For a pharmaceutical company, the HCP target list was the single source of truth for which 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 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The result was a target list that was frequently outdated, occasionally inaccurate, and always expensive to produce.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 gone to insight, 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, so any analyst can open the project and immediately understand what is happening and why. Its native Snowflake integration means data stays in the warehouse and only computation moves. And its built-in scheduling turns 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 is happening, and why.
Every manual Excel operation, from filters and lookups to ranking calculations, segment assignments, and 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 from the latest data. Territory alignment, call priority scoring, and segment thresholds are all configurable parameters, with no manual overrides each cycle. The 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 are 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 is wrong, before anyone downstream even knows to ask. Embedding QC into the flow is the difference between a pipeline your sales team trusts and one they are 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 per cycle.
Institutional account-level targeting for HCO engagement. Forecast logic applied automatically from the latest data. Dates update dynamically, no manual adjustment. Formatted for direct use by FP&A and commercial ops teams.
The impact: what changed
The results were immediate and measurable. Here is what the team gained once the new pipeline went live:
80+ hours saved per month. Analysts reclaimed time previously spent on manual pulls, wrangling, and error-checking. That capacity shifted to higher-value analysis and insight.
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 configuration per cycle.
QC built into the flow. Quality checks are no longer a separate step that gets skipped under deadline pressure. They are embedded, automatic, and non-negotiable. Errors surface in the pipeline, not in the field.
Significantly improved targeting accuracy. With cleaner, more consistent data driving the list, reps engage the right HCPs with greater frequency and confidence. High-potential prescribers no longer fall through the cracks.
Full auditability and reproducibility. Every output can be traced back to its source through the visual recipe flow. When stakeholders ask why an HCP is on the list, there is 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 is not a nice-to-have. It is 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 remove 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 decide with confidence. That trust compounds and becomes a competitive asset.
A fully automated HCP and HCO targeting pipeline built in Dataiku DSS. It processes multiple Snowflake data sources through documented visual recipes, applies priority scoring and segment logic, runs embedded QC checks, and delivers both 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 is a commercial data and analytics consulting firm for teams with lean headcount and enterprise expectations, including pharma commercial ops and sales operations teams where targeting accuracy drives revenue directly.
We work inside the tools you already have, Dataiku, Snowflake, Tableau, SQL Server, and build the automation layer that removes the manual work your team should not be doing in the first place. If your HCP targeting still runs on manual pulls and someone else’s spreadsheet, this is the conversation to have before the next planning cycle starts.