The Shocking Truth About Pharma Marketing – And What to Do About It

Pharmaceutical marketers seem to be rapidly losing the ability to understand and influence their customers. For many years, Pharmaceutical marketers were blindsided by their own successes and relied too strongly on innovative new products.


Several studies reveal that the majority of CEOs believe that the number one driver of revenue growth is new products. This could be the case in the Pharmaceutical industry also with the constant M&A activity to acquire pipeline. In addition, line extensions were also quite a common phenomenon in the Pharmaceutical industry as a quick fix to gaining a ‘new product’. Yet it has been seen many times that no matter how loved the parent product, this does not automatically transfer into the line extension. 

Nielsen BASES reported that 93% of all new products fail within the first 3 years, and product databases show that line extensions are actually more likely to fail than legitimate new products. On top of that, McKinsey reported that two thirds of pharmaceutical brand launches fail to me their revenue expectations within 3 years. 

Some Pharmaceutical marketing teams have realized that, as costs rise and differentiation becomes less and less between products, Pharmaceutical sales and marketing efficiency must grow. The key to this is a thorough understanding of customers’ needs and drivers, even more than the customers themselves understand them.
It Is Not ‘Business As Usual’ anymore.

To win in this environment, Pharmaceutical marketers need a stronger understanding of their customers than ever before; they also need to understand what their customers’ need that will drive their behavior, and how to market to these needs more efficiently and effectively than ever before.

Pharmaceutical marketing is certainly not dead but it is undergoing unprecedented changes that are not within the typical realms of many Pharmaceutical companies’ expectations. It is not ‘business as usual’ anymore.

Marketing success has become a key driver of shareholder value, and is more important than ever. But how do you achieve marketing success? In a Forbes article by Jack Trout, a survey of top executives was cited in which they were asked their business priorities in order, and the results in order were: finance, sales, production, management, legal and people. Mr. Trout noted that missing from that list were the two basics that Peter F. Drucker had cited i.e. marketing and innovation.

Marketing is important but it is slipping down the priority list of CEOs. The fault is not, however, with CEOs but with marketers as many have let their brands become commodities rather than highly differentiated brands due to poor marketing. What I mean by ‘brands become commodities’ is wherein the brand (or company services or products) is very similar to its competitors and almost interchangeable.

Brand Commoditization

A study (by Copernicus Greenfield) interestingly examined the extent of brand commoditization. In this study, an alarming thing I noted was that Pharmaceutical brands included were seen as highly commoditized compared with brands like Dunkin’ Donuts/Starbucks. The Pharmaceutical industry marketers are doing a far worse job at differentiating their brands than coffee shops! This is entirely preventable with strong marketing.

When brands are highly commoditized, and the perceived product differences no longer exist – or are small – then price becomes an important factor. When you understand this, you can see how generics and biosimilars can come straight in and claim a large share of the market. In fact, in Pharmaceutical markets that have become highly commoditized, where the product itself has little intrinsic value (e.g. G-CSF and the epoietins), the biosimilars were able to have highly successful uptake. On the other hand, in other categories where the product value proposition was seen as higher (e.g. somatropin) and more highly differentiated, the biosimilars were not able to achieve the same level of success.

Lazy marketing

One often sees lazy Pharmaceutical marketing where the marketers find an easy differentiator (e.g. ‘mode of action’) and base all their marketing on that easy, obvious differentiator. This may be successful, but only if the marketers have securely tied that differentiator to a strong driver – such as efficacy; if not, the results hoped for will not be forthcoming.

I can think of a Pharmaceutical example of a brand we worked with many years ago. The brand had great clinical data on efficacy, a minimal side effect profile as well as a strong safety profile. However, this brand was just failing to thrive; two years post launch and it was still battling with a single digit market share and not growing. When we examined the data we noticed that it didn’t look like a particularly effective drug, so we were thinking it would have to be positioned for mild cases of the condition. However, when we looked into this further, we found that it was a highly effective drug and the marketing team had neglected to even discuss efficacy as a message as they felt the fact it was launched and approved meant it was highly effective. They had decided to focus on what I would consider secondary messages, such as ‘lack of weight gain’, and completely neglected efficacy, which was the number one driver message! By changing their messaging to analytics-identified drivers, they were able to double their market share in 6 months with the same budget, something they had not been able to achieve in the preceding years. It is so important to really get to grips with the data and base your marketing on that.

It is clear that marketers must be able to understand how to deal with the challenges faced, measure and justify their activities to deal with these challenges, respond appropriately, and also be able to prove that these activities are leading to increased bottom line growth for the company. Pharmaceutical marketers need to demonstrate that they are spending wisely but often lack the information to make well informed decisions. Much of the time, instead of linking an activity to a financial result, marketers become too busy showing brand awareness and tracking survey results. They do not tie these to actual revenue figures very successfully which, of course, leads to distrust with the Board.

Sales and marketing are one of biggest – if not THE biggest – investments in a Pharma company and yet it’s a sad fact that many marketers could not argue strongly which components of their marketing are leading to what financial results. The industry is seriously behind in these measures and still rely on poor metrics, anecdotes, gut feel, basic ROI as well as competitor size, spend and actions to guide decisions in these areas. It is time to regain control and, with it, credibility. This is where using sophisticated AI-powered analytics will stop spend on knee-jerk systems and campaigns that are a reaction to competitors, and help concentrate on the real role of the marketing department: to know and understand the customer, and translate this profitably to the product and business strategy that delivers profitable revenue growth.

It Is Not an Art or Science but Mathematics and Code

The key to tying marketing to revenue and profit results successfully is not an art or science but mathematics and code…. i.e. Artificial Intelligence (AI).

There is a lot of hype about AI so it is understandable that Pharmaceutical executives may be skeptical. However, when correctly used, companies can transform their results through a thorough understanding of their individual customers, their products and competitors’ drivers, and know exactly what they need to serve up (via the algorithms) and completely personalize the marketing to the customer which results in stronger customer experience and greater revenue and profit.

There are so many ways AI is already being used in pharma sales and marketing. Here are a few examples.

Identifying Rare Disease Patients.
The average time taken to diagnose a rare disease without technology is 7.6 years and comes after countless tests and physician visits. This creates a high cost to the healthcare system, not to mention much suffering for the patient. Now, using AI, we can identify all of these patients within the data sets (EHR and claims data) within minutes after the initial time spent data wrangling and creating the algorithms. Then every time a new patient enters the healthcare system, that patient is immediately identified using the algorithms. In fact, with the potential of this approach, and the success of our work in this space, our AI project requests has moved from HCP marketing to identifying patients in both classic rare diseases, or sub populations of specific cancer types.

Recommendation Engines/Suggestion Engines for Content Engagement in Marketing or Sales Calls
A lot of the big disruptive players use AI-powered clustering linked to customer data to create very personalised product (think Amazon) or content (like Netflix and Spotify) recommendations. And they aren’t the only ones. Many content marketers use this technology to improve engagement, and so do many in pharma sales and marketing.

In sales applications we can identify the optimal next message to give an individual physician to enable greater engagement and move him or her through the customer journey faster. In marketing we use this within the same sort of approach, serving up the right content in the various channels by individual physician or patient.

And given they are AI powered, the more data they have, the more they learn. So they get even better at making very relevant recommendations.

Predicting and Modifying Patient Adherence
The only way to effectively tackle patient non-adherence is to identify the individual causes for individual patients and deliver a personalized solution on a patient-by-patient level. Automated Artificial Intelligence can help ensure that each patient gets the right message or solutions relevant for the reason of their own lack of adherence.

Eularis has been applying Artificial Intelligence to understand physicians’ prescribing behaviors to determine what specific messaging will influence individual physicians to change behavior reliably. We realized similar algorithms could be applied to understanding patients’ adherence and lack of adherence behavioral causes, enabling rapid identification of non-adherent patients while using personalized approaches to influence the individual causes for each patient.

This can be automated to consistently add new patients’ data whereby the Artificial Intelligence algorithms learn from each patient’s data input. This allows personalized approaches to each individual patient without a prohibitive time requirement from the Pharma company.

Precision Physician Targeting
Many pharma companies are still relying on historical information to make physician targeting decisions. But the market is not about yesterday, it is about tomorrow. Fortunately, today the available data allows us to make more precise predictions about tomorrow. By integrating AI into the physician targeting analysis, you can identify numerous things that can create strong physician targeting and results.

For example, on an appropriate time-frame we should be able to predict ‘Which doctor has the most potential to write a script for a patient appropriate for our brand, today?’ and to help the rep understand ‘What should be the priority, based on the most recent data, to gain more scripts of our brand?’

Customer Segmentation and Customer Personalized Marketing
While typical pharma segmentation approaches combine prescribing levels with attitude and behavior factors outside of prescribing, they tend to use a limited number of variables and are siloed by brand. You are likely to find the same physicians in multiple brand target lists within one pharma company.

The extent to which marketers can segment their consumers comes down to the data that they have – or can get access to. By unifying these data silos, we can find far richer customer information. And when we apply AI, we can combine unlimited customer variables into the algorithms for a 360 degree view of the customer and data in real time.This means you can segment dynamically, honing in on preferred channels and messaging to connect with individuals based on their individual needs and behaviors at any given time.

An individual’s behavior will change at different times for different reasons. I often use driving as an example. If I am in my home country and have to go out of the city, I will drive. However, if I am in a foreign country, I tend to use Uber. Just as location affects my transportation choice, there are variables that affect customers’ prescribing choices. And AI can account for that.

You’ll be able to align the brand strategy with value propositions that speak to a narrow market segment. For example, what type of marketing approach does your customer appreciate? Depending on who they are and where they are in the buying cycle, they may prefer educational opportunities such as webinars or calls from sales people. AI can segment these for you and maximize your marketing budget by reaching the right people through the right channel with the right messaging.

Personalized Next Best Action Modelling and Omni-Channel Marketing Using AI
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 modelling with AI where the system learns all the digital footprints of customers and what engages each one the most, 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 several billion dollars in incremental sales over all brands and countries. It is exciting work, yet complex, and one we have already successfully done. 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.

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)?

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)
• View emerging trends

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.

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.

Key Opinion Leader (KOL) / Thought Leader (TL) Mapping

Traditional and AI approaches to identifying KOLs in a given therapy area use many of the same sources, including publications, conference abstracts, Sunshine Act data and patent applications. The main difference is that with AI, the data is constantly updated, analyzed automatically and can identify things traditional approaches miss.

The AI approach uses public data to mao KOLs and TLs in a way similar to how the CIA maps terrorists and drug cartels. Our clients who are doing this are able to use the data in different ways to address questions across the organization from Sales & Marketing to Clinical and Discovery. These are just some of their wins:
• Validated the strategic brand plan
• Pressure tested the clients view regarding who were the top influencers
• Identified blind spots in the MSL engagement strategy
• New early phase pre-patented opportunities were uncovered
• Rising star and fresh faces identified for recruitment
• Better publication planning
• Congress interaction planning and communication
• Rapid identification of optimum influencers for clinical trials and research collaborations

But how do you get it all to work?

All of the above are projects that Eularis has successfully completed – on multiple occasions in several cases. To successfully deliver AI in customer experience and customer engagement companies need three things:

1. Solid strategic planning

We always begin with the business challenge to solve or objective to achieve. We never start with the technology. That is putting the cart before the horse. Start with your core business challenges and work up your use case and business case from there. If you feel you do not know enough about AI to know which business challenges are applicable – check out the training I created for healthcare commercial teams to be able to not only understand AI (not math or code but the principles) and identify and deliver a strong strategic plan to solve the challenges with AI.

2. Data

The right data must be identified and used. How to identify the correct data for your challenge is also covered in the training I created. Next, datasets have to be combined to create a single customer view. This is, of course, easier said than done. Since datasets collected together are often very different and messy, this task can be time consuming, tedious, and expensive. However, there are more and more platforms coming on the market that can make this process easier and faster – depending on the data sets.

3. Real time data insights

Real time insights and actions is the name of the game. You need to engage the customer at precise times in his or her decision making process. If you leave it till the next day or the next week, you have lost them. This is where next best action modelling comes in. It automates this process to serve up the right content, in the right channel, in the right sequence, at the right time for each customer.

4. Customer-First context
Cross-channel, omni-channel analytics allows each unique customer’s journey to be an engaging experience for his or her individual needs. But it needs context. It needs to have an understanding of the significance of each event in the journey, what each touch-point will help to shape, and what KPIs should be used to measure and track these to constantly improve the customer experience. Whatever your specific use case, and your strategy needs to firmly keep the end user and their journey in context and foremost in your planning.