---
title: "Analytics"
chunk: 1/3
source: "https://en.wikipedia.org/wiki/Analytics"
category: "reference"
tags: "science, encyclopedia"
date_saved: "2026-05-05T03:56:13.255306+00:00"
instance: "kb-cron"
---

Analytics is the systematic computational analysis of data or statistics. It is used for the discovery, interpretation, and communication of meaningful patterns in data, which also falls under and directly relates to the umbrella term, data science. Analytics also entails applying data patterns toward effective decision-making. It can be valuable in areas rich with recorded information; analytics relies on the simultaneous application of statistics, computer programming, and operations research to quantify performance.
Organizations may apply analytics to business data to describe, predict, and improve business performance. Specifically, areas within analytics include descriptive analytics, diagnostic analytics, predictive analytics, prescriptive analytics, and cognitive analytics. Analytics may apply to a variety of fields such as marketing, management, finance, online systems, information security, and software services. Since analytics can require extensive computation (see big data), the algorithms and software used for analytics harness the most current methods in computer science, statistics, and mathematics. According to International Data Corporation, global spending on big data and business analytics (BDA) solutions is estimated to reach $215.7 billion in 2021. As per Gartner, the overall analytic platforms software market grew by $25.5 billion in 2020.  

== Analytics vs analysis ==
Data analysis focuses on the process of examining past data through business understanding, data understanding, data preparation, modeling and evaluation, and deployment. It is a subset of data analytics, which takes multiple data analysis processes to focus on why an event happened and what may happen in the future based on the previous data. Data analytics is used to formulate larger organizational decisions. 
Data analytics is a multidisciplinary field. There is extensive use of computer skills, mathematics, statistics, descriptive techniques, and predictive models to gain valuable knowledge from data through analytics. There is increasing use of the term advanced analytics, typically used to describe the technical aspects of analytics, especially in the emerging fields such as the use of machine learning techniques like neural networks, decision trees, logistic regression, linear to multiple regression analysis, and classification to do predictive modeling. It also includes unsupervised machine learning techniques like cluster analysis, principal component analysis, segmentation profile analysis, and association analysis.

== Applications ==

=== Marketing optimization ===
Marketing organizations use analytics to evaluate the outcomes of campaigns and initiatives, as well as to guide decisions related to investment and consumer targeting. Techniques such as demographic studies, customer segmentation, conjoint analysis, and others enable marketers to analyze large volumes of consumer purchase data, survey data, and panel data. This helps them better understand consumer behavior and effectively communicate marketing strategies.
Marketing analytics consists of both qualitative and quantitative, structured and unstructured data used to drive strategic decisions about brand and revenue outcomes. The process involves predictive modelling, marketing experimentation, automation, and real-time sales communications. The data enables companies to make predictions and alter strategic execution to maximize performance results.
Web analytics allows marketers to collect session-level information about interactions on a website using an operation called sessionization. Google Analytics is an example of a popular free analytics tool that marketers use for this purpose. Those interactions provide web analytics information systems with the information necessary to track the referrer, search keywords, identify the IP address, and track the activities of the visitor. With this information, a marketer can improve marketing campaigns, website creative content, and information architecture.
Analysis techniques frequently used in marketing include marketing mix modeling, pricing and promotion analyses, sales force optimization, and customer analytics, e.g., segmentation. Web analytics and optimization of websites and online campaigns now frequently work hand in hand with the more traditional marketing analysis techniques. A focus on digital media has slightly changed the vocabulary so that marketing mix modeling is commonly referred to as attribution modeling in the digital or marketing mix modeling context.
These tools and techniques support both strategic marketing decisions (such as how much overall to spend on marketing, how to allocate budgets across a portfolio of brands and the marketing mix) and more tactical campaign support, in terms of targeting the best potential customer with the optimal message in the most cost-effective medium at the ideal time.