From information to insights: understanding Big Data online

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From Information to
Insights: Understanding
Big Data Online

Table of Contents
3

Introduction

4

Evaluating the Online Consumer
Data Landscape

Social Network Stream Data . . . . . . . . . . . . . . . . . .4
Data Collected from Registration + Social Proﬁle Data . . . .7
Transaction Data . . . . . . . . . . . . . . . . . . . . . . . .8
Clickstream and Third-Party Data . . . . . . . . . . . . . . .9

10

How to Collect, Store, and Manage Online
User Data

Data Collected from Registration + Social Proﬁle Data . . . 12
Social Network Stream Data . . . . . . . . . . . . . . . . . 12

14

Best Practices to Take Advantage of Online
Big Data

Email Segmentation . . . . . . . . . . . . . . . . . . . . . . 14
Product Recommendations & Content Personalization . . . 15
Ad Targeting . . . . . . . . . . . . . . . . . . . . . . . . . . 18

20

Conclusion

Copyright © 2012 Janrain, Inc. All rights reserved.

Understanding Big Data Online | www.janrain.com | 888.563.3082

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Introduction

Never before

have digital marketers
been blessed with such
immense quantities of online customer data at our disposal, yet been
so incapacitated by how to use it. From personal information collected
at registration to transaction history, clickstream data, social network
streams and third-party data, marketers are sitting on a veritable gold
mine of customer intelligence – one that remains largely untapped.
This massive volume of consumer information has led to the coining
and popularization of the term “big data”. But the advent of big data
has yet to be followed by established practices for managing and using
such data. In short, strategy and execution have not accelerated at the
pace of the technology, which has led to more questions than answers.
How can you extract value from the ever-increasing volume of
customer-related information? How do you transform information into
insight? These are the questions keeping marketers up at night. And
they are hindering our ability to truly connect with customers and justify
return on marketing investment. This paper will explore the common
sources of online data and provide practical advice for accelerating
the collection, storage and most importantly, use of this data to drive
business objectives.



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The Consumer Data Landscape

Evaluating the Online Consumer
Data Landscape
The biggest misnomer about big data is that it
only applies to large data sets. But as Gartner
pointed out way back in a 2001 report, massive
volume is only one defining element of big data.
Variety and velocity also play significant roles.
Variety refers to the diversity of data formats
that exist across multiple sources, which
traditional database solutions often are
poorly equipped to handle. These data types
may include combinations of text strings,
images, web logs, documents, numeric data,
plurals, unstructured blobs, Boolean data and
many others. Marketers and IT wrestle with
inconsistent and unstandardized data on a
constant basis. Without the tools to make sense
of the data, they are fighting a losing battle in
their attempts to understand and leverage this
data to achieve corporate objectives.

Is it really worth it to target consumers based
on behaviors logged weeks or months ago?
Much like attempting to collect rainwater with a
colander, traditional database solutions simply
are not built to handle frequently changing
data sets.
Have digital marketers ever been faced with
such a stark dichotomy? On one hand, there’s
a tremendous opportunity to leverage the
volume, variety and velocity of data out there
to connect with consumers. But on the other
hand, immense confusion exists about what big
data is and how to utilize it.
To shed some light, we have prepared a primer
on a few common online data sources and how
they are evolving.

Social Network Stream Data
Velocity refers to the rate of change for
customer data and how quickly it must be
utilized in order to be valuable. Clickstream
data is a perfect example. The pace at
which technology systems are accumulating
information on consumer online browsing
habits is staggering, yet most marketers lack
the infrastructure to capture and utilize this
information. Without near real-time utilization
of clickstream data, decay rates set in and
targeting efforts become an exercise in futility.


There is a tremendous amount of usergenerated content available on social networks.
In fact, people shared more than 8 billion pieces
of content on Facebook each day in July 2012,
up from 4 billion per day the previous year.
This information can deliver near real-time
consumer insights if marketers can find a way
to make it useful.
Purchase intent, brand advocacy and customer
service issues can all be gleaned by mining


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social network data streams. But doing so isn’t
easy – these streams contain massive quantities
of unstructured data that lack a pre-defined or
consistent model, and unearthing insights from
them is a formidable and costly task.
Accurate interpretation of sentiment is
one challenge marketers face when using
technology algorithms to analyze social network
data streams. Consider parallel tweets about the
iPhone 4S from two different people:

Consider a second challenge faced by marketers
when seeking to mine the social stream.
This tweet clearly indicates purchase intent
on the part of the Twitter user. But how many
data mining services could easily parse the
semantics of this tweet to determine intent to
purchase a replacement phone?

While mining the Twitter stream for marketing
insights at scale has remained a tough nut
to crack, Facebook, by contrast, has actually
laid the necessary groundwork to make such
analysis possible from its own data sets.
Both tweets utilize the same matching
keywords - the object of both is the iPhone 4S,
and the adjective used to describe the phone
in both is “sick”. Most social media monitoring
technologies would have trouble deciphering
the semantic nuance of these tweets. But any
human with a cursory understanding of the
English language and Generation-Y vernacular
would have few problems interpreting their
very different meanings. One of these Twitter
users should be identified as a brand advocate,
while the other should be characterized as a
candidate for customer service.


There was a time when Facebook profiles
were full of unstructured data fields. Facebook
users could type in their favorite music,
books or movies into a text area field, with
each distinct interest separated by a comma.
The loose validation rules and free form text
fields allowed for typos (“Sienfeld” instead of
“Seinfeld”) as well as vague, decoupled data
fields (such as a declared interest in “making
cool things”).


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Then, in April 2010, Facebook launched an
update to profiles. The impact of this update
slipped under the radar, but it drastically
enhanced the ability for Facebook as well
as savvy marketers to extract value from
the data within a
profile. Facebook
essentially indexed
all hobbies, music
artists, books, movies,
television shows
and other attributes,
and created public
community pages
for each. Facebook
then mapped a user’s
declared interests
and other attributes
to these pages, which
enforced a more rigid structure for the data.
The change helped standardize Facebook’s
data set on users, and eliminated the
possibility of typos, erratic free form entries, or
unstructured profile data.


As evidence of its new data validation rules, if
a Facebook user currently attempts to declare
an interest that is not indexed as a public
community page, an error message appears:

The change was a colossal step forward
toward improving the integrity and usability of
social data contained with a Facebook profile.
Facebook’s commitment to transforming
the contents of its users’ social profiles from
unstructured data to consistent, structured
data, in conjunction with the advent of tools
that enable brands to collect this data from a
Facebook profile, has uncovered exciting new
possibilities for marketers. More on this later.
So, if 2010 was the year that Facebook tackled
its unstructured data problem within its user
profiles, 2011 was the year it sought to do the
same for the Facebook news feed stream.
Similar to Twitter, the deluge of posts, updates
and shared content represents a gold mine
for marketers who can find a way to establish
meaning from the clutter. Facebook’s launch
of Open Graph is expected to be the ticket to
enable such insight.


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The cornerstone of Facebook’s Open Graph
is the ability to fuel contextual social sharing
by enabling object-verb associations within
a shared message. Rather than millions of
Facebook users “liking” products and content,
Open Graph goes a layer deeper by enabling
websites to structure a more meaningful verb
with its product or content portfolio. Instead
of simply liking a product and sharing it
back to their news feed, consumers can now
specify that they want a product, or own a
product. This context is invaluable for retailers
seeking to enable effective real-time intent
targeting and segmentation. Similarly, digital
publishers can determine whether a reader
has read an article versus commented on the
article, helping them map levels of subscriber
engagement to their content portfolio.

Purchase Intent

Customer Loyalty Target


Clearly, Open Graph gives users a more
contextual sharing experience and brands a
better opportunity to gain relevant exposure
within the news feed. But aside from achieving
those goals, what’s in it for Facebook?
Think about all of the data Facebook is now
collecting from users as they navigate across
the web. Open Graph-enabled websites are
passing large volumes of semi-structured data
back to Facebook in real-time. In essence,
Facebook has the ability to begin aggregating
clickstream data from its users as they traverse
the web – articles they read, products they
want, recipes they cook, and so on. Over
time, Open Graph could enhance Facebook’s
ability to monetize its data by functioning as a
plumbing system of real-time information from
the rest of the web. For brands to tap into this
information stream and reap the benefits of
deeper user intelligence, they will first need
to employ tools that will collect this semistructured data and extract meaning from it.

Data Collected from Registration +
Social Profile Data
Deriving insights from first-party data assets
is the single most important factor driving
increased investment in marketing data,
according to a 2012 report jointly published
by Winterberry Group and Interactive
Advertising Bureau. And more than any other
characteristics, marketers assess the utility of
a data set based on its quality and accuracy,
followed by its recency or “freshness”.


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Yet, despite these priorities, marketers are
relying on a paradigm for acquiring first-party
data, known as traditional registration, which
undermines the integrity as well as recency of
user profile data. A 2011 report commissioned
by Janrain showed that 88% of consumers
admitted to providing false information when
registering at a website the traditional way.
Compounding matters, for the 12% who actually
provide accurate information at registration,
such data exists in a time warp. Once a
consumer has registered on your site, what is
her incentive to keep her profile up to date?
If her email address changes, is she going
to let you know? How about her location? Or
relationship status?
First-party data collected from users via
traditional registration methods is littered with
flaws that inhibit its value for marketers. What’s
the solution?
Consumers already actively maintain identities
on social networks and email providers such

as Facebook, Twitter, Google and Yahoo!,
and 77% would prefer to use one of those
existing identities to register on sites. Social
login lets people securely and easily signup on a site within just two clicks using an
existing social identity. During this process,
consumers can choose to share demographic
and psychographic information from their social
profile with a brand site.
Marketers can gain a more sophisticated
understanding of their consumers by leveraging
the profile data that people already maintain on
their social networks. Social profile data includes
not only basic demographics such as name,
age, gender, geography and email address, but
also deeper psychographic information such as
interests, marital status, political views, hobbies
and friends. And because the profile information
that users maintain on their social networks is
transparent to friends, family and coworkers, it
is more likely to be current and accurate than
personal data that consumers may supply during
a traditional registration process.

“

77% of users would prefer to use an
existing identity to register on sites.



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Transaction Data
Let’s be clear – transaction data, which
describes change as a result of an event,
certainly doesn’t scale at the level of clickstream
data. The number of consumers who complete a
purchase or add a subscription on a brand’s site
will never compose more than a fraction of total
visitors to that site. But transaction data’s value
to marketers cannot be understated.
Past purchase history often serves as a basis
for merchandising, by enabling marketers to
model purchases to complementary offerings
within a product catalog. Additionally,
transaction data enables marketers to identify
current customers from suspects, and use such
intelligence to create targeted segments.

Clickstream and Third-Party Data
Clickstream data is “big” due to its sheer
volume and velocity. There’s a lot of stuff
happening online, and it’s happening very fast.
Technology companies have been analyzing
web usage since the 1990’s, and the scale of
data amassed has placed their services in high
demand. The power brokers of the clickstream
are data management platforms (DMPs) and
data aggregation services. Put simply, these
technologies use browser cookies to track
what you do, where you do it and when you


did it on the web. Thankfully, they don’t yet
explain why we do it, lest we want computers
deciphering the web’s obsession with cats!
This data is mostly anonymous, meaning it
has been stripped of personally identifiable
information (PII). Data management platforms
and personalization engines aggregate thirdparty clickstream data and develop clustered
audience segments based on users who
share similar characteristics – males in San
Francisco, CA who like surfing, for example.
Brands can use data management platforms
to build audience segments that inform
advertising campaigns.
Once marketers develop an understanding of
how to create the right audience segments
based on business intelligence, there are ways
to augment third-party data sets and make
them more relevant. Using online matching,
marketers can combine explicit first-party data
they collect on their own with third-party data
to create better segments. As an example, by
passing transaction data through to a DMP,
a big box retailer can better understand the
relationship between gadget enthusiasts and
purchase behavior on its site.


PG9

Managing Online User Data

How to Collect, Store, and Manage Online User Data
Marketers have created fences that must
be torn down. According to the Winterberry
Group/IAB report, two of the most signiﬁcant
challenges inhibiting marketers from investing
in big data are:

1.

An absence of tools to unify data silos.

2. The lack of a cohesive strategy to utilize
the data.
As described above, marketers are
overwhelmed with data. It comes from different
sources, it’s stored in different systems, and
different tools are used to analyze it. In short,
digital marketers and IT professionals lack “a


single version of the truth”, which makes it hard
to develop any version of a strategy.
About the big data transformation,
the study claims:

“

Most other marketers – saddled with legacy
technology platforms, depleted of expertise
by years of underinvestment and structured
only to support “traditional” approaches to
data usage – are ﬁnding they’re woefully
unprepared for this transformation.

”


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You see, even the best laid strategies and plans
can be undone by walled gardens. Before
marketers can start utilizing big data, they need
the right infrastructure in place to fuse silos and
build a unified view of a customer.
Consider, for example, the average retail
company within Internet Retailer’s Top 500
list. Most (72%, to be precise) use an in-house
CRM system to store customer data – things
like name, email address, geographic data,
and username and password. But customer
information also gets collected and stored in a
dedicated eCommerce system when customers
purchase products on the site. A slight majority
(53%) also use in-house systems to manage
these eCommerce transactions. Then there
is the payment system, which must collect
and store pieces of customer information as
well, the email marketing solution to enable
sending of offers to shoppers, the fulfillment
system, which consumes a shipping address,
the personalization engine to aggregate
clickstream data, and the ratings & reviews
solution, which often collects portions of a
shopper profile for community engagement.
Now, imagine trying to build a 360-degree
view of a customer by integrating data
elements across each of these disparate
technologies. Many vendor technologies
don’t integrate with other systems, and those
retailers or publishers using in-house systems


are essentially resigned to lengthy and
expensive custom development work to build
integrations. This makes it exceedingly difficult
to extract the insights and build segments
for effective marketing campaigns. Marketers
are focused on building deeper relationships
with customers, but without an easy method
of sharing data between systems, that vision
becomes hazy. There has to be a better way.
As cloud-based technologies and software-asa-service offerings have matured, integrations
across vendors are becoming more common.
Whatever user management solution you
employ as your database of record to acquire
user data and understand your customers,
make sure it provides seamless connectivity to
share up-to-date profile information between
applications. Data-layer connectors enable
marketers to easily map profile data fields
from one system to the other, and create
real-time, batch or on-demand flows of user
data between platforms. Your email marketing
system, for example, shouldn’t have a different
idea of who a particular customer is than
your CRM. Digital marketers should choose
technology systems wisely on the basis of
how well they play within a company’s existing
technology stack.
Let’s again dive into different categories of firstparty data to uncover methods for collection
and storage.


PG11

Data Collected from Registration +
Social Profile Data
Earlier, we described how social login
unlocks the door to obtaining more accurate
information about your consumers and
building richer profiles. Because the profile
data collected via social login includes
demographics, rich psychographic information,
interests and friends lists, social profile data is
more comprehensive than other first-party data
sources, and more reliable and accurate than
the clickstream or third-party data.
Once marketers have access to social profile
data, the next step is to store and utilize it.
With some effort, legacy databases can be
retrofitted through schema modifications to

Social Network Stream Data
We talked above about the wealth of real-time
updates pouring into social feeds. In order to
mine such data for semantics, you first need to
collect it. Sounds obvious, right? At first glance,
social listening technologies are a solid place
to begin your great big social stream data
excavation project. Most of these tools allow
marketers to export data from social feeds into


store social profile data. Or, marketers can
choose an off the shelf social profile storage
solution that automatically captures this
data and pre-integrates with email, content
personalization, eCommerce and other
technology systems.
If choosing an off the shelf solution, it’s critical
to grasp how the technology handles schema
for profile data. When it comes to social profile
data, the ability to enforce structure and rules
on such data, through schema, typically results
in much better data quality and makes it faster
and easier for marketers to build granular
customer segments and feed user profile
data into different technology systems (email,
personalization) for campaigns and programs.

common formats such as .CSV. The exported files
typically include Twitter handles or Facebook
identifiers to help uniquely identify consumers.
Another option is to gather such data either
by using APIs offered by social networks, or
social login vendors that manage these APIs.
Facebook, for example, offers scopes and
permissions that enable brands to access a


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list of all posts within a user’s news feed when
that consumer signs in to a brand’s site using
a Facebook identity. Searches and queries can
then be performed against that data to mine
for keywords that would provide customer
intelligence or indicate intent. Similarly, brands
can access a list of a user’s mobile check-ins with
permission in order to identify places that person
has visited and mine for targeting opportunities.
Now, determining how to store all of this
data is far from a trivial task. The volume
and velocity of data in social feeds makes it
a poor fit for traditional database software.
Beyond social listening tools, explore modern
profile storage solutions that utilize a social
data model. A social data model ensures that
the architecture and schema of the database
can handle fields of unstructured or semistructured data while enabling filtering and
high-performing queries.



PG13

Best Practices to Utilize Data

Best Practices to Take Advantage of Online Big Data
As mentioned earlier, technology has outpaced
strategy in the evolution of the digital era.
We’ve discussed the composition of big data
and how it has become more accessible and
functional for marketers. Once the technology
is in place to access and store such data,
marketers need the skills to actually take
advantage of it.
Historically, we’ve turned to the clickstream
and information contained within cookies as
a go-to data source for targeting programs.
While clickstream data can deliver an adequate
understanding of consumer behavior, past
behavior isn’t always indicative of future intent.

are much lower than with other marketing
programs. Unlike advertising and content
personalization, whose complex algorithms are
heavily reliant upon third-party data, successful
email campaigns can be executed exclusively
using data a brand owns – registration and
transaction information.
B2B marketers have been doing this for years.
Using marketing automation solutions, B2B
marketers create customer segments based on
industry, job function, and stage in the buying
cycle. Prospects within these target segments
then receive a sequence of triggered emails
that are designed to induce brand recall,
purchase intent, or loyalty.

Digital marketers need to evolve their targeting
efforts from what people have done to how
they think. Social proﬁle data holds the key
to this insight. Here are several ways to employ
big data insights into your digital marketing
campaigns, while blending social proﬁle
information with other sources of consumer
intelligence.

Email Segmentation
Email marketing is low-hanging fruit for
digital marketers seeking to apply big data
insights to connect with their consumers.
Compared with online advertising or content
personalization, the skills and barriers to entry
required to execute effective email campaigns



PG14

Certainly, B2C marketers have little use for
a potential buyer’s job role or industry. But
using social login, marketers can instantly gain
permission-based access to the information they
care about - rich demographic data and interests
straight from a consumer’s social network profile.
This data set not only includes a pre-verified
email address, name, location and birth date, but
also relationship status, political views, hobbies,
favorite books, music, movies and television
shows. The key is to create micro-segments of
consumers who share similar demographic or
psychographic characteristics and use them as
the basis for targeting.
When stored in a flexible user management
system and combined with transaction data
from an eCommerce or subscription platform,
marketers can send highly relevant offers to
consumers via email. For example, let’s say
that a registered user in your database has
declared an interest in running. Perhaps some
of your newest running apparel might be an
appropriate recommendation for this consumer.
But what if you could match that declared
interest with her past purchase history, which
indicated that she already purchased a tank
top and running shorts from your company.
Based on this insight, a more appropriate
recommendation might be that popular pair of
running shoes in your product catalog.
Research indicates that targeted emails
containing personalized content and offers
enjoy a nearly 4X greater click-through rate


than generic email offers. By selecting the
right infrastructure to store and leverage social
profiles and consumer data, and taking the
time to build intelligent segments, marketers
can dramatically improve email marketing
ROI. Ensure that your email service provider
integrates tightly with your user management
database, and that seamless sharing and
transfer of consumer data across both
applications is easily achievable.

Product Recommendations & Content
Personalization
Online retailers and digital publishers
traditionally have relied on one method to
inform product recommendations and content
personalization – predictive modeling based on
clickstream data or transaction data. Amazon.
com pioneered the use of clickstream and
transaction data for personalization years ago
by recommending products based on your
browsing behavior and purchase history.
How does the technology actually work?
Retailers and publishers generally outsource it
to specialized personalization engines. These
vendors aggregate data from multiple sources,
including browsing behavior or purchase history,
and utilize modeling techniques to group people
into clusters based on those attributes and
behavior patterns. These audience segments
form the basis of behavioral targeting and
personalization, but they are generated from
archetypes or personas and not uniquely tied to
real consumer identities.


PG15

From a privacy perspective, this is a good
thing. Unless you’ve opted in to sharing
personal information with a brand, do you
really want that company or a data mining
service tracking behaviors that are uniquely
tied to your identity?
The limitations with the most current
methods of personalization is they often
fall short at providing relevant and reliable
recommendations for consumers, and the data
sources are not always stable or actionable
for sustained use in marketing programs.
Personalization engines dynamically serve
content as part of the presentation layer on
a site, but brands often lack access to the
underlying consumer data that informs a
recommendation. And clickstream data is only
as persistent as the tracking cookie placed in a
user’s web browser upon her ﬁrst visit to a site.
Once that cookie is removed, a brand relying
on clickstream behavior for product or content
recommendations must start over from scratch.

So, the clickstream is not dependable due
to its fragility within a browser cookie.
Nor is transaction data universally reliable
as a predictor of intent, due to gift and
experiential purchase behavior. That power
tool purchase I made as a gift for Father’s Day
does not indicate that I want to be served
recommendations for similar products –
power tools are not aligned with my interests.
Similarly, recommendations for travel guides
to Costa Rica long after my trip ended are not
likely to induce future purchase intent.
Here’s the other challenge with transaction
data for retailers – purchase conversion rates
for ﬁrst-time eCommerce site visitors hover at
about 3-4%, meaning that most of your visitors
have never purchased a product from your site.
Without a historical record of past purchases
to inform recommendations, retailers lack data
scale and are left in the dark.

Personalization



PG16

If clickstream data is unreliable and transaction
data isn’t scalable across your audience or the
best predictor of future purchases or content
interests, then what is? A few retailers and
publishers ahead of the curve in this space are
hedging their bets on social data, specifically
leveraging a consumer’s interests, real-time
sentiments and social graph to
inform recommendations.
As mentioned above, audience segments used
in personalization algorithms are generated
from archetypes and not uniquely tied to real
consumer identities. But when a consumer
explicitly shares her social profile data with you,
the paradigm changes. Now you actually know
the potential customer you’re targeting. Imagine
if REI knew that a shopper was really interested
in bicycling and snow skiing from the moment
she connected a social identity on its site, and
could tailor product recommendations to prevent
her from needing to wade through troves of
camping or climbing gear.
Social profile data makes this possible by
providing brands with opt-in access to a
consumer’s interest graph – information such
as likes, hobbies and real-time status updates,
all of which can be appended to existing data
structures to augment personalization efforts.
But social profile data isn’t limited to interests.
Retailers such as Sears, Levi’s and Etsy are


starting to leverage a consumer’s friends to
create social shopping experiences. Sears.com
lets customers share their list of friends from
Facebook with the site, and then recommends
gift ideas based on the birthdays and interests
of the social graph:

Gift Suggestions
For digital publishers, the same principles of
personalization can and should be applied.
Consider the “product” in this scenario to
be your site content. Serving up articles to
readers based on demographics and interests
they have declared in their social profile, or
based on their friends’ activities, is a great way to
increase subscriptions, time on site, commenting
activity, ad impressions and CPM yield.


PG17

Ad Targeting
Complicated by countless data sources,
intricate delivery algorithms and privacy
concerns, success in online advertising doesn’t
come easy. Digital marketers lean heavily on
advertising networks and data brokers to provide
the data and technology required for audience
segmentation. Such information is collected
almost exclusively via browser cookies.
In May of 2012, Microsoft struck a blow
to publishers and online advertisers by
announcing its decision to turn on the “do not
track” specification by default within Internet
Explorer 10. The do not track flag enables
consumers to opt out of behavioral targeting.
Do not track is predicated on voluntary
compliance from ad networks and publishers,
and at this point, while advertising groups
object to Microsoft’s decision, they are offering
to honor the standard.
With Internet Explorer enjoying 32% share of
global web browser usage, Microsoft’s decision
is likely to reverberate throughout the industry.
Despite a historical reliance on tracking
cookies because of their ability to reach scale,
advertisers and publishers may soon need to
resort to other data sources in order to achieve
ROI from personalized ad units. First-party data
collected from registration could suddenly
become infinitely more valuable for advertisers.
Because not all visitors to a site will choose


to self-identify to a site via registration, firstparty data collected at account creation or via
social login will never reach the scale of thirdparty data assembled via cookies. But there
is an opportunity to apply this data within ad
campaigns to augment consumer intelligence,
improve click-through rates, lower cost of
acquisition, and generate more incremental
revenue per impression.
The key is to understand data governance
and deploy a user management system that is
capable of integrating with data management
platforms. As the Winterberry Group and IAB
study observes:

data
“Many are coming to see marketingthe road’
governance – defining the ‘rules of
for assigning distinct data sources to different
promotional tasks – as equally important.

”

Why is this important? Facebook and LinkedIn,
for example, maintain strict guidelines that
restrict how social profile data can be used in
the context of advertising. As marketers collect
social profile data and employ progressive
profiling tactics to build richer profiles on their
consumers by gradually requesting more
information over time, it is critical that their
database helps them understand the source of
a data set and how such data can be used for
ad targeting.


PG18

When handling social profile data from these
networks, a database solution should assist
data governance by helping marketers and
IT strip out personally identifiable information
from a data sample where necessary before
transferring it to a data management platform.
For example, for the social networks with
stricter terms of service about the use of
social data for ad targeting, a comprehensive
user management solution can automatically
categorize stored user data based on its
source, and prevent its circulation to data
management or targeting systems. For all
other registration and profile data stored within
these systems, a common strategy is to create
lookalike audience segments by modeling
explicit first-party information against other
aggregated data.

interest-based audience segments without
the use of clickstream data. When social login
is offered, marketers gain permission-based
access to a consumer’s Twitter data. Querying
that consumer’s Twitter bio and tweet stream
for keywords (such as politics, running or
other hobbies) delivers granular, interestbased audience segments without the use of
clickstream data. In short, Do Not Track doesn’t
need to signal the end of psychographic
targeting. While the future of online advertising
(when using more reliable permission-based
profile data) may result in lower volumes of
targetable consumers, cost of acquisition per
user and return on investment ultimately will
improve.

Other networks such as Twitter are less
restrictive about the use of interests from a
consumer’s profile for ad targeting. This is
great news for marketers seeking to build

Profile Attributes

Targeted Ad

Male + Female
Engaged

Romantic Honeymoon
Getaways

Fargo, ND



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Conclusion
In the vision

of the future web outlined by
Tim Berners-Lee, technology will
evolve to understand the meaning, or semantics, of information on the World
Wide Web. When it comes to collecting insight from the torrent of online
consumer data, this hasn’t happened quite yet, partly because computers
are not smart enough but also because we’re constrained by data silos and
lack a cohesive strategy. But with time, the data silos will disappear, and
advanced tools to mine social network streams and utilize social proﬁle
data will become a necessity for brands. As they do, marketers will gain the
technology interoperability and insight needed to truly harness the power of
big data. Will you be ready?



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About Janrain
The Janrain User Management Platform (JUMP)
helps organizations succeed on the social web
by providing leading technology to leverage the
popularity of social networks and identities for
user acquisition, engagement, and enhanced
customer intelligence. Our solutions, including
social login, social sharing, social proﬁle data
collection and storage, access to the social


graph, and digital strategy services, improve the
effectiveness of online marketing initiatives for
leading brands like Fox, Universal Music Group,
Whole Foods, MTV, Purina, Avis and Dr Pepper.
Founded in 2005, Janrain is based in Portland,
Oregon. For more information, please call 1-888563-3082 or visit www.janrain.com and follow
@janrain.


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From information to insights: understanding Big Data online

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From information to insights: understanding Big Data online