Selected Work — Shahin Mirshekari

Advanced Analytics — Asentech

Impact, in context — not just a number.

Pharmaceutical advanced analytics: turning claims, CRM, and digital engagement data into executive dashboards and decisions.

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Analytical projects delivered

Across marketing, commercial, and executive reporting requests.

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Interactive dashboards developed

Power BI experiences built for recurring executive decision-making.

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Recurring weekly reports supported

Ongoing reporting cadences owned end-to-end.

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Hours of responsive and agile analytical support & consulting

Ad hoc analysis turned around for time-sensitive business questions.

Selected Analytics Work

Anonymized case studies.

No client names, patient-level data, or proprietary figures — the pattern of thinking is what's shown.

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Case study 01

Connecting customer interactions to commercial outcomes

“Do fragmented engagement, CRM, and digital touchpoints add up to a customer journey we can act on?”

Data complexity: Engagement logs, CRM activity, digital touchpoints, and sales signals lived in separate systems with no shared timeline.
Analytical approach: Combined the sources into a single sequenced journey, then classified accounts as new, sustained, or declining growth based on interaction patterns.
Visual solution: An interactive journey map that reads left to right in time, with growth state encoded by color instead of a table of disconnected metrics.
Recommendation: Prioritize outreach on accounts showing early decline signals rather than waiting for the outcome to show up in sales.
Business value: Turned fragmented activity into an interpretable story leadership could act on the same week.

Case study 02

Designing executive decision dashboards

“How do we move leadership from “what happened?” to “what should we do?””

Data complexity: Multiple data sources with inconsistent definitions of the same KPI across teams.
Analytical approach: Established one shared KPI system, then designed the Power BI experience around the three decisions leaders actually needed to make.
Visual solution: A layered dashboard: headline KPIs at a glance, with drill-down paths for anyone who needs the evidence behind a number.
Recommendation: Standardize KPI definitions before adding more visuals — clarity came from agreement, not more charts.
Business value: Replaced competing spreadsheets with one dashboard multiple teams now trust as the source of truth.

Case study 03

Finding opportunity inside market decline

“Inside an overall declining market, which accounts are actually at risk — and which are quietly gaining?”

Protect

Rescue

Monitor

Deprioritize

Data complexity: Aggregate trend data masked meaningful variation at the account level.
Analytical approach: Segmented accounts and layered risk and opportunity signals on top of the segments instead of relying on the topline trend.
Visual solution: A segmentation matrix that separates “declining market” from “declining account,” surfacing bright spots hidden inside bad news.
Recommendation: Redirect commercial attention toward the segment gaining share, not just the segment losing the least.
Business value: Gave commercial teams a specific, defensible place to focus instead of reacting to the topline number.

Case study 04

Resolving data-quality and identity problems

“Can we trust this number, and if not, what would it take to?”

Matched82%

Fuzzy, reviewed11%

Unresolved7%

Data complexity: Mismatched identifiers and incomplete source coverage were producing numbers that didn't reconcile across systems.
Analytical approach: Investigated the mismatches directly, established defensible analytical logic for what could and couldn't be reconciled, and documented the limitations.
Visual solution: A before/after data-quality map showing exactly which records were resolved, excluded, or flagged — and why.
Recommendation: Ship the analysis with its limitations stated plainly rather than either hiding the gap or blocking on a perfect fix.
Business value: Preserved business usefulness and trust in the analysis without overstating what the data could actually support.

Digital Fingerprints

Evidence of thought, not decorative screenshots.

Select an artifact to see the question behind it. All snippets use abstract or sample data.

Raw Data

Structured Analysis

Visual Insight

Business Decision

The thinking behind it

What question was I trying to answer?

Are accounts sticking around after the first touch, or is engagement one-and-done?

What was unclear?

Whether 'active' should mean any touch, or a qualifying threshold of touches.

How did I structure the problem?

Defined a cohort by first-activity month, then measured retention by month offset.

What decision did the work support?

Set the retention threshold used across every engagement dashboard downstream.

Communication & Storytelling

The same finding, in three languages.

Value isn't limited to producing the analysis — it's helping people understand what it means.

The finding

Engagement is up 12%, but conversion is down 4% for the same accounts.

Touchpoint volume increased 12% quarter-over-quarter, while the conversion rate for the same account cohort declined 4 percentage points, concentrated in the mid-funnel stage.