Growth in market research spending has been modest for the past few years, according to ESOMAR’s annual Global Market Research (GMR) report. “Revenues being generated a year ago have broadly been maintained,” ESOMAR reported in its 2015 edition, saying the industry’s market growth is 0.1 percent.
That statistic from ESOMAR might suggest that the demand for market research is waning, but that can’t be further from the truth. As I discuss in my new e-book Winning the Research Revolution, researchers are more important than ever because businesses are invested in being customer-centric.
New advances and trends in customer intelligence will drive the industry’s growth. For instance, the Greenbook GRIT study, a biannual report on the industry’s biggest trends, found that mobile surveys and research communities are already mainstream. To remain relevant, researchers must pay attention to emerging trends that may shape market research in the next decade.
Here’s a look at four developments in research and how they will impact insight professionals.
Text analytics—which includes text data mining or simply text mining—is the use of computer-assisted techniques to make sense of free, natural language text and its associated metadata. Adoption of related software has made steady progress for a number of years and underpins several interesting developments in the business world.
Text analytics enables researchers to use more open-ended questions in surveys, reducing the total number of questions that need to be asked. This is useful because closed questions are typically less informative.
Text analytics can perform two key tasks:
- Find the story in large amounts of text
- Convert open-ended text into codes and numbers, allowing the quantification of qualitative responses
The rise of text analytics means researchers will need to develop new skills. You need to be able to write better open-ended questions (e.g. ones that elicit fuller and deeper responses), use text analytics software in new and efficient ways and extract insight from this new information.
Text analytics will drive developments in social media listening research, elevating the practice from a simple counting of likes, mentions, and positive and negative comments to a more sophisticated way of gaining actionable feedback. In terms of online discussions, insight communities and other collaborative conversation tools, text analytics will allow researchers to analyze a larger amount of text and facilitate conversations with more participants.
The traditional approach to research is to plan everything in advance and then execute it. While this approach has been in place for a long time, more companies are realizing it’s too slow—businesses can’t wait around to make data-backed decisions. They also find that it limits their ability to optimize solutions.
Enter agile methodologies. This trend, borrowed from the practice of agile software development in the technology industry, is driven by a growing need to employ a “test and learn” mentality. The finished idea or product benefits from multiple rounds of improvement.
Agile research refers to professionals who use an array of tools to enable quick, fast and often iterative solutions. A growing number of resources support agile approaches in research, including insight communities, social media, DIY research tools (e.g. simple survey platforms and online discussions) and new sampling options.
The business world will see more individuals, companies and groups within companies specializing in agile solutions. They will be the go-to people for fast solutions across an increasing range of research needs, such as concept tests, rapid insight and customer feedback. In many cases, these agile solutions will be cheap, often good, but the real focus will be on speed.
Data is becoming more abundant and cheaper, which creates a growing opportunity to use analytics to extract insight. In the past, most use of advanced analytics related specifically to designed research projects, such as a conjoint analysis project. The growth opportunity is in applying advanced analytics to data streams created by options such as big data, longitudinal information, passive data and crowdsourced information.
The future of analytics will likely be driven by freelancers, specialized agencies and advanced analysts embedded within agencies and client-side insight departments. The skills required to produce advanced analytics involve several years to acquire and constant use to maintain and develop—these approaches are generally beyond the skill set of the generalist. To take advantage of advanced analytics, researchers need to be proficient in statistics, data science and experimental design. They also need to become experts in tools like the mathematical/statistical language R and open-source software Hadoop (the leading tool for analyzing big data).
Researchers also need to master mapping business problems to techniques and turning results into insight. The industry doesn’t need more people who can run the software but can’t turn numbers into business outcomes.
In my e-book, I highlight the democratization of research. Customers want to help shape the brands and services they use—and every part of an organization today wants to interact with customers. Insight professionals will need to become guides instead of gatekeepers in the business. The need for a strategic frame of mind will grow and develop over the next few years.
Customers want to help shape the brands and services they use—and every part of an organization today wants to interact with customers.
Researchers have many skills that can effectively drive the democratization of insight. This role includes training, helping shape software and processes, and consulting during both the design and analysis stages. It also involves providing pathways to more complex solutions when needed.
As I describe inWinning the Research Revolution, research will continue to evolve to become a skill, not an industry. Researchers need to move away from being the people who run the scorecard and instead improve the business through better, evidence-based decision-making.
So, those are my four predictions for growth. What other research areas do you think will grow strongly over the next few years?