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digital marketing and analytics > W1L1 > Flashcards

W1L1 Flashcards

1
Q

digital landscape is expaning, what does this mean?

A

Online data getting more and more complex and challenging now
But the data is also getting rich and diverse to have a better understanding and then, predict and manage customers’ behavior.

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2
Q

what makes todays digital platforms substantially different than traditional offline marketing 2

A

Personalization (Individualisation) and UCG

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3
Q

goals of digital marketing can be achieved through … by using …

A

designing and managing seamless customer journeys on digital platforms – by using
right digital touchpoints synchronized with offline platforms & touchpoints.

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4
Q

what is the most widely used classification in marketing and advertising

A

paid owned earned touchpoints

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5
Q

Owned Media

A

. your web site, your app, your social media platforms – so where you can post your own information for ‘free

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6
Q

Earned Media

A

is the most difficult, yet a valuable type of media. Word of mouth, conversations, comments, likes and shares

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7
Q

Paid Media

A

: where you pay including > advertising, search engine advertising and promoted social media content.

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8
Q

Why do we need paid owned earned classification

A

Hundreds of online (and offline touchpoints) – hard to make managerial decisions for each touchpoint

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9
Q

Extension on Paid-Owned-Earned framework:

A

Category Media

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10
Q

Category Media 2uitleggen

A

: relates to the category that do not mention the advertiser’s brand neither published by the focal brand.
Captures (i) competitors’ own, paid, and earned media and (ii) independent publications related to the product category

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11
Q

Category Media: Motivation:

A

: Control Level on Earned (Individuals) and Category (publications, competitors) different. Control: Earned > Category

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12
Q

Structured Data: +example

A

Mostly Quantitative (numeric). Examples: Sales in Euro, Click/No Click (0-1), Session time, Number of Likes etc.

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13
Q

Unstructured Data:

A

Mostly Qualitative: Textual or Visual Data: Photos/Videos, Examples: Instagram Videos, Online Revies, Social Media Posts

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14
Q

Recent projections indicate that unstructured data is over …% of all marketing dat

A

80

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15
Q

3 sources of data

A

On-Site Data: Clickstream/Session Data ) Online CRM data >

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16
Q

) Online CRM data >

A

customer’ online history with the firm (i.e: purchases, demographics, etc.)

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17
Q

) On-Site Data:

A

what you see on website: available and visible to anyone. extracted through: data/web scraping

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18
Q

Clickstream/Session Data

A

traffic on website-app: what, when and how people do online – not visible to visitor (i.e: Google Analytics)

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19
Q

On-Site Data - Web Scraping

A

This is what you see on the website (product lists, comments, reviews etc) – visible to all viewers & visitors

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20
Q

Clickstream-Session Data: what data do you get?

A

This is NOT a data on what is visible on the website, but what customer do/ what they click on the website !

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21
Q

COOKIE

A

Tiny text file in your web browser storing information about your browsing experience (e.g. login information, user preferences,
shopping cart) > SOURCES: Google Analytics, Double Click etc.

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22
Q

Clickstream-Session Data: Individual Level Data

A

recording each visitors’ individiual behavior with a time-stamp. i.e what each customer does step by step.

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23
Q

Clickstream-Session Data: Agrregate Data:

A

total numbers. not focusing on individual level behavior instead > total number of visitors etc.

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24
Q

Clickstream-Session Data: most common tool

A

google analytics

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25
Q

GOOGLE ANALYTICS: 2

A

Good enough for most small/medium companies.
Aggregated data (limitation if you want to do advanced analysis)

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26
Q

6 things wwe know by using clickstream data

A

1 How are websites found by the customers ?
2 What is the content used by visitors ?
3 How often do users come back to website?
4 How many new users are there?
5 What do we know about the users?
6 What devices are used to visit the websites?

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27
Q

what data dou get with onine CRM &complementary data

A

Mostly Backoffice data (online) of the company encompasses historical-retrospective data on customer relational history:

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28
Q

2 examples of historical-retrospective data on customer relational history

A

Purchase/relation history with firm (# number of purchases, customer service contacts) * Campaign history, Feedback / Support queries, demographic information, address, location

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29
Q

Digital data can also include 2

A

e (i) business outcomes and (ii) customer attitudes/emotions

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30
Q

how can you make emotiona and attitudes observable? 2

A

1 Integrating survey (online marketing research) data with click-web data and business outcomes
2 Interpreting/ Drawing conclusions on possible emotional outcomes as a result of online behavior: Example: analyzing EMOJIS on social media

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31
Q

first party data

A

owned by brand, collected by brand directly form users Data is obtained from reputable, trustworthy third-party data sources and customize it for a specific target

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32
Q

second party data

A

first party data that can be purchased with antother brand.

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33
Q

third party data

A

collected by data collection vendors from disparate data sources and sold to brands to use in campaigns etc

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34
Q

why is first praty data valuable

A

Significantly valuable because you get your information firsthand from your
consumers, eliminating any misinterpretations and errors. It is, by far, the
most effective and reliable form of data collection.

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35
Q

Some examples of First Party Data

A

*Google Analytics
*Customer surveys, feedback, interviews, etc. *Website
*Email

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36
Q

Some examples of Second Party Data:

A

Google Big-Query (360) Attribution Data (through partnership)* Facebook campaign specific data (through partnership) * Data media publishers sell to advertisers (through partnership)* Supermarket selling its customer loyalty data to a credit card company (through partnership

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37
Q

Here are some examples of Second Party Data:

A
  • Oracle Data Cloud
  • SalesForce Marketing Cloud
  • Acxiom
  • Google
38
Q

ZERO-PARTY DATA

A

Can be confusing because it is the same as first-party data in many ways. Difference: “data that a customer intentionally and proactively shares with a brand, which can include preference center data,
purchase intentions, personal context, and how the individual wants the brand to recognize her.”

39
Q

zero party data examples

A

communication preferences, interests and hobbies, purchase intents

40
Q

Marketing Metrics:

A

: Tools helping companies quantify, compare, and interpret their own performance from marketing activities (Kotler & Keller, 2007). In other words: Measuring the impact of marketing activities

41
Q

The most important digital marketing metrics are referred to as

A

key performance indicators (KPIs).

42
Q

bottom of funnel 4

A

bofu: prospects, sales, loyalty, customer advocates

43
Q

middle of funnel 3

A

mofu: bounces, readers, leads

44
Q

top of funnel 2

A

seo/smm/ppc, click-througs,

45
Q

prospects

A

respond to email call to actions

46
Q

bounces

A

percentage which immediately leaves

47
Q

click throughs

A

visitors, page views,

48
Q

seo/smm/ppc

A

reach, ad impressions, keyword searches

49
Q

5 customer journey stages

A

awareness, consideratiom, purchase, retentio, advocacy

50
Q

Impression

A

: % content is displayed once on a web page (the customer is exposed to an online content on a website)

51
Q

In-View Rate (%

A

% customer who actually SEE the online content he is exposed to

52
Q

in view rate in verhouding tot impressions

A

Mostly below 50% of the impressions - people do not scroll down the page, or slips their attention (out of sight) or the viewer is not a actually human user

53
Q

berekening viewability rate

A

total measured viewable ad impressions / total mwasured ad impressions (x100%)

54
Q

total measured ad impressions

A

number of time a ad was loaded on a page

55
Q

total measured viewable ad impressions

A

number of times an ad wasloaded on a page which was 50 percent on screen for one second or longer

56
Q

CTR: good measure?

A

Simple, fast and easy to measure. BUT not a good indicator marketing effectiveness (Fulgoni, 2016) Mostly used if we want to measure
awareness, engagement or we are not able to measure conversions.

57
Q

how many impressions generate a click?

A

Early days of online advertising (nineties): 1-2% - sometimes went up to 3% for successful campaigns. TODAY: went down to mostly below 0,1-0,2
%. Thus: Less than 1 in 1000 impression can generate a click !

58
Q

bereknening CTR

A

total measured clicks/total measured ad impressions (x100)

59
Q

Bounce Rate:

A

The (%) percentage of visitors to a particular website who navigate away from the site after viewing only one page

60
Q

Bounce = Not per-se a bad thing: May mean

A

May mean your page is not engaging – But can also mean your customers are able to find quickly
what they are looking for while searching for a product or information.

61
Q

berekening bounce rate

A

total one page visits/ total entrance visits

62
Q

exit rate:

A

visito enters site, than to a page than leaves, last page determins exit rate

63
Q

berekening exit rate

A

total exits from page/total visits to page

64
Q

CLICK THROUGH RATE

A

number of clicks that your ad receives divided by the number of times your ad is shown: Clicks ÷ Impressions = CTR.
For example, if you had 5 clicks and 1000 impressions, your CTR would be 0.5%.

65
Q

CONVERSION RATE

A

number of conversions divided by the number of total clicks (tracked) to a conversion during the same time period.
For example, if you had 50 conversions from 1,000 clicks: your conversion rate would be 5%, since 50 ÷ 1,000 = 5%.

66
Q

A Conversion Funnel refers to

A

different and multiple stages in a buyer’s journey leading up to a purchase

67
Q

uitleg attribution model

A

Assume we have 3 ads: 2% of the people that click on our CNN ad, 1.5% on our Facebook ad and 2.2% on our Google ad convert
What about people that saw all 3? We’re counting the same conversion multiple times
In other words: For those who saw/click all these three ads – which ad/ads matter more – have the real influence on CONVERSION
How do we ATTRIBUTE the conversion over the 3 different channels?

68
Q

Attribution is quantifying

A

quantifying the VALUE of marketing ACTIVITIES with regard to DESIRED OUTCOME.

69
Q

Three main types of atttibution techniques

A

1 simplistic: single touch assins 100% of the credit to last or first exposure
2: rule based: assigns credit to each interaction based on speific business rules
3: statically driven: assigns credti to each interaction based on a data driven model

70
Q

approach of three main types of attributin techniqus

A

single touch: first/last click
rules based: even weights, custom weigts, time decay, position based
statistaclly driven: regression or porbalisitc model

71
Q

multi touchpoint: particiaption

A

all touchpoints get all credit

72
Q

multi touchpoint: lineair

A

equal credit

73
Q

Rule Based Techniques: Time Decay Model

A

Time-decay model adjusts credit so that the closer an impression is to a conversion, the more credit it receives
ASSUMPTION on Memory Decay effect > More recent experiences = more influential

74
Q

multi touchpoint: latency

A

identifies lag time between touchpoint and conversion

75
Q

multi touchpoint: SPC starter-player-coser

A

tp get credit based upon custom wights for each position

76
Q

multi touchpoint: custom

A

user-specified custom model

77
Q

multi touchpoint: agorithmic

A

statitics determine how much credit each touchpoint should get

78
Q

what attributin technique is theindustry standard

A

last click, lot of critic

79
Q

Simplistic Techniques: Last Non-Direct Click

A

100% of the conversion success is attributed to the last-non direct click before the conversion. Used as default in Google Analytics

80
Q

Simplistic Techniques: First Click Model, when can this be useful?

A

when the firm wants to focus on the first touch points with their brand, or if a firm especially focuses on building
awareness on brands or product, highly value starting new-journeys.

81
Q

when the firm wants to focus on the first touch points with their brand, or if a firm especially focuses on building
awareness on brands or product, highly value starting new-journeys. drawback and when used

A

DRAWBACK: Because the shares of each conversion are divided equally among all channels. Model’s assumption that each interaction in the
customer journey has equal influence on the user’s purchase decision thus is NOT LOGICAL.
When you plan to buy something do are clicks equally important/ remarkable for you? Probably NOT
USED: (Over)simplifies the attribution – when a fast/simple model needed taking all touchpoints in consideration

82
Q

Rule Based Techniques: Time Decay Model`drawback and used

A

touchpoints that close the deal benefit more from this attribution strategy, because the value is weighted progressively higher for
channels nearest to the last impression. However a previous/ less recent experience moment could well be the main driver behind the conversion
Mostly Used: for short-lived deals or promotional offers

83
Q

Rule Based Techniques: Position Based Model

A

Places more importance on the FIRST AND LAST TOUCH POINTS.
Assumes: First impression is important since it attracts the user’s attention, and last one because of the role it plays in concluding the transaction

84
Q

Rule Based Techniques: Position Based Model drawback

A

biased heavily toward the first- and last-click channels, arbitrary – assumption-based distribution of credits

85
Q

Rule Based Techniques: Calculating Attribution Value

A

there are 7 customer journeys, 5 of them with converson, only look at the 5: STEP 2: You count (take a look at the share of each touchpoint) according to your technique.
Example: Last Click Technique > B is the most influential touchpoint (3/5) = %60 Attribution Value

86
Q

Model Based & Data Driven Attribution Models 3 commonly used

A

Logistic Regression, Probabilistic Models, Game Theoretic Models (Shapley Value)

87
Q

Logistic Regression

A

Each observation is a click path that ends with a conversion or not

88
Q

Logistic Regression pros 3

A

Easy to use and interpret
Insights on channel effects
Additional explanatory variables can easily be added (i.e. time on site)

89
Q

Logistic Regression CONS:1

A

Does not account for touch-point order, example:
Effect of total # AdWords clicks √
Effect of AdWords at begin or end of path X

90
Q

Probabilistic Models

A

What IF we add/drop certain touchpoints to a journey? What is the impact of adding NU.NL as an additional advertising outlet ?

91
Q

probabilistic Models FEW RULES TO KNOW :3

A

1 poition of otuchoint matters, we use that in calculation 2gives us a more precise/accurate picture on the real effect of each touchpoint
3 Then we know the value of each touchpoint depending on its position in a particular journey

92
Q

Game Theoretic Attribution: Shapley Value (Google)

A

Algorithm based on a concept from cooperative game theory called the Shapley Value. developped as an approach to fairly distributing the output of a team
among the constituent team members. Digital Marketing: Team Players = Each touchpoint (media-mix element) in a customer journey

digital marketing and analytics (9 decks)

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