Market research came to fruition nearly a century ago, during the Golden Age of Radio in the 1920s. At that time, radio had long been used for maritime purposes—notably to attempt rescue aboard the Titanic in 1912—but the Golden Age marked the first decade in which radio was broadcast continuously in the type of model that’s familiar today. With this constant broadcast format, stations came to a realization: radio was expensive, and they needed to secure advertisers in order to help pay for airtime and operating costs. AT&T eventually became the first advertiser to sponsor a radio program and pay for airtime, in exchange for brand mention. As radio stations looked to scale this model, advertisers slowly began to recognize the significance of demographics revealed by sponsorship of different radio programs. Following the upsurge of radio consumption came television, and then of course the Internet—which among other things is a market research vehicle for consumers. The Internet gives anyone with access the ability to research nearly any product or service, from high-level messages to ratings and reviews to pricing. But scouring the Internet for customer insights is certainly not a scalable option for any business. The Internet harbors massive amounts of unstructured data in the form of blog posts, social media posts, photo and video, comments and more. The amount and availability of information available to brands is enormous,
but brands and even traditional market research firms aren’t able to harness all this data in a way that makes it useful. In this brief, we’ll give you an overview of traditional marketing research as well as
how modern data science has revolutionized market research in ways that can help brands of all sizes identify current and desired audience members.
There are an abundance of brick and mortar and online market research firms who make their money consulting for huge corporations looking to drive strategic marketing and general business efforts. Market research is typically divided into two sectors: qualitative and quantitative research, each of which has glaring flaws. Qualitative research is what many people consider traditional market research, and is narrowly focused—this research typically includes interviews, focus groups and data obtained from surveys and questionnaires. Unfortunately, the information derived from these methods may not be very accurate, especially if the questions are misleading, are biased or lack detail. Quantitative data is extracted from data analytics platforms like Google Analytics, marketing automation platforms and Customer Relationship
Management platforms (CRMs). This type of research is valuable, but leaves an abundance of questions about the people in the audience. For instance, Google Analytics enables you to understand site and search behavior, but when you drill down to the individual level, what can you actually learn about them as a person? There is no way to know if they predisposed to your brand. Marketing automation platforms can also provide valuable data. However, their focus is primarily on onsite data, which leaves unanswered questions about demographics, interests, buying behavior, etcetera. If a business has a prospect list of a certain size, a marketing automation tool can’t provide a comprehensive understanding of their audience at scale. CRMs store a wealth of customer data, but again lack the intimacy of understanding your audience on a personal level.
With deft market research using modern data science, marketing can be targeted, personalized and made more effective by better understanding their audience and the people in the audience. Once marketers are able to harness market research efforts and identify groups of individuals who have similar traits and behavior patterns, they can begin to practice highly targeted, personalized marketing. What qualitative and quantitative information is imperative to building and activating data-driven market research? Leveraging data to identify your current and desired audiences enables you to market to the individuals who are more likely predisposed to your brand. And focusing marketing efforts on those audiences can help drive business objectives. To better understand what brands should know about their current and desired audience members, consider the list of attributes below: • What challenges do they face, both personally and professionally? • What is their age, race and gender? • Where do they live? • What is their approximate household income? • Are they parents? • What are their interests? • What are their buying behaviors? • Who are their key influencers? • What do they value?
The more data a brand can gather about both their current and desired audience, the better they will be able to establish an authentic voice that speaks directly to those audience members.
Once a brand understands its current and desired audience, modern data science can group consumers with similar characteristics into categories—or personas—such as “driven professional,”
“outdoor weekender,” or “overscheduled parent.” When marketing efforts are tailored to communicate directly to a persona, the brand can address how their products and services solve specific problems meaningful to that persona. Some product features and benefits can overlap personas as well; for example, both the driven professional and the overscheduled parent may be interested in learning more about quick breakfast options. When a brand can solve a problem or enhance a solution for a particular persona, those consumers are likelier to become customers, repeat customers and—ideally—attract more like them. Defining personas using data science also enables brands to better predict customers’ future behaviors via passive collection, natural language processing and machine-learned algorithms. This includes nuances never before thought possible, including:
• Predicting credit history (Will this customer pay his credit card bill on time?) • Predicting life stage (Who in my audience is about to get engaged? Have a baby?) • Predicting buying behavior and purchase intent • Predicting health • Predicting interests based not on what consumers say to a focus group, but unbiased predictions straight from the horse’s mouth Although these insights are gleaned passively, gathering and analyzing data even at a highly granular level is not only significantly faster and more accurate than any traditional market research method, but also provides the audience insights brands need to make intelligent, informed business decisions.
The market research revolution is here, and the future of market research is more sophisticated than ever before. Market research has never been optional for businesses of massive proportion or for the smallest businesses, all of whom want to eloquently stretch budgets. But what is optional is the approach to obtaining the right data at the right time. When brands and agencies are open to new and innovative ways to explore market research to uncover pockets of performance-enhancing data, they stand to find new audiences, steal customers, gain revenue and reinvent brands.
People Pattern is a Software as a Service platform that supplies meaningful Audience Insights to the world’s biggest brands. Via semi-supervised machine-learned algorithms and natural language processing, People Pattern turns vast, messy public expression into actionable persona sets, helping brands gain an edge in the race to win, retain and serve customers.