Behavioral data
Behavioral data is gained as output from behavioral experiment, collected from individuals or groups [1]. It is measurement of a behaviour or action. It is collected to predict future, for example to estimate how consumer might behave or react in the future, but also to see the customer's characteristics right now, for example to estimate how loyal the customer is. It answers question: what customer do when he interact with the business? by recording his actions [2]. Depending on which industry behavioral data is collected, it may refer to [3]:
- amount of sales,
- dates of purchases,
- types of purchases,
- dates of payments,
- dates of purchases,
- customer service activities,
- claims of insurances,
- behaviour of bancrupcy,
- webside activity,
- sales on websides,
- path which user takes in webside,
- other.
Data types and charactericts
Behavioral data is considered as the most predictive type of data, however it is the most expensive one and the most difficult to gain if the company wants to get it from outside source. If company has access to its behavioral data, which means knowledge about operations by product, it is available at zero cost [4]. Other types are demographic data and psychographic data[5].
| Type of data | Power of prediction | Stability | Cost |
|---|---|---|---|
| Demographic | Medium | High | Low |
| Behavioral | High | Low | High |
| Psychographic | Medium | Medium | High |
Challenges in modeling behavioral data
Fennell P. G. points several reasons why behavioral data is challenging to model [6]:
- scale - of research, of data and size of final database,
- sparseness - because of several observations per individual,
- heterogeneity - because individuals behave differently,
- class imbalance - several observations of the outcome of interest,
- interpration of models - required some time to learn it, usually there are many complex algorithms to predict behaviour of potencial customer.
Tools of behavioural data analysis
Behavioral data is usually huge size database, there are several approaches used in analyzing. Example of ready to use model is structured sum-of-squares decomposition (S3D). Such model includes [7]:
- algorithms,
- decision trees,
- variant of regression trees,
- variation of the outcome,
- correlations between the features,
- statistical models,
- support vector methods.
Examples of Behavioral data
- Survey responses: This can include surveys that ask questions about the customer's experience, satisfaction and preferences.
- Online activity: This can include website activities such as page visits, clicks and conversions, as well as social media posts, likes and shares.
- In-store behavior: This can include data on the types of products customers purchase and how long they spend in a store.
- Transactions: This includes data on the customers' purchases, returns, and other financial activities.
- Interactions with customer service representatives: This includes data on phone conversations, emails, and other interactions.
- App usage: This includes data on how customers are using your apps, what features they use, and how often they use them.
- Wearables: This includes data from wearable devices such as fitness trackers, smartwatches, and other devices that track a person's activity and other metrics.
Advantages of Behavioral data
Behavioral data can provide a wealth of information about an individual or group, which can be used to inform decisions and strategies. The advantages of using behavioral data include:
- Enhanced understanding of customer and user behavior - Behavioral data provides insight into how people interact with products, services, and other individuals, allowing businesses and organizations to better understand customer and user behavior.
- Improved decision-making - Behavioral data helps organizations to make more informed decisions, as it provides real-time feedback on the effectiveness of initiatives, strategies, and products.
- Enhanced customer experience - Behavioral data can be used to optimize customer experience, as it allows businesses to identify trends and patterns in customer behavior that can be used to create more engaging and personalized user experiences.
- Improved efficiency - Behavioral data enables businesses to identify wasteful processes and procedures and allows them to focus resources on more effective activities.
- Increased ROI - Behavioral data can be used to optimize marketing and advertising campaigns, resulting in increased return on investment.
Limitations of Behavioral data
Behavioral data has many limitations, including:
- Lack of control: It is difficult to control the environment in which the data is collected, which can introduce bias into the results.
- Limited access: Behavioral data is often collected from individuals or groups in a certain context, which can limit the reach and accuracy of data.
- Subjectivity: Behavioral data is often subjective, as it relies on individuals to interpret and report their behaviors.
- Self-reported data: As individuals are reporting on their own behavior, there is often a risk of inaccuracy or bias.
- Limited time frame: Behavioral data is often collected over a limited time frame, which can limit the depth and accuracy of the data.
Behavioral data is collected from individuals or groups as output from behavioral experiments. Other approaches related to behavioral data include:
- Neuropsychology - This field studies the structure and function of the brain and nervous system and how they relate to behavior and cognition.
- Cognitive Psychology - This field explores the mental processes that are involved in the acquisition and use of knowledge, including problem-solving, decision-making, and memory.
- Social Psychology - This field focuses on how individuals think, feel, and behave in social situations and how individuals’ behavior is influenced by others.
- Developmental Psychology - This field looks at how people develop over the course of their lives, including physical, cognitive, and social-emotional development.
In summary, behavioral data is collected from individuals or groups as output from behavioral experiments, and there are several different approaches related to this, such as neuropsychology, cognitive psychology, social psychology, and developmental psychology.
Footnotes
| Behavioral data — recommended articles |
| Data and information — Analysis of customer — Marketing information system — Secondary data sources — Customer segmentation model — Qualitative research techniques — Case study analysis — Case study research — Analysis and interpretation |
References
- Bari A., Chaouchi M., Jung T. (2016), Predictive Analytics For Dummies, John Wiley & Sons
- Fennell P. G., Zuo Z., Lerman K., (2018),Predicting and Explaining Behavioral Data with Structured Feature Space Decomposition
- Gerber L. R., (2006), Including behavioral data in demographic models improves estimates of population viability, in "Front Ecol Environ 2006; 4(8)", The Ecological Society of America, Arizona State University
- Hartmann P. H., Gotrman J. M., Jones R. R., Gardner W., Kazdin A. E., Vaught R. S. (1980), Interrupted time-series analysis and its application to behavioral data in "Journal of applied behaviour analysis 133, number 4 (winter 1980)"
- Rud O. P. (2001), Data Mining Cookbook: Modeling Data for Marketing, Risk, and Customer Relationship Management, John Wiley & Sons
- Turner M. B., Forstmann B. U., Steyvers M. (2019), Joint Models of Neural and Behavioral Data. Computational Approaches to Cognition and Perception, Springer
Author: Aleksandra Puchała
