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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
GOOGLE ANALYTICS: 2
Good enough for most small/medium companies. Aggregated data (limitation if you want to do advanced analysis)
26
6 things wwe know by using clickstream data
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?
27
what data dou get with onine CRM &complementary data
Mostly Backoffice data (online) of the company encompasses historical-retrospective data on customer relational history:
28
2 examples of historical-retrospective data on customer relational history
Purchase/relation history with firm (# number of purchases, customer service contacts) * Campaign history, Feedback / Support queries, demographic information, address, location
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Digital data can also include 2
e (i) business outcomes and (ii) customer attitudes/emotions
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how can you make emotiona and attitudes observable? 2
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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first party data
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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second party data
first party data that can be purchased with antother brand.
33
third party data
collected by data collection vendors from disparate data sources and sold to brands to use in campaigns etc
34
why is first praty data valuable
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.
35
Some examples of First Party Data
*Google Analytics *Customer surveys, feedback, interviews, etc. *Website *Email
36
Some examples of Second Party Data:
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
37
Here are some examples of Second Party Data:
* Oracle Data Cloud * SalesForce Marketing Cloud * Acxiom * Google
38
ZERO-PARTY DATA
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
zero party data examples
communication preferences, interests and hobbies, purchase intents
40
Marketing Metrics:
: 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
The most important digital marketing metrics are referred to as
key performance indicators (KPIs).
42
bottom of funnel 4
bofu: prospects, sales, loyalty, customer advocates
43
middle of funnel 3
mofu: bounces, readers, leads
44
top of funnel 2
seo/smm/ppc, click-througs,
45
prospects
respond to email call to actions
46
bounces
percentage which immediately leaves
47
click throughs
visitors, page views,
48
seo/smm/ppc
reach, ad impressions, keyword searches
49
5 customer journey stages
awareness, consideratiom, purchase, retentio, advocacy
50
Impression
: % content is displayed once on a web page (the customer is exposed to an online content on a website)
51
In-View Rate (%
% customer who actually SEE the online content he is exposed to
52
in view rate in verhouding tot impressions
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
berekening viewability rate
total measured viewable ad impressions / total mwasured ad impressions (x100%)
54
total measured ad impressions
number of time a ad was loaded on a page
55
total measured viewable ad impressions
number of times an ad wasloaded on a page which was 50 percent on screen for one second or longer
56
CTR: good measure?
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
how many impressions generate a click?
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
bereknening CTR
total measured clicks/total measured ad impressions (x100)
59
Bounce Rate:
The (%) percentage of visitors to a particular website who navigate away from the site after viewing only one page
60
Bounce = Not per-se a bad thing: May mean
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
berekening bounce rate
total one page visits/ total entrance visits
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exit rate:
visito enters site, than to a page than leaves, last page determins exit rate
63
berekening exit rate
total exits from page/total visits to page
64
CLICK THROUGH RATE
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
CONVERSION RATE
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
A Conversion Funnel refers to
different and multiple stages in a buyer’s journey leading up to a purchase
67
uitleg attribution model
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
Attribution is quantifying
quantifying the VALUE of marketing ACTIVITIES with regard to DESIRED OUTCOME.
69
Three main types of atttibution techniques
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
approach of three main types of attributin techniqus
single touch: first/last click rules based: even weights, custom weigts, time decay, position based statistaclly driven: regression or porbalisitc model
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multi touchpoint: particiaption
all touchpoints get all credit
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multi touchpoint: lineair
equal credit
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Rule Based Techniques: Time Decay Model
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
multi touchpoint: latency
identifies lag time between touchpoint and conversion
75
multi touchpoint: SPC starter-player-coser
tp get credit based upon custom wights for each position
76
multi touchpoint: custom
user-specified custom model
77
multi touchpoint: agorithmic
statitics determine how much credit each touchpoint should get
78
what attributin technique is theindustry standard
last click, lot of critic
79
Simplistic Techniques: Last Non-Direct Click
100% of the conversion success is attributed to the last-non direct click before the conversion. Used as default in Google Analytics
80
Simplistic Techniques: First Click Model, when can this be useful?
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
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
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
Rule Based Techniques: Time Decay Model`drawback and used
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
Rule Based Techniques: Position Based Model
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
Rule Based Techniques: Position Based Model drawback
biased heavily toward the first- and last-click channels, arbitrary – assumption-based distribution of credits
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Rule Based Techniques: Calculating Attribution Value
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
Model Based & Data Driven Attribution Models 3 commonly used
Logistic Regression, Probabilistic Models, Game Theoretic Models (Shapley Value)
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Logistic Regression
Each observation is a click path that ends with a conversion or not
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Logistic Regression pros 3
Easy to use and interpret Insights on channel effects Additional explanatory variables can easily be added (i.e. time on site)
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Logistic Regression CONS:1
Does not account for touch-point order, example: Effect of total # AdWords clicks √ Effect of AdWords at begin or end of path X
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Probabilistic Models
What IF we add/drop certain touchpoints to a journey? What is the impact of adding NU.NL as an additional advertising outlet ?
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probabilistic Models FEW RULES TO KNOW :3
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
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Game Theoretic Attribution: Shapley Value (Google)
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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