Ignite your brand messaging with science and algorithms.

Messaging is the essence of a brand’s communication strategy, and all successful pharmaceutical brands put a lot of effort into optimizing their messaging campaigns. Messaging optimization requires delivering:

• The right messages
• To the right customer
• At the right time
• Through the right channel

Pharmaceutical brand teams need messaging optimization to make sure they are getting the highest ROI on their marketing spend. Messaging campaigns account for the biggest share of marketing budgets for a drug, and the difference between a good vs. great messaging campaign can be worth $10s of millions in lost revenue or unproductive media spend.

Messaging optimization requires the use of advanced messaging science, algorithms, and databases in addition to the conventional “creativity-driven” approach.

Messaging optimization is at the core of every effective pharmaceutical marketing strategy. Whether it’s undertaking new drug launches or refreshing messaging for established drugs, pharma companies need to message to many customer stakeholders, communicate complex medical information, and even change treatment behaviors with messaging.

The complexity of messaging in the pharmaceutical industry is amplified by the fact that all pharma brands have unbranded/educational and branded/promotional messaging, which is very unique to the industry. Additionally, being a heavily regulated industry, pharma companies must navigate a number of strict HIPAA regulations and FDA laws, ensuring that all messaging abides by these limitations.

Here are reasons why message optimization for the pharma industry is important, and how innovation can make it better.

• Diverse Customer Base: The pharma industry serves to different customer segments, each with unique demands and expectations. These include physicians specializing in different fields, patients, caregivers, health insurance executives, policymakers, and others. Behavorial science or decision heuristic science can help to build an emotional connection with these target groups and drive immediate decision-making through more impactful communication.
• Complex and Technical Communication: Pharmaceutical communication is inherently complex and highly technical. Being precise is important to ensure that information about products, treatments, or offers is conveyed accurately. By leveraging Messaging AI, pharma marketers can simplify the technical information and make it easy for physicians, patients, and payers to digest the complex messaging.
• Revenue Generation Urgency: The pharmaceutical industry operates under constant pressure to generate revenue on a molecule before it loses the patent. Pharmaceutical messaging has to maximize product uptake as soon as it launches, while also defending against future competitors. If the uptake of a new drug also requires HCPs or patients to change their treatment behaviors in a disease state, messaging has to work even harder to drive behavior change. Achieving this level of precision and efficiency can be made possible with advanced science and AI innovations.
• Multichannel Engagement: Pharma companies use different communication channels to reach different customers, like HCPs vs patients. Personal promotion through pharmaceutical sales reps is the #1 channel of messaging for HCPs, although digital and social media channels have grown significantly now because of limited access to HCP offices.
  
  Messaging to patients has rapidly moved to programmatic media because finding patients with specialty or rare conditions requires targeting precision. Brands are using sophisticated propensity models to reach patients through different channels and there is significant upside potential from using advances in AI to improve omni-channel engagement for both HCPs and patients.
• Complex Messaging Landscape: Unlike consumers goods, where messaging primarily targets consumers, the pharmaceutical industry requires a dual approach of both push and pull messaging. This creates a complex messaging landscape. Achieving alignment between messages designed to "push" products to physicians and messages intended to "pull" patients into treatment can be challenging. Advanced messaging science and algorithms help bridge this gap and streamline communication strategies to create a unified and coherent brand image.

Bringing carefully optimized messaging campaigns to market is not easy and requires perfection along every step of the messaging cycle, from insights to action.

• Customer Insights: Effective message development starts with identifying deep customer insights that be translated into effective messages quickly and easily. Novel behavioral science-based market research techniques offer a new way to uncover hidden insights that explain how customers make decisions subconsciously using heuristics and biases.
  
  Behavioral science-based medical market research offers pharmaceutical brands a new approach to identify insights that explain why HCPs, patients or payers behave the way they do and how to change their behavior through messaging.
  
  Conventional market research asks customers to “state” how they make decisions. Behavioral market research “derives” subconscious drivers of customer’s decision making through carefully constructed decision experiments. If and HCP chooses A vs. B in a decision experiment and choice A was based on Loss Aversion heuristic, it can be concluded that the HCP makes decisions based on Loss Aversion and should be messaged to using that heuristic.
• Message Development: Message development in the pharmaceutical industry has historically been an exercise in medical copy writing, i.e. how to present complicated medical jargon and clinical details to HCPs, patients and payers in an understandable way.
  
  Pharmaceutical marketers are now realizing that unless their messages talk to the customer at a human level and address their human decision heuristics and biases first, they will always be sub-optimal. Developing messages using behavioral science or decision heuristic science can help to take the messaging from “creatively strong” to “scientifically perfect”.
  
  By creating messages that not only cut through the noise but also drive behavior change, pharma marketers can differentiate their messaging from competitors, establish brand equities, and maximize product uptake quickly. Decision heuristics science also serves as a great tool to customize messages for different channels and customer segments, which is critical for omni-channel marketing in the pharmaceutical industry.
  
  AI is also transforming the generation of messaging and marketing content in many industries. Currently, generative AI makes too many errors in producing legally approvable commercial messages for pharma brands, but it can definitely be used for exploring copy options and thought starters.
• Message Testing: While there has been significant innovation over the years in how pharmaceuticals brands develop and execute omni-channel messaging campaigns, there has been little innovation in how they test messaging with customers to optimize their campaigns before launch or in-market.
  
  Since pharma marketing depends heavily on the personal promotion channel, A/B testing is not a solution practical to continuously learn and optimize messaging. Pharmaceutical brand teams have historically relied on conventional qualitative and quantitative market research techniques to test messages with physicians, patients and payers before launching campaigns. However, conventional message testing methodologies have not evolved in 30+ years and are not keeping up with the pharma marketing needs of the future.
  
  
  • Most message testing methodologies can only test a small number of messages in a survey or require a large sample size
  • Most methodologies produce similar scores/ranks for messages and can’t tease out enough differentiation among messages
  • Most methodologies produce a hierarchy of messages and a core message bundle which is insufficient for the marketing team’s needs because they ideally need omni-channel message bundles and storyflow.

  Latest innovation in behavioral science and AI has the potential to transform how pharma brand teams can test messaging in market research and identify the optimal message bundles and storyflow for each customer segment and channel out of billions of possibilities.

  • Behavioral science can help predict customer behaviors in real-time during surveys after only a idea exposures, thereby allowing more ideas to be tested.
  • AI can be used to analyze survey data and identify optimal messages bundles/storyflow for every segment and channel using rules-based engines.

  Innovation in market research can improve the testing of messages and identification of optimal message bundles and storyflow to improve campaign readiness.

• Messaging Campaigns: Ideally campaigns should be optimized to deliver highly tailored messaging to different segments and channels exactly when it matters most. The latest advances in decision heuristic science and AI/ML can help achieve this goal of perfecting omnichannel campaign planning; and processes.
  
  From segmenting customers on more actionable variables to activating segment-based messaging and determining the campaign frequency and time, brand teams can streamline the entire campaign execution process with laser precision.
  
  AI can be trained on data from past campaigns to predict the effectiveness of new campaigns so that they can be optimized before launch. AI can also predict which messages are likely to perform best with each customer segment and even propose message sequencing and cadence strategies.
• Messaging Performance Analytics: Measuring the performance of campaigns versus competitors and best-in-class databases is a major need gap for marketers or market researchers. Deep learning algorithms can fill this crucial gap by predictively analyzing the effectiveness of omni-channel messaging against competitors, even in the absence of tracking data. This involves breaking down campaign effectiveness analysis to the message level and implementing a continuous learning system to iteratively optimize messages.
  
  Testing every single message for a brand and all its competitors in market research is not practical but AI can be used at scale to evaluate 1,000s of messages in a disease state and predict how effective each message is individually. The analysis can then be rolled up to the brand level and show how effective each brand’s messaging is vs. competition and industry databases.

In the past, pharmaceutical communication was mostly driven by creatives. However, as advances in artificial intelligence (AI), Machine Learning (ML) and data analytics continue in 2023, the creative-driven or one-size-fits-all approach alone isn’t enough to win in the market. Pharma marketers look beyond these traditional methods to optimize their messaging and actively engage with physicians, patients and payers. Harnessing the power of advanced messaging science, algorithms and databases can help marketers influence these segments and “nudge” their decisions towards the brand.

Specializing in pharma messaging science and algorithms, Newristics has created the Ultimate Pharma Messaging Guide with 20 guiding principles that have been scientifically proven to turbocharge pharma messaging. Access it here!

Unlike many other industries that can test and optimize messages in-market using A/B testing, the pharmaceutical industry typically tests messages prior to market launch using market research techniques. Marketing research plays a very important role in helping pharmaceutical brands optimize their messages before they are launched in campaigns. Using a combination of qualitative and quantitative healthcare marketing research techniques, pharmaceutical brands test messages with physicians, patients and payers on a regular basis, often in multiple rounds of testing.

Innovations in market research are transforming how messages are optimized in the pharmaceutical industry. Some of them include:

• Agile Research Technologies: Today, market research can be conducted through agile research technologies and microsurvey DIY platforms that can be programmed directly by anyone on the insights team. Cloud-based customer feedback and intelligence platforms are making it easier for insights teams to test ideas, capture customer behaviors, and evaluate brand health.
• Changing Approach to Respondents: The process of engaging respondents has also witnessed a substantial shift. Many research teams now rely on their first-party databases, rather than 3rd party panel providers. By engaging customers on a continuous basis, brand teams can get higher quality feedback and insights from surveys.
• Emotion and Behavioral Science: In primary market research, emerging methodologies inspired by emotion and behavioral science are being used. These innovative approaches aim to gain deeper insights into customer behavior and understand the reasons behind it.
• Enhanced Data Visualization: The visualization of survey data, whether qualitative or quantitative, has become more advanced and engaging. The advent of new tools and technologies has enabled researchers to present data in interesting and innovative ways, enhancing the reporting and sharing of research findings.
• AI-driven Insights: Artificial Intelligence (AI) is playing an increasingly significant role in market research. AI technologies, such as text analytics, are employed to mine past research and derive fresh insights. This approach helps streamline research efforts, making use of existing data that may otherwise go untapped.

In an Intellus-sponsored webinar, NEWRISTICS shared how an innovative approach to message testing that combines the power of behavioral science and artificial intelligence can improve the effectiveness of messaging for drug launches.Watch it here!

Individuals do not necessarily rely on complex processes to arrive at decisions. Rather, they often use decision shortcuts or “heuristics” to help them choose one option over the other. Heuristics-based messaging leverages this predisposition of the human mind to nudge customers towards making purchase decisions.

Talking to the dominant heuristics of the customer can make all brand communications significantly more persuasive and heuristics-based messaging does exactly that to increase the appeal of brand communications.

In the ever-evolving landscape of the pharma industry, marketers actively look for ways to optimize their brand communications and make their messaging more effective and efficient at the same time. Decision heuristics science can help to better understand and influence the decisions of different target segments by HEURISTICIZING messages.

If commercial teams can segment different groups of customers based on how they use heuristics to make decisions, they can deliver highly effective, customized messaging to each segment. Attaching decision heuristics to segments can provide strong hypotheses for WHY they behave the way they do. This information can then be used to develop messaging that is more effective in converting or changing their behavior. Moreover, pharma brands can develop segment-based messaging and train sales reps to personalize messages in personal promotions.

Behavioral science is a field of research focused on how humans make decisions and why they behave the way they do. Behavioral science-based messaging speaks to the dominant heuristics of the target customer with the aim of influencing choices and getting the highest degree of message acceptance.

HCPs, patients and payers can be segmented into different groups based on their behaviors and decision-making processes. Decision heuristics science can be used to each segment’s behavior and nudge their decisions using heuristics-based messaging.

• Segmentation: Attaching heuristics to your existing behavioral segmentation

  Historically, pharmaceutical marketers have segmented HCPs and patients using variables like behaviors, attitudes, demographics, psychographics. Latest advances in segmentation include use of decision heuristics and biases to group customers based on how they make decisions differently.

  By figuring out how customers use mental shortcuts to make decisions, pharma marketers can categorize the customer segments based on their unique decision heuristics.

• Segment-Based Messaging: Use heuristics to customize messaging to segments

  Identifying the Top 3-5 decision heuristics of each segment can simplify the development of customized messages for them. Every heuristic has a finite set of words and phrases associated with it and writing messages to each segment’s heuristics is easier that writing to their attitudes.

  Some decision heuristics are so dominant in a disease state that they can be considered as cross-segment heuristics and can be used to create cross-segment messages.

• Continuous personalization and optimization

  Experimentation is the key to continuous optimization of messaging campaigns but it's not always easy to execute. In the personal promotion channel, it's nearly impossible to implement experimentation with hundreds or thousands of reps, making experimentation a challenge. But in digital and non-personal promotion channels, experimentation is not only easy, it's important if you want to see real results. Using heuristics to set up your experiments take learning to a whole new level and optimize the delivery of pharma messaging.

Many pharma marketers want to leverage Behavioral Science-based messaging, but don’t know how to. Newristics' Heuristic Science Institute is the world’s most comprehensive resource on behavioral science built just for commercial pharma teams. Access more than 100 decision heuristics and biases explained in plain English, with real-world examples from dozens of disease states and heuristics-based pharma messaging examples.

Designed by practitioners (not professors) of behavioral science, HSI is built exclusively for pharma commercial teams. Check out HSI now!

100% of pharma brands have a customer segmentation, but less than 20% of them are currently able to translate it into segment-based messaging.

Why? Because most pharma brands are missing a key ingredient that is essential to activate segment-based messaging: DECISION HEURISTICS.

Decision heuristics science offers a different model for segmenting customers and converting segments into segment-based messaging. It can serve as a unifying tool that easily brings together all the stakeholders involved in activating customer segments with personalized messaging.

Segmenting customers on the basis of heuristics helps explain the hidden drivers of their decision making, which help everyone working on the brand. Having the top 3-5 decision heuristics of each segment makes it easy to create heuristics-based messaging for pharma. Even, identifying cross-segment heuristics that apply to all or multiple segments can help create cross-segment messaging.

In a recent webinar sponsored by Intellus, NEWRISTICS outlines the barriers pharma brands face in bridging the gap between customer segmentation and segment-based messaging. Watch the session here!

20 years ago, brands with better reps won the messaging battle in pharma. Today, the messaging battle is OMNI-CHANNEL, and brands with better alignment and field and digital channels are winning.

As the pharma promotional model becomes increasingly omni-channel, messaging science and algorithms are playing a bigger role in optimizing campaigns than creative.

Harnessing behavioral science-based message optimization and AI can help pharma brands to optimize omnichannel performance and maximize the impact of non-personal promotions.

• Segment: By figuring out how customers use mental shortcuts to make decisions, pharma marketers can categorize customer segments based on their unique decision heuristics.
• Customize: Leveraging behavioral science can quickly convert unused HCP segmentation into segment-based messaging.
• Experiment: Setting up experiments using heuristics can turbocharge learning from them and optimizing the delivery of non-personal promotions.
• Train: The tools, technologies, and platforms that help train algorithms to start predicting, or even generate messaging.

In this webinar, Newristics shares the latest developments in omni-channel marketing and provides recommendations to use behavioral science-based message optimization to get more performance out of non-personal promotion channels.

AI applications are gaining traction in almost every field imaginable and message optimization is no different. AI-based message optimization services are utilizing AI to rework marketing messages to deliver campaigns that promise better email open rates, conversion rates, CTRs, and overall increase the ROI on marketing dollars. Such AI-based message optimization services leverage their databases of millions of messages to create messaging that resonates with the intended target audience.

The advancements in AI can help transform how data from message testing surveys is used to drive campaigns.

• Campaign-Ready Insights: The data collected from different research surveys can be fed into AI platforms, enabling the identification of optimal message bundles and story flows from billions or trillions of possibilities. Rule engines can help create segment-specific message maps and communication strategies for different channels.
• Personalization Messaging: AI can help in reducing marketing waste by enabling personalized messaging campaigns for every customer segment, based on their decision heuristics. Identifying the top 3-5 decision heuristics of each segment makes it easy to create heuristics-based messaging for pharma.
• Message Refresh: Predictive models can be trained using AI, allowing for more frequent campaign refreshes without the need for additional research.
• Enhancing Campaign Accuracy - Traditionally, research data was analyzed using conventional statistical methods such as SPSS or SaaS. However, leveraging artificial intelligence (AI) on survey data presents a new opportunity where researchers can generate outputs that directly reduce the number of steps required to prepare campaigns for launch. This direct line of sight from research to execution not only accelerates time-to-market but also enhances campaign accuracy.

This Newristics infographic demystifies the complex landscape of how AI is being used in market research, making it easier for marketing and insights professionals to get up to speed on AI. From low-hanging applications of AI like NLP for coding of open-ends and sentiment analysis to the most advanced applications like training models to replace primary market research altogether, this infographic will quickly bring you up to speed on all relevant use cases of AI in market research. Access it here!

Typically, pharma brands update their messaging every 12 months, but marketing teams often lack new clinical data or customer insights to refresh their messaging. Even if there is nothing new to say creatively, messaging AI can provide a quicker, more cost-effective, and improved way to refresh pharma messaging.

• Predictive AI – Machine learning algorithms can be used to predict the effectiveness of competitor’s messaging and identify opportunities to win
• Generative AI – Computational linguistic models can be used to generate many alternative versions of a brand’s messages, even if there is no new clinical data
• Evaluative AI – Genetic algorithms can be used to evaluate 100s of messages with customers in primary market research and create an omnichannel message playbook

In our recent whitepaper, you can learn about the latest developments in AI that can help streamline the message refresh process for your brand. Explained in non-technical terms, the whitepaper describes different kinds of AI for messaging applications and explains how to use it. Learn from a real-world case study of how a $1 billion brand implemented a message refresh in just 12 weeks by leveraging AI.