Proof in the work, not the pitch

A mid-size emerging pharma with an established oncology brand was experiencing declining rep productivity and flat market share despite significant promotional investment. Leadership suspected misaligned targeting but lacked a clear diagnostic.

We conducted a full SFE assessment — analysing call data, prescribing patterns, and territory alignment against market potential. We redesigned the segmentation model, realigned 180 territories, and rebuilt the call planning logic to concentrate effort on high-potential, under-called accounts.

A emerging pharma preparing its first commercial product launch needed to build commercial infrastructure from the ground up — including master data management, analytics setup, segmentation, targeting, and field readiness — within a constrained timeline.

We led the end-to-end commercial build: establishing the MDM framework and data architecture, designing the segmentation and targeting model, structuring the launch KPI framework, and supporting field training design. Work was sequenced against regulatory milestones to ensure readiness at each stage.

A large pharma commercial team was operating on a fragmented legacy analytics infrastructure — disparate data sources, slow reporting cycles, and limited ability to integrate patient-level insights with field performance data. The commercial organisation was data-rich but insight-poor.

We led a full analytics modernisation programme — rearchitecting the data infrastructure, consolidating sources into a governed commercial data asset, rebuilding the reporting layer with role-specific dashboards, and introducing patient-level analytics capabilities including journey mapping and source-of-business analysis.

A pharma brand team was investing heavily across personal and non-personal promotional channels but lacked visibility into which activities were actually driving prescription behaviour. Budget allocation decisions were being made on share-of-voice assumptions rather than measured response.

We designed and executed a promotional response model using sales data, call activity, digital engagement metrics, and market research. The model quantified the marginal contribution of each channel and identified several high-investment, low-response activities that were consuming budget without measurable impact.

A pharma company's Medical Affairs team was investing significantly in MSL engagement and KOL programmes but had limited ability to demonstrate the commercial and scientific value of that investment to leadership. Impact was anecdotal rather than evidenced.

We designed and implemented a Medical Affairs impact analytics framework — integrating MSL call data, publication activity, congress engagement, and brand performance metrics to build a measurement model that quantified the contribution of scientific engagement to prescribing behaviour and payer access outcomes.

A pharma company's Medical Affairs function was managing KOL relationships, MSL activity, and scientific engagement data across disconnected spreadsheets and email systems — limiting leadership visibility, creating compliance risk, and preventing meaningful analysis of engagement patterns.

We led the design and implementation of a Salesforce CRM configured for Medical Affairs workflows — covering MSL call logging, KOL profiling, congress management, and activity reporting. Configuration was built around Medical Affairs-specific compliance requirements and integrated with the commercial CRM to enable cross-functional visibility where appropriate.

A pharmaceutical company with a growing commercial portfolio was operating on a fragmented data infrastructure — separate data feeds, inconsistent master data, and reporting built on unvalidated extracts. The commercial organisation lacked confidence in its own data, slowing decision-making and creating audit risk.

We designed and led the implementation of a governed commercial data warehouse — consolidating claims, CRM, market access, and financial data into a single validated source of commercial truth. Work included master data management framework design, data quality governance, and role-specific reporting layers for brand, field, and leadership audiences.

A specialty pharma company's commercial leadership team was under pressure from the board to develop an AI strategy. Multiple vendors were pitching platforms and point solutions, but the team lacked the internal clarity to evaluate competing claims or prioritise investment against commercial objectives.

We led a structured AI strategy engagement — beginning with a commercial AI readiness assessment across data infrastructure, capability, and governance. We then facilitated an AI use case prioritisation process with cross-functional commercial leadership, scoring 14 candidate use cases against commercial value, feasibility, and risk. The process produced a sequenced AI investment roadmap and a governance framework for the first phase of deployment.