Executive reporting
Leadership-ready narratives, reports, and presentations that show what changed, why it matters, and what warrants attention.
Insights · Strategy · Data storytelling · Better systems
Clarity for decisions
that matter.
How I work
Strategy grounded in evidence.
Systems designed for use.
Research & synthesis
Mixed-method research and rigorous synthesis that connect customer, market, and behavioral evidence to the decision at hand.
AI-enabled workflows
Repeatable research systems with validation, quality control, documentation, and human review built in.
Trends & futures
Structured monitoring and analysis that separate meaningful movement from noise and translate it into practical implications.
Selected work
From signal
to system.
Representative work drawn from real engagements. Client names and proprietary details are intentionally withheld.
01
Audience intelligence & executive reporting
Owning the full path from raw data to leadership-ready story
The context
Four recurring audience-intelligence pipelines combined survey exports, benchmarks, title metadata, open-ended responses, written interpretation, and multiple client-facing formats on weekly or monthly cadences.
The approach
Owned or substantially managed the source-to-deliverable workflow: preparing inputs, reconciling data, analyzing movement, writing the narrative, populating trackers and presentations, and completing final quality checks.
The value
Created consistent, decision-ready reporting across different cadences, metrics, and formats—and built the hands-on process knowledge needed to improve the system responsibly.
02
Global research & product strategy
Leading a multi-market study from lived behavior to product direction
The context
A global technology organization needed to understand how younger users relied on laptops across academic, creative, and social life—and make the evidence useful to product, marketing, and experience teams.
The approach
Led a multi-stage program across four markets, three languages, several time zones, and multiple partners, combining interviews, digital diaries, video, participant artifacts, and quantitative validation.
The value
Translated immersive behavior and sized opportunity areas into shared direction for product-roadmap thinking, feature priorities, and messaging.
03
AI-enabled research operations
Turning learned processes into reusable, reviewable workflows
The context
Recurring research work involved the same demanding tasks—data restructuring, workbook consolidation, qualitative coding, cross-file reconciliation, and tracker standardization—but the know-how lived inside individual projects.
The approach
Performed the work manually until the edge cases were understood, then generalized five processes into a documented workflow library with defined uses, quality checks, limits, and human-review points.
The value
Converted individual process knowledge into a portable capability packaged for management review and broader evaluation, with a clearer path to repeatability and safe adoption.
04
Analytical judgment & data storytelling
Catching a misleading topline before it became a decision
The context
An unexpected result in a multi-market study was mathematically correct but easy to misread because one market represented a disproportionate share of the total sample.
The approach
Traced the pattern to sample composition, flagged the risk before delivery, added a market-balanced comparison, and built an accessible interactive report explaining the difference and its implications.
The value
Prevented a potentially misleading interpretation while preserving the original data and giving stakeholders a more decision-useful view of the result.
About Rachael
A senior insights strategist who modernizes how the work gets done.
For more than 20 years, I’ve helped leaders make sense of customer, market, competitive, communications, and behavioral evidence.
I’ve led research and intelligence programs from the first business question through methodology, fieldwork, analysis, reporting, presentation, and next steps. The through-line is practical: What changed? Why does it matter? What should we do about it?
Today, I bring that same research judgment to AI-enabled workflows and analytical products. My principle is simple: automate after understanding. Make the repeatable work more consistent, keep the path back to the evidence visible, and preserve human judgment where it matters most.
Connect on LinkedInHave a complex question?
Let’s make it
clearer.
Open to conversations about insight strategy, executive storytelling, AI-enabled research operations, and analytical products.
rachael.soucie@outlook.com