Product analysis involves examining product features, costs, availability, quality, appearance and other aspects. Product analysis is conducted by potential buyers, by product managers attempting to understand competitors and by third party reviewers.
Product analysis can also be used as part of product design to convert a high-level product description into project deliverables and requirements. It involves all facts of the product, its purpose, its operation, and its characteristics.
Analytics is the systematic computational analysis of data or statistics. 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.
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.
Objectives:
- Empowering others to make data-informed decisions through education and self-service analytics tools.
- Helping set and track goals that are achievable and measurable.
- Ensuring that Wikimedia products collect useful, high quality data without harming user privacy.
- Extracting insights through ad-hoc analyses and machine learning projects.
- Building dashboards and reports for tracking success and health metrics.
- Designing and analyzing experiments (A/B tests).
- Developing tools and software for working with data, in collaboration with Data Engineering and Product teams.
- Addressing data-related issues in collaboration with teams like Data Engineering, Security, and Legal.
Applications
People analytics
People analytics uses 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. Additionally, HR analytics has become a strategic tool in analyzing and forecasting Human related trends in the changing labor markets, using Career Analytics tools. The aim is to discern which employees to hire, which to reward or promote, what responsibilities to assign, and similar human resource problems.
Marketing optimization
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.
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 activities of the visitor. With this information, a marketer can improve marketing campaigns, website creative content, and information architecture.
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 an individual’s delinquency behavior and are 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 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.