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Digital Analytics > Lesson 2: Variables and Data > Flashcards

Lesson 2: Variables and Data Flashcards

1
Q

Data that cannot be counted, measured, or easily expressed using numbers.

A

Qualitative

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2
Q

Information a company collects directly from its customers and owns.

A

First-party data

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3
Q

Is the process of cleaning, and transforming raw data prior to processing and analysis

A

data preparation

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4
Q

Is any information collected by an entity that does not have a direct relationship with the user the data is being collected on

A

third-party data

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5
Q

Indicates how reliable a given dataset is across key dimensions like completeness, consistency, accuracy, and more.

A

Data Quality

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6
Q

Information all about consumers, from their household, demographic and lifestyle details to their behaviors and buying

A

Consumers analytics data

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7
Q

A numerical type of data that includes whole, concrete numbers with specific and fixed data values determined by counting

A

Discrete data

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8
Q

Ensure your data is consistent within the same dataset and/or across multiple datasets.

A

Consistency

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9
Q

Data that can take any value

A

Continuous

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10
Q

Data that can be counted or measured in numerical values.

A

Quantitative

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11
Q

Characteristic of members of a population.

A

Variables

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12
Q

Observations of variable

A

Data

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13
Q

Contains variables and observations

A

Dataset

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14
Q

Numbers with known differences between variables, such as time.

A

Interval

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15
Q

Numbers that have measurable intervals where difference can be determined such as height or weight.

A

Ratio

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16
Q

Data used for naming variables, such as hair color.

A

Nominal

17
Q

Data used to describe the order of values, such as 1= happy, 2= neutral, 3 = unhappy.

A

Ordinal

18
Q

Ensure data is close to the true values

A

Accuracy

19
Q

If it measures what it is supposed to measure.

A

Validity

20
Q

The degree to which all required data is known

A

Completeness

21
Q

The degree to which the data is specified using the same unit of measure.

A

Uniformity

22
Q

Is the process of fixing or removing incorrect, corrupted, incorrectly formatted, duplicate, or incomplete data within a dataset

A

Data Cleansing

23
Q

A script is a series of analytics commands that are executed sequentially and used to automate work within analytics.

A

Scripting

24
Q

To extract data from a source, convert it into a usable format, and deliver it to a destination. This entire process is known as ELT (Extract, Load Transform)

A

Data Transformation

25
Q

A central repository of information that can be analyzed to make more informed decisions.

A

Data warehousing

26
Q

Data Life Cycle

A

Create - Store - Use - Share - Archive - Destroy

27
Q

Data shared by another organization about its customers ( or its first-party data.)

A

Second - party data

28
Q

DATA COLLECTION METHODS USED IN ANALYTICS

A

• Surveys
• Transactional Tracking (POS System)
• Interview and Focus groups
• Observation
• Online Tracking (Internet Cookies)
• Forms

29
Q

CONSUMERS ANALYTICS DATA

A

Descriptive
Behavioral
Interactive
Attitudinal

30
Q

Six Effective Ways to use your first-party data: Segment your audience and create engaging customized emails that are relevant to that group.

A

Emails

31
Q

Six Effective Ways to use your first-party data: A/B test using customer data on your landing pages to see which message resonates amongst a particular audience.

A

Landing pages

32
Q

Six Effective Ways to use your first-party data:
Target distinct personas through paid advertising across channels

A

Paid ads

33
Q

Six Effective Ways to use your first-party data:
Create tailored content in a variety of formats (e.g. blogs, videos,
ebooks).

A

Social media

34
Q

Six Effective Ways to use your first-party data:
Offer early-bird access to sales, discounts or
free content (e.g. a relevant eBook)

A

Loyalty programs or membership

35
Q

Six Effective Ways to use your first-party data:
If you’re a B2B marketer, use this data to create relevant and personalized content

A

Account-based marketing

36
Q

Data Types: Pass/Fail

A

Binary

Digital Analytics (7 decks)

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