By: Satish Nair, Consulting Partner, LTI

How to deliver better business outcomes, and higher return on investment (ROI)? That’s the pressing narrative for pharmaceutical commercial spend executives like you today. And, these outcomes could vary from reducing the time-to-market for new drugs, to boosting digital adjacencies across the ecosystem for sales growth, or harnessing deeper customer insights for stronger brand loyalty.

The imperative for you to reimagine your value proposition is pretty clear. For many pharma companies, external commercial spend–comprising expenditure levers across marketing, sales and payor rebates–has been outpacing revenue growth in the last few years. According to a Mckinsey study, rebate spend and direct-to-consumer spend spiked at a compound annual growth rate of 34% and 20%, respectively, between 2013 and 2015, with gross sales increasing by 11% only.

Moreover, many of the pharma industry’s conventional sales and marketing practices, which PwC estimates account for up to 25% of organizational revenues on average, are increasingly proving ineffective. Yet, even as direct-to-consumer advertising yields disappointing returns, payers aggressively seek price reductions, and providers curb the access of sales representatives to primary care physicians, pharmacos keep investing large sums in direct sales forces.

And, with both patients and physicians using the Internet and mobility to be better-informed and connect with each other, you need to swiftly alter your marketing mix for impactful messaging.

Existing gaps
So, how can you address these challenges effectively? Before we look at possible solutions, it’s useful to examine the gaps in your existing approaches toward commercial spend analysis, such as media-mix-modeling/marketing-mix-modeling (MMM). The foremost lacuna, I see, is the inability of such approaches to comprehensively capture the complex interactions between sales, marketing, patient support and other commercial levers.

Also, many of you currently find it difficult to eliminate the silos across various functions including sales, marketing and IT, with each operating according to its own agenda. For instance, while marketing is focused on national brand performance trends, analytics seeks to improve the efficacy and efficiency of query response and data reconciliation. On the other hand, commercial operations need accurate, granular data to monitor sales execution, and accordingly revise planning and support for budget needs. IT, meanwhile, is tasked with fixing data problems and addressing internal requirements.

Most data analytics solutions, therefore, fail to deliver the intended outcomes, as the development cycle involves too many compromises around business rules, timelines and applications to accommodate the varying requirements of different constituents.

Spend optimization with advanced data analytics
Is there a better approach then for you to adopt, so that you can reap the desired benefits of data analytics, when it comes to making better-informed decisions on pharma commercial spend? I believe the following four steps, if implemented, can go a long way in helping you deliver enhanced business outcomes:

• Create and manage core database: First, build a core database that covers all pertinent variables concerning sales, marketing and payor. This database must be exhaustive, capturing information on all marketing channels, sales visits and messages, customer relationship management, payor access, rebate levels, and so on. It also has to be granular, in terms of segmenting relevant data at the account, health care professional (HCP) and other levels.The good news is you can compile such a database, of a critical mass, within weeks or months by repurposing existing internal databases alongside “off the shelf”, third-party data sets. And, then you need to build on this core database to create more comprehensive data sets, and glean deeper insights.
• Formulate and iterate hypotheses: Base your data collection and analytics initiative around certain working assumptions, which will require constant refining based on emerging data insights. In order to build a workable database faster, keep the scope practically feasible. So, concentrate on sourcing the data that’s absolutely necessary to answer the defined business questions, rather than seeking to capture every single metric. Once the preliminary database is ready, test your hypotheses for different client segments to validate various assumptions, or otherwise.
• Use hybrid data analytics: Given that the performance of pharma commercial spend primarily depends on sales, marketing and payor, you should make sure the impact measurement model you adopt reflects that reality. Exclusive, standalone approaches like mix modeling or econometrics won’t give you the requisite insights on this front. What you need is a hybrid approach entailing regression analysis and test-and-control approaches, rather than one, or the other.You can leverage advanced platforms that facilitate usage of multiple analytics tools in conjunction, including machine learning, natural-language processing and conventional statistical approaches, to mine structured and unstructured data simultaneously.Using such a hybrid analytics method would help you gauge the impact of multiple variables in tandem, as compared to relying on a single approach. Plus, it would enable you to unearth potential investment opportunities that had previously gone unnoticed.
• Calibrate spend allocation with optimization algorithms and predictive analytics: Use turnkey, intuitive tools for applying advanced algorithms to granular response curves, and identifying specific action items for swift implementation. Doing so, would empower you to deepen cross-functional engagement. For instance, a pharmaco leveraged cloud-based tools to zero in on sales territories that could deliver enhanced top line growth in relation to a crucial customer segment. The firm’s analytics team then used these tools and coordinated with the business team to chalk out a revised digital spend strategy for higher ROI. Consequently, the organization realized deeper engagement across its various departments, leading to enhanced sales performance.

Conclusion
The rapidly evolving business reality of the pharma industry requires you to revisit some of your long-held operating models and working assumptions. Embracing some of the next-generation data analytics practices, I have highlighted would help you respond effectively to the various dynamic business challenges. Of course, reimagining commercial-spend optimization won’t be easy, but what you can certainly do is to harness data insights for making effective trade-offs, and better navigating the complexity of pharma commercial models.