In this presentation, Chris Matty from Versium describes the company's Real-Life Data Intelligence Platform. "Versium aggregates and analyzes vast volumes of real life data, deriving actionable insights
and enabling data driven applications. LifeData: Billions of demographic, psycho-graphic and social-graphic data points on businesses and consumers that are created through real life actions and activities. Purchase
 interests, education level, family status, social behavior, etc. Versium gathers, indexes and analyses this data ultimately making it available to its partners. In consumer marketing, LifeData allows ever-narrower segmentation of customers and therefore much more precisely tailored products or services. Greater real life insights about people and businesses enable optimized communication strategies. Sophisticated analytics can predict behavior and greatly improve decision-making. LifeData can make existing applications more intelligent and enable entirely new ones. Versium’s powerful data intelligence platform enables all of this and more." Watch the presentation video: http://inside-bigdata.com/slidecast-versium-real-life-data-intelligence-platform/ Learn more at: http://versium.com

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Real-Life Data (LifeData) Intelligence

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Versium)(www.versium.com))is)a)data)
technology)company)that)operates)a)real$
life)data)(LifeData))services)and)analyPcs)
plaQorm.))
)
These)technologies)enable)organizaPons)
to)be)more)data$driven)by)powering)and)
creaPng)soluPons)that)help)opPmize)
consumer)engagement,)improve)
markePng)efficiencies)and)be-er)
understand,)retain)and)find)new)
customers.)))
)
Versium)brings)together)disparate)sets)of)
observaPonally)available)data,)comprised)
of)over)100)billion)real$life)a-ributes)and)
combines)these)insights)with)an)
organizaPon’s)exisPng)enterprise)data)to)
provide)a)richer,)more)acPonable)profile)
of)each)individual)consumer)and)more)
intelligent)analyPc)models.)
)
WWW.VERSIUM.COM
About
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A"person’s"real"life"profile"is"a"tremendously"powerful""
predictor"of"how"that"person"will"interact"with"an"enterprise"

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CHRIS MATTY - CEO)
• Has held executive positions including GM, CMO,
SVP of Sales/Business/Corporate Development
• Founding executive of InfoSpace
- Grew revenue to $200M+
- General Manager, Merchant Services Division
- SVP Business Development and Sales
- Led wireless data strategies
• Start-up executive at: DepotPoint, Payscale,
Smartworks
• Previously McCaw and AT&T
)
Founders
3
http://www.linkedin.com/in/chrismatty/

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KEVIN MARCUS - CTO)
• Co-founder and Chief Technology Officer
of Intelius
• Co-founder and Chief Software Architect
of InfoSpace
• Author and co-author of several issued
patents related to user authentication,
messages, geographic search and CPU
emulation
• Has advised on over 30 mergers and
acquisitions)
Founders
4
www.linkedin.com/in/kmarcus

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What We Do
" LifeDataServices
" LifeDataAnalytics

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Real-Life Data Services (LifeData)
" 100+ Billion consumer real-life data (LifeData) attributes
" Information created as a person interacts in the real
world outside an enterprise – Both online and offline
" Social data, demographics, interests, purchase histories,
real-time life event trigger data, More…
" 3.2 billion email database for matching LifeData to
consumers
" Patent pending validation and consumer profiling technology
" All available via API; priced per query and bulk license

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LifeData – Data Services Platform
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100+ Billion LifeData Attributes
3.1 Billion Emails 200 million Mobile Phones with Name/Address
145 Million Social Profiles and Likes
Email)
Address)
Phone)
First)Name))
Last)Name)
Address))
City)
State)
ZIP)
DOB)
IP)Address)
OpPn)URL)
Source)
Timestamp)
Line)Type) Carrier)
Email)is)Socially)AcPve)
Mapping)to)Social)Network)
Social)Profile)Behavioral)Data)
Number)of)ConnecPons)
Facebook)Likes)
Facebook)Profile)ID)
LinkedIn)Profile)ID)
Twi-er)Profile)ID)
375 million Unique Opt-In
Email Addresses
And More…

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60M Profiles)
500M$Likes$Mapping$to$Email$
Like$analy4cs$
Num$friends,$subscribers,$likes$
Social$graphic$details;$ac4vity)
45M Profiles)
$$$$$Number$of$Followers$
$$$$$Number$of$Tweets$
$$$$$Tweet$Stream$Analysis$
)
65M Profiles)
$$$$Number$of$Connec4ons$
$$$$Career$Details,$Educa4on$Etc$
$$$$Professional$Network$AHributes$
)
LifeData - Social
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And More…

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Full)Name)
Address)
City)
State)
ZIP)
County)
Phone)
Date)of)Birth)
Date)of)Birth)
Home)Value)
Purchase)Price)
Purchase)Amount,))
Purchase)Date)
Purchase)Lender)Code)
Lender)Name)
Interest)Rate)Type)
Purchase)Interest)
Loan)to)Value)
EsPmated)Wealth)
Credit)RaPng)Range)
)))Est.)Income)
Est.)Household)Income)
Net)Worth)
Range)of)New)Credit)
Credit)Card)User)
Visa)Card)
Visa)Gold)Card)
MasterCard)
American)Express)
Discover)Regular)
Financial$
Demographic$
240 Million Records With Interests & Purchase Data
Apparel))
Audio)Books)
Books/Magazines)
Career)Improvement)
Cat)Owner)
Dog)Owner)
Charitable)Donor)
ChrisPan)Families)
Computers)
Consumer)Electronics)
DiePng/Weight)Loss)
DIY:)Home)
Improvement)
Electronics)
Gaming)
Gardening)
Health)&)Beauty)
Health)Medical)
High)End)Appliances)
InvesPng)
Military)History)
Music)
Online)EducaPon)
Online)Purchasing)
ParenPng)
Self)Improvement)
Single)Parent)
Spectator)Sports)
ParPcipant)Sports)
Camping/Hiking)
Fishing)
Golf)
Scuba)Diving)
TV/Cable/)Sat/Dish)
Veteran)in)Household)
Interests$
And More…
100+ Billion LifeData Attributes

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LifeData – Event Trigger Data
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Life Events Change
Behavioral Patterns,
Interests and Desires

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" Analytics using enterprise data combined with real world LifeData
" LifeData signals and patterns mapped to enterprise segments –
yield actionable insights and improved ROI
" More accurate machine learning and analytic results
" Analytic output includes: Predictive scoring, predictive
segmentation, social influence level, predicted behavior…
" SaaS Pricing Model: Monthly Fee plus variable component based
on size of customer base to be modeled or scored
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LifeData ANALYTICS

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Analytics built using enterprise data combined with LifeData
LifeData ANALYTICS

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Objectives – Social Commerce MLM
! Problem: Limited insights and understandings about selling base
! Can better understanding different member segments provide
insight into how to improve member performance?
! Can we use analytics to predict or reclassify members in order to
identify members most likely to improve in performance?
! Can we leverage Social Influence scoring to predict viral success
! Can we compare member base to other top MLM companies using
Social and LifeData analytics
$

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CLIENT:
MLM)Company)
)
PROJECT:
LifeData)insights)and)segment)comparison)
Compare)members)to)other)MLM)groups)
Social)influence)scoring)
Predict)viral)members)
)
Results:
50%)Match)of))members)to)LifeData)
Successful)LifeData)a-ribute)idenPficaPon)
More)male)then)female)vs)other)MLM’s)
Viral$45%$more$likely$to$have$SI$score$8N10$
19%)Non$viral)modeled)as)“likely”)viral)
Prospect)gen)modeled)to)other)MLMs)
))
)
)
CASE STUDY – Social Commerce MLM
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Social Analytics

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Viral vs Non-Viral SI Score

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Chris$MaHy$
$
Phone:)425$941$9080)
Email:)cma-y@versium.com)
Thanks for your time
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Address:$
7530)164th)Ave.)NE)
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Redmond,)WA)98052)