The top 8 things causing sleepless nights in pharma execs in the commercial space

I regularly speak with Pharmaceutical execs around the globe and in the past few years have asked about their biggest challenges and what is keeping them awake at night.
My favourite answer came from someone in Singapore with multi-country responsibility, and his answer was, “Making sure my boss sleeps at night”. I am sure that can be said for everyone. Even the CEO must answer to the board. The answers vary depending on an executive’s role and country, but there are many consistent themes no matter who answers. 

1. Getting pricing right

Almost every launch team has concerns around whether they have got the price right and whether they will get reimbursement. This is not an unfounded concern. The majority of the top 10 launch failures failed due to issues relating to these. Pricing is an important but highly complex element in market access. It involves analyzing large amounts of data from ever more diverse sources—something AI excels at. Rather than spending hours pouring over clinical trial and real-world data (RWD), past drug submissions and evaluations, and global, regional, and historical pricing data, market access teams can simply feed this data into an appropriately structured AI system for fast results, largely free from human error and easily translatable into relevant, compelling insights. Accurate predictive value pricing enables pharma companies to better adjust their approach to approval and reimbursement, while the ability to use and intelligently analyze ever more data from patient outcomes and clinical trials allows for value-based pricing that appeals to payers, all while providing maximum profits.
These types of AI-powered technologies have helped market access and pricing executives assess the performance of any new drug years in advance, with an advanced understanding of each factor influencing the final outcome and provide an opportunity to proactively build a strong data-driven case, which means that they can enter pricing negotiations with strong big data-driven facts having all the different scenarios to hand.

2. Getting faster reimbursement

It’s important to understand what causes delays after a drug is approved by FDA/EMA and must be submitted to the different formularies in the US, or the different country payers if in Europe. The goals of these payers can be different – outside the obvious one of lowering costs – depending on their specific responsibilities, and if your submission doesn’t align with them, you’re wasting time and resources. For example, showing significant savings in hospitalization in favour of your drug would be beneficial if a payer is responsible for total healthcare costs. Both the drug cost and impact of the drug factor into that goal. However, showing data on reduced hospitalizations to a payer whose responsibility is solely around drug costs may not be the best use of that meeting.

To ensure the financial success of a drug, one needs to understand and incorporate the real payer drivers (for each payer influencing the decisions) in your strategy for each stage of development and commercialization. Artificial Intelligence gives us the means to do things such as uncover these. The benefits of AI in this area aren’t limited to just gaining insights in drafting submissions. It can also streamline the execution of those plans and procedures. Using AI techniques, you can develop the submission documents utilizing both the drivers for that formulary as well as the optimal language shown in previous submissions to work best for that formulary committee. AI can also assist by better identifying qualifying populations with greater certainty around efficacy, informing manufacturers on where OBCs are most likely to be advantageous, and, of course, tracking and analyzing outcomes. Indeed, there are a number of ways AI can be used for simple faster reimbursement.

3. Ensuring launch and go-to-market success

Many people we speak with are concerned about launches and getting it right. The world has changed and having a good drug, and a large growing market, does not assure you of success. I have already covered pricing and reimbursement but these are only 2 of the pre-requisite factors for launch success. Some key concerns are around getting the strategy and tactics right for products in therapy areas or segments that they do not have strong data for, knowing the optimal sub-segments to target, as well as resourcing and ensuring that they are focusing their resourcing on the right channels in the optimal amounts.

AI can help with this task in a number of ways. First, it can be used with great success in identifying the most lucrative opportunity spaces within a market. We have done this numerous times for launch brands and in every case achieve the top 1 to 3 spot in the most successful drug launches of the year for our clients. Market segmentation can also be done far more successfully using Artificial Intelligence. This can only be achieved by compiling and analyzing vast amounts of data from a variety of traditional (e.g., prescribing levels) and novel (e.g., CRM, publications, physician forums, social media behaviour) sources—the kind of thing AI excels at. I cover personalized marketing and customer experience in later points. These are just some of the ways AI can be successfully used to boost sales and marketing in pharmaceuticals.

4. Delivering value to the customer and driving strong customer experience

Customer experience in pharma has often played second fiddle to product-driven strategies. Historically, this worked well for pharmaceutical companies, which were able to leverage their sheer size and unique capabilities in research and production. In fact, even today, a majority of pharma CEOs believe that new products are the most important factor for revenue growth, despite the fact that returns from R & D are lagging. But by focusing on products rather than practitioners and what they need to perform their jobs, pharma companies are missing opportunities to provide a better overall experience for practitioners, i.e., customers.

All customers (e.g., patient, HCP and payer) today expect highly personalised solutions and experiences that put their needs (as patients, practitioners, etc.) front and centre. Some pharma companies are already taking the lead and employing a variety of value shift models to ensure value is delivered to the customer and the business and ensure future-proofing from the market changes that will be driving the ‘selling a pill’ model into the ground in the coming years. There are many models and ways AI can be embedded into the pharma value chain to add value and enhance customer experience. In pharma and beyond, focusing on a holistic customer experience, rather than individual touchpoints, is associated with anywhere from a 50% to 115% increase in customer satisfaction, and up to 15% more revenue.

4. Finding opportunities for growth in brands

This is a common request and one that is probably the easiest with artificial intelligence approaches, which take every relevant variable and sift hundreds of millions to billions (depending on the data used) of combinations in order to pull out the top differentiators that will grow a brand – either overall or within various segments. It can be identifying patients previously undiagnosed; it can be identifying new patient segments or a myriad of other things. In fact, AI now is the only approach we would recommend for this type of activity if you want accurate results.

5. How to Integrate and Personalize Marketing across all Channels, both Digital and Traditional as well as by individual physician.

For omnichannel to be effective, it must be personalised.

Personalisation here means two things.
1. First, a personalised experience. Going beyond simple multichannel approaches, an omnichannel approach offers a holistic, consistent experience, informed by each customer’s unique journey which can only be done with Artificial Intelligence due to the large variation in customer journeys across channels.
2. Second, a personalised offering. By understanding practitioners’, patients’ and payers’ preferences, needs, desires, perhaps even those they aren’t aware of themselves, you can offer better-fitting solutions, services, and messaging. It should look like a happy coincidence that the right information is appearing just when you were about to search for it. Have you had that experience yourself? I have, and immediately I have realised – it was driven by prescriptive and cognitive AI.

For the best results, a certain synergy between in-person and digital channels is necessary. Sales reps, community managers and others can engage in open-ended, dynamic conversations with practitioners, payers and patients, capturing information and data that may otherwise be missed, and yet is necessary for a holistic, 360º view of one’s customer. For personalisation at scale, pharma needs AI. This kind of personalisation can only be achieved at scale, for hundreds or thousands of practitioners and patients, with the use of AI.

7. Digital Transformation.

Pharma is facing many market challenges in numerous areas – including paying for results. The old business models alone won’t solve many challenges moving forward. I cover these in this article in more depth. 
Although Covid-19 accelerated digitization, business leaders in the pharmaceutical industry often confuse digitalization and digital transformation, seeing as synonymous these two very different concepts.

Digitalization means improving existing business processes with the aid of new technologies. Switching from paper to digital record-keeping is one example. Importantly, digitalization is often accompanied by automation, whereby machines and artificial intelligence carry out processes with little to no human intervention. Digitalization can result in both important productivity increases and cost reductions, but the processes themselves remain largely unchanged.

Digital transformation, on the other hand, involves critically examining business processes from a customer-centric perspective and leveraging new technology to radically change and improve the overall customer experience. Digital transformation is a bit like trying to manoeuvre a massive container ship. The sheer size of the beast makes it difficult to turn about and a lot of energy is required to get it moving. Once it’s underway, though, all that’s needed is a clear destination, good navigation, and strong leadership.

Digital transformation is a journey, not a destination. Pharmaceutical companies should look to leverage disruptive technology like digital twins, smart materials, 5G and the Internet of Things (IoT), sensors and wearables, nanotechnology, decentralized production like 3D printing and smart factories, and platform
enablers (components that improve the function of an existing platform, like iOS or Android). But, before doing that, they should first carefully plan an effective digital transformation strategy! Too many companies go for ‘cool’ solutions without having done thorough strategic planning first and this always ends in failed projects!

Transformation needn’t come at the cost of day-to-day business or quarterly objectives. With the right strategy and approach, it’s possible to enact change without disrupting ongoing business or threatening your bottom line. There’s no reason a company can’t undergo a successful digital transformation while continuing to thrive in the short term by implementing intelligent, incremental changes in light of its overall transformation strategy.

8. Sales Force Effectiveness in the new hybrid world

What many companies are seeking more of are automated sales force systems so that their reps deliver the right messages to the right customers at the right time to change what doctors prescribe and integrate this with all the other marketing activities seamlessly. This is covered in point 6. But AI can help the sales force in so many other ways from precision doctor targeting (for stronger results), predicting brand switch, personalized messaging, auto-CRM completion, and whisper coaching during a call. Every pharma company already has data that can allow for these and more.