Location brings an opportunity to gain a competitive edge by unlocking new insights that others have not considered. Why have they not considered location in they past? Because location is messy! Lat/longs, shapes on a map, lines on a map, routes, distance calculations… all of this data is often messy and difficult to bring into an analytics application. Learn how Pitney Bowes and Ironside of applied sophisticated methodologies to Organize, Enrich and Analyze data from a location perspective.

1. Tim Kreytak
Co-founder, CEO, Ironside Group
Dan Kernaghan
Technical Director, Pitney Bowes

2. Providing context, accuracy, and relevance to location through
 Software – Spatial, Geocoding, Address Validation, Routing, and more
 Data – Global Streets, Property Attributes, Boundaries, Demographics
 Services – ETL, Integration, Analytics, Machine Learning, AI, and more
Every Address has a Location but not every Location has an Address
We leverage our experience with addresses to enrich them with 9100+
attributes that describe a location

3. Typical Database Options
 Traditional – RDBMS
 Generally use GIS retrieval
 Partitioned and indexed
 Updates are direct replacement
Big Data equalizes data access
- and the balance of fast ingestion versus fast retrieval
Traditional processing
GIS Database
Big Data processing
File/Data Frame
Typical Big Data Options
• Traditional – HDFS
• Sharded – Hbase, Phoenix,
Redshift, Big Table, etc, etc.
• Partitioned by location, time
• Updates – Defined as SCD Type II

4. Latitude-
Longitude
Advantages
• Hierarchical
• Easy to generate
Disadvantages
• Not tied to Address
• 2-dimensional
• Not always accurate
Geohash
Advantages
• Very Accurate
• Hierarchical
• Easy to generate
• Easy to partition
Disadvantages
• Not tied to Address
Pitney Bowes
pbKey
Advantages
• Persistent
• Hierarchical
• Tied to Address
• Unique
Disadvantages
• Limited Coverage

5. TIME
DEMOGRAPHICSUSER PROFILE
CONTEXT
PSYCHOGRAPHICS
BEHAVIORAL
AUDIENCE PERSONA
HOUSEHOLD TYPE
AFFLUENCE
LIFESTAGE
BRAND AFFINITIES
TECHNOGRAPHICS
APP USAGE
+ LIFESTYLE
+
+
+
+
DEVICES

6. Push to Cloud Native
Replacing persistent Hadoop with Ephemeral Processing
 Apache Spark, Databricks
 Amazon EMR
 Google Cloud DataProc
Movement toward Elastic Processing
 Containers: Docker, Apache Mesos, LXC, OpenVZ…
 Managers: Kubernetes, Nomad, Swarm…
 Microservices
And don’t forget about Openshift, Pivotal Cloud Foundry, DC/OS…..

7. We help our clients use data to make
better decisions about their business.
7Client Privileged & Confidential - © 2018 Ironside Group, LLC

8. Customer Analytics &
Single Customer View
Data Visualization,
Dashboards & Reporting
Financial Planning,
Forecasting & Analysis
Data Science &
Machine Learning
Data Integration &
Warehousing
Data Governance &
Master Data Management
Data Quality &
3rd Party Data Enrichment
Data Engineering &
App Dev / Integration
Decision Support
Experience Design
Data & Analytics
Strategy
Data & Analytics
Governance
Data & Analytics
Architecture
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9. Using data to inform and automate
decision making in pursuit of
better business outcomes.
9

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Grow Revenues
• Increase Existing
• Create Net New
Maximize Efficiency
• Decision Support
• Decision Automation
Understand and
Minimize Risk
• Forecasting & Planning
• Predict or Prevent Fraud
In business, data science is a means for business process optimization, and
business leaders can, and should, think of it as a powerful tool to that end.

11. 11
Which
action
should I
take?
Reinforcement
Learning
How much
or how
many?
Regression
How do
these things
group
together?
Clustering /
Recommendation
Which
category is
this in?
Classification
Which one
of these is
not like the
others?
Anomaly
Detection
Client Privileged & Confidential - © 2018 Ironside Group, LLC

12. 12Client Privileged & Confidential - © 2019 Ironside Group, LLC
Data &
Information
What data is required?
What data do we have
today?
What data might we need
to acquire?
How much history?
Of what quality?
Analysis
Who wants to know?
What do we need to
know?
What do we wish we could
know?
What methods and tools
can we avail?
Decisions &
Actions
What are the “business
moments”?
What decisions are made?
How are they made?
Who is deciding / acting?
How often / many?
What are the levers of
change?
Goals &
Outcomes
What are our business
goals?
What are the activities that
will lead to success?
What will prevent success?
What is the data
opportunity?
Can we quantify value of
this goal?

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What else about the
consumer, outside of my data,
correlates to value?
What if I could estimate the
customer’s affluence and
spending power before they
ever transact with me?
How does traffic and drive
time impact our access to the
market both broadly and
individually?
How does the presence of
accessible competitors impact
consumer frequency and
wallet share?
How do elements such as
weather impact performance
of our products and services?
Would I interact differently
with a given consumer with
this information at the outset?

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Model Target Definition
The target for this model is Summer Revenue which is estimated by the number of
reviews a listing received in summer months multiplied by the daily price and then
multiplied by 4 (based on assumption that average stay is 4 days).
Training dataset = 1070 listings in greater Boston
Assumptions:
1. 100% of visitors left a review
2. An average stay in summer = 4 days
3. The price listed is the average daily price in the summer months
4. Availability for summer months does not change year over year
Filters:
1. Had bookings in 2017 and 2018
2. Had availability for 2019
3. Addresses could be enriched

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Feature
Property Attributes:
 property type
 max # of people accommodates
 # bathrooms
 # bedrooms
 bed type
 room type
 property description (text)
 neighborhood description (text)
 amenities (flag/dummy code per
amenity, composited created for
“handicap accessible” and “family
friendly”)
Host Attributes:
• # listings
• host is superhost
• picture available
• prior reviews
• host is local
• host in same neighborhood
• host identity verified
• host description (length)
• policies:
• cancellation policy strictness
• requires guest profile picture
• house rules (text and length of
text)

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Location Attributes:
 distance to nearest public transit
 CAMEO segment
 financial stress of city block (ranked within state and nation)
 presence of children (ranked within state and nation)
 population of city block
 households in city block
History:
 summer availability (days marked as available for summer 2019 calendar)
 2016 and 2017 summer revenue

17. © 2019 Ironside Group, LLC - Client Confidential 17
Location data such as distance to
airports, Amtrak, highway exits, mbta
stations, block population/density
measures and block socio-economic
measures surfaced as some of the
most important predictors of
summer demand

18. 18
DataRobot leaderboard:
● Ranks 50+ models in performance
● Balances performance and accuracy
● Performs intensive cross validation to
protect against overfitting
● Allows manual retraining of specific
models/blueprints on various feature sets

19. 19
Client Privileged & Confidential - © 2019 Ironside Group, LLC
Time:
Derived from
PB Accurate
GeoCoder and
Address location
information --
most impactful
Model Feature

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ANALYTICAL
SKILLS &
TOOLSDOMAIN
KNOWLEDGE
BROADER,
COMPLETE &
ACCURATE DATA
Demand for Better Analytics
Analytics Supply

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Law Enforcement
• Where should I locate
officers to prevent
crime?
Retailer
• Which stores should I
open or close to
maximize profits?
Manufacturer
• What ingredient
amounts should we use
to produce the highest
quality foam?
Utility Company
• What is the probability
that a contractor
damages a gas line
while digging?
Health Insurer
• Which of these health
insurance claims can I
subrogate to the auto
insurer?
University
• How much is this
person likely to donate
to our endowment?
Real Estate
• How many employees is
this company going to
relocate across the
globe in the next 12
months?
Mortgage Bank
• What is the probability
that this mortgage is
going to default or be
pre-paid?
Insurer
• What procedures were
performed in these
health insurance claims?

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Business Function,
Process, Role,
Persona etc.
Frequency of
Occurrence
Number of Users
Technology
Dependencies
Skill
Dependencies
Data Sources
Data Quality &
Completeness
Cost & Effort to
Develop
Cost to Operate
and Sustain
Time to Value
Steering
Committee Ranking
Executive
Sponsorship
Demographics Business Impact Data & Analytics Qualitative
Important?
Unsatisfied?
Tangible?
Lucrative?
Cost