Closed-Loop Prescription Intelligence (Visit Impact Score)
Revenue attribution and HCP ROI scoring powered by the correlation of field visit activity with downstream sell-out data — transforming FieldOrchestrator into a commercial intelligence platform that continuously refines where your field force invests its time.
Overview
The Closed-Loop Prescription Intelligence (Visit Impact Score) module transforms FieldOrchestrator from a visit-tracking tool into a revenue intelligence platform. Visit Impact Score correlates visit activity with downstream sell-out data — pharmacy and wholesaler purchases — to measure the real commercial value of each HCP relationship and continuously optimize future routing priorities.
Scientific Foundations
Visit Impact Score is built on peer-reviewed econometric and marketing science methodologies. The following references are the academic pillars of the approach.
Difference-in-Differences (DiD)
The core causal inference engine. Compares treated HCPs (received visits) against matched control peers to isolate the visit effect from market-wide trends.
Ashenfelter & Card (1985). Review of Economics and Statistics, 67(4), 648–660. · Card & Krueger (1994). American Economic Review, 84(4), 772–793.
Log-Compressed Score Normalisation
Raw lift percentages are transformed to a 0–100 scale using a logarithmic compression function, preventing extreme outlier HCPs from dominating rankings — standard practice in commercial analytics (IQVIA Score-Card methodology).
Box, G. E. P. & Cox, D. R. (1964). Journal of the Royal Statistical Society, Series B, 26(2), 211–252.
Diminishing-Returns Frequency Decay
Each successive visit to the same HCP contributes less marginal lift than the previous one — a concave response curve well-documented in pharmaceutical call-frequency optimisation research.
Nair, H. S., Manchanda, P. & Bhatia, T. (2010). Asymmetric Social Interactions in Physician Prescription Behavior. Journal of Marketing Research, 47(5), 883–895. · Szilagyi, T. (1991). Optimal detailing frequency. Pharmaceutical Executive.
Pharmaceutical Seasonality Adjustment
Control-group lifts are normalised by calendar-month seasonality factors (product and brick-specific, or industry benchmarks) before the DiD computation, preventing seasonal prescribing demand from inflating or deflating visit attribution.
IQVIA Q-MAT prescribing seasonality benchmarks. · Cleveland, R. B. et al. (1990). STL: A seasonal-trend decomposition procedure based on Loess. Journal of Official Statistics, 6(1), 3–73.
The specific model parameters, thresholds, and weighting schemes applied within FieldOrchestrator are proprietary to Symbiowave Technologies and protected by the applicable software licence agreement. The academic references above describe the published methodological frameworks that inform the design; they do not constitute a full specification of the implementation.
The Closed-Loop Concept
Visit Impact Score operates as a five-step intelligence cycle. Each step feeds the next, creating a self-reinforcing loop that continuously improves the commercial efficiency of field operations.
Visit Execution
The representative visits the HCP and captures the full interaction — products detailed, objections, outcomes, and engagement signals — through the mobile execution module.
Sell-Out Ingestion
Pharmacy retail sell-through and wholesaler secondary sell-out data is imported into the platform via the weekly batch process or automated ERP integration.
Correlation
The platform correlates field visits with post-visit prescription behavior within a product-adaptive attribution window (default 30 days; 14–21 days for acute therapies; up to 60–90 days for chronic conditions), controlling for baseline prescription activity to isolate the incremental impact of the visit.
HCP ROI Scoring
Each HCP receives an ROI score (0–100) reflecting their commercial contribution relative to the visit investment required to generate it.
Route Re-weighting
High-ROI HCPs receive increased priority weight in future route generation. Low-ROI HCPs are deprioritized. The loop restarts with the next cycle of visits.
HCP ROI Score
The HCP ROI Score (0–100) is the central output of the Visit Impact Score module. It measures the commercial return on visit investment for each HCP, normalised across the territory to enable objective prioritization decisions.
| Score | Label | Implication |
|---|---|---|
| 80–100 | Strategic | High prescription conversion rate and strong commercial contribution. Maximize visit frequency and invest in tailored engagement strategies. |
| 60–79 | Active | Consistent prescriber with reliable commercial return. Maintain regular contact and ensure product messaging remains current. |
| 40–59 | Developing | Commercial potential not yet fully realized. Focus on message quality and objection resolution to accelerate conversion. |
| 20–39 | Passive | Low conversion rate relative to visit investment. Review targeting approach and consider reducing visit frequency pending re-evaluation. |
| 0–19 | Inactive | No measurable correlation with sell-out data detected. Assess whether continued visit investment is justified for this HCP. |
Visit Impact Score Dashboard
Navigate to Analytics → Visit Impact Score Dashboard to access the full commercial intelligence view for your territory or the territories you manage.
Access is role-scoped: representatives see their own territory, managers see their team's territories, and national directors see the full portfolio view.
Territory ROI Heat Map
A geographic overlay showing HCP ROI score density across your territory. High-concentration ROI zones are highlighted to guide resource allocation decisions.
Top & Bottom HCPs by ROI
Ranked lists of the highest and lowest ROI HCPs in the selected period, with score delta from the previous period to identify emerging trends.
Weekly Trend Charts
Time-series charts overlaying visit activity volume against prescription lift, enabling visual correlation analysis and campaign impact assessment.
Rep Attribution Table
A breakdown of commercial attribution by representative, showing visit count, attributed prescriptions, ROI score distribution, and comparison against team benchmarks.
Sell-Out Data Integration
Visit Impact Score's correlation engine requires sell-out data from your distribution channels. Two data sources feed the attribution model:
Pharmacy Data
Direct retail sell-through data by product, prescribing HCP, and geographic brick. This is the primary attribution signal — it reflects actual patient-level dispensing associated with specific prescribers.
Wholesaler Data
Secondary sell-out by distribution zone, providing market-level demand signals where pharmacy-level granularity is not available. Used to supplement the attribution model in lower-data territories.
Import Methods
- Manual import: Administration → Sales Data Import — upload standardised CSV/XLSX files following the format specified in the Integration Guide.
- Automated ERP integration: continuous or scheduled data feed via the /api/v1/sales-data ingestion endpoint. See the Integration Guide for authentication and schema specifications.
Adaptive Attribution Window
Visit Impact Score uses a product-adaptive post-visit attribution window rather than a fixed period. The window length is configured per product in the product catalogue via the Sales Response Window setting, and defaults to 30 days when not explicitly set.
| Therapy Class | Typical Window | Rationale |
|---|---|---|
| Acute therapies (antibiotics, antivirals) | 14–21 days | Short prescribing cycle; rapid post-visit response expected. |
| Standard therapies | 30 days (default) | Industry benchmark; covers the typical prescribing decision lag. |
| Chronic / specialty therapies | 60–90 days | Long prescribing cycle; HCP may require multiple visits before conversion. |
When multiple visits occur within the attribution window, attribution is split across visits using a time-decay weighting model. The most recent visit receives the highest attribution weight.
Data Quality Grades
Every Visit Impact Score value carries a data quality grade (A through FAIL) that reflects the statistical confidence of the underlying attribution model for that HCP. Scores are always displayed alongside their grade so decision-makers can correctly weight them.
| Grade | Criteria | Interpretation |
|---|---|---|
| A | ≥10 visits, full matched control group, product seasonality data present. | High-confidence score. Safe to use for targeting decisions and ROI-based route prioritisation. |
| B | 6–9 visits, partial control group or missing seasonality factors. | Good confidence. Use with standard care; directionally reliable for planning purposes. |
| C | 3–5 visits, limited control group, or incomplete sell-out data coverage. | Moderate confidence. Score provides a directional signal but should not be the sole basis for major targeting changes. |
| D | 1–2 visits, minimal control group matches, or very sparse sell-out data. | Low confidence. Treat as preliminary only. Increase visit frequency and data collection to improve grade. |
| FAIL | Zero visits in lookback period, no sell-out data, or control group construction failed. | Score is not statistically meaningful. Displayed for completeness only — do not use for prioritisation. |
Grades improve automatically as more visit and sell-out data accumulates. A newly onboarded territory will typically reach Grade B within 2–3 months of active sell-out data ingestion.
Weekly Model Updates
Visit Impact Scores are recalculated weekly by the scheduled scoring job using the latest available sell-out data. This cadence aligns with the standard sell-out data delivery cycle from most distribution partners.
Score change alerts: Score changes exceeding ±20 points between weekly recalculations automatically generate an alert visible in the Analytics dashboard. Use these alerts to identify HCPs experiencing significant commercial momentum shifts.
Using Visit Impact Score in Planning
Visit Impact Score data is most powerful when integrated into your regular planning and management cadence. The following best practices are recommended for field force leaders and territory managers.
Quarterly Territory Rebalancing
Review Visit Impact Scores each quarter when assessing territory coverage. Use ROI score distribution to validate or challenge current geographic assignments.
Visit Frequency Calibration
Use ROI scores to set evidence-based visit frequency targets per HCP tier. Strategic HCPs should receive higher frequency allocations than Passive or Inactive HCPs.
Coaching Conversations
Identify "Passive" HCPs in a representative's territory to structure data-driven coaching conversations about targeting and messaging effectiveness.
Campaign Impact Measurement
Compare Visit Impact Scores before and after major campaign launches to measure the incremental prescription impact attributable to campaign activity versus baseline.
Related Resources
Integration Guide
Sell-out data format specifications, ERP connector setup, and authentication.
Route Optimization
How Visit Impact Scores influence route generation priorities and visit frequency targets.
Administration Guide
Configure Visit Impact Score thresholds, alert settings, and role-based dashboard access.
Questions about Visit Impact Score setup or data ingestion?
Our solution engineers can assist with sell-out data onboarding and Visit Impact Score configuration.