Lecture 3 Flashcards
How to Collect Identity Data
POS System & Online Transaction Database
Clienteling
Social Network Profile & Other Customer Profile Features
3rd party data sources
How to Collect Quantitative Data (4 ways)
Transaction Database
Web Analytics Tool
3rd Party Pixels
In Store Tracking
On an average, consumers in the US use __ each day
predicted to worsen with IoT
4 devices
In addition, companies use multiple tools to store different customer __ (i.e. CRM, Email, Ecommerce, POS, Social Media)
attributes
Businesses are left with isolated data sets and are paralyzed when it comes to
connecting these data silos
Identity Graph
a database that stores all identifiers that correlate with individual customers, creating a unified customer view and breaking silos
identifiers
anything from usernames to email,
phone, cookies and even offline identifiers like loyalty card number
Across a consumer’s journey multiple __ may be associated
with an individual
identifiers
The ID graph collects these identifiers and connects them to
the customer’s profile and any related data points, including behavioral data like browsing activity or purchase history
ID graphs use 2 different data matching methodologies:
Deterministic
Probabilistic
Deterministic:
uses known customer information (i.e. log-in data, hashed email addresses) to match and recognize individuals across devices with 100% certainty
Probabilistic
uses anonymized data signals (ex: IP address, device, browser, location, OS) to create likely statistical connections across devices, achieving greater scale but lower accuracy
Proliferating data sources (including sensors and social media) are creating torrents of information. However the value of data still comes down to 2 elements:
- How unique is it?
2. How will it be used and by whom?
Many organizations see the potential and are hungry to use data to grow and improve performance, but:
- 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
Data has several characteristics that make them a unique asset:
Non-Rivalrous Nature
Sheer Diversity
Non-Rivalrous Nature:
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)
dmp
cmp
data management platform
customer management platform: data aggregated at the customer level
Sheer Diversity:
data types (behavioral, transactional, etc…), structured vs. unstructured (images, videos), diversity of sources (web, social media, sensors, etc…)
Type of Uses For Data
Cost & Revenue Optimization
Marketing & Advertising
Market Intelligence
Market-Making
Training for AI
Cost & Revenue Optimization:
Predictive maintenance, talent management, procurement, micro-target segments, product improvements
Marketing & Advertising:
Function relies on customer transactional & behavioral data aggregated from multiple sources
Market Intelligence
Data is compiled with an economy-wide, regional, industry-specific, functional or market perspective to deliver strategic insights
Market-Making
Firm plays role of matching the needs of buyers and sellers though platforms that collect the necessary data to enable efficient matching
Training for AI
Machine learning requires huge quantities of training data, some generated through simulations and some in the public sphere
Roles Within The Ecosystem
Data Generation & Collection
Data Aggregation
Data Analysis
Data Infrastructure
Data Generation & Collection
Source and platform where data are initially captured
Data Aggregation
Process and platforms for combining data from multiple sources
Data Analysis
The gleaning of insights from data that can be acted upon
Data Infrastructure
Hardware & software associated with data management
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
Value in data collection is driven by
supply and demand forces
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)
On the supply side, the market is shaped by
difficulty of collection,
access and availability of substitute data
On the demand side, the market is shaped by
ease of use,
network effects,
and value of the ultimate uses of data
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)
Aggregation can produce significant value but is becoming easier for users to
perform many aspects of this function themselves
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…)
Translating data into business insights is the __ step in the ecosystem
most important and valuable
On the demand side, the value generated by analysis is clearer since is often the __ step
last
While companies are uncertain about what to do with huge volume of data they are
willing to pay for insights
On the supply side, highly specialized talent needed for analytics and interpretation is
scarce
The most successful analytics providers combine
technical capabilities with industry/functional expertise
Biggest Opportunities Within Data Generation
As data become easier to collect and storage costs go down, many types of data will become commoditized
Biggest Opportunities Within Data Aggregations
New tools are allowing end-users to aggregate information themselves
Biggest Opportunities Within Data Analysis
most lucrative niche with companies willing to pay for insights that are applicable to strategy, sales or ops
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
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)
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
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)
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
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)
__ __ enables finer levels of distinctions among individuals
Granular data
Outcomes and responses data allow businesses to
estimate relationships b/w individual characteristics and improved value from customized goods/services
Industry preconditions
The good or service has a differentiated value for each individual
Mass customization creates possibility of meeting individual demands
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)
Stores have practically unlimited amounts of
data of any format and type
Silos minimized, and single source of
truth accessible by the whole organization
Data lake
Offers an improved platform to run analytics and data discovery
__ to the data lakes environment can be done gradually
Transformation
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)
Archetype of disruption:
Enhance Decision Making
Analytics can improve 4 aspects of decision making:
- Speed/Adaptability
- Accuracy
- Consistency/Reliability
- Transparency
(smart cities, health care, insurance, human capital/talent)
- Speed/Adaptability:
machine and algos can react in an instant
- Accuracy:
predictive models can give a clearer view into the future leading more effective use of resources
- 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
- Transparency:
When two parties in a transaction have different sets of information, it can lead to sub-optimal decision making
Enhance Decision Making Preconditions
Human biases and heuristics are predominant in decision making
Human error and physical limitations lead to mistakes and lost value
