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Business intelligence consists of strategies and technologies used by enterprises for the data analysis and management of business information. Common functions of business intelligence technologies include reporting, online analytical processing, analytics, dashboard development, data mining, process mining, complex event processing, business performance management, benchmarking, text mining, predictive analytics, and prescriptive analytics.
Retail is the sale of goods and services to consumers, in contrast to wholesaling, which is sale to business or institutional customers. A retailer purchases goods in large quantities from manufacturers, directly or through a wholesaler, and then sells in smaller quantities to consumers for a profit. Retailers are the final link in the supply chain from producers to consumers.
Marketing research is the systematic gathering, recording, and analysis of qualitative and quantitative data about issues relating to marketing products and services. The goal is to identify and assess how changing elements of the marketing mix impacts customer behavior.
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.
Database marketing is a form of direct marketing that uses databases of customers or potential customers to generate personalized communications in order to promote a product or service for marketing purposes. The method of communication can be any addressable medium, as in direct marketing.
Demand management is a planning methodology used to forecast, plan for and manage the demand for products and services. This can be at macro-levels as in economics and at micro-levels within individual organizations. For example, at macro-levels, a government may influence interest rates to regulate financial demand. At the micro-level, a cellular service provider may provide free night and weekend use to reduce demand during peak hours.
Predictive analytics is a form of business analytics applying machine learning to generate a predictive model for certain business applications. As such, it encompasses a variety of statistical techniques from predictive modeling and machine learning that analyze current and historical facts to make predictions about future or otherwise unknown events. It represents a major subset of machine learning applications; in some contexts, it is synonymous with machine learning.
The target audience is the intended audience or readership of a publication, advertisement, or other message catered specifically to the previously intended audience. In marketing and advertising, the target audience is a particular group of consumer within the predetermined target market, identified as the targets or recipients for a particular advertisement or message.
Revenue management is the application of disciplined analytics that predict consumer behaviour at the micro-market levels and optimize product availability, leveraging price elasticity to maximize revenue growth and thereby, profit. The primary aim of revenue management is selling the right product to the right customer at the right time for the right price and with the right pack. The essence of this discipline is in understanding customers' perception of product value and accurately aligning product prices, placement and availability with each customer segment.
Business analytics (BA) refers to the skills, technologies, and practices for iterative exploration and investigation of past business performance to gain insight and drive business planning. Business analytics focuses on developing new insights and understanding of business performance based on data and statistical methods. In contrast, business intelligence traditionally focuses on using a consistent set of metrics to both measure past performance and guide business planning. In other words, business intelligence focusses on description, while business analytics focusses on prediction and prescription.
Angoss Software Corporation, headquartered in Toronto, Ontario, Canada, with offices in the United States and UK, acquired by Datawatch and now owned by Altair, was a provider of predictive analytics systems through software licensing and services. Angoss' customers represent industries including finance, insurance, mutual funds, retail, health sciences, telecom and technology. The company was founded in 1984, and publicly traded on the TSX Venture Exchange from 2008-2013 under the ticker symbol ANC.
Fashion merchandising can be defined as the planning and promotion of sales by presenting a product to the right market at the proper time, by carrying out organized, skillful advertising, using attractive displays, etc. Merchandising, within fashion retail, refers specifically to the stock planning, management, and control process. Fashion Merchandising is a job that is done world- wide. This position requires well-developed quantitative skills, and natural ability to discover trends, meaning relationships and interrelationships among standard sales and stock figures. In the fashion industry, there are two different merchandising teams: the visual merchandising team, and the fashion merchandising team.
Sales operations is a set of business activities and processes that help a sales organization run effectively, efficiently and in support of business strategies and objectives. Sales operations may also be referred to as sales, sales support, or business operations.
Trade Promotion Management (TPM) is a software application that assist companies in managing their trade promotion activity.
Fashion forecasting began in France during the reign of Louis XIV. It started as a way of communicating about fashion and slowly transformed into a way to become ahead of the times in the fashion industry. Fashion forecasting predicts the moods of society and consumers, along with their behavior and buying habits and bases what they may release in the coming future off of the forecast. Fashion trends tend to repeat themselves every 20 years, and fashion forecasting predicts what other trends might begin with the rotation of fashion as well. Fashion forecasting can be used for many different reasons, the main reason being staying on top of current trends and knowing what your consumer is going to want in the future. This method helps fashion brands know what to expect and what to begin producing ahead of time. Top name brands and high end companies such as Vogue and Gucci even use this method to help their designers become even more informed on what is to come in the fashion industry.
The fields of marketing and artificial intelligence converge in systems which assist in areas such as market forecasting, and automation of processes and decision making, along with increased efficiency of tasks which would usually be performed by humans. The science behind these systems can be explained through neural networks and expert systems, computer programs that process input and provide valuable output for marketers.
Behavioral analytics is a recent advancement in business analytics that reveals new insights into the behavior of consumers on eCommerce platforms, online games, web and mobile applications, and Internet of Things (IoT). The rapid increase in the volume of raw event data generated by the digital world enables methods that go beyond demographics and other traditional metrics that tell us what kind of people took what actions in the past. Behavioral analysis focuses on understanding how consumers act and why, enabling predictions about how they are likely to act in the future. It enables marketers to make the right offers to consumer segments at the right time.
Mobile location analytics (MLA) is a type of customer intelligence and refers to technology for retailers, including developing aggregate reports used to reduce waiting times at checkouts, improving store layouts, and understanding consumer shopping patterns. The reports are generated by recognizing the Wi-Fi or Bluetooth addresses of cell phones as they interact with store networks.
Psychographic segmentation has been used in marketing research as a form of market segmentation which divides consumers into sub-groups based on shared psychological characteristics, including subconscious or conscious beliefs, motivations, and priorities to explain and predict consumer behavior. Developed in the 1970s, it applies behavioral and social sciences to explore to understand consumers’ decision-making processes, consumer attitudes, values, personalities, lifestyles, and communication preferences. It complements demographic and socioeconomic segmentation, and enables marketers to target audiences with messaging to market brands, products or services. Some consider lifestyle segmentation to be interchangeable with psychographic segmentation, marketing experts argue that lifestyle relates specifically to overt behaviors while psychographics relate to consumers' cognitive style, which is based on their "patterns of thinking, feeling and perceiving".
Data-driven marketing is a process used by marketers to gain insights and identify trends about consumers and how they behave — what they buy, the effectiveness of ads, and how they browse. Modern solutions rely on big data strategies and collect information about consumer interactions and engagements to generate predictions about future behaviors. This kind of analysis involves understanding the data that is already present, the data that can be acquired, and how to organize, analyze, and apply that data to better marketing efforts. The intended goal is generally to enhance and personalize the customer experience. The market research allows for a comprehensive study of preferences.
References
- ↑ Kioumarsi et al., 2009
- ↑ "The futre of retail supply chains". www.mckinsey.com. Retrieved 22 November 2018.
- ↑ Bain & Co.[ clarification needed ]
- ↑ Levy, Barocas, Karen, Solon (2018). "Refractive Surveillance: Monitoring Customers to Manage Workers". International Journal of Communication. 12: 2–10.
{{cite journal}}: CS1 maint: multiple names: authors list (link) - ↑ Rygielski, Chris; Wang, Jyun-Cheng; Yen, David C. (2002-11-01). "Data mining techniques for customer relationship management". Technology in Society. 24 (4): 483–502. doi:10.1016/S0160-791X(02)00038-6. ISSN 0160-791X. S2CID 16056151.