OBU Media Analytics Exam 1

Data Definition

Sets of symbols or artifacts that can be analyzed, discussed, and used to plan

Data Types

quantitative and qualitative

Quantitative

Can determine relationships between two variables and frequency of observations. These are mostly numerical in nature and can provide analysts the ability to draw generalizable inferences.

Qualitative

Explore new topics and the overall human experience.

Examines unique occurrences

Can influence quantitative research

Little "t" truth

Little "t" truth

estimation or lived experience in reality

Deriving meaning

context, measurement, and intention/goals

context

difference of content

measurement

sample sizes and ranking can be different with "success"

intention/goals

what is the overall goal? one company will have a different meaning of success than another

Data collection - 3 types

Self Reported

Digital Exhaust

Profiling Data

Self reported Data

Information people "volunteer" - emails, personal histories, age, gender, etc

Digital exhaust data

Information generated during media use -location/browsing histories

Profiling Data

interest and behavior information compiled to create personal profiles

Data use

· Improving a product/service

· Improving targeted marketing

· Generating revenue by selling data

Frameworks for ethical use

Data ownership and control

Stakeholder education

Transparency

Equitable Returns

Data ownership and control

How can we encourage better data control for consumers

Stakeholder education

- People that are using the products and devices. How are they involved?

- What could transparency look like in the workplace?

Transparency

- Giving people the ability to know what's going on

- How may education help constituents?

Equitable returns

- If I give you something, what am I getting in return?

- How will returns need to change as people give more data?

social media definitions

· Internet-based channels that allow users to interact with others and present themselves on their terms.

· Spaces where interaction can occur synchronously and asynchronously to both narrow and broad audience groups

· Spaces where value is derived from user-generated content and the interactions with others and their perceptions about those interactions

Data use (social media)

· Optimize social media content and strategy

· Optimize web content and strategy

· Monitor brand image

· Develop robust user profiles

Social listening

· Monitoring online conversations on a chosen platform

· Exists within and outside curated circles

· Provides insight into consumer sentiment, consumer perceptions of brand, and competitive insights

uses

· Brand health and reputation management

· Identifying pain points

· Competitive and industry analysis

· Event monitoring

segmentation definition

Breaking down large, heterogenous markets into submarkets (segments) that are more homogenous

Positioning definition

Representations of service that holds a distinct and valued place in users' mind

internal goal influences

- Product/service category- Content strategy

- Organizational aspirations

- Current situation

External goal influences

Platform

Audiences

Data measurement scales and variables

- categorical

- quantitative

- nominal

- ordinal

- interval

- ratio

categorical data

separation into a limited number of groups with distinct qualities

quantitative data

Scales/data that can quantify and be used in calculations

nominal data

- categorical scales that act like labels

- distinguish between different individual objects or groups

ordinal data

- Describe order in a series

- Include notion of less, more and equal

- Can't tell us the whole story

interval data

- Scales that preserve equality of intervals between successive levels of the scale

- The "0" on an interval scale is arbitrary

- Opinions

ratio data

- Scales that have an absolute zero

- Provides an easier way to understand growth and loss

5 forms of data analysis

- descriptive

- inferential

- diagnostic

- predictive

- prescriptive

descriptive data analysis

what happened in the past?

inferential data analysis

what about the rest? is this sample biased?

diagnostic data analysis

what is going on the surface?

predictive data analysis

what is likely to happen next?

prescriptive data analysis

what should we do about it?

basics or reporting

- goal

- organization

- strategy

- transparency

web analytics definition

The process of monitoring and making sense of the behavior of users on a website

web importance

· Primary platform for online shopping

· Vital for search engine visibility

· Establish credibility

· Websites help determine other digital campaign efficacy

web analytics data types

behavioral and traffic sources

behavioral data type

- Pageviews

- Entrances

- Bounce rates

- Events

- Conversions

- Search terms

traffic sources data type

- direct

- referral

- organic

- paid

- cross network

why do we track

- To understand user information and trends

- To provide better services online

- To aggregate and sell data

what can be tracked

- IP addresses

- browser and device info

- web-interactions

how to be more privacy-minded

- collecting/using less data

- preventing unwanted user identification

- pooling data when possible

ethical frameworks

- privacy

- transparency and consumer control

- education

- accountability

what number of web users want personalized content?

2/3

what number of web users and uncomfortable sharing?

1/2

transparency

- Being transparent doesn't have to equate to losing customers

- Customers want personalized content but are hesitant to share data

- User agency encourages greater data sharing

- More info can lead to more comfort

- More choices can also provide nuance for collector

- Transparency can increase trust and long-term loyalty

client education

- what data is necessary

- what regulations are allowed

- what their audience currently thinks about data sharing

consumer education

- when and where data is collected

- how to prevent/control collection

- how and why to share mindfully

regulatory accountability

- is your company following the rules?

- what rules apply?

internal accountability

- are data collection conversations happening?

- who can currently access your data?

- how is data being used in decision making? for what?