From my earliest memory, I’ve had a natural inclination toward all forms of exploration, adventure, and inquiry: ‘How does XYZ work?’ ‘Why would he/she/they act like that?’ Life's how's and why’s have always been at my fingertips.
Consequently, I enjoy engaging with the world in ways that support my fascination with humans and all forms of life, from traveling abroad and experiencing other cultures, traditions, and food to observing alternative ways of being in relationships and connecting to natural landscapes.
Every bit of my education and life experience has taught me that human beings are immensely complex creatures. I cannot remember a time when I was not completely fascinated by the mind and the human experience. My interest in how our thoughts, beliefs, and emotions inform and motivate our behavior is longstanding. Starting with my first Introduction to Psychology course in high school, I was truly hooked. And 22 years later, I’m still just as fascinated as I was then.
For the past seven years, as senior director of cognitive sciences at AnalyticsIQ, I’ve used my knowledge of how humans think and process information to power the research behind our predictive marketing data.
At AnalyticsIQ, we blend cognitive and data sciences to create consumer and business data that provides our clients with accurate consumer information and reliable marketing insights. Our primary aim is to assess who people are and how they think to understand their behavior and (maybe most importantly) the reasons behind their actions.
What does that look like day-to-day? And what exactly are cognitive sciences?
The importance of cognitive psychology in data science
As an umbrella discipline, psychology is the broad study of human thoughts and behaviors. In contrast, cognition is a sub-discipline that specifically studies the mental processes that drive those thoughts and behaviors.
To me, being a cognitive psychologist is like being an engineer: We essentially take a scientific approach to create a working infrastructure that reliably predicts the inner workings of the human mind.
Perhaps you’re wondering, “How does cognitive psychology fit into the traditional functions of a data company?”
AnalyticsIQ’s predictive data process begins with our in-house, survey-based research approach, led by my team and me in the Cognitive Sciences department. We utilize the scientific method to investigate the consumer beliefs and behaviors most desired by our clients. My scientific experience and understanding of human psychology ensure we are asking the right questions in the right way to collect accurate, high-quality data.
My typical day
My daily activities shift and change depending on the stage and status of individual projects; however, a key set of responsibilities is necessary to successfully execute every task.
Organization and documentation
My day begins with emails and triaging, immediately followed by organizing the many tasks that fall to a research scientist. These often include organizing active projects and timelines, tracking immediate and long-term goals, assigning tasks to staff, data processing (collection, cleaning, analyses), problem-solving, documentation, background research and summarization, and more. The list is never-ending.
My job cannot be done without a very clear organizational system. And with four or more research projects active at all times and new requests coming through the door every week, documenting each stage, action, setback, and progress toward the goal is absolutely essential.
Problem-solving and research
A scientist is essentially a problem solver, and a cognitive scientist is no exception. I constantly think critically about what questions we need to ask on our next survey to meet the client’s needs. What behaviors should we include in an upcoming assessment to measure the desired construct accurately? How do we reconcile unplanned missing data in our datasets? How can we improve respondent attrition? (I could continue with hundreds more examples.)
This is perhaps the most important part of my job. It takes up about 80 percent of my time and requires the most mental heavy lifting.
Mentorship
In my opinion, every good scientist is good because they had dedicated teachers and mentors who took the time to guide them through the most common research setbacks and roadblocks and empowered them to become confident, skilled problem-solvers. Around 20 percent of my working time includes mentoring a growing department. This is both an important responsibility and a critical skill that I am continually trying to improve upon.
Communication
One of the biggest challenges scientists face is the need to communicate detailed and mentally robust processes in a way that almost anyone could easily understand. As such, a critical part of my job is sharing our research ideas, methodology, data analyses, and primary outcomes with partners inside our organization (including product teams, data scientists, executives, and marketing teams), as well as partners outside the organization who also might be interested in our research insights (including current and future clients, as well as marketers).
Steps in a research project
As a scientist, I follow the same well-defined process for every project:
Identify the research question(s): The first step is to identify what we hope to understand from the project. How does X affect Y? Why (and how often) do X people purchase Y products? Why are X customers loyal to Y brand? By engaging in discussions with in-house partners as well as our clients, we can identify behaviors to design a study that offers unique insights not found anywhere else.
Conduct background research: Once we know what we hope to learn, we can dig into previous research on the same topic. This involves summarizing existing content, linking key ideas and theories, noticing gaps in literature or application, and discovering assessment tools created and validated by other scientists.
Design the research study: Next, we reach the important task of designing the survey itself. This includes identifying the population to target, the beliefs and behaviors central to the research question, finding or creating comprehensive motivational (and/or behavioral) assessments, clarifying the necessary sample size, identifying the data collection source, and finally, creating a data analysis plan that utilizes each survey item to reach those all-important conclusions.
Collect data: Here, we communicate with our research partners to deploy our surveys to thousands of respondents to gather a statistically valid and representative sample of the United States population.
Analyze the data: Once completed, our research team brings together all survey responses and begins reviewing and cleaning the raw data, transforming variables as needed, and conducting primary project analyses.
Interpret the data: At this stage, we review the outcome of our data analyses and compare it to our a priori hypotheses. Were our hypotheses supported? If so, then what does that mean relative to our original research question? We are also likely to do some additional problem-solving here. Is more data needed to increase the breadth and depth of the research findings? Are we ready to build a predictive data model?
Report the findings: Finally, my team summarizes the research process from start to finish, including any supplementary graphs that aid in answering the primary research question. We then share the research findings with our internal teams before sharing it more broadly with clients or other outside audiences.
At the end of the day
No workday is exactly the same for a cognitive scientist. So, if you have a curious heart and an inquisitive mind and enjoy problem-solving, consider a career in cognitive sciences. You never know what other unlikely industries or businesses could benefit from such measured and methodical expertise.
[ What is a ‘day in the life’ like in your role? If you’d like to participate in this series, reach out here! ]
