- Analytics
- Churn / Attrition and Retention / Loyalty Models
- Clustering
- Customer Analytics
- Customer Life-Cycle Analysis
- Customer Segmentation
- Customer Value Models
- Data Analysis
- Data Mining
- Data Segmentation
- Decision Trees
- Market Analysis
- Market Basket Analysis
- Predictive Modelling
- Profiling
- Recency, Frequency, Value (RFV) Analysis
- Response Analysis and Models
Data Analysis
Data analysis is a process of gathering, modeling, and transforming data with the goal of highlighting useful information, suggesting conclusions, and supporting decision making. Data analysis has multiple approaches, encompassing diverse techniques under a variety of names, in different business, science, and social science domains.
Below are some varieties of data analysis:
- Data mining is a particular data analysis technique that focuses on modeling and knowledge discovery for predictive rather than purely descriptive purposes. Data mining essentially aims at discovering hidden patterns in vast chunks of data.
- Business intelligence covers data analysis that relies heavily on aggregation, focusing on business information. It serves as a foundation for making informed business decisions and hence devising strategies at a business level.
- In statistical applications, some people divide data analysis into descriptive statistics, exploratory data analysis, and confirmatory data analysis.
- Descriptive statistics are used to describe the basic features of the data in a study. They provide simple summaries about the sample and the measures. Together with simple graphics analysis, they form the basis of virtually every quantitative analysis of data.
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Exploratory data analysis focuses on discovering new features in the data. Various techniques like Decision trees, Neural networks, can be employed in this analysis, to explore different variables that are available in the database and understand the inherent relationships between them.
- Confirmatory data analysis focuses on confirming or falsifying existing hypotheses. This analysis is mostly undertaken to validate a known business phenomenon so that further steps can be taken either to accelerate or decelerate the phenomenon to the benefit of the organisation.
- Predictive analytics focuses on application of statistical or structural models for predictive forecasting or classification. This methodology has wider applications for predicting future behaviour when a strong relationship is known to exist between predictive variables and the outcome that we aim to extrapolate.