Marketers in every industry, including B2B and B2C, strive to understand what inspires their targeted users to be brand loyal. Market segment sizes range from a single potential customer representing millions of dollars to multiple consumers spending as little as a dollar at a time.<\/p>\n
Typical examples are:<\/p>\n
Everyone’s different, right? That goes for global industry market segments where humans making or influencing the buying decision display unique traits and behavioral patterns. So, here’s the challenge, explained in two questions and answers:<\/p>\n
Conversely, when the segment’s scale is Coke Zero-like, interacting with all potential and actual diet beverage drinkers is impossible, aside from being uneconomical and counter-productive.<\/p>\n
Representative sampling is the solution to this obstruction.<\/strong> The good news is, you don’t have to probe every market member of mass markets to find the diamonds in the rough. If you’re wondering why (or how!), you’ve come to the right place.<\/p>\n Laser-accurate market segmentation<\/a> depends on a significantly smaller, representative subset precisely profiling the population\u2019s demographics<\/a> as a platform to probe the latter\u2019s hidden behavioral energizers.<\/p>\n The keyword is “representative,” describing how a relatively small number of surveys or interview interactions can signify crucial motivational trends relevant to aggregated members of the targeted universe. Why? Big data analytics has demonstrated that shared demographics drive people to behave similarly under the same circumstances in every market situation.<\/p>\n For example, we can all see that Nike consumers respond in droves to the brand’s “Just do it” theme. It’s safe to assume the latter emerged when Nike researchers discovered that a significant emotional pain point was \u201ca lack of spontaneity or confidence in doing something actively daring.\u201d<\/p>\n How? They surveyed a small percentage of the many (i.e., a representative sample), drew conclusions from the responses, and applied the insights to deliver one of the most successful brand slogans of the 21st century.<\/p>\n Let\u2019s rephrase the first part of the question above:<\/p>\n It depends on a set of vital activities:<\/p>\n And that\u2019s only half of it. From there, success depends on<\/p>\n Representative samples structured with forethought<\/a> put us on the right path to accurate predictions, confident decision-making, and a deeper understanding of the market\u2019s emotional and cognitive complexities.<\/p>\n However, this vision depends on data analytic methods to ensure the sample\u2019s vital demographics (e.g., age, gender, income, education, occupation, etc.) mirror the target segment\u2019s.<\/p>\n Now, we\u2019re ready to take the two essential steps that ensure our representative sample generates our envisioned results.<\/p>\n The two primary categories available<\/strong> to researchers are probability and non-probability sampling. Both work well to establish representation, depending on the circumstances. However, researchers should lean toward probability methods as a default for maximized unbiased results. Consider the following two categories: Probability Sampling and Non-Probability Sampling.<\/p>\n This method functions around the principle of randomness. What does this mean? Every segment member has an equal and statistically calculated chance of being selected (similar to a grand lottery). The latter relies on \u201dThe Simple Random Sampling<\/a>\u201d technique, where a spinning number cylinder is the equivalent of drawing names from a hat.<\/p>\n However, marketplace research, constrained by budgets, favors a modified randomness style reflected in two methods:<\/p>\nWhat is a representative sample, and why is it important?<\/h2>\n
What is a representative sample in research and how can you ensure it works?<\/h2>\n
What is a good representative sample?<\/h3>\n
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Choose the sample type that works for you<\/h2>\n
1. Probability Sampling<\/h3>\n