16 Examples of Successful Applications of Artificial Intelligence in Pharma Marketing Part 2

I will continue listing examples to stimulate your mind as to what can be done, and has been done, with AI in pharma sales and marketing. The examples in this article series are by far not the only ones and only the tip of the ice berg.
 
 
7. Next Best Action Modelling and Omni-Channel Marketing
As you may know, it can take 20-30 sales and marketing touch points before customers start prescribing or purchasing your product. The more value you can add at each touch point, the more successful you’ll be. But providing a cohesive experience across the entire customer journey can be challenging, especially in an omni-channel campaign.
 
With NBA modeling, you can add contextually relevant and personal experiences based on the activity and needs of the individual. But it needs to be well planned to be successful. Luckily, the technology exists to predict the most likely outcome from a set of interactions with a customer or customer segment.
 
Combining data and advanced AI modeling allows us to identify, by customer, the next best content, in the next best channel, in the right sequence, at the right time. This results in maximum customer engagement and faster journey to the brand.
 
We conducted a project doing this across numerous brands and countries and it added over a billion dollars in incremental sales. It is exciting work, yet complex, and requires the right experts be involved to achieve your goals. At the heart of the approach is a customer-focused strategy, data and data sharing, AI analytics, and technology integration. The customers’ needs must be blended with the business objectives so that it is win-win for both.
 
8. Customer Journey Mapping
Although content management systems were a wonderful leap in technology, Artificial Intelligence takes things to a new level. You can now uncover the changing nature of the customer’s relationship with the brand, ensure that you disrupt the journey in a positive way, and fulfill all the customer’s expectations in order to maximize engagement.
 
These are sorts of questions we are now answering:
    •    What is the unique journey for each customer?
    •    What is the optimal sequence of content for that customer to drive brand adoption?
    •    What are the optimal sequences of touchpoints to drive brand adoption?
    •    Which profiles of customers are best predictors of potential for increased business?
    •    Which tactics drive more customer adoption in this journey?
    •    What is the optimal resource allocation across digital and non-digital channels?
    •    When a customer drops off the journey, which are most valuable to re-engage and what is the best way to re-engage them?
    •    Which customers should we not engage reps with?
    •    Which customers use a competitor brand but are vulnerable to switch with the right content and touchpoints?
    •    What is the portfolio cross-sell for any specific customer (i.e. given a large portfolio of brands, we can determine the optimal sales and profit outcome)?
 
You can find more on this topic here:
https://www.eularis.com/blog/are-you-getting-the-most-from-the-new-digital-customer-journey
 
9. Social Listening Analysis
With so much data streaming in 24/7, social media is an obvious big data set for marketers to analyze conversations around their brand. Applying AI, brand marketers can analyze this data for all sorts of helpful insights. Here are just a few:
    •    Discover who are real influencers
    •    Predict future influencers
    •    Know what it is about your brand(s) that’s hindering uptake
    •    Perceive threats to your brand
    •    Gain insight into your competitors
    •    Identify how to improve brand perception and engagement
    •    Pinpoint what caused any spikes in traffic and predict what you need to do if there is a potential problem brewing
    •    Understand what content your customers are more engaged with (and by combining this with your other data, you can also strengthen the details on who should get what content to move them up the adoption curve)

10. Switch Prediction of Physicians
 
Knowing that AI can make accurate predictions on a individual level, we realized we could use the data we had access to for a specific client to actually predict which physicians were showing signs of switching brands. This is useful for both retention and acquisition.
 
If it’s your brand they are switching away from, you need to get in front of them – with the right message and insights – to keep them loyal. If it’s a competitor brand, you need to get in front of them to demonstrate why your brand is a good match for their specific needs.
11. Pricing and Market Access
Pre-AI, pricing in Pharma was all focused on the clinical attributes of a drug versus its competitors. This focus will not get a drug approved and reimbursed by payers anymore.
 
With the shifts in this space, Eularis has been working on Artificial Intelligence powered pricing analytics that utilize real-world data on patient populations and analyze the clinical trial data alongside this. This delivers a value-based price designed to appeal to more payers than the competitors due to the increased value to them, and also weighed up to provide maximum profit to the company.
 
12. Sales Forecasting Using AI
Many years ago, sales forecasting with AI was one of the more common requests we got. This is essentially a prediction-based application which uses previous sales data, competitor sales data, product comparison and relatively simple AI techniques to predict future sales.