Building vs buying: How to best implement data analysis systems in pharma sales

Sales & Marketing
data analysis

That data analytics can help pharmaceutical firms advance their efforts – including the effectiveness of their marketing and sales teams – is clear. A McKinsey study shows that such analytics can help pharma firms significantly increase net revenue and build and maintain relationships with customers, among other things.

Building these relationships and delivering the right messages to potential customers is especially important today, as HCPs are inundated with drug choices and marketing information from pharma companies. This situation is so common it even has a name, “promo fatigue”, with HCPs basically “shutting down” when confronted with an unmanageable flood of information.

To overcome that, pharma sales and marketing teams look to data analysis to help them personalise messages in order to reach specific customers based on their particular circumstances, patient needs, and market segment – thus increasing the chances that messages will get through the incessant digital din HCPs are subjected to. The big question for pharma firms is how best to conduct this analysis.

A middle ground between outsourcing and building a data analysis platform in-house

Traditionally, pharma firms outsource this work to consulting firms that specialise in developing target lists and engagement messaging. But this often proves to be a slow and expensive process, and many pharma companies feel such outside consultants do not leverage their uniqueness or competitive advantage enough and just provide cookie-cutter solutions.

Even if those consulting firms do specialise in pharma, clients often still feel they are losing out. The objective of data analysis, after all, is to develop precision approaches that will help companies better reach their customers. For that to happen, you need to know the customers well, to be able to put insights into context, and to revise approaches and messages on the fly in response to new data, revising insights and guidance as needed. While an outsourcing firm may be able to pull off a general plan, they are unlikely to do well on particular engagements – despite the high prices they charge.

This makes it tempting to build and maintain a machine-learning-based data-analytics platform in-house. Unfortunately, this is often a mammoth task that does not prove worth it. But companies still try, because they assume it may be cheaper, give them more governance, and keeping data “in the family” makes it less likely that valuable, proprietary data will leak. Often, the bottom line is that pharma companies want to own and operate their data analysis platforms in order to be more independent, nimble, and in control of their marketing and sales strategies.

But there's a third way that trumps both relying on outside consultants and building their own platforms from scratch: a data analysis platform built by data professionals that provides the foundation for data integration and AI/ML based analysis that is tweaked to become a robust in-house solution. This hybrid approach ensures flexibility and agility, and gives companies control. At the same time, it also saves companies a lot of hassle, time, and wasted resources, and ensures a powerful, efficient, and continuously updated data analysis platform.

The challenges of building in-house pharma sales data analysis systems

Studies show that building and utilising in-house data analytics platforms from scratch is extremely challenging. Nearly half of pharmaceutical industry executives admitted in a study that they were unable to successfully leverage data with in-house systems in order to gain insights. Another 48% said that, while they did engage in data analytics, they were not doing it as well as they could. A PwC study confirmed these findings, saying that, even among those who do conduct in-house analytics, a large percentage aren't even sure they are doing it correctly. Those that do set out to build systems also often do not finish the project or end up with one-and-done models, rather than data-analysis models that are robust and can evolve to continuously meet new needs.

Pharma firms, like everyone else, are contending with a shortage of data talent (which is not likely to improve any time soon). Other technical challenges include the need for extensive, multi-year data analysis with constantly-changing conditions. This is especially true as the number of available prescription drugs is constantly increasing, and medical care in general is rapidly changing and advancing. This is set to become a bigger challenge as more drugs are set to come to market faster as technology helps speed up the drug discovery, testing and clinical trials phases, and the regulatory environment is constantly in flux. Pharma firms also deal with a plethora of scattered data that needs to be properly integrated in order to be used for data analysis.

And, of course, there is the expense. Setting up an in-house operation is not cheap, as organisations need to hire a large team of highly-paid professionals (if they can even find them) – and in-house systems come with hidden expenses as well, including management costs, data accuracy evaluations, and employee turnover expenses.

Finding the right platform provider or partner

Given all this, relying on a vendor or partner to provide an advanced AI and ML-driven insight platform, which enables sales and marketing teams to make the most of in-house, as well as market data, and develop accurate and flexible campaigns, is the best – and perhaps only – option for companies serious about utilising data in an effective manner. Such platforms allow pharma firms to access cutting-edge analysis tools, while giving them full control over their data – and full control of how that data will be used in order to gain the maximum insights possible.

Choosing the right partner is crucial. Experience in the life sciences industry and data science is, of course, important, and pharma firms need to closely examine the experience of their potential partners, looking not just at their work, but at their outcomes, ensuring that results are high quality.

There's no question that pharma firms need to engage with data analysis – immediately. Using an external platform or data analysis partner has been shown to be the most effective and efficient way to do this. It not only leads to more sales, but also means that the right drugs will reach the right doctors, and ultimately, the most relevant patients, at the right time.

Doron Aspitz
profile mask
Doron Aspitz