How do you define insight, and how is it different from information?

Insight is a broad term. It could have many definitions, but for the sake of our conversation, let’s focus on “insight helps businesses make better, more informed decisions.” When you unpack this premise, you get to an “insights value chain” that goes from data to information and then to insights.

Data is the raw material. Data can be observed and measured. There are quantitative or qualitative data.

Information is descriptive. By describing the data, we uncover information from the data. With quantitative data in market research, we often use indicators like a “mean score” or “top two box” to describe the data. Creating insight here is first interpretive and then inductive.

Interpretive means putting a context around the information so you can give it an application, and you can start with interpreting the information for the defined-use case. This is a key step in generating insights from data and information.

Inductive takes it one step further. We make inferences based on the information in the context of the use case. A decision maker draws conclusions about the decision to be made or to challenge a decision that has been made.

The better you contextualize the information, the more likely the arising insights will have an impact on the decision to be made.

Do you think the impulse for decision makers is to fall back on institutional knowledge?

We’re seeing this behavior right now as budgets tighten. But there is always a propensity to rely on institutional knowledge. It’s human. It feels safe to trust our own knowledge and experiences. Right now, it’s also a budget question. Institutional knowledge is mostly free. It is readily available, so we wonder: “Do I really need to invest in additional insights?”

What’s more, many decision makers may have had an unpleasant experience with generating additional insights. It didn’t help them. They invested in an insight-generation effort, and it was not helpful — or even harmful — because it was not done in the right way, often because it failed to distinguish between information and insights.

Institutional knowledge generally comes with a bias. GLG regularly has this conversation with clients. “Why do we need a voice-of-customer study? Our salespeople know their customers inside and out — all we need is to talk to our sales team.” But such knowledge can have different types of cognitive biases, like confirmation bias, contextual bias, self-serving bias, or framing effects. Research influenced by even one of these biases can lead to bad or even wrong decisions.

In our current situation, with budgets tightening, falling back to institutional knowledge is understandable but fraught with risk. Institutional knowledge and experience can be outdated. We don’t necessarily know how to operate in market conditions with increasing inflation, disordered supply chains, and energy shortages all happening at the same time and across geographies.

The book Strategy from the Outside In was published in 2010, near the end of the financial crisis that began in 2008. It makes the case that companies viewing the world from the inside out run the risk of losing sight of the market, shifts in the competitive landscape, and evolving customer needs.

Consider the budget process as an example. An inside-out company asks, primarily, what are we good at? What are our capabilities, people, and products? How can we sell more? Gain more share? Outside-in companies start by asking themselves, who are our customers and competitors? Are there new players or products in the market? What are our customers demanding? What are their new and possibly unmet needs? What new capabilities do we need to deliver the value that our customers desire?

Insights that address outside-in questions are a critical building block of making effective business decisions. They can make the difference between a good and a bad decision for the business.

Those companies that find the right balance between institutional knowledge and fresh outside-in insights will get to higher levels of decision intelligence in their organizations, which has never been more worthwhile than today.

How can market research go beyond information to deliver insight?

Understanding the business context when we design and execute the research is essential. What is the business problem or the decision to be made? What is the problem statement that provoked the need for the research? Knowing this, you can tailor the research to the business issue and ensure the collected data will be meaningful.

In market research, insight generation breaks down to three parts.

First, what is the sample? Who are the respondents, the target audience? If we know the business context, we can be more precise in identifying the target audience. Think, for example, of a survey in the pharma sector. The target audience can simply be healthcare providers or physicians, or maybe more precisely defined specialists, like oncologists or rheumatologists. Or in case of product-related research for a technology company, is the target audience the buyers or the users of the product? Knowing and understanding the business context and the genuine business problem can tighten up the definition and design of the sample. You lay the foundation for insight generation, or you limit the opportunity to unlock impactful insights.

Second, what is the right research approach and method? There are qualitative or quantitative methods, or some combination of the two. Which approach to use depends on the problem statement. Earlier this week, GLG talked to marketing executives at a pharma company who wanted to conduct message testing for a new product they are planning to bring to market. Their target audience is physicians. They wanted to convince the physicians to prescribe the new product instead of something they have trusted for years.

The pharma company wanted to conduct a series of qualitative interviews with selected physicians about their messaging. But when we drilled into the real problem statement for the research, we found that their messaging was already fixed and approved by compliance. The challenge was deciding between several messages by identifying the ones that resonated better with their target audience across various countries. Instead of doing qualitative interviews, the best way to get to the insights the pharma team needed was quantitative research.

Third, how do you analyze the data and visualize the information? The biggest challenge for data analysis and data visualization is not having a defined business context or even hypotheses to be analyzed and tested. Sometimes “trial and error” in data analysis can lead to unexpected new insights, but mostly it leads to effort without practical outcomes. If a research report is not tailored to a business context and built to address a problem statement, it will merely remain descriptive and thus cannot deliver more than information.

Take a simple example: In how many research reports have you seen a pie chart with sizes of companies and the commentary says that 70% of buyers of a certain product are from big organizations? That’s descriptive information. A simple business context might be to look at the total market in comparison with a specific market segment, which might tell you that in total 40% are buyers from large organizations, whereas for the specific target segment it is 70%. The comparison provides you with insight that makes the decision maker, possibly a financial investor, focus even more on how the considered business can create value from targeting buyers in large companies.

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To learn more, read GLG’s Insights vs. Information: Why Only Insights Have Impact guide.