Brainscape's Knowledge GenomeTM


Top Flashcards (Certified)
All Flashcards

Martech Ecosystem > Lecture 3 > Flashcards

Lecture 3 Flashcards

(66 cards)

Start Studying
1
Q

How to Collect Identity Data

A

POS System & Online Transaction Database

Clienteling

Social Network Profile & Other Customer Profile Features

3rd party data sources

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
2
Q

How to Collect Quantitative Data (4 ways)

A

Transaction Database

Web Analytics Tool

3rd Party Pixels

In Store Tracking

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
3
Q

On an average, consumers in the US use __ each day

predicted to worsen with IoT

A

4 devices

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
4
Q

In addition, companies use multiple tools to store different customer __ (i.e. CRM, Email, Ecommerce, POS, Social Media)

A

attributes

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
5
Q

Businesses are left with isolated data sets and are paralyzed when it comes to

A

connecting these data silos

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
6
Q

Identity Graph

A

a database that stores all identifiers that correlate with individual customers, creating a unified customer view and breaking silos

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
7
Q

identifiers

A

anything from usernames to email,

phone, cookies and even offline identifiers like loyalty card number

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
8
Q

Across a consumer’s journey multiple __ may be associated

with an individual

A

identifiers

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
9
Q

The ID graph collects these identifiers and connects them to

A

the customer’s profile and any related data points, including behavioral data like browsing activity or purchase history

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
10
Q

ID graphs use 2 different data matching methodologies:

A

Deterministic

Probabilistic

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
11
Q

Deterministic:

A

uses known customer information (i.e. log-in data, hashed email addresses) to match and recognize individuals across devices with 100% certainty

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
12
Q

Probabilistic

A

uses anonymized data signals (ex: IP address, device, browser, location, OS) to create likely statistical connections across devices, achieving greater scale but lower accuracy

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
13
Q

Proliferating data sources (including sensors and social media) are creating torrents of information. However the value of data still comes down to 2 elements:

A
  1. How unique is it?

2. How will it be used and by whom?

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
14
Q

Many organizations see the potential and are hungry to use data to grow and improve performance, but:

A
  • There are many steps between raw data and actual application of data-derived insights
  • There are also opportunities to monetize and add value at many points along the way
How well did you know this?
1
Not at all
2
3
4
5
Perfectly
15
Q

Data has several characteristics that make them a unique asset:

A

Non-Rivalrous Nature

Sheer Diversity

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
16
Q

Non-Rivalrous Nature:

A

the same piece of data can be
used by multiple parties simultaneously. Few organizations list data assets on their books and most data is monetized indirectly or used for barter (hard to evaluate)

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
17
Q

dmp

cmp

A

data management platform

customer management platform: data aggregated at the customer level

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
18
Q

Sheer Diversity:

A

data types (behavioral, transactional, etc…), structured vs. unstructured (images, videos), diversity of sources (web, social media, sensors, etc…)

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
19
Q

Type of Uses For Data

A

Cost & Revenue Optimization

Marketing & Advertising

Market Intelligence

Market-Making

Training for AI

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
20
Q

Cost & Revenue Optimization:

A

Predictive maintenance, talent management, procurement, micro-target segments, product improvements

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
21
Q

Marketing & Advertising:

A

Function relies on customer transactional & behavioral data aggregated from multiple sources

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
22
Q

Market Intelligence

A

Data is compiled with an economy-wide, regional, industry-specific, functional or market perspective to deliver strategic insights

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
23
Q

Market-Making

A

Firm plays role of matching the needs of buyers and sellers though platforms that collect the necessary data to enable efficient matching

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
24
Q

Training for AI

A

Machine learning requires huge quantities of training data, some generated through simulations and some in the public sphere

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
25
Roles Within The Ecosystem
Data Generation & Collection Data Aggregation Data Analysis Data Infrastructure
26
Data Generation & Collection
Source and platform where data are initially captured
27
Data Aggregation
Process and platforms for combining data from multiple sources
28
Data Analysis
The gleaning of insights from data that can be acted upon
29
Data Infrastructure
Hardware & software associated with data management
30
Credit Card Application Ecosystem
Consumers generate data when they use and make payments Financial Institutions organize and summarize data generated by the borrower Financial Institutions share summary data with credit bureaus, which play the aggregator role Credit Bureaus form a complete view of the customer’s credit behavior and apply analytics to generate a credit score Financial Institutions will pay credit credit bureaus for access to the score Generator of data (consumer) doesn’t own the data. Agreements with various lenders outline how info can be shared. The analysis and monetization occurs at different points
31
Value in data collection is driven by
supply and demand forces
32
As supply of data available from new sources continues to expand, the generation of raw data will become
less valuable (with exceptions when supply is constrained)
33
On the supply side, the market is shaped by
difficulty of collection, access and availability of substitute data
34
On the demand side, the market is shaped by
ease of use, network effects, and value of the ultimate uses of data
35
Aggregators can capture value by serving as a
one-stop shop or adding value as combined data yields better insights (ex: benchmarking the performance of multiple entities)
36
Aggregation can produce significant value but is becoming easier for users to
perform many aspects of this function themselves
37
The value of aggregation increases only in a case where integrating data from various sources is
challenging or access is a barrier (ex: location data) Many traditional marketing data and information services providers fall into this category (ex: mailing vendors, Bloomberg, etc...)
38
Translating data into business insights is the __ step in the ecosystem
most important and valuable
39
On the demand side, the value generated by analysis is clearer since is often the __ step
last
40
While companies are uncertain about what to do with huge volume of data they are
willing to pay for insights
41
On the supply side, highly specialized talent needed for analytics and interpretation is
scarce
42
The most successful analytics providers combine
technical capabilities with industry/functional expertise
43
Biggest Opportunities Within Data Generation
As data become easier to collect and storage costs go down, many types of data will become commoditized
44
Biggest Opportunities Within Data Aggregations
New tools are allowing end-users to aggregate information themselves
45
Biggest Opportunities Within Data Analysis
most lucrative niche with companies willing to pay for insights that are applicable to strategy, sales or ops
46
Indicators of potential disruption
Assets are underutilized due to inefficient signaling Supply/demand mismatch Dependence on large amounts of personalized data Data is siloed or fragmented Large value in combining data from multiple sources R&D is core to the business model Decision making is subject to human biases Speed of decision making limited by human constraints Large value associated while improving accuracy of prediction
47
Archetype of disruption: Business models enabled by orthogonal data
* Data is proliferating, with many new types from new sources now available * In industries where most incumbents have relied on certain type of standardized data to make decisions, bringing supplemental fresh new types can change the basis of competition * New entrants with privileged access to these type of “orthogonal” data sets can pose a uniquely powerful challenge to incumbents (Insurance, health care, human capital/talent)
48
New entrants have leveraged __ __ to gain insights into behavior. This new data is orthogonal to the demographic data that had been previously used for underwriting
telematics data
49
Archetype of disruption: Hyperscale, real-time matching
* Data and analytics are transforming the way markets connect sellers and buyers * In some industries, each offer has critical variations and the buyer prioritizes finding the right fit over the speed of the match (ex: real estate) • Hyperscale digital platforms can use data and analytics to meet both types of needs and have notable impact when:  Demand and supply fluctuate frequently  Poor signaling mechanisms produce slow matches  Supply-side assets are under-utilized (transportation and logistics, auto, smart cities and infrstructure)
50
The Market for Transportation Disruption
Taxicabs rely on crude signaling mechanism Significant unmet demand with cabs spending large share of time empty Excess supply sometimes pooled in certain spots while other areas were underserved. Due to heavy regulation and static pricing, taxi markets were highly inefficient leaving an opening for a radically different model A new model combined digital platform with location mapping technology to instantly match passengers with drivers nearby Location data can be analyzed to monitor fluctuations in supply and demand allowing for dynamic pricing adjustments
51
Archetype of disruption: Radical personalization
* One of the most powerful uses for data and analytics is micro-segmenting populations based on characteristics and preferences * By gathering and analyzing an increase wealth of data, companies get to know their customers at a deeper level * Companies can feed insights back into products and services and recommend additional purchases * Disruptive in areas where tailoring offerings to personal preferences and characteristics is highly valued (health care, retail, media, education)
52
__ __ enables finer levels of distinctions among individuals
Granular data
53
Outcomes and responses data allow businesses to
estimate relationships b/w individual characteristics and improved value from customized goods/services
54
Industry preconditions
The good or service has a differentiated value for each individual Mass customization creates possibility of meeting individual demands
55
Archetype of disruption: Massive data integration capabilities
* The first step in creating value from data and analytics is ensuring access to all relevant data * While straightforward in theory, in practice most large organizations have a department and business unit structure that tends to create silos * As result, it is difficult to share information seamlessly across internal boundaries * “Data lakes” are new tools that simplify access across the enterprise by integrating all types of data into one easily accessible and flexible repository (banking, insurance, public sector, human capabilities)
56
Stores have practically unlimited amounts of
data of any format and type
57
Silos minimized, and single source of
truth accessible by the whole organization
58
Data lake
Offers an improved platform to run analytics and data discovery
59
__ to the data lakes environment can be done gradually
Transformation
60
Archetype of disruption: Data driven discovery
* Innovation is one of the components of productivity growth * Innovative ideas have historically sprung from human ingenuity and creativity, but what if data and algorithms could support, enhance, or even replace them? * In process innovation, data and analytics are helping organizations determine how to structure teams, resources, and workflows * In product innovation, data and analytics can transform research and development in areas such as materials science, synthetic biology, and life sciences (life sciences and pharma, material sciences, tech)
61
Archetype of disruption: Enhance Decision Making Analytics can improve 4 aspects of decision making:
1. Speed/Adaptability 2. Accuracy 3. Consistency/Reliability 4. Transparency (smart cities, health care, insurance, human capital/talent)
62
1. Speed/Adaptability:
machine and algos can react in an instant
63
2. Accuracy:
predictive models can give a clearer view into the future leading more effective use of resources
64
3. Consistency/Reliability:
machine and algos are generally predictable and reliable. They do not tire, miss data points, or look at the same piece of information and draw varying conclusions each time
65
4. Transparency:
When two parties in a transaction have different sets of information, it can lead to sub-optimal decision making
66
Enhance Decision Making Preconditions
Human biases and heuristics are predominant in decision making Human error and physical limitations lead to mistakes and lost value

Martech Ecosystem (12 decks)

Brainscape helps you reach your goals faster, through stronger study habits.
© 2025 Bold Learning Solutions. Terms and Conditions