a) Descriptive analysis: What happened.
Descriptive analysis is the starting point of every analytical process, and seeks to answer the question “what happened?” and does so by ordering, manipulating and interpreting data from various sources to transform it into valuable insight for your business.
Performing descriptive analysis is essential because it allows us to present our data in a meaningful way. However, it is important to make it clear that this analysis will not allow you to predict future results or answer questions about why something happened, but it will leave your data organized to conduct future analyses.
b) Exploratory analysis – How to explore the data relationship.
As the name suggests, the main goal of exploratory analysis is to explore. Before this, russia phone number data there is no notion of the relationship between the data and the variables. Once the data has been investigated, exploratory analysis will allow you to find connections and generate hypotheses and solutions to specific problems. A typical area where exploratory analysis is applied is in data mining.
c) Diagnostic analysis – Why this happened.
One of the most powerful types of data analysis, diagnostic data analysis empowers analysts and executives by helping them gain a firm contextual understanding of why something happened. If you know why something happened and how it happened, you will be able to pinpoint solutions to address a problem or challenge.
Diagnostic analysis is done to provide direct and practical answers to specific questions.
d) Predictive analysis – What will happen.
Predictive analytics allows you to look into the future to answer the question: What will happen next? To do this, it uses the aforementioned descriptive, exploratory, and diagnostic analytics methods, in addition to machine learning (ML) and artificial intelligence (AI) . This way, you can predict future trends, potential problems or inefficiencies, connections, and coincidences in your data.
With predictive analytics, you can discover and develop initiatives that will not only improve your operational processes, but also give you an edge over your competition. If you understand why a trend, pattern, or event occurred, all through data, you will be able to develop an informed projection about how things will unfold in certain parts of the market.
e) Prescriptive analysis – How it will happen.
Another one of the most powerful data analysis methods. Data prediction techniques cross over from predictive analysis in a way that involves using patterns or trends to develop responsive and practical business strategies.
By delving into prescriptive analytics, you’ll play an active role in the data consumption process, taking well-organized, visual data sets and using them as a powerful solution to solve emerging problems in key areas of your business, including marketing, sales, customer experience, HR, service, finance, logistics analytics, and more.
Without further ado, below we have the 7 essential data analysis methods along with use cases in the business world.
Cluster analysis
The act of grouping a set of data elements in a way that makes them more similar (in a particular sense) to each other than to each other in other groups. Since there is no target variable in clustering, the method is used to find hidden patterns in the data. This approach is also used to provide additional context to a trend or data set.
Let’s look at it from a business perspective. In a perfect world, marketing analysts would be able to analyze each customer individually and provide them with the best personalized service possible, but let’s face it, with a large customer base, that would be impossible to do. That’s where clustering comes in. By grouping customers into clusters based on demographics, purchasing patterns, monetary value, or any other factors that may be relevant to your business, you’ll be able to immediately optimize your efforts and provide your customers with the best possible experience based on their needs.
Cohort Analysis
This type of analytics uses historical data to examine and compare a specific segment of user behaviors, which can then be grouped with others that have similar characteristics. Using this data analysis methodology, it is possible to gain a wealth of insights into customer needs or a firmer understanding of a broader target group.
Cohort analysis can be very useful for marketing analysis, as it helps you understand the impact of your campaigns on specific groups of customers. For example, imagine that you sent an email marketing campaign encouraging customers to sign up to your website. To do this, you would create two versions of the campaign with different designs, CTAs, and ad content. Later, you can use cohort analysis to track the performance of the campaign over a longer period of time and understand what types of content are driving your customers to sign up, repurchase, or interact with your website.
