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Evolving the Pharmaceutical Sales Model with Data Exchange

How can businesses update their pharmaceutical sales model for the new competitive landscape? Below, read why it is important to reimagine the biopharma sales rep and build effective data feedback loops. 

The Organization, its Reps, and its Data 

There is perhaps no relationship more complicated in business than that between a company, its salesforce, and data analytics. Insert the added complexity of working in the biopharmaceutical industry and the relationships become even more fraught in the pharmaceutical sales model. The “love” part of the love-hate relationship is easy: companies love increased sales, salespeople love having a product to sell, and both want to know key information on their market. Far too often that is where the happy union ends, and incentives start to diverge. The operations group pleads with sales to echo messaging from marketing campaigns and incorporate expensive investments in analytics into their tactics. The sales force grumbles about data quality and autonomy while getting more requests to enter data about their interactions with their doctors. Neither party seems to receive value from it. Modern technology platforms emerge with broken promises of streamlined workflows, which further hinder this begrudging data exchange, and all trust and value of the system is lost. 

Problems with “Rep 2.0”, or 3.0… & The New Pharmaceutical Sales Model 

With the proliferation of data sources and data volume, pharma and biotech companies flow down two paths: using it to micromanage the field or letting them ignore the data. Typically, it is a perverse and often baffling combination of too much guidance and too little adoption. The long pursuit of the elusive “Rep 2.0” and then “Rep 3.0” is to blame. The issue is the language around “Rep 2.0” and “next-generation” sales reps framed reps as the barrier. The story goes: reps are stuck in their ways and headquarters must trick them into behaving in their best interests. Therefore, sales reps were micromanaged with time-consuming initiatives and methodologies, while they roll their eyes and try to shirk participation. Plus, regional managers and sales leadership had to assume the risk of trial initiatives not working out. 

There is another way, which re-imagines the sales rep as the key to a data feedback loop, as opposed to a dead end. Headquarters has information gaps reps can fill. Reps have precious knowledge from their relationship with the doctor to be captured and categorized. HQ also must realize pharma/biotech sales reps are professional salespeople, so they know when they are receiving a sales pitch. When delivering analytics-backed guidance to the field, it can’t be disguised as company policy. The answer is not top-down enforcement. It’s a great sales pitch backed by compelling analytics. Creating a harmonized approach, where reps are actively part of the data strategy, makes headquarters better at assisting execution and lets reps maintain the integrity of their relationships. This future of successful pharmaceutical sales depends on building trust in this data and trust in this process. This needs to be paired with smart investments in IT infrastructure and streamlined rollouts of data products. The field will have limited patience for setbacks, or misinformation. 

Providing Biopharma Sales Data to Maximize Rep Effectiveness 

The organization gives value back to the rep by supplying both data and data integration. Reps can do some synthesis, but the underlying analytics needs to come from analysts and data engineers in the firm. Everything headquarters does to help the rep will help the broader organization. 

On the data side, the organization knows what engagement, channels and sub-tactics are most effective. With “Rep 2.0”, reps are coordinating marketing efforts, so they need this information to do their new job. If reps want to send a follow up email after a sales call, will it be more of the same content from the call? Classification models can recommend a few pieces of appropriate content the rep can choose from. This rolls into sequencing – or “next best action” – an elusive concept to find tactics that are most effective at that moment given all past behavior. Do they know exactly what time to send it, or the exact character counts associated with highest click through rates? Headquarters can provide these analytics from corporate email campaigns. 

On the integration side, companies need to turn a lot of data into a few key figures for digestibility. HCP segmentation is a classic example. Sales leadership/ops could set business rules to parse segments and miss a doctor that is a high prescriber but is dosing incorrectly – damaging long term chances of success for the patient. Machine learning techniques can be both the filter and the harmonizing agent. Instead of defining doctor segmentation based on some corporate business rules, or letting reps define it ad-hoc – unsupervised machine learning algorithms can take both as inputs and develop brand new segments based on the best combined information. 

Integration is not just an IT problem; it is a business question. For instance, most companies cannot answer the simple question “how engaged is a given HCP at this moment”. To do so would require confident synthesis of every piece of omnichannel engagement, failed attempts, and weights for each. Instead of 1 engagement number, reps get dashboards of activity, where no one can easily see which HCPs have low engagement. When companies start considering these failed attempts – and they should – this may mean decreasing quantity to increase quality. If that is the guidance, it needs to be backed up. Reps will not want to add activities that seem low value or remove activities they feel are high value. 

Rep-Generated Data to Maximize Organizational Effectiveness 

For reps and sales leadership to be a productive core of the data-feedback loop they need to be aligned with corporate goals, understand the importance of their role, and be set up for easy participation. Headquarters also needs to understand what value can come from the rep and how to use it appropriately. It is easy to imagine that a doctor specializing in a disease would know far more than the rep. But reps may have a specific product vs. health care providers maintaining knowledge of countless products. That specific product could be for a rare disease that doctors see only a few times in their career. There is a reason medical-device reps are often right in the operating room.  

Reps still have both unrivaled access and special knowledge, but the HCP is still the connection to the patient. Based on their background and treatment experience, HPCs will have tendencies and sentiments the reps can capture. Prescription volumes will never reliably represent how a doctor feels about the drug and when to use it. Data generated by reps can provide this sentiment, but they have their biases. A data science team can help the reps capture that information and translate it into data. Predictive models can reveal and correct biases before predicting sentiment or even prescribing patterns. When prescription volumes are non-existent or infrequent, i.e., at launch or in rare disease, these custom strategies with non-clinical data become a strategic pillar.  

With any employee generated data, incentives need to be aligned with accurate and honest entry. In one instance, a client setting up these collection pathways unknowingly created incentives which would incorrectly generate depressed figures. When the rep collected data on patient potential, the HCP hedged down, because they do not want to be hounded by the company as a prime target. Then the rep took that number and hedged down further, to give themselves extra space to meet quotas. If reps benefit from supplying bad data, they will supply bad data. If reps go through the trouble of collecting and entering accurate data, they want to see it used effectively. Groups supporting sales with analytics have a responsibility to do impactful things with any data reps collect. 

Where We Are Now – Access and the Pharmaceutical Sales Model in the Future 

Face-to-face access has become increasingly challenging, even pre-COVID. Short term access was entirely restricted, but its long-term effects are more important. Certain regions, or certain hospital systems – especially larger ones – are continuing to push back wholesale on in-person interaction. Doctors may prefer face-to-face, in person interactions with their “favorite” reps and push others towards digital platforms. This will accelerate the trend of less  access, or even less intimate virtual access. While face-to-face visits are more impactful on the relationship with the doctor, virtual visits may enable greater data collection. Live data entry is easier when you are already on a laptop, compared to standing in an office trying to keep eye contact. This is a key moment to collect qualitative information from the HCP. Firms can get creative with means of collection – the rep can use prebuilt surveys and complete it with the doctor as part of their conversation. Workarounds are not only needed in the in-person sphere, as data collection on the digital side is facing its own limitations. Privacy concerns are creating new difficulties analyzing digital marketing effectiveness. Organizations will have to be at their best to push forward creative solutions. Building effective data feedback loops between the organization and the sales reps will create trusted, impactful analytics and insights that will benefit the entire business. 

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Coauthor and contributions by Andrea Weeks

Tags: Performance Management, Data & Analytics, Organizational Effectiveness
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