Dynamic Site Generation - INFORMS NY- OR/MS Monthly Meetings

11/30/2010

[ + ] Make every interaction count

Dynamic Site Generation

PDF compression, OCR, web optimization using a watermarked evaluation copy of CVISION PDFCompressor

http://www.cvisiontech.com/

Introduction to [x+1]



© [x+1] 2010, Proprietary and Confidential

[x+1] Overview

3

Founded: 1999

Positioning: Predictive analytics solutions and services focused on „multi-touchpoint‟ marketing optimization across all digital channels

Technology: Patented Predictive Optimization Engine (“POETM”) and Open Data Bridge; Proprietary campaign management platform

Management: Leading team of industry veterans from AOL, DoubleClick, Modem Media, and Third Screen Media

Headquarters: New York, NY

RepresentativeCustomers:




The [x+1] Platform

4

The [x+1] Platform optimizes results from display media, search landing

pages and websites.




• Massive amounts of data from website interactions

• Even more data from off-site interactions in search and media

• But the data is siloed…

• …and its mostly anonymous data…not easily linked to everything we know offline.

• Its expensive to store, to process and interpret

• It‟s even harder to make it actionable at the point of contact with the consumer… because real time contact on websites and online advertising requires data to be collected and analyzed and content decisions made in milli-seconds

The online marketer’s challenge:




[x+1] provides an audience targeting platform that enable marketers and agencies

to optimize prospect and customer interaction across multiple digital channels

Relevant communications




A common data and reporting infrastructure ties the products together and enables advanced custom analytics

The decision server (SSM) delivers the decision or the content to the 3rd party ad or content server for each channel

Display SiteSearch / LPO

Data and Reporting

Targeting /Decisioning

Serving

Tools and Interface

Email Offline:

Direct MailCall Center

Courier

Video

Unified Interface Integrated Partners and Open API‟s

Reporting and Analytics

[x+1] Technology Platform

POE models provide algorithmic optimization

decisions in real time

List builder creates contextual, geographic/demographic data,

and behavioral lists

Rules engine allows for marketer defined decisions, alone or

combined with POE




POE™ is the heart of the solution

identifies consumer attributes correlated with positive responses to a marketer's advertising and uses this information to identify target audience profiles

HTTP and IP DataDemographic

DataContextual Data Customer DataBehavioral Data

• Northern California

• White male, age 50

• Low bandwidth

• Middleburg manager

• Thursday

• Home

• 3:30 p.m.

• Clicked on search results

• Long-term customer

Audience 1

• Northern Utah

• Teenage female

• Medium bandwidth

• Suburban sprawl

• Thursday

• Work

• 6:30 p.m.

• Repeat customer

• SkyMiles user

Audience 5

• Central Missouri

• Professional services

• Male, age 35


• Wednesday

• Home

• 9:30 p.m.

• Saw online ad

• Gold loyalty member

Audience 8 Audience 12

• Urban New York


• Graduate student, male

• Bohemian mix

• Friday

• Home

• 11:45 p.m.

• Visited marketer’s site

• Frequent purchaser




0%

20%

40%

60%

80%

100%

120%

0% 20% 40% 60% 80% 100%

% of Display Media

% C

on

vers

ion

s

Without [x+1] Initial Predictive Model Refined Predictive Model

Our value proposition

$4.20

$2.53

$2.12

$1.05$0.80

$0.32

$0

$1

$2

$3

$4

$5

1 2 3 4 5 6

Seg

em

en

t V

alu

e

[x+1] Technology Predicts the Response Rate and Value of Online Users, One Impression at a Time…

…in order to Maximize the Performance of Online Media and Websites

Without [x+1], in a typical display campaign, 20% of the impressions account for 80% of the conversions

Audience Segments

[x+1]‟s predictive marketing engine substantially increases conversion rates among highly-valued customers and prospects



Digital Marketing Hub




Search

Landing page

Display

E-Mail

Offline Lists

Client Website

POE-enabled

Real-Time

Targeting

Client

Marketing

Database

Mobile

Video

Product portfolio

Offline transactions

Direct mail exposure

Tenure

Manage all prospect and customer touch-points

through a single decision engine



Client Marketing Data Warehouse

Search

Display

E-Mail

Lists (other channels)

3rd Party Data

Multi-Channel Prospect/Customer Engagement Management & Delivery

Targeting

Operations

ModelingRules,

Testing Lists

Reporting

Execution Response Attribution

List Mgt CampaignSet-up

Real-TimeProspect / CustomerTargeting Database

Case study: Travel Company optimizes customers

and prospect interaction across digital channels




First the hero shot…

then other placements made dynamic

B

D

C

E

ADynamic Variables:

Headers

Headlines

Copy

Action Buttons

Images

Dynamic pages enable a personalized experience that is integrated with

any banner ad or search keyword campaign.

A

B

C

D

E

The starting point was a homepage used to show the same static

message to all site visitors




After [x+1] Optimization

• Optimized Pages:

• Tailored to the unique needs of each individual.

• Leverageanonymous + customer data and continuously adapts to changing customer needs in real time.




• [x+1] identified discrete audience segments for the advertiser, and can serve those segments unique pages• Personalization of pages drives lift and

produces significant incremental revenue

• In 6 months, [x+1] has generated 40x ROI

• Through conversion tracking [x+1] has been able to advise which creative copy and imagery are most successful at driving ticket sales

Audiences are identified

and addressed with uniquely relevant content

Hero Message




An end-to-end solution synchronizes messaging

across channels

lists / attributesemail

digital media

Landing pages

websites

The vision:

• Unifies digital marketing campaigns• Acts on insights in real time• Imports and manages audience data• Finds audiences across the entire internet• Brings value to every customer interaction

Consumer

Interaction

Database



High-Level Solution Overview




High Level Solution Overview

Inbound

TrafficThank You

POE Decision

Data from the [x+1] decision tag, tracking tag, and offline exchange are inputs into POE, which determines the optimized experience at an audience level.

Tracking Tag

Personalized Page Conversion PagesBehavioral Pages

Conversion data

passed real-time

and supplemented

in batch file

Behavioral tags collect

anonymous and

custom data, passed

real-time to [x+1]

[x+1]’s POE™ analyzes

site visitor attributes to

determine best

personalization recipe

4

Clients display

personalized creatives

based on [x+1] response

via decisioning tag

312

Tracking TagsDecision Tag

Search

Display

E-Mail




Content Decision Flow

Properties/Placements

Call to [x+1] for content decision

[x+1] reads visitor data and makes

content decision (using business

rule or model)

[x+1] passes content decision back to

COF with JSON response

Clients receive content decision,

builds page

30

0 m

illise

co

nd

s

• Integration via javascript tags / pixels, calls to [x+1] CDN

• 300 millisecond response time / SLA

Page request…

Decision response…

Page rendering…

12

34

Smartserve Ad Server




• Site+1 integration requires clients to implement three types of tags, which will pass data to [x+1] through the tag’s query string. • Content Decision Tags: Single tag implemented on any page where [x+1] serves

content decisions. (Ex. www.charter.com)

• Behavioral tags: Collect anonymous and custom data, that can be deployed within modeling and decisioning

• Tracking Tags: Required on all confirmation pages and any pages where behavioral tracking is required (Ex. Application Submission Page)

• Each tag will utilize unique on-site attributes to collect real-time user information within the [x+1] tag request

Tag Overview




• An Offline Data Feed can supplement conversion data collected onsite, when a final disposition is not available.• The offline data feed is particularly useful when approvals happen through an offline

process (credit cards, insurance, etc)

• Working with on-and offline data is relatively simple, and requires:• A common ID to coordinate data sets (ex. Member ID, application ID)

• Capacity to post data to sFTPserver, for download by [x+1] Analytics

Offline Data Feed




[x+1]’s Personalization Approach

Design and Implement Learn and Build Optimize and Report1 2 3

• Define audience

segment hypotheses

• Generate content

delivery tags, install on

sites.

• Set up programs in

[x+1] systems

• Establish control group

• Develop content

• Integration testing

• Launch program

• Measure results across

segments and content

alternatives

• Provide reporting UI

access, training and

ongoing assistance

• Continual model

refinement

• Analyze and report on

performance.

• Provide ongoing

assistance with analytic

interpretation and best

practices

A

B

C



Model Methodologies




• [x+1] utilizes product based response modeling to drive acquisitions

• A wide range of model techniques are tested to produce the best model to meet the business goal

• Different methodologies are used for different business goals

Model Methodology

Model Goal:

• Response: Segments audience based on response rates for specific marketing offers. Typical

• Lookalike: segment existing customer base. Most relevant pre targeting of media campaigns.

• Value: 2 step model first identifying response and then weighting based on fulfillment / lifetime value

Model Techniques:

• Logistic regression

• OLS (linear regression)

• Regression/Decision trees (C&RT, CHAID)

• Neural networks




The modeling approach is progressive

25

1 Learning Period• Assumptions/Rules defined

• Tracking and Observing

• Build significance

Optimization

Modeling

Learning

Period

Insights/

Refine

2 Modeling• POE identifies influencing attributes

• Models Generated, Lists identified and built,

Rules implemented

• POE supports tree, regression, an neural

net modeling

3 Optimization• POE Core Value

• Ongoing, real-time optimization of program

Insights/Refine• Audience Reporting & Analysis

• Additional Tests, Model and rule refinement

4




• Standard Attributes

• Geographic, demographic, technographic, and behavioral

• Custom Attributes

• Attributes provided by our clients for decisioning

• Passed real-time through onsite attributes or offline through batch process

• 3rdParty Attributes

• [x+1] partners with 3rd party data providers to enhance targeting/testing

• Attributes provide demographic, category-propensity, social graph, organic search, brand safety, in-market intender and contextual data

Models are built using a combination of [x+1]

standard, client custom, and 3rd party attributes




• Low conversion rates, especially within digital media that can see rates of much less than 0.1%

• Need to be especially careful to avoid over fitting data

• Various technique, akin to bootstrapping, are deployed

• Important to track live results and adjust to changing conditions

• And, compare everything to random controls and BAU cells

Our Challenge

27PDF compression, OCR, web optimization using a watermarked evaluation copy of CVISION PDFCompressor


Some Key to Success




What Drives [x+1]?

Creative drives only incremental gains




• Know what you are optimizing• Clicks (highly not recommended –not proven to date)

• Conversions

• Product-based revenue (avg.)

• Dynamic revenue

• Customer segment level product revenue

Keys to Success




• Selective targeting• Model to our intended audience

• Differentiated (& Track-able) Experiences• With at least some offers that perform nearly as well as the Champion

• Personalization hypotheses (how to differentiate right offer for right person) are identified and evaluated• Cannot create personalization magic where no personalization opportunity exists

Keys to Success




• Temporary / seasonal assets• Group into categories, analyze/model the categories, and then change individual assets

within the categories as needed

• Pros: Individual assets can be switched into and out of categories (ideal for promotional efforts)

• Cons: Analytics is compromised to some extent as the dependent variable is the category rather than the asset itself

• If temporary assets cannot be grouped into categories, segregate temporary assets from ongoing analytics and modeling

• Pros: Does not require categorization, nor does it introduce categories into modeling as dependent variables

• Cons: Further complicates the user experience / analytic plan / reporting; removes conversions from the learning pool

• Ever green assets• Enter new assets into learning cells and perhaps overweigh them (randomly show them

more often that other assets in the control) until suitable quantities are obtained

Adding Additional Assets

- No Perfect Answer



Thank you



Documents

Dynamic Site Generation - INFORMS NY- OR/MS Monthly Meetings