For a pharmaceutical company, the HCP Target List was the single source of truth for which healthcare providers the sales team should be calling on. Manual SQL queries, Excel wrangling, and a QC process that caught errors only after they reached the field had turned every planning cycle into a multi-day ordeal. Rower replaced the entire workflow with a fully automated Dataiku pipeline—delivering accurate, auditable HCP and HCO target lists with a single click.
The Problem: Manual HCP Targeting Was Costing Accuracy and Time
Healthcare Provider (HCP) targeting is central to pharmaceutical sales effectiveness. A well-built target list tells reps who to see, how often, and why—directing finite field capacity toward the highest-value opportunities. Get it right, and the sales team operates like a precision instrument. Get it wrong, and reps waste calls on low-priority physicians while high-potential prescribers go unvisited.
For this client, the process had grown organically over years and accumulated significant technical debt. The workflow looked like this:
The result was a target list that was frequently outdated, occasionally inaccurate, and always expensive to produce. Analysts spent an estimated 80+ hours every month just maintaining the process — time that should have been spent on insight generation, not data plumbing.
The process was built for a moment in time, not for a team that needed to run it reliably, every cycle, without heroics. No version control. No embedded QC. No scheduling. Every cycle depended on the same people doing the same manual steps in the right order.
Our Solution: Rebuilding the Pipeline in Dataiku
Rower brought the entire HCP targeting workflow into Dataiku DSS, an enterprise AI and data platform built for complex, regulated analytical environments. Rather than patching the existing process, we redesigned it from the ground up — translating every manual step into a documented, testable, automated visual recipe.
Why Dataiku?
Dataiku is purpose-built for exactly this kind of challenge. Its visual recipe interface makes complex data flows transparent and auditable — any analyst can open the project and immediately understand what’s happening and why. Its native Snowflake integration means data stays in the warehouse; only computation moves. And its built-in scheduling capabilities turn a one-time build into a recurring, hands-off production pipeline that runs automatically every cycle.
How We Built It: Phase by Phase
We parsed all existing Snowflake SQL queries and rebuilt them as Dataiku visual recipes. Complex multi-table joins that previously lived in undocumented scripts became interactive, labeled flow steps with clear inputs and outputs. Version control is automatic. Any change is tracked. Any analyst can now open the project and immediately understand what’s happening — and why.
Every manual Excel operation — filters, lookups, ranking calculations, segment assignments, territory alignment — was codified into replicable Dataiku recipes. Logic that previously lived in someone’s head (or a forgotten formula in a hidden column) is now explicit, parameterized, and version-controlled. Any parameter can be updated without touching the underlying logic.
Visual forecasting recipes generate the HCO target list automatically based on the latest data. Territory alignment, call priority scoring, and segment thresholds are all configurable parameters — no more manual overrides each cycle. The HCO list updates consistently and reproducibly every time the pipeline runs.
We replaced manual Excel QC with an automated quality control step embedded directly in the Dataiku flow. The pipeline now validates its own outputs before they’re ever delivered — checking for data completeness, threshold violations, and logical consistency. Issues surface in the pipeline, not in the field after reps have already acted on bad data.
The best data pipeline is one that raises its hand when something’s wrong — before anyone downstream even knows to ask. Embedding QC into the flow isn’t optional; it’s the difference between a pipeline your sales team trusts and one they’re constantly second-guessing.
Two Outputs, One Automated Run
Ranked and scored list of individual healthcare providers for field rep engagement. Call priority, segment assignment, and territory alignment all calculated automatically. Single-click delivery — no manual configuration required per cycle.
Institutional account-level targeting for HCO engagement. Forecast logic applied automatically using the latest data inputs. Dates update dynamically — no manual adjustment needed. Formatted for direct use by FP&A and commercial ops teams.
The Impact: What Changed
The results were immediate and measurable. Here’s what the team gained once the new pipeline went live:
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80+ hours saved per month — Analysts reclaimed time previously spent on manual data pulls, wrangling, and error-checking. That capacity shifted to higher-value analysis and insight generation.
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Single-click report generation — What once took a multi-day manual effort now runs end-to-end with a single pipeline trigger. Dates update automatically; no manual configuration required per cycle.
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QC built into the flow — Quality checks are no longer a separate step that gets skipped under deadline pressure. They’re embedded, automatic, and non-negotiable. Errors surface in the pipeline, not in the field.
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Significantly improved targeting accuracy — With cleaner, more consistent data driving the list, sales reps are engaging the right HCPs with greater frequency and confidence. High-potential prescribers are no longer falling through the cracks.
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Full auditability and reproducibility — Every output can be traced back to its source through the visual recipe flow. When stakeholders ask “why is this HCP on the list?” — there’s a clear, documented, reproducible answer.
Why HCP Targeting Automation Matters
The pharmaceutical commercial landscape is more competitive than ever. Sales force sizes are under pressure, digital engagement is rising, and the window to reach a busy clinician is narrowing. In this environment, targeting precision isn’t a nice-to-have — it’s a strategic advantage that compounds over every cycle.
Manual targeting processes introduce latency and error at every step. By the time a list makes it through SQL exports, Excel merges, and manual QC, it may already be weeks out of date. Automated pipelines in platforms like Dataiku eliminate that latency. The list reflects current data, is validated against current thresholds, and is ready to deploy as soon as the cycle opens.
Beyond the operational gains, automation builds institutional trust. When analysts can explain exactly how a target list was built — step by step, in a reproducible visual flow — commercial leadership can make decisions with confidence. That trust compounds over time and becomes a competitive asset.
A fully automated HCP and HCO targeting pipeline built in Dataiku DSS — processing multiple Snowflake data sources through documented visual recipes, applying priority scoring and segment logic, running embedded QC checks, and delivering both HCP and HCO target lists in a single automated run. What was previously a multi-day manual effort is now a repeatable, auditable process that runs automatically every planning cycle.
Why Rower for Pharma Commercial Analytics
Rower Consulting builds data automation for lean commercial analytics teams with enterprise expectations — including pharma FP&A and sales operations teams where targeting accuracy directly drives revenue.
We work inside the tools you already have — Dataiku, Snowflake, Tableau, SQL Server — and we build the automation layer that eliminates the manual work your team shouldn’t be doing in the first place. If your HCP targeting process still runs on manual data pulls and someone else’s spreadsheet, this is the conversation to have before the next planning cycle starts.
Technologies Used in This Engagement
