I help organizations turn fragmented customer, member, donor and applicant data into clear audience strategies, defensible decision frameworks and governed, AI-assisted analytics workflows, taking the work from raw data to executive action.
* Owner estimate for selected repetitive tasks (not whole projects), with human validation kept in place. How I report numbers
Executives rarely ask for "a segmentation" or "a DAX measure". They ask where to grow, whom to protect and what they can trust. These are the questions I have answered for clients, with the work to show for each.
I size high-potential lookalike audiences, then drill from region to city to postal area to find where interest is high but conversion is low.
Q.02 · Data trustI build the analytical universe first: entity keys, classification rules, exclusion reasons and a log of confirmed decisions, so every number traces back to its source.
Q.03 · Customer strategyI connect segment composition with tenure, products, channels and life stage, then turn it into specific acquisition, retention and wallet-share direction.
Q.04 · AI adoptionI turn proven work into Markdown SOPs, reusable prompts and version-controlled workflows, and keep human validation as a required step.
Q.05 · FundraisingI roll transactions up into reconciled donor profiles (recency, frequency, value) and document the method so any analyst gets the same answer.
Q.06 · Reporting at scaleI redesign Power BI products with dynamic bilingual logic, schema-safe Power Query and version control, so one template does the work of many.
Anonymized by design: no client names, invented segment labels and synthetic visuals. The value is in the analytical decisions, and those are shown in full.
I designed and piloted a governed workflow: business request → source-of-truth SOP → clarification → execution → validation → documented output. It pairs natural-language prompts with Markdown SOPs and version-controlled Power BI templates.
I owned the data-assessment and classification workstream that made a national segmentation program possible.
I turned a five-year segment comparison into acquisition, retention and channel direction.
The same operating system runs every engagement: clarify the decision, define the universe, validate on my own before relying on a result, then tell the story. Select a step to see what it produces.
Every deck follows the same six-part arc, so senior readers always know where they are and what is being asked of them.
What decision or audience are we examining?
What does the data show?
Why does this pattern matter?
Where is reach or engagement underdeveloped?
What should we protect, improve, prioritize or test?
Which segment, channel, product or place comes first?
Four separate gates check data, calculations, interpretation and delivery. When something fails, it goes back for correction. Nothing is quietly patched.
Hover a gate to see what it checks. The red item failed analytical QC and is routed back.
Duplicates, blanks, invalid codes, date windows, exclusions
Counts, indices, penetration, rollups, denominator logic
Materiality, direction, caveats, overstatement
SOW coverage, slide flow, labels, footnotes
Share of records by match outcome in one audit
| Outcome | Share (rounded) |
|---|---|
| High-confidence match | ~70% |
| Routed to review | ~15% |
| Left unmatched | ~15% |
This is the workflow I designed for analytics teams. It starts from work that is already proven, writes down the method, puts open questions to people before running anything, and validates outputs independently before relying on them. Watch it run:
Choose a repetitive task to see what the AI does and what stays with a person.
Prompts and SOPs have defined inputs, checks and deliverables.
Validation is a required step, and confidence builds in stages.
VS Code, Git and Power BI project files replace risky manual edits.
The method stays valid even when the tool changes.
Roadmap · exploring now
Business context, data logic, audience strategy, visualization, governance and AI-enabled execution, combined in one delivery model.
I turn fragmented data and ambiguous questions into decision frameworks people can understand.
I connect findings to clear strategic direction and to specific places, segments and channels to act on.
I build QC, traceability and stakeholder confirmation into the workflow itself.
I turn successful work into reusable SOPs, templates, prompts and knowledge assets.
I apply AI where it reduces friction and keep human judgment and validation in place.
"Who are our members, how do they engage, and where are the acquisition opportunities?"
"Which member groups are most valuable or expandable, and how should branch and digital strategy differ?"
"How should donor records roll up, and where should fundraising focus?"
"Where do high-potential lookalikes live, and where is conversion below market potential?"
"Who visits and who gives, and what is the real trade area?"
"How do we describe customer households clearly enough for stakeholders to act?"
"How do we standardize geographic reference data for mapping and reporting?"
"How do templates, bilingual reporting and SOPs become scalable?"
Also: transportation, business improvement areas and consumer / market strategy. Sector labels are generalized on purpose so that no client can be identified.
Since 2022 I have worked on client engagements at a national insights and analytics consultancy, with responsibility growing at each stage.
Review sequencing, feedback integration and quality preparation: disciplined delivery before anything else.
Audience sizing, segmentation interpretation, psychographics and multi-layer QC. A consultative role responsible for the narrative.
SOW interpretation through data rules, segmentation, geographic opportunity, storytelling and stakeholder confirmation.
Reverse-engineering proven work into SOPs, reusable prompts, validation routines and version-controlled workflows.
Each engagement has a fixed scope and clear deliverables, and ends with a handover so your team can continue without me. Timelines are typical and depend on data readiness.
For teams about to segment, model or report on data they are not sure they can trust.
For leaders deciding where to grow, whom to protect and where to act first.
For reporting teams maintaining duplicated templates, fragile queries or separate language versions.
Built on the bilingual redesign
For analytics teams that want AI productivity without losing control, auditability or knowledge.
Built on Case B
Every figure on this site comes from one claim register. Each claim has a source, a verification status and a set of approved wording. Client work stays confidential, so figures are rounded or generalized and every chart is rebuilt with synthetic data.
Practical perspectives for analytics leaders. Coming soon.
When IDs repeat across source systems, your "customers" may not be people at all. How source-aware keys fix it.
Coming soonReverse-engineer what already works, write it down, and put validation where automation could fail quietly.
Coming soonData, analytical, insight and delivery QC are different jobs. Treating them as one proofread is where errors get through.
Coming soonI work where consumer insight, segmentation consulting, Power BI delivery, quality assurance and workflow transformation meet. My job goes beyond producing analysis. I define the analytical population, translate business questions into methods, document assumptions, coordinate stakeholder decisions and turn findings into practical recommendations.
I work well with imperfect data. I build defensible approaches without pretending the limitations aren't there, and I add validation before I automate anything. What I leave behind lasts: SOPs, prompts, decision logs and templates that let your team continue the work.
I'm now bringing that combination to organizations as an AI Strategy & Insights Consultant. I focus on analytics consulting and on responsible, decision-focused AI adoption.
Tell me the decision you're facing and the data you have. I'll reply with a first read on how I'd approach it.