As a marketing analyst, I spend half my week answering the same three questions: “What’s working?”, “Should we put more budget into it?”, and “Can you prove it?”. Last month, I explained that to really address digital marketer’s performance questions, Return on Advertising Spend (ROAS) wasn’t the best guidance and that instead of reporting on…
Category: Data Analytics
From measuring performance to predicting outcomes, how can we design the models, metrics, and methods that turn raw data into business advantage. This section explores the analytical engine behind decisions like scoring systems, financial diagnostics, clustering methods, predictive models, and the architecture that makes it all run.
Stop Trusting ROAS and Start Measuring Incrementality
The Return on Advertising Spend (ROAS) tells you how many conversions the platform claims you got. Incrementality tells you how many of those would not have happened without your marketing efforts. If you optimise for the first and ignore the second, you are flying blind on actual business impact. As a marketing data analyst, your…
Mary Anning, the 19th-Century Paleontologist Who Teaches us about Modern Analytics
When we think of pioneers in data analysis, we often envision statisticians, machine learning engineers, and analysts diligently working through intricate datasets. However, after reading about Mary Anning, the 19th-century fossil hunter known for inspiring the tongue twister “she sells seashells by the sea shore,” I was struck by how her work exemplifies the core…
Beyond Categorization: Rethinking the Partner Scoring Model
As a data analyst working in a tech company, I was tasked with developing a scoring system to rank our business partners worldwide based on past performance and future potential. The goal was to support planning targets and budget allocation, ensuring that resources were directed toward the most valuable partners. At the time, we lacked…
My Playbook for B2B Market Growth
During my time supporting quarterly marketing and sales planning, I was responsible for overseeing and validating targets and budget allocation, program strategies, and then ensuring their successful implementation along the quarter. A process known as building and executing the Playbook in the tech industry. To effectively support this mission, I had to develop a 360°…
Crack your Case Like an FBI Analyst: Build a Bulletproof Analysis
The other night, I was watching Law & Order SVU when a scene cut to Morales, the analyst. It was funny, because that’s my name too … Well the analyst was briefing the team on critical data he’d pulled from a suspect’s USB key. It was an Excel spreadsheet. But Morales didn’t just dump a…
Financial Analysis To-Go: No Fluff, Just Liquidity Ratios
Ok, you’re in HQ, coffee in one hand, phone buzzing in the other. The Quarterly Business Review (QBR) is in full swing, and the execs are flashing financial results on the big screen live. No time to blink. Your boss leans in: “So… we need to brief the team after the QBR. Can we pay…
My Go-To Tech Stack Recipes for Building Practical Data Science Solutions
In competitive industries like Tech SaaS and Finance, data isn’t just an asset, it’s the engine behind every strategic move. As a Data & Business Intelligence (BI) Analyst, I specialize in designing scalable, automated, and stakeholder-facing analytics workflows that transform complex data into business insight. A strong analytics workflow seamlessly integrates data extraction, transformation, modeling,…
Exploring Essential KPIs and Tactics in Data Analytics
In the dynamic world of data analysis, a Data Analyst plays a pivotal role in steering a company towards success. The primary goal of a Data Analyst is to provide actionable insights based on data to improve performance. His responsibilities span a broad spectrum, encompassing performance assessment, future performance forecasting, target development, and the shaping…
Mastering the Art of Data Engineering: A Step-by-Step Guide to Building Robust Data Systems
A Data Engineer (DE) is responsible for building a robust data environment by developping and maintening scalable databases, data pipelines and architectures. He focuses on the infrastructure and mechanics of data handling, ensuring that data is properly collected, stored, processed and made accessible for various analytical and operational needs. By enabling efficient data analysis, the…