Weekly KPIs Report
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Tools & GuidesPrioritising a List You Cannot Agree On: 5 Ranking Methods Compared
Compare pairwise ranking, weighted scoring, MoSCoW, RICE, and Productboard to choose a method that fits your list, evidence, and decision.
Tools & GuidesHow to Document a CSV: 5 Ways to Build a Data Dictionary Compared
Compare five ways to build a CSV data dictionary, from a spreadsheet and pandas to dbt, Atlan and a browser tool, tested on the same export.
Data & ReportingGood Tracking Architecture Starts With One Source of Truth
A practical framework for building clean tracking architecture where each tool has a clear role, attribution has one owner, and reporting disagreements become explainable instead of political.
Data & ReportingDo You Need Old Affiliate Data?
A practical decision framework for operators migrating affiliate software: what history can stay in an archive, what breaks if it does, and when native continuity still matters.
Data & ReportingWhy Dashboards Fail
Why dashboards fail before anyone opens them, what business teams get wrong upstream, and where AI actually helps turn reporting requests into decision-ready tools.
Data & ReportingReporting engagement — are you paying attention?
A case study on how engagement reporting changed the interpretation of Magic6 from an acquisition tool into a repeat-activity system.
Data & ReportingYou’re Scaling the Wrong Customers (Until You Measure Retention)
How retention marketing reporting connects acquisition channels to real long-term value and changes business decisions.
Data & ReportingYou’re Missing Half Your Data. Here’s Why That’s Fine.
Why acquisition reporting is never complete, and how to build a system that still drives decisions.
Data & ReportingRetention Marketing Reporting: How to Actually Measure Customer Value
A practical guide to building retention marketing reporting using waterfall tables, retention curves, and CLTV to understand real customer value.
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Who writes this
Nikita Goncharenko — Data Analysis
Nikita Goncharenko is a Belgium-based digital delivery professional who connects analytics, execution, websites, and AI-assisted workflows. As a Data Analyst, he turns messy reporting into trusted KPI views, budget signals, retention analysis, and decision support that teams can act on.
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Quick answers on Data Analysis
Short answers about this topic. Open only what you need.
Which reports are worth building first?
The ones a team already argues about: KPIs, budget, retention and cohorts. If a number changes a decision every week, it deserves a reliable report before anything else does.
Which tools does this kind of reporting need?
Less than vendors suggest. Excel, Power BI or Looker Studio on top of GA4 and GTM, SQL for the queries, and Python where a step repeats. The exports from the business platforms you already use are usually the real source.
How do you make a dashboard useful for decisions?
Start from the decision, not the chart. A good report shows what changed, why it matters, and which action the team should consider.
When is reporting worth automating?
When the manual version causes delays, errors, or depends on one person being available. Automating a report nobody reads only makes it wrong faster.