Analytics
Analytics is the systematic computational analysis of data or statistics.[1] It is used for the discovery, interpretation, and communication of meaningful patterns in data. It 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.
For other uses, see Analytics (disambiguation).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.[2] 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.[3] According to International Data Corporation, global spending on big data and business analytics (BDA) solutions is estimated to reach $215.7 billion in 2021.[4][5] As per Gartner, the overall analytic platforms software market grew by $25.5 billion in 2020.[6]
Applications[edit]
Marketing optimization[edit]
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.[10]
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.[10]
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.[11] Those interactions provide web analytics information systems with the information necessary to track the referrer, search keywords, identify the IP address,[12] and track the activities of the visitor. With this information, a marketer can improve marketing campaigns, website creative content, and information architecture.[13]
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.
People analytics[edit]
People analytics uses behavioral data to understand how people work and change how companies are managed.[14]
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.[15] Additionally, HR analytics has become a strategic tool in analyzing and forecasting Human related trends in the changing labor markets, using Career Analytics tools.[16] The aim is to discern which employees to hire, which to reward or promote, what responsibilities to assign, and similar human resource problems.[17]
For example, inspection of the strategic phenomenon of employee turnover utilizing People Analytics Tools may serve as an important analysis at times of disruption.
[18]
It has been suggested that People Analytics is a separate discipline to HR analytics, representing a greater focus on business issues rather than administrative processes,[19] and that People Analytics may not really belong within Human Resources in organizations.[20] However, experts disagree on this, with many arguing that Human Resources will need to develop People Analytics as a key part of a more capable and strategic business function in the changing world of work brought on by automation.[21] Instead of moving People Analytics outside HR, some experts argue that it belongs in HR, albeit enabled by a new breed of HR professional who is more data-driven and business savvy.[22]
Portfolio analytics[edit]
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.[23]
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[edit]
Predictive models in the banking industry are developed to bring certainty across the risk scores for individual customers. Credit scores are built to predict an individual's delinquency behavior and are widely used to evaluate the credit worthiness of each applicant.[24] Furthermore, risk analyses are carried out in the scientific world[25] and the insurance industry.[26] It is also extensively used in financial institutions like online payment gateway companies to analyse if a transaction was genuine or fraud.[27] For this purpose, they use the transaction history of the customer. This is more commonly used in Credit Card purchases, 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 in reducing loss due to such circumstances.[28]
Digital analytics[edit]
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 automation.[29] This also includes the SEO (search engine optimization) where the keyword search is tracked and that data is used for marketing purposes.[30] Even banner ads and clicks come under digital analytics.[31] 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[edit]
Security analytics refers to information technology (IT) to gather security events to understand and analyze events that pose the greatest security risks.[32][33] Products in this area include security information and event management and user behavior analytics.
