If your reports are confusing, your data needs a translator.

I build and audit data systems to surface signal before KPIs degrade.

Applied analytics projects focused on trust signals, governance, and operational systems.

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Meet Gina

Founder of PixelKraze — turning spreadsheets into clear insights.

Hi, I’m Gina—turning spreadsheets into clear business insights.

Behind PixelKraze

Hi, I’m Gina Aulabaugh. I turn spreadsheets and exports into clear KPIs and next steps.

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What I Do

Data System Diagnostics
Investigating definitions, incentives, and metric distortion.

Trust Signal Modeling
Identifying friction, drift, and repeat-contact patterns in operational data.

Actionable Decision Translation
Turning statistical outputs into process and governance recommendations.

Proof

Real-world case studies and notebooks that show how I translate messy data into clear decisions. Explore NovaWireless — Trust Signal Health, then browse the full case study library.

Customer & offer-level analysis to support pricing, bundling, and retention decisions.

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From Spreadsheet to Insights

Real examples of charts and tables I build from spreadsheet/CSV exports—so you can compare KPIs, spot trends, and make decisions faster.

Privacy Note: No sensitive data needed—IDs only are fine.

KPI Snapshot: Avg Heating Oil Use

Bar chart from spreadsheet/CSV data showing average heating oil used by heating type—an easy way to compare categories and spot cost drivers.

PixelKraze, LLC | Clean Data. Clear Decisions.'s image

Trend Signal: Repeat Rate vs Friction

Line chart showing repeat rate rising as friction increases—useful for spotting where people get stuck and where fixes reduce repeat issues.

De-identified dataset prepared for analysis.

Clean Data → Decision-Ready Table

I clean and structure spreadsheets/CSV exports so they’re consistent, de-identified, and ready for KPI reporting and dashboards.

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Operational Systems Experience

Before building governance-aware analytics frameworks, I worked inside high-volume telecom retention operations under KPI pressure. I handled churn cases, metric escalation, and customer trust breakdowns in real time.

That frontline exposure shapes how I audit definitions, incentives, and reporting systems today. I’ve seen how misaligned metrics distort behavior — and how small data-definition gaps create large operational consequences.

Problems I Work On

• Metric misalignment under incentive pressure

• Conflicting KPI signals
• Repeat-contact churn loops
• Dashboard noise masking operational friction

How I Work

I treat data as a product of real systems—people, incentives, tools, and processes. Most problems aren’t ‘bad analysis’ — they’re bad definitions, broken incentives, or missing context.

Then I move quickly through three steps:
• Clarify the decision and what “success” actually means
• Validate the data (definitions, gaps, bias, incentive effects)
• Translate results into actions: process changes, experiments, or models

The goal is clarity you can act on—not another dashboard you ignore.

Want a second set of eyes on your data?

Tell me what decision you’re trying to make and what data you have. I’ll tell you what’s possible and what I’d recommend as a first step.

No sensitive data needed—IDs only are fine.

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View Resume

All original content, models, documentation, and frameworks on this site are the intellectual property of PixelKraze, LLC unless otherwise stated.

This work is licensed under the Creative Commons Attribution-NonCommercial 4.0 International License (CC BY-NC 4.0).

Commercial use, redistribution for profit, or incorporation into proprietary systems requires prior written permission.

Independent work using synthetic or public data. Not affiliated with or endorsed by any employer.

Contact: Gina.Aulabaugh@PixelKraze.com