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Can AI Improve Sales Productivity in Pharma?

Pharmaceutical ExecutivePharmaceutical Executive-07-01-2017
Volume 37
Issue 7

With the physician-access climate as daunting as ever for sales reps, four best practices in implementing artificial intelligence-based technology to help achieve engagement wins are explored.

For years, pharmaceutical sales representatives have been dealing with access challenges and shrinking availability of prescribers, stemming from a more restrictive regulatory environment, changing economic conditions and new healthcare business models. These factors, in turn, have pressured physicians to see more patients, leaving less time to learn about even potentially life-saving drugs. Whether it’s with key opinion leaders (KOLs), high prescribers, administrators or any other decision--makers, yesterday’s sales strategies and tactics are not only less effective, they’re also highly restricted and regulated. 

Regulatory requirements are creating hurdles for salespeople. Federal and state regulations, and guidelines from industry associations such as the Pharmaceutical Research and Manufacturers of America (PhRMA), set boundaries for how pharmaceutical sales reps can interact with customers. The “lunch ‘n learn” meeting between reps and physicians, once a key part of the sales playbook, has been significantly curtailed, reducing both the frequency and average time per rep/physician interaction. In addition, the Open Payments program, created by the Physician Payments Sunshine Act (part of the Affordable Care Act), requires drug and medical-device manufacturers to report to the Centers for Medicare & Medicaid Services (CMS) every “transfer of value” of $10 or more to physicians and teaching hospitals. Only 44% of physicians routinely meet with sales reps and there is even less access to many specialists. For example, according to a 2016 ZS Associates report on physician access,  just 17% of oncologists are available to sales reps.

Today, the most successful reps are always prepared to present the right content and employ best practices at every opportunity, making a greater impact in less time. Most reps simply aren’t able to achieve this on their own. According to Kapost, 65% of sales reps report that they cannot find the most impactful content to send to, let alone find it on the spot when a physician suddenly gives them two minutes to either present impactful content or become a “no-see” physician to them.  

Reps need help from sales leadership, marketing, training and their peers to know and implement best practices in each sales scenario. There is generally even less time to meet with the busiest and most vital customers, KOLs and high prescribers. On average, reps aren’t earning more time, as physicians have only a 38% recall of sales rep activity, the ZS report notes. In the new age of the “30-second detail,” reps must be armed with guided selling so that they can give a more targeted, impactful message. When the message and content is more timely and relevant, detail time increases. When detail time increases, sales increase. 

The tech imperative

Due to the current pharmaceutical sales environment, sales teams must find new and different ways to engage with their customers and close more deals. In today’s competitive business climate where growing top-line revenue is a constant struggle yet sales reps are expected to do more with less, the implementation of technology solutions has become increasingly critical.

More recently, machine learning and artificial intelligence (AI) technologies have emerged to aid in this effort. These technologies have the potential to make a real difference for pharmaceutical  companies when it comes to helping their sales teams improve productivity, win rates and customer satisfaction. An AI-based selling solution, for instance, can help pharmaceutical sales reps do more in the restricted time that they have by serving as a virtual sales assistant. It can provide relevant content recommendations that guide the salesperson and the customer down the most effective path to a sale. 

Here are just a few of the ways AI can help drug manufacturers looking to achieve and sustain a more productive sales team:

1. Prioritizes pre-call planning. AI technology can provide the required training and certification materials needed to make sure that the pharmaceutical sales rep is ready for the meeting in advance. AI can also make recommendations on what information and content is going to have the most impact in the meeting, including sharing and recommending best practices and specific sales collateral to reps. This, in turn, minimizes the time consumed by the rep looking for and gathering these materials. By implementing more effective pre-call preparation, sales reps can maximize their effectiveness in every customer meeting. 

For instance, sales reps can ascertain in advance that a particular doctor is more interested in making time for reps who have more treatments to discuss. Recognizing this ahead of time empowers the rep to come armed with details on all the drugs that would be relevant for this particular physician. AI can also recommend the most important reprints, detail aids, abstracts, and leave-behinds for each call with a prescriber as determined by the sales teams’ best practices, marketing, and sales leadership. By optimizing pre-call planning, more time can be spent meeting with physicians, physician assistants (PAs), and nurse practitioners.

2. Reduces administrative tasks. Reducing time spent on administrative tasks will help maximize the time reps can spend in the field. One way to do this is by using technology that automates manual tasks, freeing up reps to cultivate relationships. For instance, technology can help by automatically logging calls with physicians, nurses, pharmacists and other key customers into customer relationship management (CRM) systems like SalesForce or Veeva. In addition, taking advantage of smart forms, which enable digital data entry and connect to back-end systems, allows reps to complete them while in the field from their mobile devices. This eliminates time-consuming paper-based forms-enabling reps to place orders on-site and in real time versus later when they’re back in the office-and increases overall productivity. This content, in turn, can be fed into an AI-powered tool to help inform the self-learning algorithms. The result: automatic content recommendations provided to sales people based on factors such as where they are in the sales cycle, their role or what their peers are using-and a significant reduction in time spent hunting for the right content.

Now, pharmaceutical sales reps are able to stay in the field longer and call on more customers since the admin-heavy tasks that have traditionally forced them to spend more time at their desks, like inputing sales calls into their CRM systems and emailing a reprint to a physician, are now automated. 



3. Identifies and shares best practices. By measuring every user action and learning from this process, an AI-powered solution can identify what works and what doesn’t for engaging prospects and closing deals. From this, AI solutions can make real-time content recommendations for a sales rep, tailored to where they are in the sales cycle, and greatly improve their chances for a successful outcome. 

For instance, if the top 10% of sales people use a particular presentation during their initial meetings, the system would push that content to other sales people as they were headed toward their own introductory session with a prospect. By having a means to better measure and learn from the successes and failures of

other sales people across the team and identify the habits of its best reps, organizations can improve outcomes by elevating the performance of the entire sales teams. In other words, AI can become a sales trainer and mentor that is always by the sales rep’s side.

With pharmaceutical sales reps typically only meeting to share thoughts and advice with their peers at quarterly plan of-actions (POAs) or national sales meetings, which occur one to two times per year, AI technology enables the reps to share and implement these best practices in real time and potentially translate to immediate improvements.

4. Enables real-world, step-by-step guided selling. By taking input data, AI-powered sales enablement technology helps guide the sales person through the entire sales cycle, suggesting the best next steps, activities, and assets based on that information. Acting as a virtual mentor, this technology guides a sales person and recommends the right content to be successful, while also allowing them to benefit from the marketing’s expertise. In fact, according to SiriusDecisions, aligning sales and marketing is proven to deliver 19% more growth. 

In addition, this technology allows sales teams to take advantage of the best reps’ experience and wisdom, while also alerting them to missing information or other issues that could put a deal in jeopardy. Often called guided selling, this approach offers a real-world way to help sales people be successful, especially when they have limited time with the physician. This could include providing a customized prescriber profile and call history to better prepare  sales reps before engaging with physicians, PAs, and nurse practitioners to sharing the best practices of the top sales reps in the organization to maximize the time in front of the physician.

Future ‘rep’ in balance

Leveraging technology where a machine is taking on manual work typically done by a human offers significant opportunity to boost efficiencies, improve sales team preparedness, increase time reps can spend with physicians, KOLs, and other key decision-makers, and enhance cross-team collaboration. In turn, sales teams can better prepare for meetings, successfully present to those time-strapped physicians, more effectively follow-up after meetings, and share best practices with their colleagues. Ultimately, AI has the potential to help pharmaceutical sales reps increase their productivity, close deals faster and help drive revenue. 


David Keane is the Co-founder and CEO of Bigtincan

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