Spend less time assembling reports.
Consolidate sources, align definitions and build repeatable reporting that gives teams a shared view.
Typical output: a reporting model, dashboard, QA checks and a documented handover.
I help brands and agencies consolidate marketing data, check measurement and turn campaign reporting into practical decisions.
A focused reporting, measurement or campaign-analysis engagement, with a clear deliverable and an agreed way to assess it.
Consolidate sources, align definitions and build repeatable reporting that gives teams a shared view.
Typical output: a reporting model, dashboard, QA checks and a documented handover.
Review tracking, campaign identifiers and joins so reporting answers the intended question without inflating results.
Typical output: a measurement review, prioritised issue list and validation plan.
Connect media plans, budget pacing and post-campaign analysis. Separate delivery problems from performance questions.
Typical output: platform-level analysis, decision rules and an investigation backlog.
Start with the measured reporting improvement. Then inspect the experiment analysis and try the tools that show how I reason through campaign and customer data.
At Drumroll, weekly reporting effort fell from 20–30 team hours to approximately 6. The work brought reporting ownership together and gave teams visibility across the process.
Measured from data receipt to stakeholder export. Operational efficiency outcome; no sales uplift is claimed.
Read the delivery caseExplore how media planning, read-only validation, HTML5 dashboards and invoice tracking contribute to a coherent system. Follow the architecture, run synthetic checks and see where each tool fits.
An experiment report suggested B was underperforming. I checked the excluded users, corrected the SQL and set out what the evidence could actually support.
Anonymised assessment reconstruction · synthetic miniature records · no claim of realised business uplift.
Read the case & inspect the workCompare platform spend, plan shapes and performance signals. See which gaps need action even when the total looks on track.
Explore multi-platform pacingUse the query explorer to see how the grain and aggregation change an answer. For a complete SQL investigation, inspect the experiment case above.
Calculate customer churn, gross revenue churn and net revenue retention. See how new business can hide losses in the existing customer base, then identify what to investigate.
I use public creative to develop hypotheses, then separate those observations from what a controlled experiment can establish. Explore brand reviews, creative audits and incrementality planning.
I would assess the data, measurement, customer journey, decision process and ownership before deciding what to improve. The implementation plan gives each next step an owner and an acceptance criterion.
I’m Sibs, a Cape Town-based marketing analytics professional and consultant with a BSc in Chemical Engineering from the University of Cape Town.
My work has progressed from campaign execution into ad technology, paid-media optimisation and reporting leadership. At Incubeta, I progressed through three roles before moving into paid-media work at Addesu and reporting leadership at Drumroll.
I work end to end with SQL, including complex joins and analysis in BigQuery. If the numbers disagree, I want to understand why. Once the evidence is strong enough, I want a clear recommendation and a practical next step.
My progression across paid media, ad technology and analytics, supported by engineering and continued professional learning.
Drumroll
Apr 2026 – Present (8 months)
Addesu
Oct 2025 – Mar 2026 (6 months)
Incubeta
Sep 2023 – Oct 2025 (2 years 2 months)
Incubeta
Jul 2022 – Sep 2023 (1 year 3 months)
Incubeta
Feb 2022 – Aug 2022 (7 months)
The Consulting Academy Cape Town
Jul 2019 – Jun 2021 (2 years)
University of Cape Town · Graduated 2023
I start with what needs to be decided, who will use the answer and what evidence would change the recommendation.
I check the definitions, denominators and missing data. If the result depends on an assumption, I make that assumption explicit.
I explain what I found, what remains uncertain and what I would do next. I want the reasoning to stand up to a challenge.
Tell me what you want to improve.
I’ll come prepared with relevant context.