There are different analytics methods and techniques that data analysts use depending on the type of data in question and the kinds of insights they want to uncover.

The first six analytics methods are applicable in case of quantitative data, while the last method applies to qualitative data.

1. Regression analytics

Analysts use regression analytics to estimate the relationship between variables. It is about finding the correlation between a dependent variable and any number of independent variables. Moreover, analysts can estimate how different variables might impact the dependent variable. This analytics is helpful to identify trends and patterns, make predictions and forecast future trends. It is important to note that regression analysis does not reveal the cause and effect of the variable relationship.

2. Monte Carlo analytics

Analysts apply Monte Carlo analytics to determine possible outcomes and estimate the probability distributions. Random variables can make it tough to predict the scenarios. For this reason, Monte Caro analytics helps to make the predictions. This analytics method finds its application in advanced risk analysis helping to forecast future events and make the best decisions. Besides, it also helps to perform risk analysis, and estimate the effect of unpredictable variables considering a specific output variable.

3. Factor analysis

Factor analysis helps to reduce multiple variables into a lesser number of factors. It works upon the correlation between the observable variables. Besides, it helps to unearth hidden patterns and explore patterns that are not easy to measure. For example, factor analysis can determine happiness, customer loyalty, and customer satisfaction.

4. Cohort analysis

Cohort analysis involve breaking down datasets into relative groups (or cohorts) for detailed analysis. Generally, these cohorts share common characteristics at a point of time. Besides, cohort analysis helps to uncover valuable insights. Consequently, organizations can provide tailored services to specific customers. And the customers can gain from the targeted and personalized experience.

5. Cluster analysis

Cluster analysis is an exploratory method for determining structures within a dataset. The different data points are organized into clusters based on their characteristics. This method finds its application in data preprocessing. Furthermore, it also helps to determine the data distributions in each dataset. Generally, cluster analysis enables to understand the data and apply the knowledge for further analysis.

Some of the application areas of cluster analysis are machine learning, insurance companies, and geology firms. Insurance companies use cluster analysis to determine why some locations contribute to a high number of insurance claims. Geology experts apply cluster analysis to find the cities which are prone to a high risk of earthquakes.

6. Time series analytics

Time series analysis is a method to determine trends over a period. It enables to forecast economy trends, sales trends, and stock market trends.

7. Sentiment analysis

Sentiment analysis is necessary to understand how customers feel about organizations and their products. Furthermore, it helps to identify areas for improvement. Sentiment analysis belongs to the broader category of text analysis. It involves sorting (usually automated) and understanding textual data. Moreover, the aim is to interpret and classify the emotions conveyed within textual data. This allows businesses to ascertain how their customers feel about various aspects of their brand, product, or service.

Below are the three types of sentiment analysis models:

• Fine-grained sentiment analysis: This method helps to focus on opinion polarity (i.e., positive, neutral, or negative). For example, this method is used to interpret star ratings given by customers, on a scale ranging from very positive to very negative.

• Emotion detection: This method applies machine learning algorithms to choose the different emotions from the textual data. It uses emotion detection models to identify words associated with happiness, anger, frustration, and excitement, giving insight into how customers feel when they write a comment or a review.

• Aspect-based sentiment analysis: This type of analysis helps to identify what specific aspects the emotions or opinions relate to. If a customer writes that they “find the new Facebook ad so annoying”, then the model should detect not only a negative sentiment, but also the object towards which it is directed.