(DSBA 6276) Chapter 1: Introduction to Marketing Analytics

(1.1-1.9 Objectives) 1. Define Marketing Analytics. 2. How to identify the right business problem? 3. Identify data sources. 4. Describe different data types. 5. Differences between predictors and target variables. 6. Supervised vs unsupervised modeling 7. 7 step marketing analytical process. 8. ethical considerations. 9. explain value of learning marketing analytics

Artificial Intelligence

_______ is a branch of computer science that is designed to mimic human-like intelligence for certain tasks, such as discovering patterns in data, recognizing objects from an image, understanding the meaning of text, and processing voice commands.

Binary

_______ categorical data can have only two values—for example, yes or no. This can be represented in different ways such as 1 or 0 or “True” and “False.” Binary data is commonly used for classification in predictive modeling.

Categorical Data

_______ exists when values represent a group of categories. Categorical variables can be one of three types: binary, nominal, or ordinal.

Continuous Data

_______ includes values with decimals: 1, 1.4, 3.75, . . .

Dependent, target, outcome variable

The variable being predicted is referred to as the dependent (target) variable (Y).The variable being predicted is referred to as the dependent (target) variable (Y).he dependent variable is the target variable (y). Its outcome is impacted by other variables.

Descriptive analytics

_______ are a set of techniques used to explain or quantify the past.

Discrete data

_______ is measured in whole numbers (integers): 1, 2, 3, . . .

Independent variable

_______ is the predictor or feature variable (x). This variable could potentially influence or drive the dependent or outcome variable(s).

Integer

An _______ is a whole number.

Interval

_______ data has an equal distance between data points and does not include an absolute zero.

Machine Learning

_______ is the development of algorithms and statistical models that allow computers to learn and improve from experience, without being explicitly programmed.

Marketing analytics

_______ uses data, statistics, mathematics, and technology to solve marketing business problems.

Nominal

_______ categorical data consist of characteristics that have no meaningful order.

Ordinal

_______ categorical data represent meaningful values with a natural order but the intervals between scale points may be uneven.

Predictive analytics

_______ is used to build models based on the past to explain the future.

Prescriptive analytics

_______ identifies the best optimal course of action or decision.

primary data

_______ is collected for a specific purpose. For example, companies conduct primary research with surveys, focus groups, interviews, observations, and experiments to address problems or answer distinct questions.

Ratio

_______ values can have an absolute zero point and can be discussed in terms of multiples when comparing one point to another.

Secondary data

_______ relies on existing data that has been collected for another purpose.

SMART principles

_______ are used as a goal-setting technique. The acronym stands for specific, measurable, attainable, relevant, and timely.

Structured data

_______ is made up of records that are organized in rows and columns and are easily searchable and analyzable using computer algorithms. This type of data can be stored in a database or spreadsheet format.

Supervised learning

In _______ , the target variable of interest is known and is available in a historical dataset.

Testing dataset

A _______ is used to evaluate the final selection algorithm on a dataset unique from the training and validation datasets.

Traning dataset

The _______ is the data used to build the algorithm and “learn” the relationship between the predictors and the target variable.

Unstructered data

_______ does not have a predefined structure and does not fit well into a table format (within rows and columns).

unsupervised learning

_______ has no previously defined target variable. The goal of unsupervised learning is to model the underlying structure and distribution in the data to discover and confirm patterns in the data.

validation dataset

The _______ is used to assess how well the algorithm estimates the target variable, and helps select the model that most accurately predicts the target value of interest.

variables

_______ are characteristics or features that pertain to a person, place, or object. Marketing analysts explore relationships between variables to improve decision making.

Cognitive analytics

_______ uses advanced analytics capabilities to draw conclusions and develop insights hidden in data without being explicitly programmed.