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AI in Sales: Accelerating Efficiency in Pharmaceutical Sales with Artificial Intelligence

The adoption of technology in the pharmaceutical industry has been pretty lopsided over the years. Perhaps owing to being a fairly regulated industry, companies have been extremely wary in embracing new technologies that can transform their business. After all, these companies have a lot of very sensitive patient data as well as confidential research and patent information. Given the landscape of business they operate in, pharma companies have to be extremely discerning in the technology they adopt and how they implement it — while being highly choosy in terms of the vendors they work with and their credentials.

However, Artificial Intelligence is one of the sub-areas within the larger technology sphere that pharma companies are massively interested in. With healthcare costs fast spiraling out of control, AI’s promise to help run a more efficient business is more important than ever. Today, we see a plethora of small inroads that AI is making in the pharmaceutical industry and delivering strong business outcomes.

AI and Healthcare:

So, what is AI and what makes it so valuable to Pharmaceutical Companies?

In simple words, AI is the area of computer science that helps imbue human intelligence onto machines. So on one hand, AI typically behaves like a human by mimicking human capabilities — perception, learning, reasoning, problem-solving — and combines it with the processing power and speed of machines. And it does this by observing huge amounts of data. Today, we have huge amounts of data to train the machine and huge computational power to sustain that level of learning. The ready availability of both — data and computional power — is the reason why we are seeing the emergence of the Age of AI today.

The healthcare industry similarly is sitting on a huge set of data they have recorded internally as well as sourced externally from data syndication companies, pharmacies, payers and laboratories. All of this data when used — with appropriate security apparatus in place of course — can deliver competitive advantage while running a tight ship.

What pharma companies need to do now is to integrate this intelligence into their business processes — and augment their reliance on human instinct with evidence-based, data-driven decision-making. Especially in the realm of sales — where data-driven insights can be invaluable to get one over your competition — practical application of AI can be hugely consequential. With the help of AI, it becomes possible to quantify physician potential, their attitudes and other aspects, allowing companies to profile, segment and target HCPs to optimize multichannel marketing and run brand diagnostics

For a pharmaceutical rep, AI-driven approaches can help them transform the physician and patient relationship without violating doctor-patient confidentiality.Bringing AI in place can make it easier for the sales and marketing team to customize their outreach and engagement with HCPs and to deliver better results in an almost effortless way.

How AI is improving Pharmaceutical Sales

Pharmaceutical companies rely on the marketing model to grow. And with the increased competition and the various modes of promotion, it gets hard to attribute marketing efforts to sales. And this is the main solution that AI can bring to the healthcare industry. Specifically, AI helps in the following ways:

1: Optimizing MCM:

The sales team can leverage AI to analyze the impact of Multichannel Marketing ideas and predict their likelihood of success. This allows their team to focus on segmenting ideas against the overall strategy to optimize the time of sales reps in the best way possible.

2: Identifying Specific Patient Population:

Pharmaceutical companies can bring predictive analytics capabilities from within AI to get an insight into the ongoing patterns used for candidates part of clinical trials. AI can augment physicians’ decisions to determine when a patient should start their treatment. It will also help them understand when patients should move on to the next phase of the treatment.

3: Customizing Digital Engagement:

Using AI, healthcare systems can optimize channel effectiveness as well. AI can provide smart recommendations on the timing, frequency, and content of the messages and emails used for engagement with HCPs. It is a targeted approach that is used to increase the precision with which sales reps engage with physicians, enhancing the overall impact of digital campaigns.

4: Leveraging Multi-Indication Analytics:

We all know that drugs are distributed through various channels at once. With the help of AI and machine learning, the healthcare system can eliminate the old and traditional analytics methods. This way, companies will get a better picture of the use of multi-indication products. The marketing team can then track the performance of the product by region, specialty, indication, and the source of business. This helps the company to make an informed decision regarding future marketing.

5: Enhancing HCP Profiling/Segmentation/Targeting:

AI can help deliver the targeted list of physicians to sales reps with precision. It highlights the prescription potential of each physician and their behavioral profile. Furthermore, the sales team can then strategize future steps such as offering discounts and value-added opportunities in the pharmacies that will possibly drive high sales.

These pointers are only just the tip of the iceberg when it comes to the potential impact that AI can deliver to pharmaceutical companies in the area of sales. While the upside is immense, it is important however to address one key aspect when it comes to data in this hugely regulated space — confidentiality of patient information and data security. Bringing in AI within this space means that there needs to be a solid ethical framework within which it works in order to not compromise the very sensitive data that pharma companies own. Hence it is critical that pharma companies while seeing the massive opportunity also keep a close watch on data governance and security protocols when they embark on these interventions.

Alternative Text Amit Das