3 artificial intelligence use cases for pharma marketers

In recent years, artificial intelligence solutions have become increasingly ubiquitous in a number of industries and 2019 is poised to be the year AI creates significant change for pharma marketers.

Recently, MM&M reported that healthcare companies plan to spend an average of $32.4 million on AI implementation. The majority of surveyed executives believe their organizations will see a return on investment within five years. In addition to reducing costs, most stakeholders believe AI will make the healthcare industry better in a number of ways, from patient experience to improved outcomes.

Pharma marketers can utilize these same technologies to uncover new opportunities to engage physicians and other buyers. Many of the trending AI solutions seen today focus on providing personalized engagement at scale. An effective AI strategy can optimize interactions between sales representatives and physicians.

1. Chatbots

Automated messaging systems have been around for several years, but they are becoming increasingly sophisticated. Modern chatbots can not only engage in-bound leads, but also qualify and elevate leads to a human expert.

According to research from Oracle, 80 percent of all companies will employ AI-powered chatbots by 2020. The numerous benefits offered by this versatile technology mean pharma marketers stand to gain much from this investment.

One of the major benefits of a chatbot is 24/7 availability. Rather than leaving potential customers to interact with a phone tree to leave a recorded message, a chatbot can immediately answer questions. Importantly, chatbots can deliver this advantage at scale - a human representative might only be able to answer a set number of calls in a day, but a chatbot can speak with as many customers as server space allows. This saves the organization time and money without sacrificing engagement metrics.

2. Value pricing strategies

As prices on resource-intensive drugs become higher, many pharmaceutical leaders are looking for ways to curb costs. One potential solution is value-based pricing, similar to the strategy utilized by the Centers for Medicare and Medicaid for reimbursements.

Many value-based systems take the form of a pay-for-performance model, in which payers can seek reimbursements for ineffective treatments. This type of solution has already seen some success for specialty drugs, though there's still a long way to go before such a system could be implemented at scale.

It's possible that emerging AI solutions could support companies as they determine which pricing model works best for each buyer. Such a solution could analyze revenue models, discount opportunities, ACO volume and other considerations - such as government regulations - to develop optimized pricing models.

3. Sale messaging optimization

Today's pharma companies have access to comprehensive databases of buyer and stakeholder information. Sales representatives who utilize this data are better prepared to close deals and provide relevant information at the right time.

However, it's often difficult for human representatives to parse all of the available information in a short amount of time. Modern AI integration can augment this ability, giving salespeople a greater advantage when speaking with potential clients. With more information, sales reps can effectively engage buyers on their terms, without wasting any time. For instance, integrating an AI solution with SalesForce could provide reps with these advantages within a software environment with which they are already familiar.

AI is just once piece of a larger marketing strategy. Combining these technologies with a display advertising campaign can generate leads, spread brand awareness and educate a targeted audience. To learn more, contact an expert consultant at Elsevier today.