift, or was it coincidence?" Applying it to attribution modeling.
Learning dbt (data build tool) to write modular, version-controlled SQL pipelines. Goal: build analytics stacks that don't break when the data changes upstream.
Experimenting with using LLM APIs to auto-generate plain-English summaries from dashboard data. Testing if non-technical stakeholders can get insights without opening a BI tool.
Deep-diving into retention curve analysis and cohort LTV modeling. Building frameworks to tell SaaS founders exactly which user segments are worth acquiring vs. optimizing.
Specialized consulting for data-driven growth
Find Hidden Money
Payment failure analysis, revenue leak detection, failure root cause diagnosis. Built on real transaction pattern analysis.
Predict Behavior
XGBoost churn models, engagement scoring, early warning systems. Built and validated on real student behavior data.
Maximize ROI
Multi-touch attribution modeling, channel credit analysis, budget reallocation frameworks. U-shaped and data-driven models.
Protect Revenue
Pattern detection, anomaly alerts, fraud scoring systems. Typical outcome: 5-10% fraud reduction.
Independent data projects — real datasets, real methodology, real output
End-to-end analysis of 3.5M payment transactions — identified 9.98% failure rate, ₹4.84M revenue at risk, and 5 actionable fixes with SQL + Python + Power BI.
Built ML model achieving 90.3% accuracy, enabling early intervention on 280+ at-risk accounts monthly.
Multi-touch attribution model across 2,847 customer journeys — revealed Instagram was undervalued by 3.75x under last-click. Budget reallocation projected 2.3x ROAS improvement.
Don't just read about my work - interact with it. These models run in your browser.
Predict if a student will churn based on behavior
Will this payment succeed?
Optimize your channel budget allocation
Internship, certifications & independent project work
Self-Directed Portfolio Work | 2024 – Present
RIT × Excelerate Remote Internship | Oct 2025
Google (Coursera) · Infosys Springboard · IBM SkillsBuild
Validated expertise in data analytics and AI
Google & Coursera
Completed 8-course professional certificate covering foundations of data science, Python programming, statistical analysis, regression modeling, machine learning fundamentals, and advanced analytics capstone.
Rochester Institute of Technology × Excelerate
Recognized as Star Performer for outstanding achievement in AI-powered data analysis remote internship. Applied advanced analytics, machine learning, and visualization techniques to real-world business problems.
Data analyses and business perspectives
Analysis of UPI transaction data showing 3% lower success on Monday mornings. Hypothesis: Weekend system maintenance + Monday traffic spike.
Read analysis →Analyzed 5 D2C brands. Found inverse relationship between CAC and LTV in 3/5 cases. Premium customers often come from organic, not ads.
Read analysis →Most SaaS churn prediction models look at 30-day behavior. But engagement data shows the decision happens by Day 3.
Read analysis →Simple process. Fast delivery. No surprises.
You explain the problem. I tell you exactly what I'd look at and whether I can help. No pitch. No commitment.
Exact scope, deliverables, timeline, cost. Everything in writing before any work starts. No vague retainers.
SQL, Python, Power BI — whatever the problem needs. You get progress updates. Not silence for a week then a surprise.
Not a 47-slide deck. You get a dashboard, 3–5 clear actions, one walkthrough call, and 7 days of follow-up support.
Full credentials, projects, and experience — view inline or download
Analytical data professional with experience building decision-support analysis across payments, customer behaviour, and marketing performance. Analysed 3.5M+ transaction records to identify ₹4.84M in recoverable revenue through systematic failure analysis and operational cross-validation. Skilled in SQL, Python, Power BI, Tableau, and Excel (Pivot Tables, Large Dataset Management) with focus on structured research workflows, data verification, and concise business communication. Strong interest in Gulf market dynamics, commercial strategy, and regional investment trends.
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Open for remote freelance & consulting work globally
Fast execution. No lengthy processes. 3-7 day delivery.
Available for freelance projects, remote contracts, and consulting engagements. Fast turnaround. Timezone-flexible.