Behavioral Data Science: A Complete Guide

Marketing researchers have previously introduced the concept of using Behavioural Data to gain insights into consumer preferences. However, in the past, marketing researchers have faced challenges in ensuring the reliability of their data, such as survey design flaws, incorrect analytical techniques, and a limited sample size of respondents at one time. According to Statista, over the next five years up to 2025, global data creation is projected to grow to more than 180 zettabytes. 

Behavioural Data Science resolves these limitations as it involves collecting, analysing, and interpreting large datasets to gain insights into patterns of behaviour and decision-making. Explore the fascinating world of Behavioural Data Science and how it provides valuable insights into human actions and decision-making processes. 

Table of Contents 

1) What is Behavioural Data Science? 

2) How is Behavioural Data Science different from Behavioural Analytics? 

3) What are the three strands of Behavioural Data Science? 

4) Who is a Behavioural Data Scientist? 

5) Roles and responsibilities of a Behavioural Data Scientist 

6) Skills required to be a Behavioural Data Scientist 

7) Career path in Behavioural Data Science 

8) The future of Behavioural Data Science 

9) Conclusion 

What is Behavioural Data Science? 

The field of Behavioural Data Science is a rapidly evolving area of research that has the potential to revolutionise the way researchers conduct studies. It involves the integration of various fields, such as Behavioural Science, Data Science, engineering, statistics, finance, and more. This interdisciplinary approach allows for the creation of better prediction models and algorithms, which in turn can lead to more accurate results and insights.  

Contrary to popular belief, the focus of Behavioural Data Science is on something other than predicting human behaviour. Instead, it is concerned with the conduct of algorithms and systems.  

It is a branch of Behavioural Science that uses a large volume of data to determine what is likely to occur. Therefore, the importance of Behavioural Data Science cannot be overstated. It plays a crucial role in enhancing our understanding of complex systems and improving decision-making processes. 

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How is Behavioural Data Science different from Behavioural Analytics? 

Behavioural Data Science is a broad field that goes beyond just analysing consumer behaviour, which falls under the umbrella of Behavioural Analytics. Instead, it encompasses the study of how humans interact with technology and how they can coexist harmoniously with algorithms in the future. Behavioural Data Science seeks to understand and optimise the dynamic interplay between humans and technology.  

There are several factors to consider when examining this relationship, ranging from the impact of technology on human behaviour to the ethical implications of algorithmic decision-making. Behavioural Data Science is an innovative and impactful field that aims to develop novel approaches to studying human behaviour, enabling individuals to make better decisions and achieve improved economic and social outcomes.  

By analysing patterns and trends in human behaviour, Behavioural Science can provide insights into why people make certain decisions and how they can optimise their behaviour to achieve their goals. 

What are the three strands of Behavioural Data Science? 

Understanding human behaviour, algorithmic behaviour, and systems behaviour is crucial in the field of Behavioural Data Science. Each of these elements offers a unique set of methodological tools that can be used to effectively combine human intelligence with Artificial Intelligence (AI) and other advanced technologies. By leveraging these tools, we can gain deeper insights into the complex interactions between individuals, algorithms, and systems and ultimately improve our ability to make informed decisions based on data-driven insights.
 

Human behaviour strand 

The Human behaviour strand uses concepts from the fields of psychology, Behavioural Science, and other soft sciences that support data to understand human behaviour better. 

Algorithmic behaviour strand 

The algorithmic behaviour strand is the second strand in Behavioural Data Science. It measures the intelligence of algorithms. It employs fields like statistics and computer science to predict behaviour.  

Systems behaviour strand 

The systems behaviour strand looks at complex models like networks, connections, and cultural differences. This strand technically studies how humans interact with algorithms. In some ways, this strand needs knowledge from the above two strands. 

Who is a Behavioural Data Scientist? 

Behavioural Data Science plays a crucial role in understanding and optimising human behaviour, which helps in making informed decisions. This field helps to identify any shortcomings or gaps in the data, enabling us to make better decisions.  

Although there are deep learning models available that can help us understand why customers prefer one product over another, they may need to provide a specific solution to the underlying factors driving consumer behaviour. By gaining an understanding of human behaviour through this study, we can better prepare for and make accurate predictions regarding stocks and supply-demand scenarios. 

A Behavioural Data Scientist applies statistical and analytical techniques to develop accurate predictions and system models using data. They meticulously evaluate the influence of various social and economic factors on human behaviour and anticipate the most probable response. They help organisations make data-driven decisions and optimise their outcomes with their expertise. 

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Roles and responsibilities of a Behavioural Data Scientist 

The specific roles and responsibilities of Behavioural Data Scientists may vary depending on the organisation they work for and its primary agenda. A Behavioural Data Scientist is tasked with studying human behaviour using data as their primary tool. This involves conducting surveys, research, and experiments to gain an understanding of how people make decisions.  

A Behavioural Data Scientist also prepares questionnaires, focus groups, and research models to gather data. Once they have collected the data, they clean the data and analyse it to identify patterns that can be used to develop hypotheses and publish papers. 

As a Behavioural Data Scientist, it is essential to be able to communicate the findings and solutions to clients in an easy-to-understand way. This includes preparing dashboards and reports for client presentations. Working with clients to understand why people behave the way they do is an essential part of the job. Behavioural Data Scientists may also conduct A/B Testing to see what triggers certain behaviours. 

Behavioural Data Scientists may also assist policymakers and institutions in developing better solutions for the public. Data can be used to train AI to predict more accurate patterns. Behavioural Data Scientists may also be involved in creating computational and cognitive systems to further their understanding of human behaviour. 

Skills required to be a Behavioural Data Scientist 

As a Data Scientist, it is essential to have a wide range of skills. The following list highlights some of the most required skills in this field:
 

a) Statistics: It is the ability to analyse and interpret data using statistical techniques. 

b) Behavioural Science: An understanding of human behaviour and psychology to better understand user behaviour and preferences. 

c) Programming and coding: Proficiency in programming languages such as Python, R, and SQL to manipulate data and build models. 

d) Predictive modelling: It is the ability to create models that can accurately predict future outcomes. 

e) Machine Learning and deep learning: It is the knowledge of advanced techniques for building models that can learn and improve over time. 

f) Data wrangling and preparation: The ability to clean, transform, and prepare data for analysis. 

g) Model deployment: It is the ability to deploy models in production environments to make predictions in real-time. 

h) Business knowledge: Understanding the industry and business needs to create solutions that are relevant and valuable. 

Career path in Behavioural Data Science 

Data Science presents numerous employment opportunities for those who are interested in building a career in this field. With its multiple professions and areas of competence, the scope for growth and diversification is immense. If you aspire to become a Behavioural Data Scientist, there are two paths that you can choose from - academic and professional. It is essential to assess your options before deciding. 

Academic route  

Pursuing a career in the field of Behavioural Science requires a formal education that typically involves obtaining a master's degree and a doctorate. By completing these advanced degrees, you will be equipped with the knowledge necessary to conduct research in both the public and private sectors, teach others about the subject, and even write your thesis or books.  

With each of these methods, you will be able to strengthen your understanding of the subject matter further, giving you a competitive edge in the field. 

Professional route  

As you progress along this path, you will learn to adopt a more application-based approach, incorporating critical concepts from Behavioural Science into your work. It is important to note that the study of human behaviour can vary significantly depending on the industry or sector with which you are working. For instance, in the public sector, the focus is on achieving sustainable development, encouraging timely tax payments, and enhancing overall quality of life.  

In contrast, if you were working for a private company like Google, your mission would be to delve into the intricacies of human behaviour related to product selection to optimise user satisfaction and drive business growth. 

The future of Behavioural Data Science 

The field of Artificial Intelligence aims to incorporate human values into the core of AI systems. As we continue to advance in technology, it is crucial to ensure that humans can still verify the integrity, answerability, and resilience of these systems. This will enable us to build successful, community-driven AI systems that prioritise human values.  

Behavioural Data Science has the potential to transform our predictive capabilities while creating a secure and efficient flow of information between institutions worldwide.
 

Conclusion 

One in-demand profession is Data Science. There is undoubtedly an opportunity for growth in this industry. If you aspire to be a Consumer Analyst or want to make a shift into the field of Data Science, you should learn more about Behavioural Data Science. We hope that this blog has helped you understand the fundamental concepts behind Behavioural Data Science, as well as the relevant skills required, careers, and prospects. 

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