real-time decisioning for big data

Real-time Decisioning for Big Data:Evolving the Service with Audience Measurement

Presented by Marc Price, CTO AmericasJuly 2014

2w w w . o p e n e t . c o m© Copyright 2013 Openet – Company Confidential

For Use Under Non-Disclosure Only

Openet delivers the infrastructure to enable Operator Innovation

A global leader with more

than 80 customers in 32

countries, Openet

provides insight,

interaction, control, and

monetization within the

world's largest and most

complex networks.

Openet helps control costs and increase

revenue by enabling customers to innovate

how people, machines and services

engage with their network

Openet helps increase revenue and

control costs by enabling customers to

innovate how people, machines, and

services engage with networks

Cable

Mobile

Machines

People

ServicesFixed

Real-time

transaction

management



• Service Data and Customer Data can be leveraged in unprecedented ways to drive revenues, increase loyalty, and reduce costs

• New techniques for big data require real-time processing for low latency analytics, as well as pre-processing for batch analytics

• An example of valuable analytics for service usage is audience measurement for video viewing across various platforms

• New techniques enable streaming event processing, in-memory No-SQL storage, real-time decision triggers, and holistic data delivery for personalized services, recommendations, analytics, dashboards, and beyond

Highlights



The Digital Communication Age

The volume and variability of immediately available offerings make the communication

decision-making environment quite distinct from other billable services.

The Challenge is to maximise every interaction Experience

Interaction Types:

• Ad hoc

• Occasional

• Informative

• Social

• Regional

• National

• International

• Short term

• Regular

• Excessive

Interaction Types:

• Information

• Entertainment

• Purchasing

• Business

• Social Media

• Messaging

• Browsing

• Posting content

• Gaming

• Tweeting

• Communication



Growth of Personal Data

This richness of big data provides an enormous potential to personalize services - yet

customers still marvel at how little their service provider seem to know them

The volume of personal data is

growing exponentially & at a

staggering rate to the actual

number of subscribers

Per Subscriber

Personal Data

My Plan

My usage

My spend

My Contacts

My devices

My fav apps

I’ve been a customer for

years… I never receive any

relevant offers… I want more

interactive, informative contact



1998

Prepaid

2000

GPRS

Unleash the power of real time personalization

Despite the vast amount of customer data that exists, marketing continues to rely on traditional

segmentation schemas and static profile attributes that provide an incomplete view of the customer

• Traditional BI systems, data warehouses and CRM systems are ill-equipped to support dynamic customer profiling

• Operators know they are not reaching certain groups of people, and may not even identify a “category” before it is too late

• Legacy systems are not designed to deal with the volume, variety, velocity and volatility of big data to deliver value, relevance and immediate responses to market needs

2005

3G

2015

HetNets

2013

LTE

2014

IMS1996

2G/ GSM

Patchwork of legacy infrastructure

supporting the evolved technology &

services. Optimizing subscriber profit

margins is now a fine art with rising

infrastructure OPEX costs

2007

HSDPA

2010

WiFI

OPEX



Seizing the Moment

Analytics insights require useful and actionable information through

timely actions to process data when it is most relevant

Tier 1(first order

Analytics)

Tier 2(second

order

Analytics)

Tier N(nth order

Analytics)

Tier 1(first order

Analytics)

Location A

Location B

Window of Insight

Window of Insight

Window of Insight

ADS

Triggers

Batch

ie. Instructing Policy Use Cases,

Recommendations

Insights

Insights

Insights

Status

Scenario Elligibility

Condition A

Condition B

Condition C

Condition X

<15 milliseconds

1 second

15 seconds

For a particular subsriber:

Continuous stream of data

ADS: Advertising Decision Server

ie. Ad Decisions

ie. Lifecycle analytics



Operators are seeking Reporting and Analytics solutions in response to new business drivers

Delivering value, relevance and immediate response

• Intelligent Upsell use cases: creating morerelevant, timely and personalized services

Real Time Decisioning

• Real time behavior visibility & detection of network service impacts and billing impacts

Real Time Service Assurance

• Enable content provider sponsored data, Relevant advertising, B2B revenue chains

Sponsored Data andAdvanced Advertising

• Understanding viewing impressions and related applications usage to aid with marketing, upsell and content negotiations

Audience Measurement

Some Examples:



Detailed video viewing impressions and contextual usage statistics better support

marketing and advertising campaigns as well as content negotiations

Case Study: Audience Measurement

Audience measurement incorporates analytics for:

• Linear television program viewing

• Video on demand viewership

• Ad campaign viewership

• Interactive content engagement



Detailed video viewing impressions and contextual usage statistics better support

marketing and advertising campaigns as well as content negotiations

Audience Measurement: Challenges

Challenges include:

• Normalization across traditional cable systems and IPTV and other platforms

• Correlating usage for “Second screen” and multiple devices with linear viewing

• Household vs. Individual user data

• Protecting Personally Identifiable Information (PII)

• Supporting Data Governance techniques



Enables operators to generate subscriber profile and segmentation with unprecedented

depth and accuracy

Audience Measurement: Highlights

Usage information is validated, normalized across platforms, then enriched with

marketing & subscriber information handling opt-in/out for behavioral viewing metrics.

The solution collects and correlates second-by-second click stream, linear TV

viewing, on-demand and interactive TV events.

Delivers profile information across systems in a useful, anonymous format to internal

and third-party marketers, advertisers and buyers to enable the planning and

measurement of addressable advertising campaigns and marketing promotions



Audience Measurement: Architecture

Data Sources:

ActIngest Analyze

Ad campaigns

HSD

Events

Wireless

Data

Events

Linear TV

Tuning

Events

VOD/PPV

Events

iTV

Events

DVR

Events

Ad Inserts

TVE

Events

Filtering

Parsing

Validation

Translation

Aggregation

Correlation

Enrichment

Multi structured

data for streaming

and batch analytics

Data Collection &

Processing

Content rights negotiation

Marketing campaigns

Recommendations

Real Time Insight

Data Analyzed in

motion

Storage



Measure and influence customer behavior

• Measure:

• Geographic/Demographic Trends

• Screen/Device Usage trends

• Ad Viewing Trends

• Content Viewing Trends

• Content Ratings

• Influence:

• Ad Placements and pricing and therefore Ad Revenue

• Content Pricing driving content revenues

• Targeted Offers driving improved service uptake and revenues

• Recommendations driving increased usage revenue

Big Data techniques applied to Video Viewership enable service providers to:



Conclusions

• Improved Customer Satisfaction

• Boost in Loyalty

• Reduced Costs

New techniques for managing service usage data enable:

real-time decisioning for big data

Technology

o p e n e t

customer data

service data

openet company confidential

data warehouses

volume of personal data

volatility of big data

richness of big data