Market Intelligence & Marketing Glossary
A-B | C-E | F-H | I-L | M-R | S-U | V-Z
This glossary for terms related to marketing and market intelligence wants to address most common terms that are explained here to better understand the market intelligence function. Please feel free to add on to this list by using our journal.
C-E
Commodity (Product): product consumed routinely and therefore bought often. Products with little differentiation / normally sold on the basis of price.
Communication Plan: a written plan describing the messages a company wishes to portray about itself or its offerings.
Competition: generally viewed by a business as those firms that market products similar to, or substitutable for, its products in the same target market.
Competitive Advantage: having a clear advantage over the competition in terms of one or more elements of the marketing mix that is valued by potential customers.
Competitive Analysis: the study of systematically gathered information about competitors’ products, research and development, production methods and costs, organizational designs, financial status, marketing strategies, and their strengths and weaknesses. See more : Competitor Analysis
Competitive Intelligence: systematically gathered information about competitors’ products, research and development, production methods and costs, organizational designs, financial status, marketing strategies, and other strengths and weaknesses.
Competitive Intelligence also identifies competitive trends, provides early warning on threats and opportunities in the evolving competitive landscape and evaluates their impact.
Competitive Position: the organization’s strategic position in a market
compared with its competitors in the relevant market: leader, challenger, follower or niche.
Competitive Strategy: the way in which a company chooses to compete within a market, with particular regard to the relative positioning and strategies of its competitors.
Complementary Product: products / services which are manufactured / sold / bought / used together.
Compound Annual Growth Rate: is the average annual percentage growth rate for a series of n observations. The formula for determining the CAGR % is as follows: ((last value/first value)^(1/n-1))-1.
Conjoint Analysis: family of statistical techniques that quantifies customer
observations about the value of offering elements, the value of alternative levels of offering elements, and trade-offs that customers might make between offering elements (including price) and levels.
Contribution Pricing: price calculation on the basis of the contribution margin.
Corporate Marketing: centralized unit in an (international) company that sets (more or less strict) rules / guidelines for marketing activities performed by the subsequent marketing units (subsidiaries etc.).
Corporate Mission: basic reason for existence of a firm. Describes its scope and its dominant emphasis and basic values.
Corporate Objectives: overall objectives of the organization that influence
the direction of marketing strategy.
Corporate Relations: implementation of communication and public relations techniques to influence the (positive) attitudes towards a company with consumers, the (financial) community, stockholders, competitors etc. (stakeholders).
Corporate Strategy: strategy that determines the means for utilizing resources in the areas of production, finance, research and development, personnel, and marketing to reach the organization’s goals.
Cost Orientation: the point of view that increasing the efficiency of production is the primary means of increasing an organization’s profits. There is little consideration of customers’ needs and desires.
Cost Plus Pricing: a form of cost-oriented pricing in which first the seller’s
costs are determined and then a specified dollar amount or percentage of the cost is added to the seller’s cost to set the final price. Normally used in monopolistic situation with low elasticity of demand.
Customer Decision Making: the multistage decision process consumer’s use in making purchases.
Customer Orientation: attempt to focus the company’s strategy strictly on the needs of the buyers in the target market.
Customer Relationship Management: The idea of establishing relationships with customers on an individual basis, then using that information to treat different customers differently. Customer buying profiles and churn analysis are examples of decision support activities that can affect the success of customer relationships.
Customer Satisfaction: degree to which the customer’s expectations of a
market offering and / or the actual performance of the company are met.
Customer: (1) the ultimate user of a product or service, as opposed to the
purchaser. (2) more generally, a person or company that uses or buys goods and services, as distinct from the producer and distributor.
Dashboard: A dashboard is a reporting tool that consolidates, aggregates and arrranges measurements, metrics (measurements compared to a goal) and sometimes scorecards on a single screen so information can be monitored at a glance. Dashboards differ from scorecards in being tailored to monitor a specific role or generate metrics reflecting a particular point of view; typically they do not conform to a specific management methodology.
Data: Items representing facts, text, graphics, bit-mapped images, sound, analog or digital live-video segments. Data is the raw material of a system supplied by data producers and is used by information consumers to create information.
Database: a centrally-held collection of data that allows access and manipulation by one or more users.
Data Access Tools: An end-user oriented tool that allows users to build SQL queries by pointing and clicking on a list of tables and fields in the data warehouse.
Data Acquisition: Identification, selection and mapping of source data to target data. Detection of source data changes, data extraction techniques, timing of data extracts, data transformation techniques, frequency of database loads and levels of data summary are among the difficult data acquisition challenges.
Data Analysis and Presentation Tools: Software that provides a logical view of data in a warehouse. Some create simple aliases for table and column names; others create data that identify the contents and location of data in the warehouse.
Data-Based Knowledge: Knowledge derived from data through the use of Business Intelligence Tools and the process of Data Warehousing. Most of our knowledge is based on a combination of our experience, perception, and intuition. Business Intelligence and Data Warehousing give us a new kind of knowledge based on data.
Data-based knowledge can have several advantages over experience/intuition-based knowledge:
1. It can be more accurate because it is based on so many detailed facts.
2. It can be more current because the data warehousing and business intelligence tools can so quickly analyze new data.
3. It can be more comprehensive because so many different perspectives are available through the rapid recombination of elements from different dimensions and different levels of the data hierarchy.
4. It can give new insights because there are complex patterns in the data that can be discovered by data mining that would never be detected by human analysis.
5. It can be less subjective because conclusions are tied directly to the physical data.
Data, Information, and Knowledge: Data is the reality that a computer records, stores, and processes. The use of computers can be referred to as data processing. At the lowest level data has no significance for people. This lowest level in the perception of reality is sometimes referred to as “raw data”.
Information is what a person is able to understand about reality.
Information systems use computers to organize data in such a way that people can understand the results.
Knowledge is what a business uses to make decisions.
The process of organizing information in such a way as to create data-based knowledge is called Data Warehousing. The software products that present this knowledge to users are sometimes called Business Intelligence Tools.
The goal of business intelligence and data warehousing - changing data into information and knowledge.
Organizations are gathering and storing more and more data. Every year the amount of data in the world is approximately doubling. This data is of little benefit unless it can be turned into useful information and knowledge.
Information by itself is an inadequate basis for business decisions because the amount of information, like the amount of data, is overwhelming. Business Intelligence Tools are designed to find what is significant - what really adds to our useful knowledge - in the piles of data and information.
Data Management: Controlling, protecting, and facilitating access to data in order to provide information consumers with timely access to the data they need. The functions provided by a database management system.
Data Mining: A technique using software tools geared for the user who typically does not know exactly what he’s searching for, but is looking for particular patterns or trends. Data mining is the process of sifting through large amounts of data to produce data content relationships. It can predict future trends and behaviors, allowing businesses to make proactive, knowledge-driven decisions. This is also known as data surfing.
Data Mart. Also Known As: Local Data Warehouse or Datamart: A database that has the same characteristics as a data warehouse, but is usually smaller and is focused on the data for one division or one workgroup within an enterprise.
There are three different (and somewhat contradictory) views of the place of the data mart in the world of data warehousing.
1. The data warehouse gathers all the information from the various legacy systems. Specialized data marts are then created with a subset of the information in the data warehouse. These data marts are easier to use because they only have the particular information the specific user group needs. The use of several data marts also allows the querying load to be spread among several different computers. This can reduce network traffic.
2. Free-standing data marts are created, independent from a data warehouse. The information for the data mart probably comes from just one legacy system. It is quicker and cheaper to build a separate data mart instead of building an enterprise-wide data warehouse with data marts derived from it. The drawback of this solution is that the company’s data is not integrated (and thereby violates one of Bill Inmon’s original defining characteristics of the data warehouse). If several separate data marts are built using this strategy, they will usually contain data that is duplicated and inconsistent.
3. The data mart is the prototype or the first step of a data warehousing process. An enterprise picks the division or group that would most benefit from data-based knowledge. A data mart is built with that group’s data. Additional types of information are added to the data mart as time goes on until it is turned into a data warehouse.
New terminology is often created and developed for marketing purposes. The term ‘data mart’ probably has a marketing advantage over the term ‘data warehouse’. The whole data warehousing process is about creating data-based knowledge and bringing that knowledge to people. A warehouse is a place where things are stored away. A mart is a convenient place to buy something. Most data warehousing professionals (including myself) include ready access to information as a defining characteristic of the term ‘data warehouse’. I think, though, that the term ‘data mart’ captures this sense of data availability more effectively.
Data Mining: The process of finding hidden patterns and relationships in the data. Analyzing data involves the recognition of significant patterns. Human analysts can see patterns in small data sets. Specialized data mining tools are able to find patterns in large amounts of data. These tools are also able to analyze significant relationships that exist only when several dimensions are viewed at the same time.
Users can ask data questions using standard queries when they know what they’re looking for. Queries can be written for questions like this: “Which of our out-of-town customers have given us the most business in the last year?”
Data mining is needed when the user’s questions are more vague or general in nature. Data mining questions would include: “What attributes characterize the customers that gave us the most business in the past year?”
Data Transformation: The modification of data as it is moved into the data warehouse. This modification can include:
- Data Cleansing - Part of the Process of Data Quality Assurance
Dimensionalization - Organizing the data into the multidimensional (OLAP) structure of a star schema. - Normalization - Organizing the data into the normal structure of a relational database
- Processing Calculations
- Changing Data Types
- Making the Data More Readable
- Replacing Codes with Actual Values
- Summarizing the Data by Various Time Periods - See Aggregations
-
Summarizing the Data in Other Ways - See Aggregations
Data Warehouse. Also Known As: Datawarehouse or Information Warehouse: A database where data is collected for the purpose of being analyzed. The defining characteristic of a data warehouse is its purpose.
Most data is collected to handle a company’s on-going business. This type of data can be called “operational data”. The systems used to collect operational data are referred to as OLTP (On-Line Transaction Processing).
A data warehouse collects, organizes, and makes data available for the purpose of analysis - to give management the ability to access and analyze information about its business. This type of data can be called “informational data”. The systems used to work with informational data are referred to as OLAP (On-Line Analytical Processing).
Bill Inmon coined the term “data warehouse” in 1990. His definition is:
“A (data) warehouse is a subject-oriented, integrated, time-variant and non-volatile collection of data in support of management’s decision making process.”
Subject-oriented - Data that gives information about a particular subject instead of about a company’s on-going operations.
Integrated - Data that is gathered into the data warehouse from a variety of sources and merged into a coherent whole.
Time-variant - All data in the data warehouse is identified with a particular time period.
Non-volatile - Data is stable in a data warehouse. More data is added, but data is never removed. This enables management to gain a consistent picture of the business.
Data Warehousing: The process of visioning, planning, building, using, managing, maintaining, and enhancing data warehouses and/or data marts.
Whether we’re building a data warehouse, a data mart, or both, we are taking part in a complex, on-going process. The emphasis in the data-based knowledge business needs to be kept on the process. That’s why you’re reading a glossary of “data warehousing terminology” instead of a glossary of “data warehouse terminology”.
There are many steps in the data warehousing process:
- Visioning - Having an idea about what could be accomplished.
- Learning - Studying the potential of data warehousing.
- Justifying - Developing a business purpose for the process.
- Budgeting - Counting the cost.
- Deciding - Making a commitment to develop and use data-based knowledge.
- Gathering Information - Examining legacy systems.
- Interviewing Users - Finding what information is needed.
- Choosing Tools - Choosing the hardware, the database management system, the data extraction tools, and the Business Intelligence tools..
- Building, Using, Testing, and Evaluating the Prototype - Repeat this step and the above steps as necessary.
- Deploying - Putting the system into operation.
- Training - Helping users make full use of the Business Intelligence tools.
- Managing - Keeping track of scheduled data replication, system usage, and query performance.
- Adding, Modifying, On-Going Development - As the system is used, new possibilities will be discovered.
Data Replication: Periodic copying of legacy data.
Data Transformation: Transforming the legacy data into the form in which it will be stored in the data warehouse.
Data Quality Assurance: Testing the data for inconsistencies and errors.
Data Storage: Storing the data in a DBMS (Database Management System).
Metadata Storage: Storing the description of the data - the data about the data.
Data Mart Population: Populating all the data marts that receive their data from the warehouse.
Setting Up Business Intelligence Tools: Giving users access to the data through multidimensional analysis, querying, and data mining.
Setting Alerts: Establishing conditions that result in an automatic message being sent.
Data Warehousing Management: Keeping track of how well all the other actions are being carried out.
Demand Curve: a line showing the relationship between price and quantity
demanded.
Demographics: demographic variable, e.g. sex, age, nationality, income, life cycle state, marital status and social grade.
Depression: stage of the business cycle during which there is extremely high unemployment, wages are very low, total disposable income is at a minimum, and consumers lack confidence in the economy.
Derived Demand: characteristic of industrial demand that arises because
industrial demand derives from the consumer demand.
Desk Research: is the systematic examination of all available secondary data in the context of a particular marketing research problem.
Differential Advantage: see Competitive Advantage.
Diffusion of Innovation: process by which a new idea / product / service is spread within a market, over time and among various adopter categories (innovators / early adopters / early majority / late majority / laggards).
Divisional Organization: form of organization that “divides” the company into two or more business units (divisions). A division is normally a profit center and the division manager is responsible for achieving the profit goals.
Domestic Market: the “home” market for a company.
Early Adopters: customers who are willing to buy a new product quite soon after launch.
Early Majority: individuals who adopt a new product just prior to the average person; they are deliberate and cautious in trying new products.
Economic Shopper: a type of customer whose (buying) behavior is seen as being totally rationally led. She / he will rationally decide between alternatives based only on economic values.
Economies of Scale: savings derived from producing a large number of units. More specifically: Decline in per unit product costs as the absolute volume of production per period increases.
Elasticity of Demand: the relative responsiveness of changes in quantity
demanded to changes in price.
End User: person or organization that consumes a good or service. The end user is the final target for all economic activities.
Environmental Analysis: process of seeking information about events and
relationships in a Company’s environment to assist marketers in identifying opportunities and threats in planning.
Event Marketing: part of promotional activities in which the target group (the stakeholders of a company) are attracted by unusual “events”.
Experience Curve Analysis: investigation into the question of how the
components of the total product costs of a company are affected by the cumulative experience.
Experience Curve Pricing: a pricing approach in which a company fixes a low price that high-cost competitors cannot match and thus expands its market share.
Exploratory Research: the preliminary exploration of a research area prior to the main data collection stage, in order to develop hypotheses and / or better understand a problem.
A-B | C-E | F-H | I-L | M-R | S-U | V-Z
Also see the most comprehensive business intelligence glossary by Vernon Prior. Market & competitive intelligence thesaurus and glossary. Terms and terminology used in business intelligence.