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Martech Ecosystem > Lecture 3 > Flashcards

Lecture 3 Flashcards

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

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2
Q

How to Collect Quantitative Data (4 ways)

A

Transaction Database

Web Analytics Tool

3rd Party Pixels

In Store Tracking

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3
Q

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

predicted to worsen with IoT

A

4 devices

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4
Q

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

A

attributes

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5
Q

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

A

connecting these data silos

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6
Q

Identity Graph

A

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

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7
Q

identifiers

A

anything from usernames to email,

phone, cookies and even offline identifiers like loyalty card number

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8
Q

Across a consumer’s journey multiple __ may be associated

with an individual

A

identifiers

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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

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10
Q

ID graphs use 2 different data matching methodologies:

A

Deterministic

Probabilistic

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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

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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

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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?

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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
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15
Q

Data has several characteristics that make them a unique asset:

A

Non-Rivalrous Nature

Sheer Diversity

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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)

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17
Q

dmp

cmp

A

data management platform

customer management platform: data aggregated at the customer level

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18
Q

Sheer Diversity:

A

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

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19
Q

Type of Uses For Data

A

Cost & Revenue Optimization

Marketing & Advertising

Market Intelligence

Market-Making

Training for AI

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20
Q

Cost & Revenue Optimization:

A

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

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21
Q

Marketing & Advertising:

A

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

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22
Q

Market Intelligence

A

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

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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

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24
Q

Training for AI

A

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

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25
Q

Roles Within The Ecosystem

A

Data Generation & Collection

Data Aggregation

Data Analysis

Data Infrastructure

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26
Q

Data Generation & Collection

A

Source and platform where data are initially captured

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27
Q

Data Aggregation

A

Process and platforms for combining data from multiple sources

28
Q

Data Analysis

A

The gleaning of insights from data that can be acted upon

29
Q

Data Infrastructure

A

Hardware & software associated with data management

30
Q

Credit Card Application Ecosystem

A

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
Q

Value in data collection is driven by

A

supply and demand forces

32
Q

As supply of data available from new sources continues to expand, the generation of raw data will become

A

less valuable (with exceptions when supply is constrained)

33
Q

On the supply side, the market is shaped by

A

difficulty of collection,

access and availability of substitute data

34
Q

On the demand side, the market is shaped by

A

ease of use,

network effects,

and value of the ultimate uses of data

35
Q

Aggregators can capture value by serving as a

A

one-stop shop or adding value as combined data yields better insights (ex: benchmarking the performance of multiple entities)

36
Q

Aggregation can produce significant value but is becoming easier for users to

A

perform many aspects of this function themselves

37
Q

The value of aggregation increases only in a case where integrating data from various sources is

A

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
Q

Translating data into business insights is the __ step in the ecosystem

A

most important and valuable

39
Q

On the demand side, the value generated by analysis is clearer since is often the __ step

A

last

40
Q

While companies are uncertain about what to do with huge volume of data they are

A

willing to pay for insights

41
Q

On the supply side, highly specialized talent needed for analytics and interpretation is

A

scarce

42
Q

The most successful analytics providers combine

A

technical capabilities with industry/functional expertise

43
Q

Biggest Opportunities Within Data Generation

A

As data become easier to collect and storage costs go down, many types of data will become commoditized

44
Q

Biggest Opportunities Within Data Aggregations

A

New tools are allowing end-users to aggregate information themselves

45
Q

Biggest Opportunities Within Data Analysis

A

most lucrative niche with companies willing to pay for insights that are applicable to strategy, sales or ops

46
Q

Indicators of potential disruption

A

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
Q

Archetype of disruption:

Business models enabled by orthogonal data

A
  • 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
Q

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

A

telematics data

49
Q

Archetype of disruption:

Hyperscale, real-time matching

A
  • 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
Q

The Market for Transportation Disruption

A

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
Q

Archetype of disruption:

Radical personalization

A
  • 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
Q

__ __ enables finer levels of distinctions among individuals

A

Granular data

53
Q

Outcomes and responses data allow businesses to

A

estimate relationships b/w individual characteristics and improved value from customized goods/services

54
Q

Industry preconditions

A

The good or service has a differentiated value for each individual

Mass customization creates possibility of meeting individual demands

55
Q

Archetype of disruption:

Massive data integration capabilities

A
  • 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
Q

Stores have practically unlimited amounts of

A

data of any format and type

57
Q

Silos minimized, and single source of

A

truth accessible by the whole organization

58
Q

Data lake

A

Offers an improved platform to run analytics and data discovery

59
Q

__ to the data lakes environment can be done gradually

A

Transformation

60
Q

Archetype of disruption:

Data driven discovery

A
  • 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
Q

Archetype of disruption:

Enhance Decision Making

Analytics can improve 4 aspects of decision making:

A
  1. Speed/Adaptability
  2. Accuracy
  3. Consistency/Reliability
  4. Transparency

(smart cities, health care, insurance, human capital/talent)

62
Q
  1. Speed/Adaptability:
A

machine and algos can react in an instant

63
Q
  1. Accuracy:
A

predictive models can give a clearer view into the future leading more effective use of resources

64
Q
  1. Consistency/Reliability:
A

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
Q
  1. Transparency:
A

When two parties in a transaction have different sets of information, it can lead to sub-optimal decision making

66
Q

Enhance Decision Making Preconditions

A

Human biases and heuristics are predominant in decision making

Human error and physical limitations lead to mistakes and lost value

Martech Ecosystem (12 decks)

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