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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
2 Multifaceted
Possesses multiple facets, each offering unique information. Example: A photo = brightness, mode, contrast, colors or a Voice: pitch, speech rate, intensity
26
Concurrent Representation
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
3 facts about unstrcted data
1 Non-Numeric: 2 Multifaceted: 3 Concurrent Representation
28
Unstructured Data 1: Tekst Data Analysis 1 Classifiers
group or tag data into a defined category (by sentiment, emotion, topic, etc.)
29
Unstructured Data 1: Tekst Data Analysis 2 Extractors
: retrieve pieces of information (like keywords, entities, phrases, numbers, etc.)
30
Sentiment Analysis
s is a sub domains of text-analytics based on the classification of statements being +/- (positive or negative)
31
Some measures to be used in visual (video and photo) analytics:
Resolution, Aspect Ratio, Hue (warm vs cool colors), Brightness, Saturation, Contrast, Smoothness
32
clustering images dichotomies example
Example 1: Clustering of social media images based on different dimensions (dichotomies)
33
How to cluster image 2
(1) Supervised and (2) Unsupervised Learning
34
Supervised learning
model like ‘Teacher’ or ‘Supervisor’ who tells the machine the Label so that the machine knows which output from the given input.
35
(Mere) presence of an image has a ...... impact on .....
Mere) presence of an image has a positive impact on customer engagement on Twitter
36
High-quality and professionally shot pictures lead to
s lead to higher engagement on Instagram and Twitter
37
Presence of human face and image–text fit can lead to
ead to higher user engagement on Twitter but not on Instagram
38
(1) Feature complexity:
i.e., unstructured pixel-level variation; color, luminance, and edges
39
(2) Design complexity
i.e., structured design-level variation; number of objects, irregularity of object arrangement, and asymmetry of object arrangement
40
results Li & Xie: is a piture worth a thousand words
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
Sentiment Analysis
: 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
In sentiment analysis, social conversations are classified based 3
n positive, negative, neutral language
43
2 types of scores sentinment anlysis textual data
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
Multi layered tecxtual seniment analys 3 different dimensions
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
Paralanguage
Aspects of speech that are not actual verbal prose, gives contextual information that allows interactors to more appropriately understand the message being conveyed
46
3 types of paralanguage
1 Auditory Non-Verbal Communication:2 Visual Non-Verbal Communication:3 Tactile Non-Verbal Communication:
47
1 Auditory Non-Verbal Communication:
information communicated by aspects of speech such as pitch, rhythm, tempo, vocal qualities and vocalizations
48
2 Visual Non-Verbal Communication:
conscious or unconscious bodily movements that possess communicative value, including human gestures and body language.
49
Tactile Non-Verbal Communication:
nonverbal communication related to physical, haptic interaction with another individua
50
Topic Models extracts and classifies
Topic Models extracts and classifies (clusters) the prominent TOPICS mentioned in a textual data (reviews, social media posts etc)
51
Topic and Sentiment analysis: used to
used to manage the pleasure-pain points along CUSTOMER JOURNEYS
52
Topic and Sentiment analysis:
The rate (%) of positive-negative comments in each stage of customer journey based on each TOPIC
53
Sentiment & Topic Analysis Used Together Brand Reputation Tracking Using Social Media
Positive-Negative words (by lexicons) are matched/associated with certain extracted topics (
54
Facial Expressions are relatively easier to detect through Machine Learning/Deep Learning techniques Research focuses on six basic categories of emotions
happiness, surprise, anger, sadness, fear, and disgust.
55
Challenges in Visual Sentiment Analysis: 3
Visual semantic is hidden in images (not expressed with words – but facial expression , 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

digital marketing and analytics (9 decks)

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