Why “Scoping” Research Could be Your Biggest Problem

Editor’s Note: This post is part of our Big Ideas series, a column highlighting the innovative thinking and thought leadership at IIeX events around the world. Ujwal Arkalgud will be speaking at IIeX North America 2019 in Austin, TX. If you liked this article, you’ll LOVE IIeX North America. Click here to learn more. 

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When it comes to understanding consumers, we turn so much attention to the issue of accuracy. We compare research methodologies, over-analyze research questions, deconstruct consumer responses, and more.

But rarely do we talk about the process of scoping the project itself. And more importantly, the role it can play in preventing new and powerful insights from presenting themselves.

If you work in a large organization you’ll likely agree that consumer research is rarely free from institutional pressures. More often than not, it is not a purely exploratory process, and you need to prove an underlying hypothesis that is driving the project Which means one of the most common unintended consequences of “scoping” research (i.e. defining boundaries) is that the research invariably heads in a particular, even predictable, direction.

I can say this with confidence since almost half of the new clients we acquire each year come to us looking to break this cycle. They realize that they keep getting the same results and they are yearning to discover “something new”.

Unfortunately, avoiding scope-bias isn’t easy. Because without defining the scope, you will invariably find it difficult to stay on budget and deliver the results on time. But this problem of budget and timing is largely a function of the traditional methodologies most organizations employ when looking to study consumers.

Most organizations conduct research using various forms of structured data gathering methods (surveys, focus groups, interviews etc.) This makes it next to impossible to proceed without preemptively dictating which areas to gather data from and which areas to avoid.

Take for example the topic of vegan diets.

If we define the scope of a study as vegan diets and their impact on ready-to-eat snacks, the researcher will seldom bother to expand their data gathering efforts outside of the snacking universe in order to keep time and resources under check, all of which are at a premium because the methods employed are services-driven and are affected by the number of hours a project ultimately takes to build and complete.

This, of course, can be detrimental to the outcome of the research. What if the cultural universe of vegan diets was more strongly connected to replacing meals rather than consuming snacks? In such a scenario, we’d end up studying a universe that isn’t naturally connected to the way the consumer uses vegan foods in their lives. The act of scoping in such a situation would ultimately result in missed opportunities that could have either saved the team from launching a failed product. Or better yet, it could have allowed the team to launch something that would have solved a truly unmet need and taken the market by storm.

New approaches can help.

When we were building our product (MotivBase.com), we worked really hard from the beginning to create a paradigm wherein our clients weren’t required to create detailed scopes for research projects. I

nstead, we focused on designing the research process in such a way that we could start with some broad boundaries and let the consumer define it better for us. We did this by automating the task of data gathering and even letting the machine get to the first layer of interpretation, whereby our social scientists would be forced to examine culture (or a project space) from the perspective of the consumer.

This type of paradigm shift is now possible because we’re living in a time when access to big data and machine learning tools are making research significantly more agile.

And most of all, they’re forcing us to put the consumer first in the process. If we think back to the example of vegan diets and snacking, what we really should be doing is allowing research to begin from the highest possible layer of abstraction so we can work our way down to understand the role of vegan diets on snacking, without compromising our ability to identify opportunities – even if they don’t directly prove the underlying hypothesis. For example, we could have uncovered that vegan diets are actually more relevant to non-vegans in the context of dinners and weight-loss, rather than in the context of snacks. Which means, vegan snacks appeal to a smaller market and show little growth potential (compared to vegan solutions for dinner or weight loss). Of course, this is just an example but it doesn’t change the underlying reality.

If we want to improve the overall accuracy of research and genuinely uncover net new opportunities for our businesses, we need to change the way we think about scoping projects.

We need to flip the switch, turning an industry perspective to scope, into a consumer-led perspective. And to do this, we need to open ourselves up to new methodologies and tools that can offer innovative solutions that don’t penalize us for having open boundaries.