Personagraph Whitepaper

© 2013 Personagraph Corporation. All rights reserved. 1
an Intertrust Company
Increasing Mobile Advertising
Revenues using Personagraph™
Phone: 1-800-393-2272 / Inquiries: sales@personagraph.com

1. Summary
2. The Problem
• How do we emulate targeting and
personalization on mobile devices?
3. An Opportunity
• The Virtues of Predictive Marketing
• Building value without creating a privacy
nightmare
4. Solution: Personagraph
• The Personagraph Security Architecture:
A Trusted Intermediary
• Personagraph Architecture
• The Personagraph SDK
• The Personagraph Consumer Dashboard
• The Personagraph Campaign Manager
5. Conclusion
Contents

Mobile phones are people’s undisputed device of choice for everything from communicating
(via voice, texts and social media), to enjoying entertainment and gaming, to storing vast
libraries of photos, music, and other media. Over 427 million mobile phones were sold in
Q3’12; 169 million of which were smartphones1
. Gartner predicts purchases of 1.2 billion new
smart devices in 20132
, making smartphones the most ubiquitous deployment of any device
in the history of mankind. Almost every adult in a developed country owns a mobile phone
they carry everywhere. Mobile usage is 93% at home, 87% on the go, and 77% in stores3
.
600 million of Facebook’s 1 billion users use Facebook from mobile devices4
. Mobile phones,
with their vast reservoir of user behavior data, present a huge opportunity to benefit from
their usage insights.
1 CNET, Zach Whittaker, November 14, 2012, http://news.cnet.com/8301-1035_3-57549482-94/smartphone-sales-up-47-percent-as-android-increases-its-lead/
2 Gartner, November 6, 2012, http://www.gartner.com/it/page.jsp?id=2227215
3 The Mobile Movement – Understanding Smartphone Users, Google/IPOS OTX MediaCT, US, April 2011 http://www.gstatic.com/ads/research/en/2011_TheMobileMove-
ment.pdf
4 The Next Web, October 2012, http://thenextweb.com/facebook/2012/10/04/facebook-now-has-600-million-monthly-active-mobile-users/
5 Internet Trends, Mary Meeker, December 3, 2012, Kleiner Perkins Caufield Byers, http://www.kpcb.com/insights/2012-internet-trends-update
The consumer rush to mobile devices has created a challenge for marketers. The advertising
revenue model that has been crucial to the success of traditional websites has not translated
equivalently into the mobile world. Today, mobile advertising and merchandising command
a fraction of the revenue that a similar campaign on a PC website would generate. Also,
mobile advertising and merchandising have not been successful in attracting ad dollars in
proportion to the time spent on mobile devices; consumers spend close to 10% of their total
time on mobile devices while advertisers only spend 1% of their advertising budget on mobile
advertising5
.
Mediaconsumption5
1
2
Summary
The
Problem

Mobile opens up engagement opportunities with users never available before. Mobile
devices have a unique owner and are equipped with sensors able to capture data beyond
user search or browser sessions. Location sensors, accelerometers, and app usage patterns
on the device provide a wealth of data that can be used to better understand a consumer
and to reach customized classes of consumers. Digital life and real life merge into a single
persona on a mobile device when we leverage data from social, location, motion, and in-app
activity sensors.
While mobile ads suffer from limited screen real estate, they have one distinct advantage:
they are served on devices that are everywhere marketers want to be—in the real world in
the hands of their target customer as they go about their life. Mobile technology introduces
the concept of predictive marketing to the world of digital advertising. Often cited as the
holy grail of advertising, predictive marketing is the key to increasing the real and perceived
value of mobile ads.
An
Opportunity
3
Marketers have experienced lower conversion rates on mobile devices, lowering the
perceived value of mobile advertising. Several key factors contribute to this problem:
Limited screen size: This shrinks the ad unit and does not provide enough space to deliver a
compelling message; this means that the promotion language must be extremely brief.
Carriers strip tracking information from devices: This drastically limits an advertiser’s ability
to place targeted ads.
Marketers are unable to target the most valuable impressions: The typical consumer uses
specialized apps for her most frequent and important activities (i.e., stock trading, news
reading, etc). However, marketers cannot use normal audience targeting techniques to tailor
ads and promotions because cookies are not allowed inside mobile apps.
These issues present a stark reality for publishers. With over 1 billion mobile Internet users,
today’s publishers will eventually see a significant drop in ad revenues purely because of the
price discount of mobile ads. In order to survive and thrive under the new mobile paradigm,
publishers and retailers need to find a way to substantially and credibly increase the real
value of mobile advertising and find other ways to monetize the media while preserving the
experience to their end consumers.
How do we emulate targeting & personalization on mobile devices?
As users navigate across websites, site owners, advertising networks, and data aggregators
(i.e. Bluekai) drop cookies on the their computers. Cookies follow users across the web and
help build user profiles for targeting them with relevant offers.
On mobile Internet phones, carriers block cookies thereby rendering this method useless.
Additionally, due to Apple’s restrictions and technology constraints, a typical cookie cannot
be placed inside an iPhone app. With most of the Internet activity occurring outside the
browser and inside the specialized apps, marketers are unable to gauge user’s interests.
Marketers who build their relationships with consumers based on these cookies are
stranded when it comes to mobile users.
To address some of these limitations, several publishers and ad networks have proposed a
system of fingerprinting, which simulates cookies found on the traditional web.

The5Dimensionsof
PredictiveMarketing
The Virtues of Predictive Marketing
Today, there are several ad services that are location-aware. They are rudimentary solutions
that serve location-relevant ads based on a user’s current location. These solutions don’t
have a detailed user profile and ignore the context around the location that could impact the
relevance of the ad or promotion.
Unlike simple location-aware solutions, predictive marketing solutions need to harness the
power of such vast amounts of data made available by mobile consumers. A predictive
marketing solution should be able to harness information such as a user’s profile (user model
and interests) and context (time, location, device type, and surrounding context) for targeting
advertisements and promotions.
Dave works in Sunnyvale, CA and prefers to eat lunch within a half-mile radius
of the office. He doesn’t go out to lunch on Wednesdays as he hosts a team
meeting. He likes ethnic food, stays clear of fast food and avoids $$$$ rated
restaurants. He prefers eating spicy Asian food on rainy days.
Dave’s Personal Agent constantly analyzes his dining habits. A new Asian fusion
restaurant 'Fusian' just opened 0.3 miles away from Dave’s office and is running
a promotion to attract new customers. On a rainy Tuesday, Dave quickly checks
for the latest news on his NY Times application at 11am, which displays a lunch
coupon for 'Fusian' as a banner ad on the front page.
It’s almost lunchtime & Dave likes Asian food. To explore a new restaurant near
the office, he clicks on the banner and checks out their menu.
PredictiveMarketing
inaction

Building value without creating a privacy nightmare
All predictive marketing solutions come at a price. The sheer amount of personal data that
mobile devices can track and collect is staggering. Traditional online publishers and digital
advertisers track online usage data via cookies. Mobile devices can passively gain access to
both mobile app usage data and device data. This sensitive data can be analyzed to draw
inferences and create accurate user profiles (reflecting real world behavior), but can also
expose a user to extreme privacy risks never before imagined.
Without clear industry standards around data privacy, publishers and retailers have been
reluctant to leverage a user’s sensitive data. Privacy concerns have limited the types of
targeted advertising that companies employ in their mobile initiatives, diminishing the
revenue potential of mobile ads. Additionally, consumers are becoming aware of the “data
footprint” they leave behind, increasingly denying access to location tracking or other
mobile usage data.
The inability of marketing solutions to protect user data is creating concern for consumers.
A Do Not Track proposal, that enables consumers to opt out, is gaining momentum among
Internet consumers because they want to protect their private information. Adoption of
such technology will diminish the effectiveness of marketing campaigns by limiting their
ability to reach relevant audiences. Consumers miss out on a personalized experience and
receive non-targeted, and at times, non-relevant marketing messages. The marketers and
consumers thereby end up in a lose-lose situation.
In order to unlock the value of predictive marketing solutions, consumers must feel
comfortable about sharing their data. This requires explicit user opt-in and adequate
measures to maintain individual anonymity. Effectively, the user must own and control her
data and any derivations arrived from this data.
What is needed is a technology that maps user interests and routines into an anonymous
persona that marketers and retailers can leverage to offer relevant advertisements without
impinging upon a user’s privacy. Addressing user privacy concerns will allow mobile
advertising to gain a larger share of the total advertising budget.
Personagraph™ creates private representations of a user’s behavior, interests, and demographics
from her real and digital life. This representation analyzes breadcrumbs of a user’s daily life to
draw inferences and create an accurate user profile:
• Location graph: user visits over time
• Activity graph: user actions
• Social graph: user connections
• Contextual graph: time of day, weather, preferred device
Personagraph uses all permitted user data to generate inferences using complex rules, axioms
& ontologies . Any user volunteered data enriches their Personagraph. This representation
can help predict consumer-buying behavior, which is invaluable for advertisers and marketers
to personalize their offerings.
Solution:
Personagraph
4
6 Do Not Track, http://en.wikipedia.org/wiki/Do_Not_Track

Personagraph represents a person’s profile of attributes and interests. This model
describes a person based on demographic, psychographic, behavioral, and geographic
terms. Interests represent the person’s tastes, hobbies, occupation, and recreational
activities. They are mapped using Personagraph’s interest taxonomy, which has over 1.5
million interests and categories. Personagraph organizes a large amount of information
on complex behaviors into a form that is simple and amenable to automated analysis for
generating trends and predicting the intent to purchase.
The Personagraph Security Architecture: A Trusted Intermediary
Personagraph is built from the ground up to ensure privacy and security by providing total
transparency and control to consumers over their data.
Service providers typically store all personal data and give consumers no visibility or
control over their own data. Personagraph stores inferences based on user data on the
Personagraph cloud and the inferences are kept anonymous. The consumer has complete
control (view, edit, add, and/or delete) over their Personagraph.
Personagraph is designed to match advertising and merchandising offers based on consumer
profiles and context without the need for consumers to reveal their personal information.
Marketers can reach their consumers via the secured distribution of Personagraph to
efficiently monetize content using targeted advertising. Ads and offers are matched to a
person’s user profile using Personagraph’s privacy protecting Personal Agent™. Leveraging
these agents, advertisers can target ads with high precision and generate high margin
revenue for their content & services.
SecurityModel

Alice has downloaded a popular mobile application so she can watch her favorite music
videos during her lunch break. When she opens the app, it tells her that Personagraph
powers the app’s ads and promotions. Alice opts-in to the Personagraph feature in order
to get more relevant promotions and fewer ads.
Although Alice only periodically watches videos on her iPhone, the Personagraph is always
on, passively collecting location data. It has learned that over the past week, she has visited
Blue Bottle for coffee, had lunch at Italian, Indian, and Japanese restaurants, visited a hair
salon, spent an hour at Brookstone, and shopped at a local wine store. Personagraph has
also learned that Alice prefers classical music through the videos she has previously viewed
in the app.
Based on Alice’s Personagraph, we infer that she is a woman who enjoys new gadgets,
international cuisine and wine. The next time Alice watches a music video on the app, she
will see a highly targeted ad regarding an upcoming pay-per-view classical music perfor-
mance.
Hypertargetedads
PredictiveMarketing
inaction!
Personagraph architecture has 3 primary partner facing components:
1. SDK that is designed to integrate into a service provider or retailer’s mobile application(s)
2. Consumer Dashboard to gain insights into their consumers
3. Campaign Manager to create highly targeted campaigns
Personagraph Architecture

How does it work?
1. A consumer downloads and uses a partner’s mobile app that is enabled with
Personagraph’s SDK
2. Consumer is given the option to create a Personagraph account to allow them to
visualize their data
3. Personagraph passively collects her data e.g., points of interest (POI) visited.
4. Personagraph processes information regarding the consumer’s in-app activity,
installed apps on the device, their social connections, the time and the context.
This information is used to infer their profile and build a Personagraph for each user.
5. Partners target the most relevant consumers for their marketing campaigns using
the Personagraph Campaign Manager.
6. The Personagraph Campaign Manager delivers the offers or advertisements to
the relevant consumers and provides rich insights into campaign performance
back to the marketer.
The Personagraph SDK
The SDK enables partners to incorporate
Personagraph technology into their mobile
applications. This integration should take
approximately 20 minutes to accomplish.
The SDK integrates into an app for several distinct
reasons:
• To enable the collection of information while the
app is running and collect information used to
model the user’s behavior. This integration provides
the partner with deep insights into their consumer’s
demographics, interests and trends.
• To provide end-user access and control over their
data. Consumers can curate inferred interests and
declare new interests
• Allow the partner app to provide a personalized
experience to their consumers.
The Personagraph SDK consists of two major
components—the Sensor Module, and the
Personal Agent. The sensor module collects data
from the GPS, the apps installed on the user’s
device, in-app clicks, and their social data. The
decoupled architecture of the SDK allows partners
to individually choose sensors to integrate with.
Modifying run-time configuration can turn on
sensors. This eliminates the need to release a new
binary. Personal Agent

CampaignManager
The Personal Agent provides access to the user’s data; including raw data – location, clicks,
apps and inferred data – demographics and interests. The consumer has complete control
over her data— she may curate inferred interests or choose to declare new interests.
The Personagraph Consumer Dashboard
Partners leverage their dashboard to gain a deeper understanding about their consumers.
Partners can visualize their consumer’s demographic segmentation (gender, income level,
education), interest segmentation (music, sports, movies), behavioral insights (store visits,
purchasing preferences) and competitive analysis (visit to competitor’s stores, social inter-
actions). These insights are aggregated and anonymized to protect consumer privacy.
The Personagraph Campaign Manager

Using the Personagraph Campaign Manager, partners can engage with their
consumers using predictive marketing in the following ways:
• Personalized messaging: Create personalized messages or select existing offers to
target select user segment(s)
• Distribute through any channels: The Campaign Manager can be integrated with
push notification, video and banner advertising, in-app feature highlights and more.
• Scheduled campaigns: The ability to run on specific days or specific times of the day,
partners can even engage their target segment instantly through immediate mobile
notifications.
• Targeted offers: Target your audience using any combination of the five dimensions
namely demographics, interests, location, time and context.
• Get rich feedback: Learn how campaigns are performing. Learn what segments of
your audience engage with the offer. This feedback can be easily incorporated to
improve your future campaigns.
With millions of consumers flocking to iOS, Android, and Windows devices, the mobile phone
revolution has officially given way to the smartphone revolution. This has created an unprec-
edented opportunity for marketers to harness the true potential of the mobile platform.
However, challenges regarding consumer trust and privacy are hindering such aspirations. A
predictive marketing solution can overcome these challenges helping marketers realize the
optimal potential of the smartphone revolution.
The Personagraph platform lets marketers engage with their consumers based on their
behavior graph without the need for those consumers to reveal their personal information.
This solution provides marketers with uniquely relevant user data to increase the efficacy and
value of their mobile ads and promotions.
Conclusion
5

Personagraph Whitepaper

Recommandé

Recommandé

Contenu connexe

Tendances

Tendances (20)

En vedette

En vedette (20)

Similaire à Personagraph Whitepaper

Similaire à Personagraph Whitepaper (20)

Dernier

Dernier (20)

Personagraph Whitepaper