What do you Mean - A Human Centred Approach?

Since releasing our report, Understanding the Experience of Farm Women, the most common question we’ve received isn’t about specific data points—it’s about what it truly means to take a human-centered approach to customer research. After all, doesn’t collecting information about farmers’ opinions, behaviors, and characteristics always involve some form of human interaction?

In this context, a human-centered approach means going beyond surface-level data to uncover the deeper emotional and psychological drivers behind decision-making. It’s not just about gathering facts and figures like brand share or product usage—it’s about understanding how farmers think, feel, and ultimately behave.

Three Key Steps in Human-Centered Research

1. Framing the Research Problem Correctly

The first step is recognizing that farmers are humans first, farmers second. Many of the topics Agri-marketers explore—how customers learn about new products, process information, and perceive brands—are fundamentally human issues more than they are farmer issues. This means we can apply insights from behavioral economics, consumer psychology, and B2B marketing to understand behavior more effectively.

One of the biggest takeaways from behavioral research is that what may seem like irrational behavior is often perfectly rational in the customer's mind. This is why we almost always start with qualitative research—to uncover the range of attitudes and behaviors. It also helps us understand the language and terminology farmers use, which can dramatically impact how we communicate.

For example, in a recent study, a client told us that farmers at all scales of operation valued "independence." Previous surveys had shown no difference in how farmers rated independence, regardless of farm size. However, our qualitative research revealed five distinct definitions of "independence." When we tested these definitions in a subsequent survey, we found significant differences in how farmers across different operations actually perceived independence. Without qualitative research, this crucial insight would have been missed.

2. Recognizing and Addressing the “Farmers, Farms, and Farming” Problem

Agri-marketers are excellent at understanding farms—the acreage, commodities produced, and products used. We also have a solid grasp of farming—methods like direct seeding, no-till, and harvest techniques. But we often overlook the farmer—the human being making the decisions.

A human-centered approach means focusing on farmers’ goals, motivations, and relationships with the world around them. In previous research, we discovered that a farmer’s adherence to rural cultural norms was a predictor of decision-making and farm management choices. Similarly, in our Farm Women study, the most critical variable in understanding a farm woman’s experience wasn’t farm size or operation type—it was her relationship with the broader rural culture. These social and psychological influences significantly shaped her sense of identity and decision-making.

Adding to the challenge, ag market research often assumes that a priori segments—such as commodity type or farm size—are predictive of behavior. While being a dairy farmer influences the products a farmer buys, it tells us very little about how they make purchasing decisions. Likewise, the assumption that "large-scale farmers" all make decisions in the same way is simply not supported by behavioral research. Human motivations, personal preferences, and decision-making processes are far more nuanced.

3. Framing the Data Analysis Correctly

As I mentioned in my previous blog  (insert link to the LinkedIn page) Nobel Prize-winning economist Daniel Kahneman once reminded us, "The human mind is a machine for jumping to conclusions." Too many market research reports are little more than tabulation exercises, where responses are categorized by demographics and firmographics—encouraging readers to draw oversimplified and often misleading conclusions.

When it comes to quantitative research, human-centered research goes deeper by seeking the root causes of variation in the dataset. In our Farm Women study, we found that traditional demographic factors—such as farm size, respondent age, commodity type, or education level—had no meaningful correlation with key insights such as farm role involvement or perceived barriers.

Instead, we took a different approach:

• Principal Component Analysis helped identify underlying themes within responses.

• Cluster Analysis grouped respondents into meaningful segments based on attitudes toward challenges and barriers.

• Discriminant Analysis modeled the relationships between segments to provide a holistic understanding of farm women’s experiences.

The result? A coherent and compelling picture of five distinct models of farm women’s experiences—replacing outdated stereotypes with a nuanced, evidence-based understanding of their roles and challenges.

The Bottom Line: Humans Make Decisions—Farms Don’t

Any research respondent is subject to human cognitive biases, including recall errors, risk aversion, and aspirational thinking. In the Farm Women study, we took extra care with terminology, question order, and gendered language to minimize bias and ensure accurate insights. We also prioritized open-ended questions, allowing farmers to define their own responses rather than forcing them into predetermined categories.

In today’s fast-evolving ag industry, where farmers must navigate increasingly complex decisions, understanding customers as humans first will be the most critical factor for success. The future of Agri-marketing isn’t just about data—it’s about empathy, psychology, and human connection.

How do you feel about this Human-Centered Approach to B2B Marketing?

Share your thoughts in the comments, and stay tuned for our next post, where we’ll dig into the underlying reasons for why a combined approach of logical information and emotional content are the optimal approach in agrimarketing.

And if you want to take 15 minutes to discuss this article, or options on working together -  let’s talk! maurice.allin@centricengine.com.