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digital marketing and analytics > W3 L1 > Flashcards

W3 L1 Flashcards

1
Q

4 main pillars in online marketing

A

website placement, social media, search engine, mobie webapp

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2
Q

social media: 7 levels of Customer engageemnt

A

inactives, spectators, joiners, collectors, critics, conversationalists, creaatoes

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3
Q

What Drives Influence (Influence Model):

A

The probability that a person (I) choose potential influencer A over B will increase with the number other people choose A (social proof)

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4
Q

what are level 2 followers

A

followers of followers

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5
Q

2 main parameters to determine influencers

A

1 Number of K (2) (chain) level followers
2 Likelihood (activation) level of each (1+…+K) level follower to share the content

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6
Q

virality rate percentage berekenen

A

shares/impressions x 100

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7
Q

What Makes an Online Content Go Viral ? 4

A

Positive content High-Emotional Arousal, Positive emotions of amusement, excitement, inspiration, and warmt, Drama elements such as surprise, plot, and characters, including babies, animals, and celebrities arouse emotions.

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8
Q

Social Media Analytics: Integrating

A

Customer ID + Social ID

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9
Q

what is customer id

A

information about one customer, purchase history, adress etc

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10
Q

social id:

A

social media, activitty, emotion social identity

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11
Q

what delivers measurable marekting ROI

A

f ACTUAL SHARING OF CONTENT

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12
Q

buyers jounrey 5

A

awareness - considration - decision - adoption - advocacy

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13
Q

Share of Voice berekenen

A

of conversations mentioning your brand / total # of industry conversation

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14
Q

Social Sales Effectiveness berekenen

A

of sales coming from social channels / # total sales

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15
Q

conversation rate

A

of comments or replies / # of posts

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16
Q

amplification rate

A

of shares or retweets / # of posts

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17
Q

applause rate

A

of likes or favourits / # of posts

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18
Q

WHAT makes a Facebook (brand) post more POPULAR? what do we need to answer this question?

A

Remember we need some X (predictors) and outcome/performance (Y) variables to answer this question and to build our model.

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19
Q

FB page popularity: what are your x and y variables?

A

1 What do we want to improve/make better ? These are your outcome Y variables
2 What can we change to achieve this objective? These are your predictors X variables

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20
Q

“What kind of social media content drives people’s engagement on social media : In other words: What kind of content should we post ? “
what could be our x and y variable here?

A

Y = Dependent Variable
Model 1 Y = Number of Likes
Model 2 Y = Number of Comments
Two separate models for (i) likes and (ii) comments
X variables = predictors (excplanatory variables)

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21
Q

Structured Data

A

: We can add, sum, get averages and make mathematical statistical operations (number of likes, number of reshares etc)

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22
Q

Unstructured Data:

A

In tekst, photo or video format. Very common. Then how do we deal with it?

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23
Q

Unstructured Data – Definition:

A

Data unit in which the information offers a concurrent representation of its multifaceted nature without predefined organization or numeric values

24
Q

1 Non-Numeric:

A

Text, Photo or Video Data. To be converted into numeric data to be analyzed.

25
Q

2 Multifaceted

A

Possesses multiple facets, each offering unique information.
Example: A photo = brightness, mode, contrast, colors or a Voice: pitch, speech rate, intensity

26
Q

Concurrent Representation

A

Each facet represents a different phenomena at the same time.
Example: An online customer review has a positivity (valence) but also represents a topic (price, service, service etc)

27
Q

3 facts about unstrcted data

A

1 Non-Numeric: 2 Multifaceted: 3 Concurrent Representation

28
Q

Unstructured Data 1: Tekst Data Analysis 1 Classifiers

A

group or tag data into a defined category (by sentiment, emotion, topic, etc.)

29
Q

Unstructured Data 1: Tekst Data Analysis 2 Extractors

A

: retrieve pieces of information (like keywords, entities, phrases, numbers, etc.)

30
Q

Sentiment Analysis

A

s is a sub domains of text-analytics based on the classification of statements being +/- (positive or negative)

31
Q

Some measures to be used in visual (video and photo) analytics:

A

Resolution, Aspect Ratio, Hue (warm vs cool colors), Brightness, Saturation, Contrast, Smoothness

32
Q

clustering images dichotomies example

A

Example 1: Clustering of social media images based on different dimensions (dichotomies)

33
Q

How to cluster image 2

A

(1) Supervised and (2) Unsupervised Learning

34
Q

Supervised learning

A

model like ‘Teacher’ or ‘Supervisor’ who tells the machine the Label so that the machine knows which output from the given input.

35
Q

(Mere) presence of an image has a …… impact on …..

A

Mere) presence of an image has a positive impact on customer engagement on Twitter

36
Q

High-quality and professionally shot pictures lead to

A

s lead to higher engagement on Instagram and Twitter

37
Q

Presence of human face and image–text fit can lead to

A

ead to higher user engagement on Twitter but not on Instagram

38
Q

(1) Feature complexity:

A

i.e., unstructured pixel-level variation; color, luminance, and edges

39
Q

(2) Design complexity

A

i.e., structured design-level variation; number of objects, irregularity of object arrangement, and asymmetry of object arrangement

40
Q

results Li & Xie: is a piture worth a thousand words

A

Results*: (read the full paper for detailed results and managerial implications)
Inverted u-shape between feature complexity and consumer liking
Regular u-shape relationship between design complexity and consumer liking

41
Q

Sentiment Analysis

A

: the process of computationally identifying and categorizing opinions expressed in a piece of text, in order to determine whether the writer’s attitude towards a particular topic, product, etc. is positive, negative, or neutral.

42
Q

In sentiment analysis, social conversations are classified based 3

A

n positive, negative, neutral language

43
Q

2 types of scores sentinment anlysis textual data

A
  1. begative or positive 2. Each word is scored on the basis of its sentiment based on a Lexicon database: every word has a score
44
Q

Multi layered tecxtual seniment analys 3 different dimensions

A

1 Valence positivity-negativity
2 Subjectivity (Emotionally) to what extent the tekst is factual or subjective: emotion-opinion based
3 Polarity (Extremity) how strong the positivity in (1) is expressed

45
Q

Paralanguage

A

Aspects of speech that are not actual verbal prose,
gives contextual information that allows interactors to more
appropriately understand the message being conveyed

46
Q

3 types of paralanguage

A

1 Auditory Non-Verbal Communication:2 Visual Non-Verbal Communication:3 Tactile Non-Verbal Communication:

47
Q

1 Auditory Non-Verbal Communication:

A

information communicated by aspects of speech such as pitch,
rhythm, tempo, vocal qualities and vocalizations

48
Q

2 Visual Non-Verbal Communication:

A

conscious or unconscious bodily movements that possess communicative
value, including human gestures and body language.

49
Q

Tactile Non-Verbal Communication:

A

nonverbal communication related to physical, haptic interaction with
another individua

50
Q

Topic Models extracts and classifies

A

Topic Models extracts and classifies (clusters) the prominent TOPICS mentioned in a textual data (reviews, social media posts etc)

51
Q

Topic and Sentiment analysis: used to

A

used to manage the pleasure-pain points along CUSTOMER JOURNEYS

52
Q

Topic and Sentiment analysis:

A

The rate (%) of positive-negative comments in each stage of customer journey based on each TOPIC

53
Q

Sentiment & Topic Analysis Used Together
Brand Reputation Tracking Using Social Media

A

Positive-Negative words (by lexicons) are matched/associated with certain extracted topics (

54
Q

Facial Expressions are relatively easier to detect through Machine Learning/Deep Learning techniques
Research focuses on six basic categories of emotions

A

happiness, surprise, anger, sadness, fear, and disgust.

55
Q

Challenges in Visual Sentiment Analysis: 3

A

Visual semantic is hidden in images (not expressed with words – but facial expression <easy>, body language or ambience: to be detected)
Visual sentiment = Visual semantic, and there is no high level visual semantic dictionary like text-analysis lexicons
Visual sentiments from images, requires high level visual semantic ontology features instead of low-level visual features</easy>

digital marketing and analytics (9 decks)

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