Implementing NBA In Pharma: Data and Other Challenges

Many if not all pharma companies are introducing artificial intelligence-powered “Next Best Action” systems to drive proactive business intelligence into commercial execution. Next Best Action, also known as Context Marketing, identifies the needs of individual physicians at specific moments of engagements to assist in the organisation of creating the right personalised content in the right channels at the right time for every individual physician in a meaningful way, in real-time. In doing so, it creates a strong customer experience alongside essential support for both sales and marketing professionals, while simultaneously providing stronger business results. Introducing NBA comes with challenges, and companies need to make informed decisions to ensure success.

The Data Conundrum

One of the biggest challenges is collecting and integrating detailed data about customers, messages, and engagement channels. Companies need to decide whether to invest in data first or create ways to stimulate behavioral change and improve their understanding of the impact. Both are required, and a pilot program can be a good way to start. Companies can pilot-test one market at a time or rank package capabilities to see what works and what doesn't work.

Data is crucial to the success of NBA, and companies can start with readily available data from their own customer engagements. However, companies can achieve even more value by injecting external data sources, such as purchased data or publicly available data. It's important to take a step-by-step approach to NBA capability at the beginning, before scaling up to a larger number of markets.

Within the current technical architecture supporting commercial execution, there are many systems that capture data, and many of those systems don't necessarily feed directly back to the user. There is definitely a virtuous data cycle associated with adding a point of value that actually helps individual users that are engaging with these technical platforms, whether they're event management systems or CRM systems, through various forms or marketing engagement, to be able to close that loop. That can not only provide value from the data that's already there, but it also can accelerate the quality of information that's actually captured.

What is required is putting in the right infrastructure, acquiring the right information, performing the right analytics, and then activating those analytics to drive behavioural change. A strategic plan needs to be made from the start about how much to invest upstream versus how much to invest in stimulating the behavioural change. The goal is to experiment and explore and gain socialization as quickly as possible because that drives the end result, and also provides a frame for how much you invest in different data sources.

Achieving balance between data sources and stimulating behaviour changes

As you are building a program, focusing on an ability to value getting that experience and building the business case as a live approach is essential. Instead of the investments being focused on building a big customer data platform / big data warehouse like they were previously, they need to build confidence, build the stories, the experience, and the anecdotes that actually help senior leadership become comfortable in this type of investment as a capability on a global scale.

The quality and reliability of data

Defining readiness is difficult and achieving 100% readiness is extremely difficult. The key is to start with what you've got. Identifying the pitfalls is where to start with the quality of data. Identifying the challenges and the gaps and then putting action plans together is how to keep improving. This journey is all part of the NBA story.

Additional Challenges of NBA Systems

There are challenges associated with the evolution from experimentation to thinking about NBA as needing to be scaled overshadowed by the need for achieving an omnichannel approach and a coordinated broader sales, marketing, and medical alignment around the strategy with individual customers. This can create a level of intensity along with confusion because of the sheer amount of factors involved in the commercial model transformation needing to be supported by new capabilities. Also, there's the fact that all of this change is happening while the market is going through a significant upheaval. The value of this journey is really more about creating the agility to continue to transform.

Questions that come about from a change management perspective:

• How do you sell it to a rep?
• How do you position it as creating value?
• How can it help the reps, themselves?
• How do you communicate and manage your change?

There’s a very strong element around data privacy. Elements of compliance make things more difficult at a minimum when implementing NBA. For example, how do you ensure that an algorithm and an AI engine are going to do something that is okay from a compliance perspective?

Having the relevant compliance engagement at the initial project design phase is absolutely critical. If it's done well, the technology can actually reinforce and strengthen the way in which an organisation behaves. This could be within a specific channel, limitations on engagement, or the coordination of the right flows of information across different parts of the organisation, which can be designed to fortify the organisation's compliance.

Conclusion

Introducing NBA requires good decision-making to ensure success. Companies need to invest in data, but they also need to stimulate behavioural change and understand the impact. Pilot programs are a good way to start, and companies should take a step-by-step approach to NBA capability and market-wise at the beginning. Injecting external data sources can add value, and there needs to be a virtuous data cycle in the technical architecture. Companies should experiment, explore, and get socialization as quickly as possible to provide a frame for how much they should invest in different data sources. There are some unique challenges and opportunities associated with delivering NBA at scale. Accessing artificial intelligence and machine learning is essential for success.

Found this article interesting? Listen to the AI For Pharma Growth Podcast episode about the Data Conundrum in Implementing NBA here. For more information, contact Dr. Andree Bates?at?[email protected].