Apple, Microsoft, Amazon, and Google/Alphabet are the first companies to reach market capitalizations of over a trillion dollars, with no end in sight for continued exponential growth across the technology sector. At the core of their jaw-dropping success is a revolution in the customer experience, triggered by digital and empowered through data-driven personalization.
The result has been a stunning transformation in the everyday lives of most people on the planet, raising expectations for the benefits brands must now provide their customers. Entire industries from entertainment to hospitality to commerce are conveniently activated in real-time from anywhere with a click, tap, or swipe, the user experience improving with every interaction.
Although great strides have been made to similarly improve the patient experience, healthcare remains a sluggish sector along this road to customer-centricity. Numerous obstacles abound, including the legacy of a paternalistic approach that marginalizes the patient voice; privacy, compliance, and interoperability restrictions that limit the sharing of data; and other factors. The disappointing and often tragic irony of such healthcare digital inertia is the obvious truth that nothing is more important than our health and wellness.
Compounding the frustration is the tangible progress that has nonetheless been made across digital health, from diet and fitness apps for consumers to significant advances in diagnostics and telemedicine for professionals.
One of the silver linings of the global pandemic of 2020 is the acceleration of these digital health trends, encouraging a greater appreciation of how digital technology could further improve the patient experience. A central but often overlooked element of this hopeful paradigm shift is the increasing digitization of market research, and its striking implications for healthcare brands.
The incredible potential for a digital revolution in market research begs the question of the specific gaps digital can immediately fill, and how fresh insights from innovative technology can improve the overall patient experience. So let’s take a look at how healthcare brands usually use market research, and show recent advances changing the way brands learn and grow.
The Role of Market Research in the Patient Experience
Broadly described, the “patient experience” is not only an individual’s reaction to illness and treatment, but an aspirational goal healthcare companies strive to maximize. Metrics are often used to define what a good or even an exceptional patient experience is like, with various indicators of satisfaction often going beyond direct engagement with the healthcare system.
An optimal patient experience is therefore somewhat analogous to a personalized customer experience, where buyers feel their needs are met and expectations exceeded at each touchpoint of the customer – or within the healthcare context, treatment – journey. In both cases, the end-user demands convenience, responsiveness, support, and ongoing guidance.
Patients differ from consumers, however, in that being treated for an illness is nothing like purchasing a product or service. Unlike consumers, who embrace a brand choice as a flattering reflection of themselves, patients consider their treatment as a means to an end, namely getting back to their healthy lives. If nothing else, that makes a good experience even more important.
Healthcare brand teams understand this, and like their counterparts in consumer packaged goods, entertainment, and other industries know the power of market research to try and get that experience right. At its best, market research provides actionable insights into what patients need and expect, and how they react to information from and interactions with their treatment.
With origins that go back to the Golden Age of Radio in the 1920s, market research is ultimately designed to gain transparency into marketplace competitors and trends, audience behaviors and triggers, and the effectiveness of marketing and communications efforts. Tools have ranged from landscape to ATU and SWOT analyses, and fall into qualitative and quantitative buckets.
Healthcare marketers typically rely on a broad arsenal of these research capabilities, choosing a combination that best reveals the hearts and minds of patients to improve their experience throughout the continuum of care. Compromises have had to be made between the strengths and weaknesses of these methods, though, a challenge digital tech is finally beginning to meet.
Either Quant or Qual: An Unnecessary Compromise
Optimizing the patient experience starts with understanding it, no easy task given the uniqueness of each patient, and the complexity of their journey through the healthcare system. From symptom awareness to point-of-care diagnosis, treatment decision to reimbursement, adherence to advocacy, the holistic patient experience continuously morphs and evolves.
Depending on the specific need, brand teams tend to choose between market research methods that have a reputation for being most effective: quantitative research is typically associated with larger sample sizes and the need for speed; qualitative research is used, in contrast, for more focused, hands-on studies of fewer subjects to extract more nuance.
Given the inherent dichotomy between quant and qual, brands often feel forced into a tradeoff: go broad and shallow, or deep and narrow. For example, a team might release thousands of surveys to statistically assess the diabetic patient experience surrounding a new type of insulin, or conduct a far more limited but intensive focus group to ask a tiny N more detailed questions.
Strategic brands with sufficient resources often opt for both methods: they may use quant to acquire a general understanding of the larger patient population, then follow the initial research with better informed one-on-one interviews across a select cross-section of subjects. But even such a hybrid approach frequently falls short of providing the actionable insights brands crave.
Part of the challenge is the methodology of market research itself, which is only as good as its data 1) input, 2) processing, and 3) output. Regardless of technique, the thoughts and feelings of patients must be translated into information researchers analyze and populate into a report the brand team can understand and act upon. Any weak link destroys the entire data chain.
When viewed through this three-step lens, opportunities abound for applying similar technologies the trillion-dollar big tech giants use to improve the customer experience. Although surveys and statistical analytics continue to serve quant researchers well, and first-hand participant observation remains the bedrock of qual, Next Gen market research is worth exploring.
Step 1: The Digitization of Data Input
The average human body contains over 37 trillion cells, each a treasure trove of complex health information. Our technological ability to capture biometric data of ever-increasing sophistication and volume is the foundation of the “Quantified Self,” and gateway to digital health ultimately enabling the optimal diagnosis, treatment, management, and even prevention of disease.
A legacy challenge of traditional market research is the need to translate biometrically rich behavioral data into fill-in-the-blanks survey and questionnaire bubbles, unnatural interview and focus group responses, and other limiting formats. Rigorous observational studies can be subject to bias, and force responses from uncomfortable subjects within artificial settings.
In contrast, the tech giants have mastered the science of understanding the customer experience through passive digital surveillance. Arguably controversial and often violative, the ability to monitor every action in real time provides an unprecedented and astonishingly accurate window into the attitudes, intentions, and behaviors of billions of active consumers.
Although these “Big Brother” tactics remain problematic, they reveal the incredible potential for similar data being utilized to better understand the patient experience. Under consensual research circumstances, for example, consider the power of a patient voice recording compared to a written response from a survey or questionnaire. Skeptical of the inputs? Go ask Alexa.
Imagine patients sharing their thoughts and feelings about their experience with a disease state or treatment from their own smartphones. Not only does a voice recording contain 100x or more data than the written word, but no data is lost or filtered during the input process. The data is also acquired conveniently and efficiently, and in a manner that can seamlessly scale to need.
Unlike eye-tracking widgets, heat mapping, and other emerging yet gimmicky technologies, asynchronous telephony-based voice recognition enables low friction, high speed data collection on any device. The digitized input becomes a window to the heart and soul of your patients, potentially revealing the essence of their experience with the right data processing.
Step 2: The Digitization of Data Processing
Aftermarket research data is acquired, whether in alphanumeric form from a survey or as a multivariate input from an observational study, the information must be processed. For quantitative research, where by nature the amount of data is relatively large and the analysis is statistical, the sophisticated application of mathematical algorithms is usually involved.
For qualitative research, data processing can be more subjective and ad hoc. One of the many perceived benefits and possible weaknesses of qual is this “human touch,” where experienced researchers collect the data and correlate it with similar inputs from analogous scenarios. In either case, the research is only as good as the processing remains objective and accurate.
Given these differences between quant and qual data processing, brands again feel that certain trade-offs are necessary: mountains of information from quant surveys are anonymized and statistically processed for generalized insights about the patient experience, while more dimensional, deeper data sets from smaller samples in qual might deviate from broader norms.
Innovative digital technologies can again come to the rescue: Imagine a platform that combines human (linguistic) analysis with an AI-powered processing interface that utilizes machine learning tools. Such a hybrid approach can reveal vital patient attitudinal insights from deep biometric voice data otherwise lost or never inputted in the first place through mere surveys.
By layering advanced sentiment analysis on top of transcribed textual responses, dimensions of additional emotional detail, subtle nuance, and behavioral indicators become transparent. While survey responses or observational feedback might communicate the what and how of the patient experience, emotional insights reveal the resonant why behind patient behaviors.
All too often, the data processing step of market research is ignored or considered a “black box” sandwiched between traditional inputs and outputs. A combination of sophisticated software and human-powered analytics can bridge the gap between signal and noise to extract patient experience insights precisely tailored to reveal underlying needs and fulfill unmet expectations.
Step 3: The Digitization of Data Output
Market research can input the best data and process it with accuracy and finesse, but if the insights are incomprehensible or confusing then the entire effort is for naught. The whole point of such research is to better understand and hopefully improve the patient experience; to do so, brands need not only receive intelligible insights, but simple and prescriptive recommendations.
Big tech again leads the way, having developed dynamic dashboards for their customers. From Google Adwords and Analytics to Tableau to Microsoft Azure, the presentation of data results in a visual, intuitive, and compelling way is the hallmark of digital supremacy. Gone are the days of generic PowerPoint slides of bar graphs and pie charts, as digital storytelling takes the prize.
Unfortunately, most quant and qual outputs demand significant attention and effort from the marketing team. Not only should the results be intuitive and straightforward, but key findings and recommendations must resonate and be impactful. Insights, however broad or detailed, must ultimately lead to specific action. Brands want the why, but they really need the what next?
Yet another advantage of biometric data as input is layered, dimensional output. Otherwise, it’s garbage in, garbage out. Processed voice data, to continue our tech example, lends itself well to a dynamic dashboard with insights that drive prescriptive action: from study design to endpoints to a lexicon, emotional connection is arguably one of the best indicators of future behaviors.
Megabytes of actionable intelligence across multiple modalities also lends itself to sophisticated digital features, including advanced search, audiogram creation, and listening studios. Being able to play source files instantly connects brands back to the raw input; hearing the subjects speaking for themselves infuses the output with transparency, credibility, and patient passion.
“Without data,” W. Edwards Deming once notoriously said, “you’re just another person with an opinion.” And without a visual, intuitive, and KPI-driven dynamic interface, your brand is lost at sea regarding how patients feel about their condition and its treatment – and the steps you need to take to ensure their voices are heard, their needs met, and their expectations exceeded.
The Best of Both Market Research Worlds
Getting to the core of the patient experience through custom-designed market research is a winning strategy. Adept brand stewards identify research opportunities across functional teams and the product life cycle, from clinical trials and R&D to primary and health economics and outcomes research, et al. Tailored combinations of quant and qual are the industry staples.
But what if your quant study suggests insights that need a further evaluation? Or your qual observational project reveals anomalies that need to be tested? The current gap in market research capability is a service that scales as fluidly and rapidly as quant, while offering the depth and dimensionality of a thorough qual. You need agility, but with the extra firepower.
Although privacy and other ethical concerns continue to plague big tech, many of their proven innovations for understanding the customer experience are capable of responsibly transforming market research. These include voice recognition, AI and machine learning, and dynamic dashboards that can take both quant and qual to a whole new level with a bold third option.
Ideal use for such capabilities to better understand the patient experience include situations where patient recruitment is time-consuming or tricky; additional research depth is required with minimal ramp-up time; campaign stimuli need testing during the creative development cycle, and any other research scenarios where quant may fall short in-depth, and qual in breadth.
Innovation springs from creativity thrown at an unmet need. Harnessing the innate power of the patient’s own voice, a fresh approach to market research gives additional flexibility and strength to the arsenal of tools already at a brand’s disposal. Taken together, all healthcare stakeholders can be heard and brands more responsive by embracing the potential of biometrics and data.
“The patient needs an experience,” said Dr. Frieda Fromm-Reichmann, “not an explanation.” When done right, market research helps explain what that optimal patient experience should be like, and recommends how brands can assist in creating it. By extracting learnings from big tech and the customer experience, the digitization of market research can bring out the best in each.