Anto A Chittilappilly from 12 Ruane Cir, Newton, MA 02465, age 57

Resumes

President, Co-Founder And Chief Technology Officer At Visual Iq

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Location:

75 2Nd Ave, Needham, MA 02494

Industry:

Marketing And Advertising

Work:

Visual IQ, Inc. - Boston, New York, San Francisco, Chicago, London, India since Aug 2005
President, Founder and CTO
Abacus Software Pvt Limited Jan 1996 - Jan 2007
President and Chairman
Sun Microsystems Inc May 2001 - May 2004
Software Architect
Oracle Corporation May 1994 - May 2001
Software Architect
IRI Software May 1994 - Apr 1995
Software Architect

Education:

Harvard University 2000 - 2001
University of Calicut

Skills:

Analytics
Business Intelligence
Digital Marketing
Big Data
Strategy
Software Development
Digital Strategy
Data Warehousing
Entrepreneurship
Databases
Cloud Computing
Start Ups
Saas
Web Analytics
Marketing
Data Mining
Oracle
Product Management
Business Analysis
Enterprise Software
Agile Methodologies
Product Development
Marketing Analytics
Data Analysis
Project Management
Online Marketing
Online Advertising
Romi
Marketing Optimization
Marketing Attribution
Marketing Mix Modeling
Social Media Analytics
Statistical Modeling
Architecture
Quantitative Analytics
Software Engineering
Engineering Management
Innovation Management
Media Mix Modeling
Marketing Roi
Marketing Automation
Software Architecture
Algorithm Development

Interests:

Science and Technology
Table Tennis
Skiing

Languages:

English

Publications

Us Patents

Method And System For Determining Touchpoint Attribution

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US Patent:

20130332264, Dec 12, 2013

Filed:

Jun 8, 2012

Appl. No.:

13/492493

Inventors:

Anto Chittilappilly - Waltham MA, US
Madan Bharadwaj - Billerica MA, US
Payman Sadegh - Arlington VA, US

International Classification:

G06Q 30/02

US Classification:

705 1445

Abstract:

A system and method for allocating credit for an advertising conversion among various advertising touchpoints encounter by the consumer is provided. The system and method comprise receiving data pertaining to touchpoints and conversions of an advertising campaign across multiple channels. Users are correlated across the channels and the various conversions, touchpoints, and touchpoint attributes are identified. Each touchpoint attribute and touchpoint attribute value is assigned a weight. An attribution algorithm is selected, and coefficients are calculated using the assigned weights. The algorithm is executed and true scores corresponding to the touchpoints encountered by each converting user are computed.

Machine Learning Techniques That Identify Attribution Of Small Signal Stimulus In Noisy Response Channels

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US Patent:

20180005140, Jan 4, 2018

Filed:

Jun 22, 2017

Appl. No.:

15/630806

Inventors:

Anto Chittilappilly - Waltham MA, US
Payman Sadegh - Alpharetta GA, US

International Classification:

G06N 99/00
G06Q 30/02
G06N 7/00

Abstract:

A method, system, and computer program product identifies attribution of small signal stimulus in noisy response channels. Using machine-learning techniques in a computer, a small signal correlation engine correlates time series stimuli data vectors to time series response data vectors, and generates correlation coefficients that identify contributions of event notifications, including small signal attributes, to aggregated response data. Also using machine-learning techniques in a computer, a learning model simulates variations of stimuli data to predict user responses using the correlation coefficients, including computing a contribution of the small signal attributes of an event notification.

A Method , Computer Readable Medium And System For Determining Touchpoint Attribution

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US Patent:

20180005261, Jan 4, 2018

Filed:

Mar 7, 2013

Appl. No.:

13/789453

Inventors:

Anto Chittilappilly - Waltham MA, US
Madan Bharadwaj - Billerica MA, US
Payman Sadegh - Arlington VA, US
Darius Jose - Thrissur, IN

International Classification:

G06Q 30/02

Abstract:

Media Spend Management Using Real-Time Predictive Modeling Of Media Spend Effects On Inventory Pricing

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US Patent:

20170337505, Nov 23, 2017

Filed:

Apr 19, 2017

Appl. No.:

15/491903

Inventors:

Anto Chittilappilly - Waltham MA, US
Payman Sadegh - Alpharetta GA, US

International Classification:

G06Q 10/08
G06N 5/04
G06N 99/00
G06Q 30/02

Abstract:

A method, system, and computer program product for media spend management. An Internet media planning and purchasing application executes on a management interface device. Servers execute operations to predict various inventory and pricing effects that result from a particular Internet media planning and purchasing plan. Machine learning techniques are used to form a stimulus attribution predictive model based on stimulus data records and respective response data records received over a network path. Additional predictive models are formed, including (1) an ad inventory predictive model derived from ad inventory data records and (2) an ad pricing predictive model derived from ad pricing data records. A set of media spend allocation parameters are received from the management interface, and those parameters are used to produce predicted inventory changes that in turn affect parameters in the ad pricing predictive model. Media spend allocation performance parameters are predicted based on the affected media prices.

Dynamic Media Buy Optimization Using Attribution-Informed Media Buy Execution Feeds

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US Patent:

20170337578, Nov 23, 2017

Filed:

Apr 19, 2017

Appl. No.:

15/491908

Inventors:

Anto Chittilappilly - Waltham MA, US
Payman Sadegh - Alpharetta GA, US
Philip Gross - Newton MA, US

International Classification:

G06Q 30/02

Abstract:

Touchpoint encounters, which represent exposure to messages transmitted through a network to users, include attributes that define universal unique identifiers (UUIDs) for user devices and at least one cross-device user engagement stack. The cross-device user engagement stack consolidates the touchpoint encounters from the user devices with different UUIDs but associated with a single user. A stimulus attribution predictive model outputs attribution parameters to estimate an effectiveness of the messages to elicit positive responses from the users. Media buy execution feed parameters are generated to quantify a set of spending amounts, based on the attribution parameters, so as to specify a cost-effective amount to deliver one of the messages to the users. The media buy execution feed parameters are delivered to programmatic media buying execution platforms that attempt to deliver the messages in accordance with the spending amounts.

Cross-Device Message Touchpoint Attribution

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US Patent:

20170337588, Nov 23, 2017

Filed:

Apr 18, 2017

Appl. No.:

15/490757

Inventors:

Anto Chittilappilly - Waltham MA, US
Payman Sadegh - Alpharetta GA, US
Philip Gross - Newton MA, US

International Classification:

G06Q 30/02
G06N 7/00
G06N 99/00
G06N 5/02

Abstract:

Fragmented user engagement stacks are generated from users that use multiple devices to view messages. The fragmented user engagement stacks include a universal unique identifier (UUID). A computer platform stores cross-device mapping information, derived from a shared characteristic between two or more devices, that associates the UUIDs of multiple devices to a single user. The computer platform processes the cross-device mapping data to identify the UUIDs from different devices associated with a single user and to join touchpoint encounters from the single user to generate at least one cross-device user engagement stack. The computer platform uses the cross-device user engagement stack and the response data to determine attribution as a measure of influence attributed to touchpoint encounters from a single user.

Media Spend Management Using Real-Time Predictive Modeling Of Touchpoint Exposure Effects

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US Patent:

20170323330, Nov 9, 2017

Filed:

Apr 18, 2017

Appl. No.:

15/490751

Inventors:

Anto Chittilappilly - Waltham MA, US
Payman Sadegh - Alpharetta GA, US

International Classification:

G06Q 30/02
G06Q 30/02
G06N 99/00

Abstract:

A touchpoint exposure predictive model defines the relationship between a number of messages deployed in a message campaign and the response so as to model diminishing returns on the response due to the number of messages. A predicted message deployment—response curve is rendered on a display of a user computer depicts the effectiveness of the response to the messages. The user runs a simulation to increase the number of the messages in the campaign, and a modified message deployment—response curve for the messages, which incorporates diminishing returns, is rendered from the touchpoint exposure predictive model.

Cross-Channel Predictive Model

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US Patent:

20170300832, Oct 19, 2017

Filed:

May 23, 2017

Appl. No.:

15/603352

Inventors:

Anto Chittilappilly - Waltham MA, US
Madan Bharadwaj - Billerica MA, US
Darius Jose - Thrissur, IN

International Classification:

G06N 99/00
G06Q 30/02
G06N 5/04

Abstract:

A method, system, and computer program product for advertising portfolio management. The method form processes steps for determining effectiveness of marketing stimulations in a plurality of marketing channels included in a marketing campaign. The method commences upon receiving data comprising a plurality of marketing stimulations and respective measured responses, then determining from the marketing stimulations and the respective measured responses, a set of cross-channel weights to apply to the respective measured responses, where the cross-channel weights are indicative of the influence that a particular stimulation applied to a first channel has on the measure responses of other channels. The cross-channel weights are used in calculating the effectiveness of a particular marketing stimulation over an entire marketing campaign. The marketing campaign can comprise stimulations quantified as a number of direct mail pieces, a number or frequency of TV spots, a number of web impressions, a number of coupons printed, etc.

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