Predictive modelling is a data driven, induction based modelling that is continuously used by big sized companies to gain useful insights into trends and risks budding in the future. The modelling on the basis of data extraction, cleansing and analysis helps in predicting the value of a target variable (Fortuny, Martens, & Provost, 2013). Most of the analytical softwares developed are used to efficiently understand how things move for an organisation as per trends indicated by a relevant factor. One of the software that helps in prediction is R, summarization and estimation of the target variable with respect to different factors (Varian, 2014). The software holds a wide scope to develop predictive models.
Easy user interface
R is a text based programming by entering commands at the prompt and getting executed one by one. It is continuously evolving to create a more graphical interface where code editors interact with the package installed and present an image of the command through the interface (Valero-Mora & Ledesma, 2012). Also, development of R Studio, a code editor that interfaces with R for Windows, MacOS and Linux platforms has become popular. Kilburn (2015) cited in that R studio is commercial software that is built on the basis of R and provides additional features with respect to predictive modelling, data analysis and others.
The picture above represents four sections in R Studio. Firstly, the script section is the one where the data is imported. Secondly the next section, R environment shows the number of variables present in the given set of data. Next, R console where all the commands run and lastly, the graphical output display as per commands run in the console.
There are other user interfaces of R software such as Rattle, Red-R and Rkward which makes it accessible for its users to enjoy free services.
Availability of different types of predictive modelling techniques
The relevance of prediction differ from one software to another. R was primarily built to run complex data science algorithms, but holds good package for predictive analytics. It helps in data visualization through graphs and diagrammatic representations. Usually there are 3 types of predictive modelling in R:
- Propensity modeling,
- clustering modeling,
- collaborative filtering (Strickland, 2015).
Firstly, propensity Models make predictions about customers’ future behavior with a firm. Secondly, clustering modeling is used for customer segmentation and classification into different groups. Lastly, collaborative filtering is about implementing recommendations based on user feedback. It allows development of User-User and Item-Item collaborative filtering algorithm.
Since the time of its inception, R software is evolving and trying to make it easier for users to predict their models. In order to see the response of analytical models, it is better to link them directly to the marketing execution systems.
Companies using R for predicting consumer behavior
In conclusion, companies generating huge database try to predict customers’ behavior through statistical analysis and knowledge. Smith (2014) argued that use of R in marketing data analysis is becoming increasingly common as per customers’ habits and backgrounds.
Furthermore, financial and insurance industries are lead users of advanced statistical analysis where they develop new trading, pricing and optimization strategies (Mcneil, Martinez-miranda, Engelhardt, & Shanahan, 2013). In addition, R also plays a strategic role in weather forecasting, detection of changes in climate, estimates of war casualties in volatile regions (Fraley, Raftery, & Gneiting, 2011).
- Amirtha, T. (2014). Why the R Programming Language is Good for Business. Fast Company. Retrieved from https://www.fastcompany.com/3030063/why-the-r-programming-language-is-good-for-business.
- Fortuny, de E. J., Martens, D., & Provost, F. (2013). Predictive Modelling With Big Data: Is Bigger Really Better? Big Data, 1(4), 215–226. https://doi.org/10.1089/big.2013.0037.
- Fraley, C., Raftery, A. E., & Gneiting, T. (2011). Probabilistic weather forecasting in R. The R, 3(June), 55–63. https://doi.org/10.1198/jasa.2009.ap07184.
- Kilburn, W. (2015). Getting Started with R *. Retrieved from http://www.gvsu.edu/datainquirylab
- Knott, C. L. (2010). Using Excel in an Introductory Statistics Course : a Comparison of Instructor and Student. Retrieved from http://www.nedsi.org/proc/2010/proc/p091112001.pdf.
- Lam, L. (2010). An introduction to R. https://doi.org/10.1515/text.1.19126.96.36.199.
- Mcneil, A., Martinez-miranda, M. D., Engelhardt, A., & Shanahan, H. (2013). R in Insurance Biographies of presenters. Retrieved from https://rininsurance17.sciencesconf.org/data/pages/R2013.pdf.
- Ripley, B. (2013). R Data Import / Export (Vol. 2). https://doi.org/10.1111/j.1365-2699.2008.01969.x.
- Smith, D. (2014, June). How Companies use R to compete in a Data-Driven World. Data Informed. Retrieved from http://data-informed.com/companies-use-r-compete-data-driven-world/.
- Strickland, J. (2015). Predictive Analytics Using R. Lulu PR. Retrieved from http://www.humalytica.com/uploads/5/8/0/8/58082827/predictive modelling_analytics_using_r.pdf.
- Valero-Mora, P. M., & Ledesma, R. D. (2012). Graphical User Interfaces for R. Journal of Statistical Software, 49(1), 1–11. https://doi.org/10.18637/jss.v069.i12.
- Varian, H. R. (2014). Big Data: New Tricks for Econometrics. Journal of Economic Perspectives, 28(2), 3–28. https://doi.org/10.1257/jep.28.2.3.
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