15.12.2022 - by Davide Rigoni

The promise & pitfalls of observational research methods


In observational research, subjects and phenomena are observed in their most natural settings. Many famous examples from various domains come to mind, think of Jane Goodall’s studying of chimpanzees in the wild to understand their social interactions, or Jean Piaget’s observing of children playing at different ages to examine their stages of cognitive development. Also in consumer research, the observational method is well established. In this article, we will first introduce some new and exciting applications of observational (consumer) research, and then explore their advantages and potential pitfalls.

New applications in observational consumer research

Recently, many new applications in observational consumer research have emerged thanks to technological advancements, often accompanied by a quantification in the study design. Let’s consider three innovative examples.

  1. Accurat is a location intelligence company that helps to improve brand & store performance‍ using real-world consumer insight. By tracking the location data of over 800.000 users, Accurat is able to provide detailed store traffic analytics (number of visits, visit moments, dwell time etc.), and track important KPI’s as market penetration and loyalty. All metrics can be benchmarked in the competitive landscape.
  2. SightCorp makes lightweight AI edge software solutions that bridge the gap between the online and real world. They developed an in-app AI-based face analysis to measure emotions, attention and crowd demographics. By simply infusing the analysis into any online-enabled app or service, you can get relevant insights into the customer experience of your app/website users.
  3. Neurons applies neuroscience tools and insights to help businesses better understand consumer behavior. They developed a cloud-based, eye-tracking AI solution that predicts customer responses, attention and behavior, yielding interesting insights on e.g. communication campaigns, product packaging, usability etc.


Real behavior

The main promise shared by observational research methods is straightforward: by focusing on real behavior instead of self-reported behavior, a researcher is able to collect objective data capturing both conscious and unconscious consumer decisions. Knowing that the majority of consumer behavior is determined by unconscious mental processes, this is no trivial matter. Traditional research (surveys, focus groups etc.) often fails to predict real consumer behavior: respondents have been shown to answer strategically, socially desirable, or have a false self-knowledge, making them to a certain extent ignorant of their own choices and decision processes.

Larger datasets

Another advantage of observational research is that oftentimes a very large number of data points can be collected (in the Accurat case even billions of interactions a year), improving data reliability and allowing for deep dives on a very specific sublevel (e.g. per store instead of on the general brand/company level). In traditional research, the data set is often limited to a couple of thousands at best.


A third advantage of observational research is that it can impact decisions in real-time: behavior is e.g. instantly captured in a live dashboard, allowing for brand/product owners to immediately act on the insights the dashboard provides. In traditional research, the time lapse between the behavior and the reporting on that behavior is often more pronounced: a researcher needs to develop a questionnaire first, then contact the sample, and finally interpret the results, which often takes a couple of weeks in lead time.


What vs. why

There are also some potential pitfalls in observational research. For example, a researcher is usually able to establish the 'what' but not necessarily the 'why'. The observations made will reveal a consumer engaging in a certain behavior (e.g. visiting a shop, buying a product), but the drivers for that behavior may not be immediately clear. Therefore, self-reports could prove to be a useful addition for observational research (painting a complementary part of the picture).


Another possible pitfall is representativeness: how representative are the study subjects of the entire population? Is there a bias on the sample because only specific profiles choose to download an app (SightCorp case) or share their location data (Accurat)? It is always good to be aware of this when interpreting the data.


Finally, an observational study should always make a good commitment to participant privacy. App users might not always be well aware of what they are consenting to when downloading or using the app. It is important to be as transparent as possible about this and to provide all necessary processes through which the user's data can be safely managed.


Observational research offers many exciting opportunities and is an important tool for measuring real (consumer) behavior. If you want to know more about our behavioral and observational toolbox at Sapience, our previous blog post on mystery visits might prove a good starting point. Or else, just drop us a message!

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