A Changing View of Sales In Pharma

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New approaches to pharma sales: Marrying data analytics with the art of personal engagement

 

The lock-down impact for COVID-19 was stark, looking at available March data cuts from IQVIA which signal serious additional healthcare consequences beyond infected patients. A decline of 72% of in-person doctor visits, -70% Rx volume, -70% lab test (excl. COVID-19), across the board declines in new therapy starts (e.g., -11% autoimmune, -9% cholesterol, -12% depression, -11% epilepsy) and drops in new scripts (e.g., –47% gastro, -42% dermatology -40% Ped etc.). In a world post-COVID, customer engagement still matters, and considering the numbers above, it perhaps matters more than ever before. Ulrich Neumann is sharing a few reflections as we’re rethinking engagement in the field for the post pandemic era. He argues that we owe it to patients to bring our engagements back as soon as circumstances allow. As long as we respect the rules of the game, data and analytics can play a decisive role.  

 

It’s been roughly two decades since conferences first brought industry leaders together to discuss the pressing needs in commercial pharma. Back then, the discussion ensued under the banner of sales force effectiveness. That was a time when consumers ‘surfed’ the internet, hooked up to it via dial-up modems, and what we today refer to as the generation of digital natives was yet to be born. Most physicians had precious few digitized tools in their office. Communication on the benefits of pharmaceutical products occurred over the phone and via print brochures. Reps met customers (often at lavish dinners). 

It seems an eon ago in today’s uber-mediated world of digitized consumerism. No surprise, the nature of the work of pharma’s commercial teams has changed in so many ways. Those of us most concerned with access questions like to point out that, as marketing colleagues identify new means to engage with physicians, they first need to understand the effects of managed care formularies on the ability to prescribe treatments. Yet there is truth to the saying ‘the more things change, the more they stay the same’. The sales function has stayed the same across a few central dimensions: To effectively communicate a value proposition requires a personalized approach, one which focuses on a very clear understanding of the target customer’s needs. Pharma is still striving to secure insights about total and new scripts, adoption sequences, the tendency to use a variety of products and patient demographics. 

 

Digital transformation

What has changed is that the digital revolution created a profusion of available and relevant information to act on. We are in an era in which you, as a commercial leader, have no alternative but to mine, link and position various data sets for commercial success. The way we use that data, for instance as with pre-call planning has really been a rapid transformation, no doubt. Let’s take the typical situation where an MSL field force has established strong physician relationships through their education around unmet needs, prior to the approval of a novel treatment. What now is the best deployment strategy for key account management and sales post-launch? Well, in any of the leading pharma companies your strategic response to this classic scenario is today driven by predictive analytics, leveraging hyper-targeted insights and some form of algorithmic intelligence. Our new data points stem from the proliferation of alternative channels through which doctors now interact with and consume product and disease information. As my colleagues driving the transition in industry caution me, we shouldn’t forget that supporting the learning curve of both the sales team and the physicians they engage with has become critical in successfully leveraging these new technologies. The most exceptional data points require certain sets of new skill to also enable the added value effectively, on all sides.  

 

Training before targeting, infrastructure before insights

In the new environment, we all know that field engagement needs to become more centered around consultative selling, which is sometimes equated with deeper capacity for medical knowledge transfer and the call to deploy more MSLs. But it isn’t just that. Having a profound understanding of the clinical value proposition is a foundational core of today’s rep, of course. But, as a senior leader in charge of large field forces reminds me, the ability to create human connections, “to ignite interest and establish rapport in personal relationships is as vital as it ever was in a pre-digital world”. So is an excellent understanding of the business realities a physician’s office faces. This translates into our need to focus training curricula on B2B selling, storytelling skills and new types of messaging. If your company hasn’t as of yet, it is time now to double down on training programs for your team around business acumen, data interpretation and use of electronic systems in the field, for instance. 

 

Changing buyer profiles

Back in 2000, the vast majority of offices were physician-owned, whereas following years of consolidation, institutional actors carry a lot more weight today. Integrated delivery networks now own 6 in 10 group practices. Treatment protocols and formularies are defined by P&T committees, not individual prescribers. What happens when the doctor becomes an employee? Well, it certainly requires you to continue to provide deep disease expertise and helpful education as a resource for health systems. But it inevitably leads to new account models, and opportunities such as outcomes-based contracting, which address the novel reimbursement configurations and value-based care objectives many of the institutional actors are invested in. Considering the major issue of abandonment of scripts and high discontinuation rates, other care stakeholders have grown in importance to us when it comes to optimizing patient outcomes. The average patient sees their pharmacist a lot more than they see their physician, for example. We should ask, within each of our teams, what resources can we get in front of the pharmacist to help them help and educate patients. 

 

Not just in the Ivory tower, excellence in data science matters

What needs to happen in central command, you ask? If the first couple of slides of any leadership presentation these days are any guidance, pharma has undergone a data-driven, customer-centric transformation. Many consultants have told you that you have witnessed the convergence of data from different disciplines and historically organizational siloes. And yes, that is what happened. The process is what ultimately enables commercial success, requiring the coordination, linking and triangulation of insights between managed markets analytics, social media, customer segmentation, longitudinal prescribing information, geo-location data, and DTC marketing statistics, to name just a few commercial data sources we now deal with it on a day-to-day basis in the channel mix. 

As the amount of available information continues to expand, the journey from information to insights has indeed begun and data scientists are some of the most important stewards in your commercial organization – they are combining various disciplines of data science, analytics, sales and marketing ops. The widespread fear of a few years back, that this new environment would signify the beginning of the end of the traditional sales force, didn’t materialize. Rather, new capabilities seem to supplant the old approach with more precise targeting. The overall objective is still to enhance every stakeholder conversation, taking the interaction with customers to a new level. Many still use PDFs, because their organization requires it, or because that is how they can make the magic happen.

Results from PwC’s 2018 Digital Pharma & Life Science IQ survey show that only 21% of pharma leaders see digital disruption as a threat to salesforce numbers, but when it comes to actual execution, the same research reveals critical gaps: Only 43% of respondents would agree that their company is making effective use of all the data that are being collected today. 

 

Driving internal change for external effect, same old?

So what is needed to succeed organizationally in this new era? Our clients and colleagues in large pharma tell us repeatedly that the amount of change management and learning on adoption, such that is required to leverage their analytics, is routinely underestimated. There’s always a demand for the next ‘AI’ and fancy model application but hiring the right talent can often solve for the analytics. It is actually rarely the bottleneck. Driving change across sales and marketing orgs takes a very different effort though. Even for fairly simple programs, implementation, measurement and learning necessitate vision, executive stewardship and a good deal of patience.

So, how about I ‘drive the point home’ with a sports analogy? Truth is, as a German-born American, I am notoriously wary of comparisons to the deep world of US sports I did not grow up with. But there is one that a colleague at Novo Nordisk shared with me recently on this topic, and it resonated. His useful analogy came from the rise of so-called Moneyball in major league baseball, the use of rigorous statistical analyses for evaluating player performance and building competitive teams around various new data points. There is a Brad Pitt movie on it. Essentially, sabermetrics (the empirical analysis of baseball) have changed the nature of the game (and I’d argue have now made a notable entry into other sports like soccer which I did grow up with) – but on their own, all of these are not nearly enough to drive any team to win a World Series (or the Champions League). Why? Because it is the unique balance between the art and the analytics that makes the real difference. It is as simple as that.

The pharma industry has a similar opportunity – for commercial success, we need to marry best-in-class data science and insights with classic, proven engagement approaches to win in the field. To get going, you probably don’t need a big-name Chief Data Officer from Silicon Valley or a VC-fund wildly acquiring machine-learning startups. But, as one colleague put it bluntly, you most likely will need to invest in getting your “plumbing” in order – that is pairing strategic direction from the center with hands-on, coordinated data management on the ground.

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