Targeting with Location Precision

November 3, 2016 NYC

rob.minaglia@pb.com

914-262-2003

@ROBMINAGLIA

Pitney Bowes in Location-based Marketing

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•Mailing, shipping/cross border commerce, customer engagement

•Highly precise, global location data stack

•Data quality, visualization and spatial analytics

•Context for audience creation, insights, attribution and ad delivery

•Neutral supplier to these brands - and 1.5M SMB’s:

mailto: rob.Minaglia@pb.com

The “where” factor definition

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BY USING LOCATION DATATO DERIVE INSIGHTS AND BEHAVIORS

AND THEN BUILDING A MARKETING PROGRAM

AROUND THOSE INSIGHTS

A B O U T Y O U R P R O S P E C T S

A N D C U S T O M E R S

GAINING COMPETITIVE ADVANTAGE

The “where” factor EQUATION

=Your Customer’s

Location

The “Where”

Factor

+Location

Data

Mobile Marketing Use Case: Precise, boundary-based geofencing

and enrichment

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TARGET or DRIVE

TIME

Apply factors such as:

• Road network & traffic

• Retail trade area

modeling

• Demos & social data

BOUNDARY-BASED

• Store

• Mall

• Category

• Neighborhood

• Zip, city, state

RADIUS-BASED

Has problems of:

• “cross river”

• “false alarm”

Mobile Marketing Use Case:Location Targeting

Source: IAB Location Terminology Guide

Factual & Trade Desk Survey (Dec 2015)

Mobile Marketing Use Case:Audience Creation & Targeting

1. Match lat/long from exchange to a POI / geofence

2. Correlate affinity to POI / GF to consumer behavior

3. Build audience based on location–based behavior

(frequency and dwell time) within geo-fence

(store/mall/lot boundary)

4. Enrich audiences with range of demographic, geo-

demographic, financial vitality and purchasing power, etc.

Mobile Targeting Engine

Insights and behavior

analysis

Personalization engine

Lat / longs from Exchange

Enriching Data with a Location Stack

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For a given location:• POI (carries attributes)• Retail (Business) Footprint poly• Building Footprint• Parcel (Lot)• Isochrone (travel time)• Demographics, lifestyle attributes, financial and consumer vitality, etc.

Pitney Bowes POI’s

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• 107M points, 104 countries & territories• Businesses (fully attributed

• Identification of franchises by brand

• Financial Stress scores (opt)

• Leisure & geographic places

• Powered by D&B and other trusted sources

• Over 19,000 categories, 72 fields

• Most accurate lat/long (global)

• Consistent data structure (global)

• Monthly updates

Segment Creation using POI

Lives in urban professional neighborhood in Boulder, CO.

Works at Management Consulting location and spends lunch hours at

the gym. Frequents nightlife hotspots on Wednesday. Enjoys ethnic

foods. Visits mountains on weekends. Air travel 30% of the time.

Audience Creation

Branded Geo-fences Business...Building…Parcel…Drive Time…Neighborhood

• Retail (Business) “Polygon” -represents the space occupied by individual businesses.

• Information stored with each Building Polygon:• Geometry for polygon and centroid

• Brand code, name, alternates

• Parent/Child Relationships

• Ability to augment attributes

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Boundary portfolio

• Malls and standalone stores

• Airports

• Schools & Colleges

• Hospitals & healthcare

• Auto dealers

• Neighborhoods

• School districts

• World admin & postal

• Golf courses

• Speedways

• Train Stations

• Amusement Parks

• Ski Areas, Casinos

10/24/2016

• Administrative data - Country, district, locality, postcode

• Over 600,000 boundaries, contiguous globally

• Localization - English and local language

World & Admin Boundaries

Contextual data for segment enrichment

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Recommendations

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Leverage location-services in your apps

Use clean, accurate data

Enrich with location data stack and analytics to better understand / target your customer

Data is always changing - be vigilant with your sources and vendors

November 3, 2016 NYC

rob.minaglia@pb.com

914-262-2003

@ROBMINAGLIA