Scaling Impact from Pharma Commercial Analytics

A moment that stuck with me recently: in one organization, a sales rep described preparing for a single customer conversation by hopping across seven different systems just to assemble a 360-degree view of an HCP.

That is not a talent issue. That is commercial drag. And it is why even strong models can fail quietly: the work to use them is harder than the work to ignore them.

The industry’s own benchmarks make the gap hard to ignore. BCG’s 2025 omnichannel survey found that 97% of leaders view omnichannel as critical, yet fewer than 10% report fully integrated customer engagement and management platforms. Veeva’s 2025 research on commercial biopharma makes a similar point from the AI side: 89% of organizations were not able to scale more than half of their AI initiatives, often because the underlying data is not trusted, timely, or consistent.

So yes, technology choices, the data explosion, in-sourcing and outsourcing trends, and operating models matter. But the winning conversation starts with business value: the ability to deliver the right insight, at the right time, at the right cost, and in the right place, inside the workflow where decisions get made.

Simply put, there is a credible opportunity to make analytics better, faster, and cheaper.

But better analytics in commercial pharma is not more dashboards or fancier models. It is changed decisions and improved outcomes for patients, HCPs, payers, and brands.

Here is the equation I use to keep the conversation grounded:

Better Analytics = Better Data + Better Insights + Better Action + Better Speed + Better Cost + Better Measurement + Better Scaling + Better Human Impact

Below is what each term really means in practice, based on the realities teams are facing today.

Better Data

Data is the foundational component for commercial analytics, and where momentum most commonly stalls.

Healthcare data is intrinsically noisy. Separating signal from noise often requires real quality controls, smoothing methods, and hard decisions about what “good enough” means for commercial decisions.

The real pain is not just noise, though. It is fragmentation. Customer identity, channel signals, payer dynamics, and market events live in different places, on different timelines, with different definitions.

Modern cloud-native platforms plus privacy-preserving linkage techniques such as tokenization can dramatically reduce the time and cost to integrate diverse, multimodal datasets and make them analytics-ready.

The goal is not to build a lake. It is to build a trusted, decision-grade view of the market: timely, governed, and reusable across brands.

A mindset shift helps. Many companies still take a narrow data focus, only what is relevant for a single brand or therapy area. That is understandable, but it caps the insight ceiling.

A stronger approach is closer to a digital twin of the commercial ecosystem around an HCP: the fullest behavioral context you can responsibly assemble, so you can see what is changing before it hits your numbers.

Better Insights

Once you have decision-grade data, the next question is whether insights actually change decisions.

Many commercial teams still spend disproportionate effort on descriptive reporting. That is useful, but in volatile conditions it is the business equivalent of driving while looking in the rear-view mirror.

Better insights are explanatory, predictive, and prescriptive: why did it happen, what will happen, and what should we do?

One practical example is historical market-level promotion data linked to real-world prescription data. With the right setup, it becomes possible to test the impact of messages virtually in minutes, and get guidance on how to refine messaging for critical HCP segments before spending real dollars in market.

Similarly, physician-level and payer-level behavioral models can power scenario planning for forecasting, launch choices, payer contracting, and omnichannel mix.

The payoff is simple: you anticipate barriers and opportunities before they surface.

Better Action

This is where most value dies.

Insights to action is the last mile of analytics, and the most treacherous.

The barriers usually look like some combination of low trust in the data or model, friction to access the insight, and misaligned incentives and change management.

The seven-systems rep story is a perfect example of friction. If people must leave their workflow and stitch context together manually, analytics becomes optional. It becomes something you do after work, not something that makes work better.

The fixes are straightforward, but they require intent.

First, embed insights in situ into workflow tools such as Veeva or Salesforce, and create a true single pane of glass.

Second, make recommendations explainable. Whiteboxing gives a clear “why” behind each recommendation and increases confidence, especially for next-best-action prompts that need to be simple, contextual, compliant, and genuinely useful.

Third, treat incentives and adoption as first-class work. One company shifted incentives from volume to value maximization so the organization would stop winning the activity game and start winning the outcome game.

Another enlisted top sellers as advocates to address a real psychological barrier. When an algorithm challenges the belief “I know my customer best,” adoption becomes a change-leadership problem, not a model problem.

This all sounds obvious. Implementation at scale is not.

Many organizations underestimate the last mile and arrive there with no energy left. The better path is to over-rotate early on last-mile readiness: workflow, explainability, enablement, field leadership, and incentives.

Better Speed

Speed to insight is transformative, and increasingly necessary.

In many organizations, insights take months to compile and decisions lag market changes, such as policy shifts and formulary shifts. That lag becomes especially damaging during launch, when the cost of a slow correction is measured in quarters.

Modern analytics can compress time to insight from months to days, or even hours.

Always-on dashboards updating overnight with fresh feeds can enable agile cross-functional teams across analytics, brand, and access to shift spend, field focus, and tactics in near real time.

In work to enable real-time access insights, cycle-time compressions of upwards of 75% are achievable, an advantage that compounds.

Better Cost

Every commercial leader eventually asks the same thing: can we get better insights faster and cheaper?

The answer can be yes, if you stop funding bespoke analytics as a way of life and start productizing what matters.

The expectation today is that modern approaches and tools can make analytics 30% to 50% more productive, often proven in pilots and then scaled.

But the cost story is nuanced.

Many organizations want self-service, yet user acceptance pulls them back toward white-glove service. The latest AI tools can also be oversold.

The work-slop problem is real: outputs look polished and inspire confidence but can lack the depth and precision to be practically useful, sometimes creating extra effort to fix.

Generative AI also hallucinates. The executive question is not whether it will. The question is how far you can reduce it with better data and design, and what the system does when it makes something up.

Proceed with optimism, but design for reliability, auditability, and safe failure modes. When done well, the juice is worth the squeeze.

Better Measurement

What you do not measure, you cannot scale.

Basic reporting is table stakes. But many organizations measure too narrowly: reach, volume, or other vanity contribution metrics that do not link to what the business actually cares about.

Better measurement does three things:

1. Links insights to actions to outcomes, such as TRx lift, time to fill, and ROMI.
2. Includes adoption and behavior change, such as usage, compliance with recommendations, and enablement completion.
3. Protects experience with counter-metrics, such as HCP fatigue, opt-outs, and complaints, so optimization does not erode trust.

The goal is closed-loop learning: leaders know not just what happened, but what worked, what did not, and what they missed.

Better Scaling

Many companies are stuck in pilot purgatory. Pilots prove value but stall. Others rely on hero analysts, limiting sustainability and making results dependent on a few people.

A breakthrough requires a structured path: innovate, automate, scale.

That path needs explicit checkpoints:

1. Proof of concept: does it work?
2. Proof of value: is it valuable?
3. Proof of scale: can we deliver it repeatedly across brands and markets?

Scaling requires industrialized ML and DataOps: reusable pipelines, standardized features, shared playbooks and toolkits, monitoring and refresh cadence, and bench strength across brands and geographies.

When scale happens, every brand benefits, and cost per insight drops as reuse increases.

Better Human Impact

All of this ultimately comes down to human impact.

I talk to many organizations where analytics is framed primarily as an internal efficiency play. Efficiency pays the bills, but it can obscure the energizing purpose: use analytics to find patients faster, reduce access delays, personalize support, and ensure reps deliver information that physicians actually need and value.

The best commercial analytics transformations do not shrink the mission. They make the mission more achievable.

Monday morning actions to accelerate impact in the next 90 days

1. Pick three to five decisions that actually move outcomes, such as targeting and call planning, next best action, omnichannel mix, and access risk signals.
2. Build the minimum trusted data foundation for those decisions, not for everything.
3. Embed outputs into workflow tools with explainability, or whiteboxing, so adoption is not optional.
4. Align incentives and operating rhythms so the organization uses the capability.
5. Measure impact with outcomes and counter-metrics, then scale what works with reuse and standardization.

Better analytics is not the point. Better decisions are.

Better Analytics = Better Data + Better Insights + Better Action + Better Speed + Better Cost + Better Measurement + Better Scaling + Better Human Impact