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Martech Ecosystem > Lecture 7 > Flashcards

Lecture 7 Flashcards

1
Q

Paid search, also referred to as search advertising, search engine marketing (SEM), or pay-per-click (PPC), is the process of

A

advertising on search engines such as Google, Bing, and Yahoo!

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2
Q

Search marketers use paid search in conjunction with SEO practices and software to form

A

a comprehensive search marketing strategy

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3
Q

Why is Search Advertising Important?

A

Allows advertisers to place their product in front of people who are already looking for it

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4
Q

Search Advertising is important because…

Targeted ad based on people’s searches reduces the

A

audience to those already seeking out a product or service

• 3.5 billion searches performed per day on Google

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5
Q

SEO is about

A

optimizing your website to get better search result rankings (organic listings)

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6
Q

SEM is

A

a form of marketing focused on increasing a website’s visibility in search engine result pages through optimization and advertising

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7
Q

How are Search Ads Priced?

A

Auction style cost per click model, not based on impressions

Bid on keywords (similar to eBay auctions)

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8
Q

Ranking and CPC depends on variety of factors:

A 
 Competition
 Bid Amount
 Landing Page Relevance
 Ad Relevance
 Quality Score
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9
Q

Quality Score

A

CTR - Are users clicking on ad at an efficient rate?

Landing Page - Does the destination URL identify with the ad copy?

Historical Perform

Ad Relevancy - Does the ad copy relate to the keyword query?

Keyword Relevancy

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10
Q

Quality Score =

A

The Ad Rank of the person below you / Your Quality Score + $0.01

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11
Q

Search Advertising Trends

A

Mobile Prominence

Personalized PPC

Keyword Importance Waning

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12
Q

Mobile Prominence:

A

In May 2015, Google reported that more searches are performed on mobile devices than PCs in the US

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13
Q

Personalized PPC:

A

ads will continue to become more data driven, resulting in an increase in remarketing campaigns

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14
Q

Keyword Importance Waning:

A

Experts predict that keywords will be knocked off as top priority in PPC strategies with the adoption of product listing ads (PLAs) and search engines’ growing focus on semantic search

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15
Q

Search Advertising Software

A

Help businesses advertise on search engines
such as Google, Bing, and Yahoo!

Typically used by marketers to identify,
target, and bid on relevant keywords,
gaining more prominent positioning in SERP
for engaged users

Leveraged to create and optimize ads, as
well as track conversion and return on ad
spend (ROAS)

It can include 1st party (Google, Microsoft, and
Yahoo!) and 3rd party platforms (to manage
campaigns across multiple engines)

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16
Q

Customer Modeling

A

Process of predicting and forecasting behavioral aspects of customers’ future perspectives

Includes identification of marketing and campaigning targets and optimizing predictive analysis

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17
Q

Framework for Model Developing

A

Describe the case and the define the goal. Ex: Who will subscribe?

Select all available data types (internal and external) and remove points that are obsolete, redundant, incomplete

Select variables with predictive power and transform them if needed to avoid overfitting

Run the model and validate the data

Implement the model or select different data

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18
Q

Types of Customer Modeling

A
  1. Reactive
  2. Proactive
  3. Recommendation Engine
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19
Q

Reactive Modeling

A

Bayesian

Artificial Neural Network

Business Rules

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20
Q

Proactive Modeling

A

Logistic

Time to Event

Bayesian

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21
Q

Recommendation Engine Modeling

A

Content Based

Collaborative

Hybrid Systems

22
Q

Bayesian:

A

based on Bayes’ theorem which provides a
mathematical framework for forming inferences through probability. Allows for decision making and market research evaluation under uncertainty

23
Q

ANN:

A

AI representations inspired by the way in which the human brain functions. Interconnected elements (nodes) process simultaneously the information, adapting and learning from past example

24
Q

Time to Event:

A

recognizes the importance of time and the tenure/position in the customer life cycle. The outcome of interest is not only whether or not an event occurred but also when

25
Q

Logistic:

A

statistical model that uses a logistic function to

model a binary dependent variable

26
Q

Content Based Filtering

A

Process of recommending content based on its characteristics

Used in a number of applications, including information retrieval (i.e. search engines) as well as recommender systems (i.e. Netflix, Pandora)

27
Q

Content Based Filtering Advantages

A
  • Highly relevant results
  • Transparent recommendation
  • Users can get started more quickly
  • New items can be recommended immediately
  • Easier to implement
28
Q

Content Based Filtering Challenges

A
  • Lack of novelty and diversity
  • Scalability
  • Attributes may be incorrectly or inconsistently applied
29
Q

Collaborative Filtering

A
  • Based on the idea that people who share an interest in certain things will probably have similar tastes in other things as well
  • Method of making automatic predictions (filtering) about the interests of a user by collecting preferences or taste information from many users (collaborating)
30
Q

Collaborative Filtering Advantages

A
  • Benefits from large user base
  • Flexible across domains
  • More diverse recommendations
  • It can capture more nuance around items
31
Q

Collaborative Filtering Challenges

A
  • Complexity and expense
  • Data sparsity
  • “Cold” start issue
32
Q

Content vs Collaborative Filtering

A

Content Filtering: Recommends products based on similar products

Collaborative Filtering: Recommends products based on similar customers

33
Q

Hybrid Systems

A

Combination of collaborative and content-based filtering that can provide more accurate recommendations

34
Q

Hybrid Systems can be implemented in different ways:

A
  1. Making content-based and collaborative-based predictions separately and then combining them
  2. Adding content-based capabilities to a collaborative-based approach (and vice versa)
  3. Unifying the approaches into one model
35
Q

Netflix uses a

A

hybrid system with recos based on
comparing the watching habits of similar users
(collaborative filtering) as well as by offering movies that share characteristics with films that a user has rated highly (content-based filtering)

36
Q

Chanel-Product Level Modeling

A

Which channel should I spend my next marketing dollar?
How effective is a promotional event?
How do I balance between product and brand spending?

Solution:
Marketing Mix Modeling
Algorithmic Cross Channel
Attribution
Integrated Marketing
Attribution Modeling
37
Q

What is Attribution?

A
  • Practice of allocating partial value to different touchpoints within the customer journey which have influenced a desired outcome
  • “Give credit where credit is due”
38
Q

Marketing Channel Attribution Modeling

A

Last-Touch Attribution Model

Multi-Touch Attribution Model

39
Q

Last-Touch Attribution Model

A

Attributes entire value of a conversion to the last ad clicked or seen

Miscalculates ROI by assuming the consumer doesn’t see or click any other ads along the way

40
Q

Multi-Touch Attribution Model

A

Attributes value to observed touch point along the path to conversion

Provides a more realistic assessment of ROI by capturing a holistic picture of the customer journey across channels

41
Q

Marketing Channel Attribution Modeling

A

Same Touch

Linear

U-Shaped

J-Shaped

Time Decay

42
Q

Same Touch:

A

100% of the credit goes to the hit where the conversion

occurred

43
Q

Linear:

A

each touchpoint in the conversion path (Natural Search, Paid Search, Social and Email) shares equal credit (25% each) for the sale

44
Q

U-Shaped:

A

40% of the credit goes to both the first (Natural Search) and last interaction (Email), the remaining 20% is divided among any interaction in between (Paid Search and Social)

45
Q

J-Shaped:

A

60% of the credit to the first interaction (Natural Search), 20% to the last interaction (Email), the remaining 20% is divided among any interaction in between (Paid Search and Social)

46
Q

Time Decay:

A

follows an exponential decay with a custom half-life parameter

47
Q

Multi-touch Attribution Limitations

A

Custom MTA models can still arbitrarily distribute fixed credit to each touchpoint

Event though they are considered superior to other rule-based models they are still limited when trying to build a complete measurement and performance picture

48
Q

Multi-touch Attribution–What is the Right Tool?

A

Algorithmic Weighting

Sophisticated Approach

Integrated Attribution

49
Q

Algorithmic Weighting:

A

use machine-learning and sophisticated algos to calculate probability-based weightings

50
Q

Sophisticated Approach:

A

analyzes all media touch points of all users (even
non-converters), incorporates non-media factors that play a role in the purchase behavior (i.e. economy), uses a statistical algo to distribute credit

51
Q

Integrated Attribution:

A

“Holy Grail”, integrates multiple events stream to offer

a more complete and accurate view of the customer decision journey

52
Q

Next Generation of Attribution Modeling

A

Econometric top-down (marketing mix)&raquo_space;>

Bottom-up (algo att models)»>

Machine learning (agent-based models)

Martech Ecosystem (12 decks)

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