The Intelligence Gap That’s Costing Brand Teams Strategic Ground
Here’s a scenario that plays out more often than anyone in commercial leadership cares to admit. A competitor launches a label expansion. Prescribing patterns shift. Market share starts moving. And the brand team finds out three months later — when the quarterly sales data arrives, the analyst reports catch up, and someone has to explain at the leadership review why the numbers look the way they do.
By that point, the strategic window for response has already closed.
This is the central problem with how most pharma commercial teams approach competitive intelligence. Sales data tells you what moved through the channel — not who prescribed it, not which patients received it, not how long they stayed on it, not when and why they switched. Analyst reports synthesise public information — they reflect what companies announce, not what is happening in real-world clinical practice. Advisory board opinions capture what KOLs think is happening — not what anonymised claims data from hundreds of thousands of patient journeys shows is actually happening. And physician surveys record stated intent and recalled behaviour — not actual prescribing decisions across a representative Continental European population.
The result is a structural intelligence gap. Brand teams and business development functions are making consequential strategic decisions — launch positioning, lifecycle management, portfolio prioritisation, competitive response — based on lagged, aggregate, or synthesised data when real-world health insurance claims data could give them a faster, more granular, and more accurate picture. As Sherman et al. established in the New England Journal of Medicine, real-world evidence captures what clinical trials cannot — the full complexity of how therapies perform and are used in actual clinical practice.
The argument here is direct: competitive intelligence is not a byproduct of RWE programmes designed for regulatory or HEOR purposes. It is a standalone strategic capability. It deserves its own evidence infrastructure, its own analytical workflows, and its own seat at the commercial strategy table.
This post explains what that capability looks like in practice — and what it takes to build it.
If you’re new to how claims data supports broader pharma strategy, our post on transforming pharma market sizing with real-world insurance claims data provides useful grounding for the use cases we cover here.
What Real-World Competitive Intelligence Actually Is — And What It Isn’t
This is worth defining clearly, because the term competitive intelligence means different things to different functions. What we’re talking about here is specifically claims-based competitive intelligence — and it is distinct from three things it is often confused with.
It is not traditional market research. Surveys and focus groups capture what physicians say they do. Claims data captures what they actually do, across a representative population, without recall bias.
It is not sales data analytics. Sales data tells you sell-in volumes at the channel level. Claims data tells you patient-level prescribing — who received what, in what sequence, for how long, and in what clinical context.
And it is not the RWE that HEOR and regulatory teams typically commission. That work is designed for payer submissions and regulatory packages. It is rigorous, longitudinal, and audit-ready — but it is not designed for the recency and competitive granularity that brand strategy requires.
Real-world competitive intelligence — done properly — enables five things that none of those alternatives can deliver. Dang’s 2023 primer on real-world evidence is useful here for understanding the foundational distinctions between data types and their appropriate applications.
It tracks real-world market share by treatment setting, prescriber type, patient segment, and region — not aggregate sales volume. It monitors competitor treatment patterns across the actual sequence of clinical practice. It detects prescribing shifts in near-real-time rather than three to six months after the fact. It maps how patients move through competitive treatment sequences longitudinally. And it identifies untreated or undertreated populations that represent genuine commercial opportunity — the kind of white space that doesn’t show up in published epidemiology but is visible in claims.
That is what this capability delivers. Now let’s look at what it looks like in practice.
Five Competitive Intelligence Use Cases Where Claims Data Outperforms Everything Else
1. Real-World Market Share Tracking
Most brand teams believe they understand their competitive market share position. What they actually understand is their channel sell-in volume relative to competitors — which is a different number, often significantly so.
Claims-based market share analysis works at the patient level. It reveals which therapies patients are actually receiving, which prescribers are writing those prescriptions, in which clinical settings, and for which patient segments. This matters because aggregate market share numbers can mask very different competitive dynamics at the segment level — a brand that appears stable nationally may be losing share rapidly in community practice settings while holding or gaining in academic medical centres.
Critically, the recency of this data changes the strategic value. Near-real-time claims feeds allow brand teams to track market share shifts within weeks of a competitive event. The ISPOR Real-World Data Task Force has documented how this kind of patient-level data supports commercial decision-making in ways that traditional market data simply cannot replicate.
2. Competitor Treatment Pattern Analysis
There is consistently a gap between what a competitor’s label says about its intended patient population and where that therapy is actually being used in real-world clinical practice. Labels describe intended use. Claims data describes actual use — including off-label applications, combination use, and positioning in treatment sequences that differ substantially from clinical trial populations.
This intelligence is strategically critical for positioning. If a competitor is being used primarily as a second-line therapy in practice — despite first-line label claims — that is commercially significant information. If it is being used in combination with other agents in ways not reflected in its clinical data package, that changes the competitive framing.
The RECORD statement on observational health data research provides important methodological standards for how treatment pattern analyses from claims data should be designed and reported — relevant for anyone commissioning this work for both commercial and regulatory purposes.
3. Prescribing Shift Detection
This is arguably where real-world competitive intelligence delivers its most distinctive value — the early warning capability.
When a competitor launches, expands its label, updates its safety profile, or achieves a formulary win, prescribing behaviour changes. The signal is in the claims data within weeks. Traditional competitive intelligence infrastructure — sales data, analyst reports, market research — picks that signal up months later, often after it has already translated into measurable brand impact.
That three-to-six month lag is not a minor inconvenience. It represents a strategic window during which a proactive commercial response could have been designed and deployed. Blonde et al. demonstrated how real-world clinical data changes the interpretation of competitive dynamics when it is properly integrated into commercial decision-making — rather than treated as a post-hoc validation exercise.
4. Patient Journey Competitive Mapping
Longitudinal claims data enables something no point-in-time data source can: following actual patient cohorts through their treatment journeys over time. Which therapy did they receive before switching to yours? Which did they switch to after? How long did they persist on each? What clinical or administrative events triggered switching?
This is competitive intelligence at the patient journey level — and it is remarkably actionable. If claims data consistently shows that patients are switching from a competitor to your brand after a median of nine months, that tells you something important about the competitive dynamic. If it shows that switching from your brand to a competitor is triggered by a specific clinical event, that tells you something equally important about where your brand’s evidence profile needs to be strengthened.
Davies et al. (2023) provide a useful framework for understanding how patient journey mapping from real-world data can be structured to generate these kinds of competitive insights. Our next post — on real-world treatment pathway analysis using aggregated claims data — goes deeper on the analytical methodology.
5. Business Development and Portfolio Intelligence
BD teams and portfolio strategists are, almost by definition, making decisions about the future based on assumptions about the present. The question is whether those assumptions are built on theoretical market models or on observed real-world prescribing dynamics.
Claims-based competitive intelligence changes the quality of those inputs dramatically. White space opportunities — indications or patient segments where current standard of care is demonstrably suboptimal in real-world practice — become visible in claims data in ways they simply do not in published epidemiology or clinical trial populations. Competitive threat assessment for pipeline assets can be grounded in real-world market entrenchment data rather than analyst projections. And commercial valuations for in-licensing targets can be validated against what is actually happening in clinical practice rather than what management presentations claim is happening.
Dagenais et al. (2022) make the case compellingly for how real-world evidence should inform not just drug development strategy but the upstream commercial and portfolio decisions that shape which programmes enter development in the first place.
Why Traditional Competitive Intelligence Leaves Dangerous Gaps
Most commercial teams know, at some level, that their competitive intelligence infrastructure has limitations. The problem is that those limitations tend to feel abstract until a competitive surprise makes them concrete.
Let’s be direct about what those limitations actually are.
Sales data is retrospective, aggregate, and channel-level. It tells you what moved through the supply chain — not what happened at the patient level. It cannot reveal which patient segments are gaining or losing, which prescribers are shifting behaviour, or whether market share movement is driven by new patient starts versus switching.
Analyst reports are synthesised from public information. They reflect what companies announce in investor calls and press releases — not what is happening in clinical practice. They are also, by definition, backward-looking. The insights they contain describe a competitive landscape that has already changed by the time the report is published.
KOL advisory boards and physician surveys capture opinion rather than behaviour. Academic KOLs represent a prescribing segment that, in most therapeutic areas, accounts for a modest fraction of total patient volume. Community practice — where the majority of patients are treated — is systematically underrepresented in advisory board intelligence.
And all of these sources share a structural latency problem. By the time competitive intelligence from these sources is synthesised, distributed, and acted upon, the market has already moved. As Makady et al. (2017) documented, the gap between when real-world evidence is available and when decision-makers can access it is one of the most significant constraints on the value that data can actually deliver.
Claims-based competitive intelligence closes that gap. Our post on using claims data for market research in pharma and MedTech explores this contrast in more detail for teams evaluating how to complement or replace traditional market research approaches.
The Data Foundation: What Your Competitive Intelligence Infrastructure Actually Needs
The challenge many commercial teams face is not a lack of interest in claims-based competitive intelligence. It is uncertainty about what data infrastructure is actually needed — and how to evaluate whether a given data source is fit for this specific purpose.
Five dimensions matter most, and the FDA’s guidance on assessing real-world data sources — though US-focused — provides a useful evaluative framework that translates well to Continental European contexts.
Coverage breadth. The competitive intelligence value of claims data scales with population coverage. Narrow datasets miss prescriber and patient segments that matter commercially — particularly in community practice settings across Continental European markets where therapeutic area coverage and data completeness vary substantially by country and insurer network.
Data recency. This is where competitive intelligence requirements diverge most sharply from HEOR and regulatory requirements. For market access submissions, adjudicated longitudinal data with twelve to eighteen months of follow-up is appropriate. For competitive intelligence, near-real-time feeds — or at minimum, data with a recency lag measured in weeks rather than quarters — are what enable early warning capability. These are genuinely different requirements and they should not be conflated.
Therapeutic area depth. Coverage of the specific drug classes, diagnostic code combinations, and procedure codes relevant to your competitive landscape varies substantially across data sources. A source with broad population coverage but thin coding depth in your therapeutic area will miss the patient-level dynamics that matter most.
Geographic granularity. Regional competitive dynamics across Continental European markets often differ significantly from national aggregates. Formulary decisions, prescribing guidelines, specialist density, and payer structures vary at the sub-national level — and competitive intelligence that cannot reveal these dynamics will consistently generate misleading national averages.
Longitudinal linkage. Following patients across care settings and over time is essential for treatment sequence analysis and switching pattern detection. Without longitudinal linkage, you cannot answer the questions that competitive intelligence requires: what happened before, what happened after, and what triggered the change.
Understanding which combination of these dimensions matches your specific use case and therapeutic area requires deep familiarity with the Pan-European data vendor landscape — and the honest reality is that most commercial teams do not have that familiarity internally. The ISPOR-ISPE Good Practices guidance is the appropriate methodological benchmark for evaluating data quality — but translating those standards into source selection decisions for competitive intelligence specifically requires both methodological knowledge and vendor landscape expertise.
Building Competitive Intelligence Capability Without a Dedicated Team
Here’s the operational reality for most brand teams: competitive intelligence is nobody’s primary job. It is a secondary responsibility distributed across market research, commercial analytics, medical affairs, and brand management — without a clear owner, a dedicated analytical workflow, or a data infrastructure designed for the purpose.
And it shows. Intelligence insights get generated episodically in response to competitive events, rather than continuously as part of an ongoing monitoring capability. Different functions commission intelligence independently from different sources, producing conflicting insights that create internal confusion rather than strategic alignment. The commercial team and the HEOR team are not sharing competitive data — meaning real-world effectiveness evidence that is highly relevant to brand strategy sits unused in regulatory submissions while brand teams commission separate market research that covers adjacent ground less rigorously.
None of this has to be the case. But fixing it does not require building a dedicated competitive intelligence function with a large internal team and proprietary data engineering capability.
What it requires is three things: analysis-ready claims data with appropriate recency and coverage for the competitive intelligence use case; clearly defined intelligence questions that tell you what decisions need to be informed and on what timeline; and expert guidance for both data source selection and analytical workflow design.
The point about expert guidance matters more than it might seem. Navigating the Pan-European real-world data vendor landscape for competitive intelligence specifically — as opposed to HEOR, epidemiology, or market sizing — requires understanding which data sources have the recency, therapeutic area depth, and geographic granularity your use case demands. Getting that matching wrong means either overpaying for regulatory-grade data you don’t need, or under-investing in recency for a use case where near-real-time capability is the entire point of value.
The ISPOR Real-World Evidence Transparency Initiative sets useful standards for how competitive intelligence analyses from claims data should be structured and documented — which matters both for internal credibility and for any downstream use of competitive insights in external communications.
Competitive Intelligence Across the Product Lifecycle
The specific competitive intelligence questions your brand needs to answer change at each stage of the product lifecycle. So does the appropriate data infrastructure to answer them.
Pre-launch. Before positioning decisions are locked in, you need to understand the competitive treatment landscape as it exists in real-world practice — not as it is described in clinical guidelines or competitor materials. Which therapies are actually being used in the patient population you are targeting? In what sequence? With what persistence? Where is current standard of care genuinely suboptimal in practice, and where is it performing well enough that your launch positioning needs to offer something clearly differentiated? Data-driven launch planning with aggregated patient data is the natural application here.
Launch phase. This is where near-real-time competitive intelligence delivers its most direct commercial value. Early adoption patterns, competitive response, formulary decisions, prescribing shift signals — all of these are detectable in claims data within weeks of launch, if the data infrastructure is in place. Without it, you are flying blind during the period when commercial decisions have the highest stakes.
Growth phase. As the competitive landscape stabilises and then evolves — new entrants, label expansions, guideline updates, formulary changes — continuous monitoring of market share dynamics and prescribing patterns enables proactive competitive response rather than reactive damage control.
Lifecycle management. Real-world competitive intelligence is underutilised as a lifecycle management tool. As brands approach their later commercial phases, understanding which patient segments represent defensible positions and which are vulnerable to competitive erosion requires exactly the kind of longitudinal treatment pattern analysis that claims data enables. Forecasting loss of exclusivity impact benefits enormously from competitive prescribing pattern data rather than analogue-based erosion models.
Loss of exclusivity and post-LOE. Biosimilar or generic entry impact on real-world prescribing is not visible in traditional market data until it has already translated into significant revenue impact. Claims data can detect prescribing shifts in near-real-time — giving brand teams the lead time to execute formulary defence strategies, evidence-based differentiation messaging, and patient retention programmes before the financial impact is fully realised. The Deloitte Centre for Health Solutions research on pharmaceutical innovation returns documents the scale of the commercial stakes that LOE intelligence is designed to address.
What Good Competitive Intelligence Strategy Actually Looks Like
Competitive intelligence is often approached as a data problem. In practice, it is a strategy problem with a data solution — and the distinction matters for how you build the capability.
Good competitive intelligence strategy starts with specific intelligence questions. Not “we want more competitive intelligence” but “we need to understand, within eight weeks of competitor launch, whether and where prescribing patterns are shifting in our top ten markets — and we need that answer updated monthly for the following twelve months.” Specificity about the decision that needs to be informed, the timeline it needs to be informed on, and the geographic and therapeutic granularity required is what turns a data access question into a tractable analytical problem.
It matches data source to intelligence question. Near-real-time claims feeds for tactical brand defence and prescribing shift detection. Longitudinal adjudicated data for strategic market analysis and patient journey mapping. The recency, coverage, and analytical depth requirements differ substantially between these applications — and using the wrong data type for the use case is one of the most common and costly mistakes in competitive intelligence programme design.
It integrates claims-based intelligence with other sources rather than treating each in isolation. Sales data, market research, clinical data, and claims-based analytics each have different strengths and blind spots. The most commercially valuable intelligence programmes are those that synthesise across these sources systematically — not those that replace one with another.
And it builds continuous monitoring workflows rather than commissioning one-off studies. Competitive dynamics change constantly. A competitive intelligence programme that generates a report twice a year cannot serve as the early warning system that brand strategy requires. The infrastructure needs to be designed for ongoing monitoring, not episodic research.
The FDA’s evolving framework for real-world evidence in regulatory decision-making — detailed in both their 2021 guidance and more recent programme experience documented by Smith et al. (2025) — reflects a broader shift in how real-world data is being used across the drug development and commercial spectrum. The most forward-looking pharma commercial teams are ahead of this curve, not catching up with it. The CIOMS Working Group XIII report (2025) on real-world data in regulatory decision-making provides useful context for the standards that will increasingly apply to commercially generated real-world evidence as well.
The gap between accessing claims data and extracting the competitive intelligence value it contains is substantial. Getting data access right — selecting sources with the appropriate coverage, recency, and therapeutic depth — is a necessary starting point. But the analytical design, intelligence question framing, and integration with broader commercial strategy are where the real competitive advantage is built or lost.
That is where expert guidance becomes critical. Navigating the Pan-European real-world data vendor landscape for competitive intelligence specifically, matching data source capabilities to commercial intelligence requirements, and designing analytical workflows that generate timely and actionable insight — all of this requires both deep market knowledge and pharmaceutical commercial expertise that most internal teams do not have in combination.
If your brand team is ready to move beyond lagged sales data and analyst reports — and build a competitive intelligence capability grounded in real-world claims analytics — we’d welcome a conversation. Contact us to discuss your specific competitive intelligence requirements and how expert advisory can accelerate your path to value.
Sources & References
- Sherman, R. E., et al. (2016). Real-world evidence — what is it and what can it tell us? New England Journal of Medicine, 375(23), 2293–2297. https://www.nejm.org/doi/full/10.1056/NEJMsb1609216
- Dang, A. (2023). Real-world evidence: a primer. Pharmaceutical Medicine, 37(1), 25–36. https://pubmed.ncbi.nlm.nih.gov/36604368/
- Penberthy, L., et al. (2022). An overview of real-world data sources for oncology and considerations for research. CA: A Cancer Journal for Clinicians, 72(3), 228–245. https://acsjournals.onlinelibrary.wiley.com
- Garrison, L. P., et al. (2007). Using real-world data for coverage and payment decisions: the ISPOR Real-World Data Task Force report. Value in Health, 10(5), 326–335. https://www.valueinhealthjournal.com
- Benchimol, E. I., et al. (2015). The RECORD statement. PLOS Medicine, 12(10). https://journals.plos.org/plosmedicine
- Blonde, L., et al. (2018). Interpretation and impact of real-world clinical data for the practicing clinician. Advances in Therapy, 35(11), 1763–1774. https://link.springer.com
- Davies, E. L., et al. (2023). Reporting and conducting patient journey mapping research in healthcare. Journal of Advanced Nursing, 79(1), 83–100. https://pmc.ncbi.nlm.nih.gov/articles/PMC10099758/
- Dagenais, S., et al. (2022). Use of real-world evidence to drive drug development strategy and inform clinical trial design. Clinical Pharmacology & Therapeutics, 111(1), 77–89. https://pubmed.ncbi.nlm.nih.gov/34839524/
- Makady, A., et al. (2017). Policies for use of real-world data in health technology assessment. Value in Health, 20(3), 520–532. https://www.valueinhealthjournal.com
- U.S. Food and Drug Administration (2024). Real-world data: assessing electronic health records and medical claims data to support regulatory decision-making. https://www.fda.gov
- ISPOR-ISPE Special Task Force. Good practices for real-world data studies of treatment and/or comparative effectiveness. https://www.ispor.org
- ISPOR (2022). Real-world evidence transparency initiative: guiding principles. https://www.ispor.org
- Deloitte Centre for Health Solutions (2024). Measuring the return from pharmaceutical innovation. https://www2.deloitte.com
- U.S. Food and Drug Administration (2021). Considerations for the use of real-world data and real-world evidence to support regulatory decision-making. https://www.fda.gov
- Smith, et al. (2025). US Food and Drug Administration’s Advancing Real-World Evidence Program: initial experience. Clinical Pharmacology & Therapeutics. https://ascpt.onlinelibrary.wiley.com
- Hennessy, et al. (2025). Real-world data and real-world evidence in regulatory decision making: report summary from the CIOMS Working Group XIII. Pharmacoepidemiology and Drug Safety. https://onlinelibrary.wiley.com
