What is Analytics?

Analytics is the discovery, interpretation, and communication of meaningful patterns in data.

Especially valuable in areas rich with recorded information, analytics relies on the simultaneous application of statisticscomputer 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 predictive analyticsprescriptive analyticsenterprise decision management, descriptive analytics, cognitive analytics, Big Data Analytics, retail analytics, store assortment and stock-keeping unit optimization, marketing optimization and marketing mix modelingweb analytics, call analytics, speech analytics, sales force sizing and optimization, price and promotion modeling, predictive science, credit risk analysis, and fraud analytics.

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.

Analytics vs. analysis

Analysis is focused on understanding the past; what happened. Analytics focuses on why it happened and what will happen next.

Data analytics is a multidisciplinary field. There is extensive use of computer skills, mathematics and statistics, the use of descriptive techniques and predictive models to gain valuable knowledge from data.

The insights from data are used to recommend action or to guide decision making rooted in business context.

Thus, analytics is not so much concerned with individual analyses or analysis steps, but with the entire methodology.

There is a pronounced tendency to use the term analytics in business settings e.g. text analytics vs. the more generic text mining to emphasize this broader perspective. 

There is an 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 Tree, Logistic Regression, linear to multiple regression analysis, Classification to do predictive modeling.

It also includes Unsupervised Machine learning techniques like cluster analysis, Principal Component Analysis, segmentation profile analysis and association analysis.

Application of analytics

Marketing optimization

Marketing has evolved from a creative process into a highly data-driven process. Marketing organizations use analytics to determine the outcomes of campaigns or efforts and to guide decisions for investment and consumer targeting.

Demographic studies, customer segmentation, conjoint analysis and other techniques allow marketers to use large amounts of consumer purchase, survey and panel data to understand and communicate marketing strategy.

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 IP address, and track 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 web sites 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.

People analytics

People Analytics is using behavioral data to understand how people work and change how companies are managed. People analytics is also known as workforce analytics, HR analytics, talent analytics, people insights, talent insights, colleague insights, human capital analytics, and HRIS analytics. 

HR analytics is the application of analytics to help companies manage human resources. The aim is to discern which employees to hire, which to reward or promote, what responsibilities to assign, and similar human resource problems. 

HR analytics is becoming increasingly important to understand what kind of behavioral profiles would succeed and fail. For example, an analysis may find that individuals that fit a certain type of profile are those most likely to succeed at a particular role, making them the best employees to hire.

However, there are key differences between people analytics and HR analytics.

“People Analytics solves business problems. HR Analytics solves HR problems. People Analytics looks at the work and its social organization. HR Analytics measures and integrates data about HR administrative processes,” says Ben Waber, MIT Media Lab Ph.D. and CEO of Humanyze. Josh Bersin, founder and principal at Bersin by Deloitte agrees that people analytics is a larger industry than HR Analytics, explaining, “… over time, I believe it doesn't even belong within HR.

While it may reside in HR to begin with, over time this team takes responsible for analysis of sales productivity, turnover, retention, accidents, fraud, and even the people-issues that drive customer retention and customer satisfaction… These are all real-world business problems, not HR problems.”

Portfolio analytics

A common application of business analytics is portfolio analysis. In this, a bank or lending agency has a collection of accounts of varying value and risk. The accounts may differ by the social status (wealthy, middle-class, poor, etc.) of the holder, the geographical location, its net value, and many other factors. The lender must balance the return on the loan with the risk of default for each loan.

The question is then how to evaluate the portfolio as a whole. The least risk loan may be to the very wealthy, but there are a very limited number of wealthy people. On the other hand, there are many poor that can be lent to, but at greater risk. Some balance must be struck that maximizes return and minimizes risk.

The analytics solution may combine time series analysis with many other issues in order to make decisions on when to lend money to these different borrower segments, or decisions on the interest rate charged to members of a portfolio segment to cover any losses among members in that segment.

Risk analytics

Predictive models in the banking industry are developed to bring certainty across the risk scores for individual customers. Credit scores are built to predict individual’s delinquency behavior and widely used to evaluate the credit worthiness of each applicant.

Furthermore, risk analyses are carried out in the scientific world and the insurance industry. It is also extensively used in financial institutions like Online Payment Gateway companies to analyse if a transaction was genuine or fraud.

For this purpose they use the transaction history of the customer. This is more commonly used in Credit Card purchase, when there is a sudden spike in the customer transaction volume the customer gets a call of confirmation if the transaction was initiated by him/her. This helps inreducing loss due to such circumstances.

Digital analytics

Digital analytics is a set of business and technical activities that define, create, collect, verify or transform digital data into reporting, research, analyses, recommendations, optimizations, predictions, and automations. 

This also includes the SEO (search engine optimization) where the keyword search is tracked and that data is used for marketing purposes. Even banner ads and clicks come under digital analytics.

A growing number of brands and marketing firms rely on digital analytics for their digital marketing assignments, where MROI (Marketing Return on Investment) is an important key performance indicator (KPI). Security analytics Security analytics refers to information technology (IT) to gather and analyze security events to understand and analyze events that pose the greatest risk. Products in this area include security information and event management and user behavior analytics.

Software analytics

Software analytics is the process of collecting information about the way a piece of software is used and produced.