Audience and Consumer Insights Mid Term

One-way Audience 

The assembled spectators or listeners at an event such as a play, film, concert, TV, channel, platform and interact with the media that have certaon characteristics to define it

The different types of audiences depend on

variables both sociological and psychological of that audience like age, sex, socioeconomic level, individual habits, schedules, hobbies, signs of identity, social role, etc

Two-way Audience

a group of people, with certain characteristics + interests + motivations, attitudes that define it, who talks to you, consume and use a product or service

audience is

who you talk to as a medium or who talks to you, chooses you, consumes you, talks about you, etc

Audience measurement is useful to

decide the fate of the content programming based om the performance, understand who is my audience, the competitors, how to grow it, and understand them deeply, what they like, how to connect with them and what the impact of the content is

performance metrics

Why are audiences measured?

for accountability and knowledge

knowledge measurement 

to understand audiences deeply, what they like, how to connect with them and the impact of context

accountability measurement

to manage, to understand the audiences share, who we are competing with, what is the core audience, and how much it can grow

Insight

an accurate and deep understanding of someone or something, it gives purpose and identifies opportunities and tells us why people do what they do

conclusion

speaks of resolution, result and decisions based on data

finding 

the information that helps us identify a behavioral pattern

insights are not the same as

data, conclusions or findings

insights explain 

reasons for patterns and provide possible opportunities

observation

what people do

insights arise after 

in depth investigation of the hidden, deep, unconscious or unmentionable aspects of the consumer happens

Design Thinking

a methodology to develop innovation focused on people, researching to find the “right” answer

design thinking integrates

needs of people, possibilities of technology and requirements for business success

People do not buy the best products

they buy the products they can understand the faster

the true value of data

lies in the knowledge of people

third-party data

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

first-party data

information a company collects directly from its customers and owns, its part of the mosaic of data marketers have at their disposal

zero-party data

data that a customer intentionally and proactively shares with a company

Data principals

curiosity, empathy, observant, analytical and collaborative 

descriptive data

data that provides information about what has happened 

prescriptive data

data that provides recommendations on actions that should be taken to optimize the results to the highest degree

predictive data

data that provides information about what will happen 

Types of data

descriptive, predictive and prescriptive

3 stages of audience analysis

research design, outcome analysis and visualization and presentation

research design 

asking the right questions and defining the objectives

research design steps 

hypothesis, why, purpose, how, methodology, benchmarking and comparison, watch out for biases

outcome analysis

finding the right data by not forgetting the context, focusing on real problems, real people, staying informed, getting reals answers, reliable data and in depth data

visualization and presentation

keeping it simple, telling the story behind data, detailed personas and consumer journey maps, presenting data through visuals

samples must be

representative of a universe and statistically robust

Quantitative research

samples are representative, it focuses on the purpose of the study, measured in numbers, answers how many and how much, it shows cause and effect relationships, fixed questions, evidential research, has a directional and deductive approach to affect, to influence, to represent and it does not fully explore reasons why 

Quantitative research downside 

sampling bias 

Quantitative Collection Methods

can be face to face, non attendance, assisted or self administered: PAPI,CAPI,CAT and CAWI

PAPI

Paper assisted personal interviewing

CAPI

Computer Assisted personal interviewing

CATI

Computer Assisted telephones interviewing

CAWI

Computer aided web interviewing 

pros of computer assisted web interviewing

interviewer bias is avoided, gamification, multimedia, international studies and strong samples

cons of computer assisted web interviewing

no guidance from interviewer and coverage error due to digital gap

questions in surveys give details on

sociodemographic, socioeconomics, attitudes, behaviors, uses and satisfaction

the types of responses on surveys

single or multiple choice, closed, semi closed, open

surveys should use 

a common language for everyone, thats easily understood, clear and simple

things to avoid doing surveys 

suggestive, vague, ambiguous, compound, absolute and biased questions

in qualitative research the emphasis is on

depth, meaning and understanding

qualitative research is about 

opinions, behaviors, motivations, words and images, patterns, what? how? why?, inductive and exploratory approach, interpretative but its not representative 

quantitative research is about 

data, numbers, how many? how much?, evidential deductive approach, cause and effect, directional orientation, influence, determine but cannot fully explore reasons why

From a qualitative perspective is important

the potential of each case to meet our research objective and is based on selection 

when conducting qualitative research it is good practice to

be well documented, to know the context and prepare focus groups or interviews 

in qualitative research results are 

specific to the context, case study or individual and application t other contexts may be inferred by the researcher based on interpretation

qualitative research methods 

individual or group in depth interviews, face to face or online focus groups

focus group

small group of users discuss some topic under the guidance of a moderator

in depth interviewing 

involves conducting intense individual interviews with a small number of responders to explore their perspectives on a particular subject 

cross methodology

the sum of different methodological approaches to achieve a holistic vision and observe phenomena from different perspectives obtaining more valuable insights 

quantitative + qualitative

helps having dimension, structure, sizing and measure phenomena as well as understanding reasons why, motivation, feelings, etc

big data

quantitative information based in large data sets that may be analyzed computationally to reveal patterns, trends, associations, relating human behavior and interaction 

thick data

qualitative information that provides insights into the everyday emotional lives of consumers explaining why they have certain preferences, the reasons they behave the way they do, why trends stick, etc

big data relies on

machine learning 

big data reveals 

insights with a particular of quantified data points

big data isolates 

variables to identify patterns

big data loses 

resolution

thick data relies 

on human learning

thick data reveals

social context of connections between data points

thick data accepts 

irreducible complexity 

thick data loses 

scales

big data + thick data

provides a more complete context in any given situation with different types of knowledge at different scales and depths

thick data provides the human side and 

the necessary context to make sense of mass data 

linear (on) + digital (off) + cross media

people are consuming media through different mediums at all times so it needs to be measured in a standardized measurement to cover
advertising across all TV platforms – both linear and digital

sociogram

a tool for charting relationships within a group

A mathematical model is

an abstract representation of a real-world phenomenon, such as audience behavior, using equations, statistics, or algorithms

mathematical models in audience measurement allows to

estimate the size and profile of the audiences, predict future behavior, and correct biases in collected data

AI and machine learning have revolutionized audience measurement by enabling

analysis of large volumes of data, advanced predictive models, dynamic and personalized segmentation and cross media measurement