Commercial Analytics in Pharma and Life Sciences 

Commercial Analytics in Pharma and Life Sciences 
PUBLISHED
July 04, 2025
AUTHOR
Svitlana Denysenko
CATEGORY
AI & Data Analytics

Research and development costs keep climbing, competition is fiercer than ever, and market entry barriers are at an all-time low. So, what do you do? Right – you make smarter marketing decisions.  

Advances in technology give us deeper insights into pharma industry trends and, more importantly, the reasons behind them. Understanding the “why” helps anticipate customer behaviors and identify opportunities that ensure the largest returns.  

This article will discuss pharma commercial analytics – its models, and key strategies for successful adoption.  

What Do We Mean by Commercial Analytics? 

Pharma commercial analytics is the strategic data analysis to drive smarter marketing and sales decisions. It helps brands understand market dynamics, refine targeting, set more precise pricing, and optimize campaigns to improve the company’s commercial performance. 

Commercial analytics comes to life in four key areas: 

  • Market segmentation and targeting. Advanced analytics lets you see exactly what customers want so you can cluster those needs into groups and target the audience with military precision. 
  • Sales force optimization. With data insights, you allocate resources wisely, sending the right message to the right healthcare provider (HCP) at the right time. 
  • Pricing and market access. Pharma commercial analytics provides insights into the market landscape, competitors, and regulations, ensuring pricing is accurate and not restricting product access. 
  • Customer engagement. Data-driven insights help us connect with customers by making our communications feel personal and giving them more value. 

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Data Complexity as a Major Challenge 

It’s time to face the truth – data is a double-edged sword. It helps companies make smarter decisions, but managing it effectively is a significant challenge. Here’s a shocking statistic: the healthcare industry generates 30% of the world’s data. This data comes in various formats – structured (databases), semi-structured (emails, social media), and unstructured (research papers, medical images). It’s scattered across departments and systems, with external sources like electronic health records adding even more complexity.  

So, it’s no surprise that life sciences brands struggle to access, clean, consolidate, and process it. The result? Companies feel stuck when making decisions, miss out on valuable insights and opportunities for innovation, and face compliance risks because they cannot effectively track and manage data across sources. 

But before we get to the fix, it is important to understand the different commercial analytics models available, so you can choose the one that best suits your needs.

Key Analytics Support Models 

Pharma companies can fall on the spectrum when adopting commercial analytics. They can either a) choose the ‘fully captive’ model, b) go for the ‘vendor captive’ model, or c) find themselves somewhere between these two extremes. Let’s explore these options in more detail.  

The ‘fully captive’ model 

This model assumes building advanced analytics capabilities in-house. Brands that choose it set up several centers of excellence at a global level, offering services to local markets. It is ideal for companies that thrive on taking control of their operations. Opt for this model if you want better cost transparency, potentially greater intellectual property protection, and alignment with your corporate goals. 

But if you plan to go this route, be prepared to spend a lot. Building the infrastructure, hiring and training people, keeping up with tech, and dealing with regulations in different places all add up.  

Beyond the initial investment, you will need to cultivate organizational agility. Your analytics requirements will inevitably evolve as your product develops, new functions emerge, or older ones sunset. So, being able to shift gears quickly is key. 

Blood, sweat, and tears are also part of the process. It takes years, a willingness to go the extra mile for innovative approaches, and the ability to align global and local teams to achieve deep expertise in commercial analytics.  

The ‘vendor captive’ model 

Some life sciences brands rely on external partners for end-to-end commercial analytics support, and there are several reasons for this. The ‘vendor captive’ approach frees companies from investing in infrastructure or recruiting and training their workforce. Plus, it offers flexibility in scaling, allowing companies to keep overhead costs low. The partner aligns processes and goals with the pharma company while allowing it to oversee and fine-tune services.

Yet, the model comes with some significant drawbacks. It often leaves life sciences companies without full cost transparency and control. Some partners use ‘black box’ models, keeping brands in the dark about their processes.

The more a company relies on its partner, the harder it becomes to build in-house analytics expertise. Companies often lose access to the latest technology and best practices under this model.  

Finally, adopting this model can take up to two years, depending on how the team is currently set up. As the saying goes, it takes two to tango – strong collaboration between internal and external teams is crucial for speeding up this transition. 

The hybrid model 

If you do not want to go to either extreme, a hybrid approach lets you get the best of both worlds. For example, you can stay agile and cost-efficient by combining onsite, offsite, and offshore resources.  

The partner will assemble the team, establish governance and engagement models, and then hand over the project to you once everything is in place. With this model, you can introduce commercial analytics capabilities in less than a year and quickly scale across therapeutic areas and geographies.  

commercial analytics in pharma

And there’s no need to worry about over-dependence on the partner. You will collaborate closely to transfer industry knowledge, giving your team hands-on experience with the latest technologies while saving up resources. 

Ways to Succeed with Commercial Analytics in Pharma 

There are several common best practices you should consider when adopting commercial analytics: 

Set ROI expectations when choosing a model 

As mentioned, there is a spectrum between ‘vendor captive’ and ‘fully captive’ models in commercial analytics. To figure out where you fit, set clear ROI expectations.  

Know what return you can expect and in what timeframe. For instance, if you have business processes in place but lack strong infrastructure and technology, are you ready to invest in setting them up in-house, or would a vendor-supported model be more efficient for delivering ROI faster? 

Some large life sciences brands have organically built their commercial analytics capabilities over time. However, most mid-size and small drug manufacturers struggle to keep up, lacking the budget for such investments upfront. A smart move for them is to adopt a hybrid or ‘vendor captive’ model, which would allow them to quickly catch up and gain a competitive edge. 

Close the gaps in HCP behavior and patient journey 

Sometimes, you are simply missing a key piece of the puzzle to understand how to deliver real value to the customer. Sai Jasti, GlaxoSmithKline’s (GSK) chief data officer, put it best: “Bridging those gaps in physician behavior and the patient journey is where the biggest opportunities exist.” Life sciences companies depend on patient data to refine customer experiences.

Patient data is everywhere, from electronic health records to insurers, or social media. Yet, privacy concerns make it challenging to access and piece together a complete picture. Pharmaceutical giants like Amgen and Takeda recognize the value of customer data and are pouring resources to fill these gaps in the patient journey. This enables them to strengthen HCP-patient relationships and drive better clinical outcomes. 

One way to gain valuable customer data is by introducing chatbots. Imagine a life sciences company X commercializing a second-line treatment for lung cancer. Chatbots let the company gather data-driven insights about its oncology patients based on their questions.  

All this customer data is fed into an AI-powered tool that segments patients based on their risk of failing first-line treatment. Just as Amazon uses big data to predict what and when a customer will purchase, a sequence of patient queries can help identify high-risk groups. With this knowledge, medical representatives can proactively reach out to HCPs, even before the first-line treatment fails. This approach provides brands with critical patient data, improves clinical outcomes, and elevates the customer experience. 

Admit that data silos are holding you back 

Life sciences brands have access to vast amounts of data – prescription claims, sales force metrics, specialty pharmacy records, advertising performance, qualitative physician insights, and more. But the challenge isn’t always about collecting data – but integrating it. Without a unified, helicopter view, companies struggle to understand who their customers are and what they want. This lack of integration makes it nearly impossible to leverage commercial analytics effectively, especially for predictive, prescriptive, and adaptive insights. 

types of commercial analytics in pharma

Adopt approaches used in other industries 

Some leading life sciences brands, like Sanofi and GSK, intentionally recruit chief data officers from other industries. The logic is simple – sectors like retail, transportation, and entertainment have successfully leveraged customer behavior insights to drive long-term financial growth. Pharma companies can tap into these proven strategies by applying cross-industry expertise within their centers of excellence. 

Let’s take Netflix as an example. The company relies on customer data to predict what its audience will want to watch next. In fact, statistics show that more than 80% of shows are discovered through Netflix’s predictive model. By leveraging commercial analytics, Netflix not only delivers personalized recommendations based on viewing history but also identifies the next big hits.  

Can this approach work for the life sciences industry? The short answer is yes. However, accessing customer data in life sciences is far more challenging than in the entertainment sector. One solution is using chatbots to engage with patients and HCPs directly. This will allow brands to access and analyze customer requests to deliver highly personalized, engaging content. 

Tame the Complexity of Commercial Analytics 

Lack of data and abundance thereof are equally challenging for life sciences companies. When data is siloed or scarcely accessible due to privacy concerns, businesses struggle to interpret customer behaviors, predict trends, and adapt to the competitive landscape. When data is abundant, however, companies may lack the infrastructure to organize, standardize, and cleanse it effectively. 

If you need support in setting up infrastructure, collecting or transforming your data, and making sense of it, the hybrid or ‘vendor captive’ analytics models might be the solution you’re looking for. And if you seek a reliable partner, Viseven offers comprehensive, end-to-end analytics services.  

Navigate the complexities of Pharma with Viseven

Contact our experts to get your commercial analytics up and running for Pharma & Life Sciences

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Here’s how we approach it: We gather data from both internal and external sources to provide a holistic view. We then harmonize, clean, normalize, and enrich your data, ensuring it is accurate, consistent, and relevant for data analysis. Next, we create intuitive, interactive dashboards and reports that allow you to easily uncover hidden patterns, trends, and opportunities – and effectively communicate these insights across your team. 

But we don’t stop there. Our data scientists and analysts work closely with you to interpret the results, ensuring your decisions align with your business goals. We offer consulting services to help you optimize everything from marketing campaigns to operational efficiency.  

Frequently Asked Questions (FAQs) 

What is commercial analytics in pharma and life sciences?

Commercial analytics in pharma refers to the strategic use of data to drive smarter marketing, sales, and operational decisions. It helps pharmaceutical and life sciences companies optimize market segmentation, sales force efforts, pricing strategies, and customer engagement to improve overall commercial performance and ROI.

What are the biggest challenges pharma companies face with commercial analytics?

The biggest challenge is managing complex and siloed data. The healthcare industry generates 30% of the world’s data, coming from diverse sources such as EHRs, social media, and clinical research. Many pharma companies struggle to access, clean, and consolidate this data, which hinders decision-making and creates compliance risks.

What are the key models for implementing commercial analytics in life sciences?

Pharma companies can adopt one of three main models: Fully Captive: In-house analytics capabilities with full control and long-term investment. Vendor Captive: Outsourced analytics with lower upfront costs but less transparency. Hybrid Model: A blended approach combining internal oversight with external expertise, offering speed, scalability, and flexibility across regions and therapeutic areas.

How can pharma brands close gaps in the HCP-patient journey using analytics?

By collecting and analyzing patient data from sources like EHRs, chatbots, and insurer systems, pharma companies can better understand treatment pathways. Advanced tools such as AI can segment patients and predict behaviors, enabling proactive HCP engagement and improved clinical outcomes through timely interventions.

Why is commercial analytics important for global go-to-market strategies in pharma?

In a highly competitive and regulated industry, commercial analytics helps life sciences companies make data-driven decisions about pricing, access, targeting, and communication strategies. It supports global go-to-market efforts by providing localized insights and helping brands adapt strategies across different geographies and healthcare systems.

AUTHOR
Svitlana Denysenko
Svitlana Denysenko
Copywriter
Svitlana Denysenko brings 10+ years of B2B and B2C copywriting experience, with the past two focused on life sciences content marketing. Naturally curious, she dives deep into topics and asks thoughtful, beyond-the-surface questions in expert interviews. Her writing is grounded in evidence-based research and crafted to deliver value. Yet, Svitlana’s mantra: “No one will consume the value unless the content is interesting to read.” That’s why storytelling is often on her to-do list.