PM360 asked the industry’s brightest which new technologies are having the most impact on marketers and how they need to adjust, specifically:
- What area of marketing is being impacted the most by the addition of new technology? How is technology changing that area of marketing and how must companies today adapt?
- What advancement in virtual, augmented, and/or mixed reality technology made within the last year should have marketers most excited? How will marketers be able to best take advantage of this new development?
- What has been life sciences companies’ biggest roadblocks in using artificial intelligence and machine learning to gain insights to optimize their marketing and sales approach? What can companies do to take better advantage of AI and ML?
- What use of a (relatively) new technology by marketers in any industry has impressed you the most? Why was it so impressive and what can life sciences marketers learn from it?
All areas are being impacted by new technologies. Long gone are the days when we had a simple and clear path by which we could tell people about our brand. The challenge now is cutting through all the noise—and technology is allowing us to do this in unprecedented ways.
Marketers can now leverage Account-Based Marketing (ABM) technology to facilitate more personalized ad engagement experiences such as trying to best match content to each individual viewing the ad. Marketers are also using ABM technology to help sales teams identify customers in the most need of solutions based on intent and engagement data. And while some sales teams might be reluctant to leverage marketing-surfaced benefits from this AI/ML-derived data, it is becoming imperative for them to learn to trust their marketing partners—and trust the data—in order to help optimize their operations. Though sometimes that requires a reason to change and if sales teams aren’t experiencing pain points then they are reluctant to do so.
Still, AI is creeping into all aspects of the buyer’s journey. Discerning buyers are demanding it, and it is up to savvy marketers to deliver it.
A simple way for us to think about how virtual assistants have changed our behavior is to think about where you look when you access information. On a mobile device, your head is down looking at the screen. When using a virtual assistant, activating it by your voice, you are looking up at the world around you. So the engagement opportunities from audible and visual perspectives present new opportunities.
And when looking at how content providers such as Hulu and Netflix have changed consumption, media planners need to be aware of the state people are in. We have obviously seen a rise in “binge” watching. But we also have begun to see people watching episodes of The Office as background noise. So while the TV is on, their mobile device may still be the primary touchpoint for engagement. Smart media planners need to stay ahead of the game and understand the new opportunities that come with both streaming and virtual assistants.
Sharing information with HCPs to help their patients via meaningful dialogue and exchange of ideas builds trust. Technology is transforming how we engage physicians and deliver medical education to maximize a brand narrative.
Content design and delivery are enriched through technology. AI and predictive learning tools can assess presentation, poster, and visual content design, under simulated viewing conditions, that turns visual data into actionable insights. The benefit is improved clarity, concentration, and comprehension of materials to maximize learning.
Bring another layer of personalization to KOL selection. Use AI to analyze KOLs’ personalities and tendencies. Use this information to customize speaker programs, advisory boards, and other engagements where that HCP represents your brand. You can determine an HCP’s presentation style to drive more dynamic speaker selections, receive insight on the best way to engage the HCP, or flag scenarios where personalities aren’t aligned.
Customer relationship management platforms simplify message repetition and reinforcement to improve recall. Program measurement is also changing. New databases and AI-enhanced social listening tools can reveal HCP sentiment shifts in advance to provide guidance for managing medical education programs.
We are only at the beginning of this technology evolution in medical education.
The VR/AR space has experienced a few exciting developments, including the advancement of WebAR technology. This allows users to experience augmented reality without needing to download an app, which can be a major friction point for some consumers. This technology enables quick, bite-sized interactions from a mobile browser, which is especially helpful for mini-games and contests.
Additionally, Apple has also released some very impressive features for iOS (ARKit 3) that allow for more expressive and immersive experiences, including the ability to have apps that can capture and analyze a person’s movement, as well as blend virtual objects into views of the real world more seamlessly than was previously possible. These features are still in early access, so new and creative applications for them are emerging every day.
A great example of the use of AR is Warby Parker’s virtual try-on as it uses advanced technology to accomplish something simple and powerful. As people become more familiar with AR/VR technology, more interesting applications will emerge, but for the time being, recreating something familiar, physical, and “real” is a great step along the way, and they did a great job of that.
Visualizing data in 2D isn’t anything new, but because the brain works better in a 3D environment, using immersive data analytics really helps one to understand and analyze the data in VR. Even better: It avoids human bias and quickly connects data to insights without all the steps of manual methods. End result? You can act at the speed of business and quickly make actionable conclusions based on said data.
In a click, one can quickly show color-coded segments for target markets. It is more responsive than anything I’ve ever seen and quickly showcases trends in a way that 2D data visualization cannot. It also inherently simplifies complex information.
The best part? Since it’s platform-based it’s portable. This is huge. To have easy access to your data wherever you are seems like something that marketers can’t put a price tag on. This could mean monumental progress in revenues based on sales performance and being able to quickly pivot in response to aforementioned trends, patterns, and outliers.
Affordability and accessibility, while not the most innovative terms, should have life sciences marketers excited when it comes to the opportunity for AR and VR. With Facebook jumping into the game, and one billion consumers accessing its Oculus and Spark platforms alone in the past 12 months, AR and VR is approaching mass scale.
AR/VR very well might be an innovation that changes broader healthcare, but also provides new opportunity for life sciences. A very recent, public example is IDEGO. They unveiled their platform to the masses via America’s Got Talent where they proved that Howie Mandel could overcome his psychological fear of heights using AR/VR. The crowd went wild.
The application for healthcare is here and ready for consumption: Driving more empathy and compassion among caregivers and providers by allowing them to experience conditions such as schizophrenic hallucinations, allowing surgeons to experience surgery before performing it on a patient, allowing children to be more comfortable with routine procedures that can be terrifying to a young mind, and helping nurses find better veins before taking blood. What used to be available only to marketers with big budgets and consumers with big money to spend is becoming increasingly available and ready for healthcare at large.
Life science marketers often face several hurdles implementing AI for their organizations. One key roadblock is a lack of clarity around what is AI and what is not. Misconceptions that AI can only exist in large, multichannel ecosystems lead to concerns about costs and implementation challenges.
There can also be misalignment between the brand teams who are ready to implement and the teams who execute and pay for implementation. For example, a brand team can see immediate value and want to use AI to optimize its marketing activities, but if it does not align with the AI roadmap from their IT teams, it will take much longer to implement.
Overcoming these hurdles requires education and advocacy for AI from the top of organizations, especially in highly regulated industries like pharma that are typically slower to adapt to new technology.
The biggest thing companies can do to take advantage of AI opportunities in the near term is to take smaller, incremental steps to gain massive efficiencies. Making realistic investments such as automating content-analysis activities or personalizing media banners could see immediate returns. This requires smaller time and resource investment but could prove value for an eventual AI ecosystem that enables even more efficiencies.
One key historical roadblock to AI/ML adoption within life sciences: Data availability. Building and training accurate predictive models requires access to a large volume of standardized, reliable, and longitudinal data—medical claims data can meet these criteria, but it needs to be combined with other data sources to inform the most accurate predictive models. Commercial teams have struggled to access and integrate these supplemental sources while also protecting patient privacy.
Interoperability has also proved to be a roadblock. Linking healthcare data across traditionally siloed sources requires a centralized means of ingesting and integrating diverse data types into a structured format that enable data features to be interrogated at scale and within the proper clinical context. Healthcare data has tremendous complexity, and a sophisticated infrastructure is required to establish and maintain the underlying data integration at a scale that enables AI- and ML-driven models.
Finally, even the best data is useless without the ability to analyze it in an actionable and trusted way. To take full advantage of AI/ML, life sciences companies need the power and speed of financial services and consumer analytics platforms, along with data science and clinical expertise, in order to reach the most actionable and relevant insights.
Companies have invested into enterprise Customer Relationship Management (CRM) systems, such as Veeva, with the promise that the data and analytics is going to give them strategic insights for a market advantage—the problem is too much data is collected without a defined plan of action to make sense of it all. AI and ML holds the promise of automating the task of analyzing this data for marketing and sales.
However, a common misconception is that “Big Data” can be fed into AI systems like a hay baler. In reality, these systems are tediously trained with clean and codified data before providing valuable results. So, if you were to build a game-changing, marketing message recommendation engine, you need to start with a sizeable dataset that codifies which messages are favorable, aligned against customer profiles—herein lies the challenge.
While CRMs today solve the problem of how to capture that data, the practical problem is training people in the field to solely detail in these closed-loop platforms. The hurdle for AI is largely a user experience problem, not a technical one. Producing quality data also requires reps embracing a level of data entry as part of their job—and breaking old habits is hard.
I think advances in facial recognition software and the ability to augment mobile applications is significant and will alter how marketers deliver campaigns in the near future. In the movie Minority Report, personalized advertising was teased and based on retinal scans. In reality, facial recognition works more seamlessly and can easily be integrated in delivering geofenced, personal ads as consumers pass. This is already starting to appear in some retail outlets.
For example, consumers can turn on the camera, and select clothing to “hold” up against themselves to see the fit. Amazon is also working on a virtual dressing room, and these types of advancements will lead to even more. One concept that is being explored is the idea of an “abandoned shopping cart” that may follow you as you exit a store. So if you were to leave without making a purchase then an ad appears on your phone like: “Come back and buy—here’s incentive…”. In short, marketers need to track technology advances and determine how to use them imaginatively, while still preserving customer trust and relationships.
The use of predictive analytics to deliver hyper-personalized campaigns has produced impressive results. In traditional marketing, two patients diagnosed with the same condition would typically get an identical message. With machine learning, the data analytics from these same individuals would uncover information that’s unique to each of them, allowing marketers to create a hyper-personalized message delivered at that perfect moment in time. It’s a personalized and unforgettable experience.
Predictive analytics may lead to a shift from traditional marketing toward real-time marketing campaigns that are rapidly adjusted by both market and channel based on data. Right now, most healthcare agencies are not yet built for real-time marketing. A shift to this model would put a lot of pressure on agencies to hire more staff, develop new solutions, and execute extremely quickly. Real-time marketing would also require an updated submission approach to Med/Reg, leading to a whole new set of challenges for pharma marketers. However, real-time marketing has significant potential for building provider and patient loyalty. It’s already being applied in the consumer space. It’s only a matter of time before the brave few begin using real-time marketing in pharma to push the industry forward.
Technology that integrates with the way we naturally engage has been shown to be a distinctive advantage for innovative marketers. We learn and adopt from those we admire and hence the rise of influencers. We interact with content that resonates with us and hence the importance of data-driven, programmatic advertising. And now we are entering the screenless future where we would rather speak than type and hence the growth of conversation interfaces, one of the most impressive technologies of late.
The first conversation interfaces, or chatbots, sounded like robots at the expense of user experience, but it was clear that the vision would be realized. Fast forward several years and now we have Google Assistant, which can make restaurant reservations as naturally as if it was a human making that call. For life sciences companies and marketers, this can be revolutionary.
This powerful means of communication can provide seamless access to life-saving information—and leveraging this type of conversational flexibility is one step towards simplifying information access. As customer expectations become more demanding coupled with increasingly complex product information, voice-enabled technology enables pharma product data to become easier to find, and thus, facilitates marketers’ ability to continue to provide relevant content while simultaneously improving the user experience.