Ari Buchalter, MediaMath COO, presented "Billions and Billions: Machines, Algorithms, and Growing Business in Programamtic Markets" at ATS New York, November 2014.
An Empowered Entrepreneurial Culture: Multiple CEOsMediaMath
The document discusses the culture at digital advertising company MediaMath, which focuses on solving large problems through a collaborative team of ambitious employees empowered with accountability and innovation. MediaMath analyzes over a trillion data variables per day to power digital advertising and has been recognized as a top workplace, with many alumni going on to start their own successful companies. The company's culture of thinking big and working openly gets them bigger successes faster by avoiding echo chambers.
'Tis the Season: Leveraging Social Media Ad Campaigns for the HolidaysMediaMath
This deck was presented in a joint webinar by MediaMath and Upcast Social. Josh Wallin, Sales Director, Upcast Social, and Mike Neiss, Director, PSO, MediaMath outlined best practices to leverage social advertising during the holiday season.
This document discusses how to achieve marketing goals through programmatic advertising using data and algorithms. It emphasizes setting the right advertising goal, understanding the large programmatic opportunity in terms of available digital impressions, and managing the vast amount of data through predictive models and optimization to determine the right bid price for each impression and which impressions to purchase. The key to success is creating a strategic vision, setting a clear goal, accessing maximum supply, onboarding relevant data, using world-class algorithms, and providing insightful analysis.
Realizing Customer Centric Marketing in the Digital AgeMediaMath
This document discusses the importance of customer-centric marketing in the digital age. It outlines MediaMath's capabilities in helping companies implement a customer-centric approach through programmatic advertising. MediaMath has grown significantly since being founded in 2007 and now has over 600 employees serving over 4,500 active advertisers in 15 global locations. The document emphasizes that successful customer-centric marketing requires data-driven understanding of audiences, optimization of media buying, engaging messaging, and delivering ads across relevant media channels.
This document discusses how programmatic technology enables customer-centric marketing. It notes that a customer's journey occurs across multiple touchpoints and that programmatic advertising helps overcome barriers to understanding customers by targeting the right person at the right time and place. Programmatic spending has grown significantly in recent years and will account for most display ad spending by 2017. Several major brands are adopting programmatic advertising and seeing improved results and ROI. The document advocates using first-party data and programmatic buying to drive more profitable marketing and improve the customer experience with relevant messages.
Automating Mobile Success for the Holiday Season - MediaMath and Millennial M...MediaMath
This document discusses best practices for automating mobile success for the 2014 holiday season. It notes that mobile usage, especially on smartphones and tablets, has greatly increased and now accounts for over half of retail time spent digitally. The document provides tips for marketers transitioning to programmatic mobile advertising, such as leveraging learnings from past mobile campaigns, taking a mobile-first approach in targeting and creative design, understanding different programmatic buying options, and aligning brands with target audiences' interests. It also emphasizes rethinking mobile marketing by focusing on unique targeting and functionality, offline attribution, and being multi-screen conscious.
The Trade Desk, WTF Programmatic, December 2016 Digiday
The document provides best practices for creating an effective advertising campaign that resonates with modern consumers. It lists 5 things to consider: 1) think holistically and select the right media mix, 2) approach your campaign as a consumer would like to engage with one, 3) do your homework and understand the advertising landscape, 4) creative thinking as one size does not fit all and immersive advertising requires understanding different formats, 5) success is in measurement as data ties the advertising plan together.
The document summarizes two marketing campaigns run by Mall of America to increase foot traffic - a holiday campaign and a spring break campaign. The holiday campaign was most successful, driving over 158,000 mall visits through digital advertising. The spring break campaign saw fewer mall visits due to a heavier focus on connected TV. Analysis of data from both campaigns showed correlations between weather, day of week, and foot traffic that could inform future campaign strategies. Going forward, recommendations include increasing social media spending and testing more connected TV platforms at lower costs.
An Empowered Entrepreneurial Culture: Multiple CEOsMediaMath
The document discusses the culture at digital advertising company MediaMath, which focuses on solving large problems through a collaborative team of ambitious employees empowered with accountability and innovation. MediaMath analyzes over a trillion data variables per day to power digital advertising and has been recognized as a top workplace, with many alumni going on to start their own successful companies. The company's culture of thinking big and working openly gets them bigger successes faster by avoiding echo chambers.
'Tis the Season: Leveraging Social Media Ad Campaigns for the HolidaysMediaMath
This deck was presented in a joint webinar by MediaMath and Upcast Social. Josh Wallin, Sales Director, Upcast Social, and Mike Neiss, Director, PSO, MediaMath outlined best practices to leverage social advertising during the holiday season.
This document discusses how to achieve marketing goals through programmatic advertising using data and algorithms. It emphasizes setting the right advertising goal, understanding the large programmatic opportunity in terms of available digital impressions, and managing the vast amount of data through predictive models and optimization to determine the right bid price for each impression and which impressions to purchase. The key to success is creating a strategic vision, setting a clear goal, accessing maximum supply, onboarding relevant data, using world-class algorithms, and providing insightful analysis.
Realizing Customer Centric Marketing in the Digital AgeMediaMath
This document discusses the importance of customer-centric marketing in the digital age. It outlines MediaMath's capabilities in helping companies implement a customer-centric approach through programmatic advertising. MediaMath has grown significantly since being founded in 2007 and now has over 600 employees serving over 4,500 active advertisers in 15 global locations. The document emphasizes that successful customer-centric marketing requires data-driven understanding of audiences, optimization of media buying, engaging messaging, and delivering ads across relevant media channels.
This document discusses how programmatic technology enables customer-centric marketing. It notes that a customer's journey occurs across multiple touchpoints and that programmatic advertising helps overcome barriers to understanding customers by targeting the right person at the right time and place. Programmatic spending has grown significantly in recent years and will account for most display ad spending by 2017. Several major brands are adopting programmatic advertising and seeing improved results and ROI. The document advocates using first-party data and programmatic buying to drive more profitable marketing and improve the customer experience with relevant messages.
Automating Mobile Success for the Holiday Season - MediaMath and Millennial M...MediaMath
This document discusses best practices for automating mobile success for the 2014 holiday season. It notes that mobile usage, especially on smartphones and tablets, has greatly increased and now accounts for over half of retail time spent digitally. The document provides tips for marketers transitioning to programmatic mobile advertising, such as leveraging learnings from past mobile campaigns, taking a mobile-first approach in targeting and creative design, understanding different programmatic buying options, and aligning brands with target audiences' interests. It also emphasizes rethinking mobile marketing by focusing on unique targeting and functionality, offline attribution, and being multi-screen conscious.
The Trade Desk, WTF Programmatic, December 2016 Digiday
The document provides best practices for creating an effective advertising campaign that resonates with modern consumers. It lists 5 things to consider: 1) think holistically and select the right media mix, 2) approach your campaign as a consumer would like to engage with one, 3) do your homework and understand the advertising landscape, 4) creative thinking as one size does not fit all and immersive advertising requires understanding different formats, 5) success is in measurement as data ties the advertising plan together.
The document summarizes two marketing campaigns run by Mall of America to increase foot traffic - a holiday campaign and a spring break campaign. The holiday campaign was most successful, driving over 158,000 mall visits through digital advertising. The spring break campaign saw fewer mall visits due to a heavier focus on connected TV. Analysis of data from both campaigns showed correlations between weather, day of week, and foot traffic that could inform future campaign strategies. Going forward, recommendations include increasing social media spending and testing more connected TV platforms at lower costs.
Drive Business Results with MediaMath RetailMediaMath
The document discusses MediaMath Retail, a programmatic marketing solution tailored for retailers. It highlights how several major brands are increasingly adopting programmatic media buying. The solution combines data management, cross-channel execution, creative optimization, and decisioning tools. It also provides access to billions of global online ad impressions daily across different channels. MediaMath Retail aims to help retailers drive business results by optimizing their marketing activities and campaigns to achieve desired goals and outcomes. Case studies show how the solution has helped various retailers lower customer acquisition costs, increase returns on ad spend, and boost online and offline sales.
Realizing Customer-Centric Marketing with Programmatic Technology, Argyle CMO...MediaMath
The document discusses how marketers are increasingly focused on delivering more customer-centric and relevant communications. It notes that 53% of marketers want to be more customer-centric, and 89% of companies plan to compete based on customer experience by 2016. The document argues that programmatic technology can help companies realize customer-centric marketing by evaluating opportunities dynamically, activating data, embracing transparency, personalizing messages, minimizing fragmentation, and optimizing toward what works. It presents MediaMath as a solution that provides a connected source for addressable media across all channels to deliver more personalized messages.
Put your brand research on a digital high protein dietJoel Rubinson
Your brand research is bloated...slow, unproductive, and way to costly. Here is how to cut long -form survey tracker costs in half and get so much more by integrating digital and social data into a contemporary brand guidance system
Over the last few years, the ad industry has been decisive and diligent about demanding better media quality. As a result, we've seen dramatic reductions in waste and made huge improvements in the areas of Viewability, Fraud and Brand Safety. Now it's time for the industry to set its sights on the new leading cause of waste. Join Jake Moskowitz, head of the Emodo Institute, for a glimpse into the pervasive problem of data inaccuracy. In this session, Jake will outline the causes, scope and magnitude of today's data quality issues, and discuss tactical ways to ensure advertisers get what they pay for.
Emodo is the data arm of Ericsson, the telecommunications company that powers roughly 80% of US mobile traffic.
Presented by Annie McAndrews: July, 25, 2017
Everyone is searching, but who knows exactly what they’re looking for? Paid search allows advertisers to step in and help point users in the right direction. In this class, we’ll discuss how paid search works and why it should be a part of your client’s digital strategy. We’ll also review the different ways advertisers utilize paid search to help us find the object of our desire, be it shoes, burritos, or even a college degree.
Media Mind: Dynamic Creative PptimizationEngel Fonseca
Smart Versioning is MediaMind's dynamic creative optimization product that improves ad performance by serving different ads to target audiences based on real-time data. Analysis found direct response campaigns using Smart Versioning maintained click-through rates but increased post-click conversion rates by 59% compared to regular campaigns. For branding campaigns, Smart Versioning increased ad engagement rates by 20%. Smart Versioning streamlines targeting and creative production while maximizing returns.
This document discusses the challenges that marketers face with digital media efforts, including poor audience targeting, small audiences, and inaccurate measurement. It introduces Conversant's approach to address these challenges through integrated 1:1 marketing at scale using data onboarding, device matching, and dynamic creative optimization. This approach aligns millions of data points to create a unified view of individuals and their online and offline behaviors. A case study on Staples is presented, where Conversant's solution improved match rates, drove incremental revenue lifts, and provided comprehensive campaign analytics.
Presented to the Miami Ad School by Brandon Schifrien: August 8, 2017
A topline overview of programmatic media, along with a description of why creatives should understand what it is and how it impacts them (DCO). The latter half of the presentation includes a brief overview of DCO, before concluding with key takeaways from the presentation.
Programmatic planning: Brand vs. Performancedigitalinasia
Women in Programmatic is a professional organization with the mission of bringing together women within the programmatic advertising space to share knowledge and support. WiP holds a series of educational workshops related to the industry.
"Programmatic Planning: Brand vs Performance" was a workshop that covered the basics of programmatic and how to achieve marketing goals programmatically.
"The Evolution of Digital Media Planning" presented by Joe Pych at Aspen Group Meeting / iMedia Agency Summit on May 3, 2015 at Terranea Resort in Rancho Palos Verdes, CA. Bionic believes that in a post-programmatic advertising world, media planning will evolve to include full-cycle marketing performance management.
Programmatic Buying - DAAT Media Breakout Room DAAT_TH
The document provides advice for a new client on coming onboard with programmatic buying. It recommends: 1) building a strategy and getting started; 2) understanding key terminology and the programmatic landscape; 3) choosing the right buying model such as open auction or private marketplace; 4) considering creative assets; 5) leveraging available data; and 6) protecting the client's brand. The document outlines benefits of different approaches and challenges to address for a successful programmatic strategy.
Are Data and Creativity forming a nice couple? Do they have a happy or unhappy marriage? For any answers, please follow the presentation of George Frangakis, Managing Director Forestview, from AllThingsPerformance event in Athens.
Jenn Vlahavas gave a presentation on conquering the omni-channel challenge. She discussed how digital now accounts for 57% of global media consumption and consumers are using multiple channels in their purchasing journeys. She outlined six best practices for brands to address the omni-channel opportunity: leverage data, embrace transparency, create partnerships, own the strategy internally, achieve internal alignment, and make educated bets on new approaches. Vlahavas emphasized that brands must break down silos and use technology and data to engage customers across channels.
Sunando Das, Head Of Marketing Analytics And Data Science at TNS speaking at ad:tech London on 14th October 2015
http://ad-techlondon.co.uk/
Register for next year: http://ad-techlondon.co.uk/register
Gary Milner discusses the importance of managing the digital marketing supply chain. He notes that inefficient media management can destroy brand impact and waste up to 80% of spend. Milner advocates reducing the number of demand-side platforms (DSPs) used and curating supply through select SSP and private marketplace partners to gain transparency and reduce costs. He also warns against using audience extension without the permission of premium suppliers. Milner predicts continued shifts from open to private marketplaces and the growth of programmatic creative powered by artificial intelligence.
1) The document discusses the current state of confusion around internet measurement and outlines five principles for making measurement more clear, including moving to a viewable impressions standard and determining metrics that matter for brand marketers.
2) It outlines areas of future progress, such as measuring viewability, parsing campaign delivery into target reach and frequency, developing impression quality metrics, innovations in measuring branding and mobile, and continued convergence of web analytics and audience measurement.
3) The key challenges are developing standardized metrics for impression quality and viewability across different platforms and devices, and integrating measurement from various data sources into a unified methodology.
In this presentation you will find a timeline of how media was consumed from 1980 until today in 2015 and how these changes in the media landscape impact your business. In addition, you will see the biggest gains and losses in market share in mainstream media channels and find tips on how to adapt in an ever-changing world of new technologies and sources to consume media.
Programmatic advertising transactions generate vastly more data each day than Wall Street trades, with billions occurring daily compared to millions on the stock exchange. However, the large amount of programmatic data has lacked visibility, with an average of 10 data fields per transaction compared to just 4 for a stock trade. Increased transparency is needed to fulfill programmatic advertising's promise, as Justice Brandeis noted that "sunlight is the best disinfectant."
PROGRAMMATIC I/O 2015 - "Putting Facebook’s People-Based Marketing to the Test"Ad Exchanger
People are cross-device, cross-browser, cross-publisher, cross-everything. Because of the continuous proliferation of devices, browsers, and apps, advertisers and publishers must face this reality with new ways to serve, buy, sell, and measure advertising. In this session, Mark Trefgarne, Product Management Director of LiveRail, will give the audience a first-hand look at how people-based marketing can change the way publishers, brands and consumers think about advertising.
Drive Business Results with MediaMath RetailMediaMath
The document discusses MediaMath Retail, a programmatic marketing solution tailored for retailers. It highlights how several major brands are increasingly adopting programmatic media buying. The solution combines data management, cross-channel execution, creative optimization, and decisioning tools. It also provides access to billions of global online ad impressions daily across different channels. MediaMath Retail aims to help retailers drive business results by optimizing their marketing activities and campaigns to achieve desired goals and outcomes. Case studies show how the solution has helped various retailers lower customer acquisition costs, increase returns on ad spend, and boost online and offline sales.
Realizing Customer-Centric Marketing with Programmatic Technology, Argyle CMO...MediaMath
The document discusses how marketers are increasingly focused on delivering more customer-centric and relevant communications. It notes that 53% of marketers want to be more customer-centric, and 89% of companies plan to compete based on customer experience by 2016. The document argues that programmatic technology can help companies realize customer-centric marketing by evaluating opportunities dynamically, activating data, embracing transparency, personalizing messages, minimizing fragmentation, and optimizing toward what works. It presents MediaMath as a solution that provides a connected source for addressable media across all channels to deliver more personalized messages.
Put your brand research on a digital high protein dietJoel Rubinson
Your brand research is bloated...slow, unproductive, and way to costly. Here is how to cut long -form survey tracker costs in half and get so much more by integrating digital and social data into a contemporary brand guidance system
Over the last few years, the ad industry has been decisive and diligent about demanding better media quality. As a result, we've seen dramatic reductions in waste and made huge improvements in the areas of Viewability, Fraud and Brand Safety. Now it's time for the industry to set its sights on the new leading cause of waste. Join Jake Moskowitz, head of the Emodo Institute, for a glimpse into the pervasive problem of data inaccuracy. In this session, Jake will outline the causes, scope and magnitude of today's data quality issues, and discuss tactical ways to ensure advertisers get what they pay for.
Emodo is the data arm of Ericsson, the telecommunications company that powers roughly 80% of US mobile traffic.
Presented by Annie McAndrews: July, 25, 2017
Everyone is searching, but who knows exactly what they’re looking for? Paid search allows advertisers to step in and help point users in the right direction. In this class, we’ll discuss how paid search works and why it should be a part of your client’s digital strategy. We’ll also review the different ways advertisers utilize paid search to help us find the object of our desire, be it shoes, burritos, or even a college degree.
Media Mind: Dynamic Creative PptimizationEngel Fonseca
Smart Versioning is MediaMind's dynamic creative optimization product that improves ad performance by serving different ads to target audiences based on real-time data. Analysis found direct response campaigns using Smart Versioning maintained click-through rates but increased post-click conversion rates by 59% compared to regular campaigns. For branding campaigns, Smart Versioning increased ad engagement rates by 20%. Smart Versioning streamlines targeting and creative production while maximizing returns.
This document discusses the challenges that marketers face with digital media efforts, including poor audience targeting, small audiences, and inaccurate measurement. It introduces Conversant's approach to address these challenges through integrated 1:1 marketing at scale using data onboarding, device matching, and dynamic creative optimization. This approach aligns millions of data points to create a unified view of individuals and their online and offline behaviors. A case study on Staples is presented, where Conversant's solution improved match rates, drove incremental revenue lifts, and provided comprehensive campaign analytics.
Presented to the Miami Ad School by Brandon Schifrien: August 8, 2017
A topline overview of programmatic media, along with a description of why creatives should understand what it is and how it impacts them (DCO). The latter half of the presentation includes a brief overview of DCO, before concluding with key takeaways from the presentation.
Programmatic planning: Brand vs. Performancedigitalinasia
Women in Programmatic is a professional organization with the mission of bringing together women within the programmatic advertising space to share knowledge and support. WiP holds a series of educational workshops related to the industry.
"Programmatic Planning: Brand vs Performance" was a workshop that covered the basics of programmatic and how to achieve marketing goals programmatically.
"The Evolution of Digital Media Planning" presented by Joe Pych at Aspen Group Meeting / iMedia Agency Summit on May 3, 2015 at Terranea Resort in Rancho Palos Verdes, CA. Bionic believes that in a post-programmatic advertising world, media planning will evolve to include full-cycle marketing performance management.
Programmatic Buying - DAAT Media Breakout Room DAAT_TH
The document provides advice for a new client on coming onboard with programmatic buying. It recommends: 1) building a strategy and getting started; 2) understanding key terminology and the programmatic landscape; 3) choosing the right buying model such as open auction or private marketplace; 4) considering creative assets; 5) leveraging available data; and 6) protecting the client's brand. The document outlines benefits of different approaches and challenges to address for a successful programmatic strategy.
Are Data and Creativity forming a nice couple? Do they have a happy or unhappy marriage? For any answers, please follow the presentation of George Frangakis, Managing Director Forestview, from AllThingsPerformance event in Athens.
Jenn Vlahavas gave a presentation on conquering the omni-channel challenge. She discussed how digital now accounts for 57% of global media consumption and consumers are using multiple channels in their purchasing journeys. She outlined six best practices for brands to address the omni-channel opportunity: leverage data, embrace transparency, create partnerships, own the strategy internally, achieve internal alignment, and make educated bets on new approaches. Vlahavas emphasized that brands must break down silos and use technology and data to engage customers across channels.
Sunando Das, Head Of Marketing Analytics And Data Science at TNS speaking at ad:tech London on 14th October 2015
http://ad-techlondon.co.uk/
Register for next year: http://ad-techlondon.co.uk/register
Gary Milner discusses the importance of managing the digital marketing supply chain. He notes that inefficient media management can destroy brand impact and waste up to 80% of spend. Milner advocates reducing the number of demand-side platforms (DSPs) used and curating supply through select SSP and private marketplace partners to gain transparency and reduce costs. He also warns against using audience extension without the permission of premium suppliers. Milner predicts continued shifts from open to private marketplaces and the growth of programmatic creative powered by artificial intelligence.
1) The document discusses the current state of confusion around internet measurement and outlines five principles for making measurement more clear, including moving to a viewable impressions standard and determining metrics that matter for brand marketers.
2) It outlines areas of future progress, such as measuring viewability, parsing campaign delivery into target reach and frequency, developing impression quality metrics, innovations in measuring branding and mobile, and continued convergence of web analytics and audience measurement.
3) The key challenges are developing standardized metrics for impression quality and viewability across different platforms and devices, and integrating measurement from various data sources into a unified methodology.
In this presentation you will find a timeline of how media was consumed from 1980 until today in 2015 and how these changes in the media landscape impact your business. In addition, you will see the biggest gains and losses in market share in mainstream media channels and find tips on how to adapt in an ever-changing world of new technologies and sources to consume media.
Programmatic advertising transactions generate vastly more data each day than Wall Street trades, with billions occurring daily compared to millions on the stock exchange. However, the large amount of programmatic data has lacked visibility, with an average of 10 data fields per transaction compared to just 4 for a stock trade. Increased transparency is needed to fulfill programmatic advertising's promise, as Justice Brandeis noted that "sunlight is the best disinfectant."
PROGRAMMATIC I/O 2015 - "Putting Facebook’s People-Based Marketing to the Test"Ad Exchanger
People are cross-device, cross-browser, cross-publisher, cross-everything. Because of the continuous proliferation of devices, browsers, and apps, advertisers and publishers must face this reality with new ways to serve, buy, sell, and measure advertising. In this session, Mark Trefgarne, Product Management Director of LiveRail, will give the audience a first-hand look at how people-based marketing can change the way publishers, brands and consumers think about advertising.
(ADV403) Dynamic Ad Perf. Reporting w/ Redshift: Data Science, Queries at Sca...Amazon Web Services
This document discusses using Amazon Redshift for analytics workloads related to advertising technology. It provides examples of SQL queries for tasks like sessionization, attribution, overlap analysis, and ad-hoc queries. It also discusses best practices for data modeling, ETL processes, and optimizing workload performance and costs in Redshift.
Realizing Customer-Centric Marketing with Programmatic TechnologyMediaMath
The document discusses how companies are increasingly focusing on customer-centric marketing and the role of programmatic technology in enabling it. It notes that 53% of marketers cited delivering more relevant communications as a key demand, and 89% of companies plan to compete on customer experience by 2016. The document then outlines some of the organizational barriers to customer-centricity, how the customer journey has multiple touchpoints, and best practices like leveraging data, personalizing messages, optimizing continuously, and aligning internal teams. It positions MediaMath as providing a connected platform to access addressable media across channels for more personalized messaging.
How MediaMath Built Faster, Scalable Attribution Reporting with Hadoop-ImpalaMediaMath
At MediaMath, we deal with billions of records every day. One of our biggest challenges is hourly reporting of attribution data - the joining of billions of records to millions of events. How did we solve this hourly attribution reporting issue? We will walk through our evaluation, testing, and fine tuning of a variety of tools including Netezza, Hive, and Pig, and how we ultimately chose Cloudera's Impala.
(PFC303) Milliseconds Matter: Design, Deploy, and Operate Your Application fo...Amazon Web Services
You can't (yet) bend the law of Physics, but you can use the power of the cloud to design applications that run as fast as the speed of light! This session will focus on the best practices for optimizing performance to the very last millisecond. We'll dive into topics such as caching at every layer of your application, TCP optimizations, SSL optimizations, latency based routing, and much more. These best practices can help you to streamline your infrastructure utilization, improve performance and allow you to scale economically.
GraphLab: Large-Scale Machine Learning on Graphs (BDT204) | AWS re:Invent 2013Amazon Web Services
GraphLab is like Hadoop for graphs in that it enables users to easily express and execute machine learning algorithms on massive graphs. In this session, we illustrate how GraphLab leverages Amazon EC2 and advances in graph representation, asynchronous communication, and scheduling to achieve orders-of-magnitude performance gains over systems like Hadoop on real-world data.
A VERY high level over view of Graph Analytics concepts and techniques, including structural analytics, Connectivity Analytics, Community Analytics, Path Analytics, as well as Pattern Matching
The slides give an overview of how Spark can be used to tackle Machine learning tasks, such as classification, regression, clustering, etc., at a Big Data scale.
In this session from the London AWS Summit 2015 Tech Track Replay, AWS Technical Evangelist Ian Massingham introduces the new Amazon Machine Learning service.
Amazon Machine Learning is a service that makes it easy for developers of all skill levels to use machine learning technology. Amazon Machine Learning provides visualization tools and wizards that guide you through the process of creating machine learning (ML) models without having to learn complex ML algorithms and technology. Once your models are ready, Amazon Machine Learning makes it easy to obtain predictions for your application using simple APIs, without having to implement custom prediction generation code, or manage any infrastructure.
(DVO203) The Life of a Netflix Engineer Using 37% of the InternetAmazon Web Services
Netflix is a large and ever-changing ecosystem made up of: hundreds of production changes every hour, thousands of micro services, tens of thousands of instances, millions of concurrent customers, billions of metrics every minute. And I'm the guy with the pager. This is an in-the-trenches look at what operating at Netflix scale in the cloud is really like. It covers how Netflix views the velocity of innovation, expected failures, high availability, engineer responsibility, and obsessing over the quality of the customer experience. It also explains why freedom and responsibility are key, trust is required, and chaos is your friend.
Demand-side platforms emerged in 2007, changing how digital media is purchased and creating a more efficient way for advertisers to reach consumers at scale. Since then, programmatic has grown significantly in both media opportunity and data management. Nearly all major brands and publishers now have or are developing programmatic strategies. The presentation discusses how programmatic has grown tremendously in Europe and Brazil in recent years, with spending increasing by hundreds of millions. It also outlines how an open ecosystem supports innovation, transparency, and outcomes for both demand and supply stakeholders in programmatic advertising.
(BDT309) Data Science & Best Practices for Apache Spark on Amazon EMRAmazon Web Services
Organizations need to perform increasingly complex analysis on their data — streaming analytics, ad-hoc querying and predictive analytics — in order to get better customer insights and actionable business intelligence. However, the growing data volume, speed, and complexity of diverse data formats make current tools inadequate or difficult to use. Apache Spark has recently emerged as the framework of choice to address these challenges. Spark is a general-purpose processing framework that follows a DAG model and also provides high-level APIs, making it more flexible and easier to use than MapReduce. Thanks to its use of in-memory datasets (RDDs), embedded libraries, fault-tolerance, and support for a variety of programming languages, Apache Spark enables developers to implement and scale far more complex big data use cases, including real-time data processing, interactive querying, graph computations and predictive analytics. In this session, we present a technical deep dive on Spark running on Amazon EMR. You learn why Spark is great for ad-hoc interactive analysis and real-time stream processing, how to deploy and tune scalable clusters running Spark on Amazon EMR, how to use EMRFS with Spark to query data directly in Amazon S3, and best practices and patterns for Spark on Amazon EMR.
Organizations need to perform increasingly complex analysis on data — streaming analytics, ad-hoc querying, and predictive analytics — in order to get better customer insights and actionable business intelligence. Apache Spark has recently emerged as the framework of choice to address many of these challenges. In this session, we show you how to use Apache Spark on AWS to implement and scale common big data use cases such as real-time data processing, interactive data science, predictive analytics, and more. We will talk about common architectures, best practices to quickly create Spark clusters using Amazon EMR, and ways to integrate Spark with other big data services in AWS.
Learning Objectives:
• Learn why Spark is great for ad-hoc interactive analysis and real-time stream processing.
• How to deploy and tune scalable clusters running Spark on Amazon EMR.
• How to use EMR File System (EMRFS) with Spark to query data directly in Amazon S3.
• Common architectures to leverage Spark with Amazon DynamoDB, Amazon Redshift, Amazon Kinesis, and more.
(ISM213) Building and Deploying a Modern Big Data Architecture on AWSAmazon Web Services
"The AWS platform enables large enterprises to use data to solve business problems and uncover opportunities more easily and affordably than ever before. However, to truly take advantage of AWS, enterprises need a way to collect, store, process, analyze, and continually execute on their data.
Datapipe has been an AWS partner for more than five years. In that time, it has developed a proprietary process for the deployment of AWS environments, as well as the processing and evaluation of big data analytics to optimize these environments over time. This flexible solution includes automation tools, continuous monitoring, and cloud analytics. It protects against architectural sprawl and continually redesigns for scalability. This kind of continuous build environment allows Datapipe to examine the AWS environment as a complete picture and ensure the cloud environment is running as efficiently and effectively as possible, ultimately reducing overhead costs for the enterprise.
In this session, Jason Woodlee, Senior Director of Cloud Products at Datapipe, will discuss the technical details of designing and deploying a modern big data architecture on AWS, including application purpose and design, development environment and language overview, DevOps automation best practices, and continuous build and test frameworks. Session sponsored by Datapipe."
In this presentation, learn how an end-to-end smart application can be built in the AWS cloud. We will demonstrate how to use Amazon Machine Learning (Amazon ML) to create machine learning models, deploy them to production, and obtain predictions in real-time. We will then demonstrate how to build a complete smart application using Amazon ML, Amazon Kinesis, and AWS Lambda. We will walk you through the process flow and architecture, demonstrate outcomes, and then dive into the code for implementation. In this session, you will learn how to use Amazon ML as well as how to integrate Amazon ML into your applications to take advantage of predictive analysis in the cloud.
Presented by: Guy Ernest, Principal Business Development Manager, Amazon Web Services
Customer Guest: Pim Vernooij, Partner, Lab Digital
This document discusses 10 R packages that are useful for winning Kaggle competitions by helping to capture complexity in data and make code more efficient. The packages covered are gbm and randomForest for gradient boosting and random forests, e1071 for support vector machines, glmnet for regularization, tau for text mining, Matrix and SOAR for efficient coding, and forEach, doMC, and data.table for parallel processing. The document provides tips for using each package and emphasizes letting machine learning algorithms find complexity while also using intuition to help guide the models.
Myths and Mathemagical Superpowers of Data ScientistsDavid Pittman
1) The document discusses 10 myths about data scientists and provides realities to counter each myth.
2) Some myths include claims that data scientists are mythical beings, elitist academics, or a fading trend. However, the realities note data science requires hands-on work with data and has experienced steady growth.
3) Other myths suggest data scientists are just statisticians or BI specialists, but the realities indicate data scientists come from varied backgrounds and tackle business problems through experimentation and analysis.
How to Become a Data Scientist
SF Data Science Meetup, June 30, 2014
Video of this talk is available here: https://www.youtube.com/watch?v=c52IOlnPw08
More information at: http://www.zipfianacademy.com
Zipfian Academy @ Crowdflower
- The document introduces artificial neural networks, which aim to mimic the structure and functions of the human brain.
- It describes the basic components of artificial neurons and how they are modeled after biological neurons. It also explains different types of neural network architectures.
- The document discusses supervised and unsupervised learning in neural networks. It provides details on the backpropagation algorithm, a commonly used method for training multilayer feedforward neural networks using gradient descent.
The twin goals of customer research: inspire designers, persuade stakeholdersRashmi Sinha
The document discusses strategies for customer research to both inspire designers and persuade stakeholders. It provides examples of different interaction patterns - co-opt, pre-empt, and combat - and case studies applying each pattern. Key recommendations include being flexible in research methods, validating findings, and delivering data tailored for both designers and business stakeholders.
The document discusses the importance of measuring marketing effectiveness and provides examples of how to measure the effectiveness of various marketing channels, including mass media, social media, web, direct mail, and others. It notes that only 50% of marketers formally analyze data to measure return on investment and provides reasons why some marketers may not measure effectiveness. It also gives tips on identifying key performance indicators and examples of calculating marketing return on investment for different channels.
Overcoming the challenges of programmatic in southeast asiaBenedict Hayes
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Billions and Billions: Machines, Algorithms, and Growing Business in Programamtic Markets
1. Billions and Billions:
Machines, Algorithms, and Growing
Business in Programmatic Markets
Ari Buchalter COO, MediaMath
PhD, Astrophysics
2. What do these things have in common?
The Digital Advertising Universe The Actual Universe
• Both are complex systems
• Math can be applied to understand both
3.
4.
5. The evolution of media decision making
Past 60 years – “Audience-based” Today – “Goal-based”
Describe your audience
Figure out media they consume
Buy placements, wait, and hope
Get report, manually adjust
Define your marketing goal
Capture all the data (media, user)
Model it to identify what works
Automate the buying
Humans making coarse
decisions based on proxies,
averages, and indexes
Machines making exact
decisions based on granular
user data
8. …and we understand the benefits
Buy in batch (wheat + chaff) Buy what you want (wheat only)
Fixed price, regardless of value Variable bidding, aligned with value
Little/no insight into true drivers Full insights into “what” & “why”
Manual, labor-intensive (~5/FTE) Fully automated, scalable (~50/FTE)
And the results are typically 10x better, BUT there’s a cost….
Analyze 10-20 buys weekly Analyze 1MM opps. per sec.
9. Let’s talk about Big Data in Programmatic
~100 BILLION impressions per day
~100 variables per impression
~100 values per variable
EQUALS
~1,000,000,000,000,000
Possible combinations of data per day
(1015 = ONE QUADRILLION)
10. Making sense of the chaos
Algorithms
Optimization
Programmatic
Automation
Predictive Modeling
Machine learning
Decision engines
11. Getting inside the RTB transaction
SSP or
Exchange
Publisher
Consumer
DSP
Advertiser
Agency or
Trading Desk
12. The two (buyer) questions that matter
What is the right bid for each impression?
Which impressions should I buy?
13. Why does question #1 matter?
What is the right bid for each impression?
Too high
Overpay &
underperform
Too low
Lose out &
underspend
“Goldilocks” bid
Maximize scale &
performance
14. Why does question #2 matter?
Which impressions should I buy?
• ~$100MM/day of RTB supply
• Typical campaign spends ~$1K/day
(0.001% of total supply)
• Not buying the RIGHT 0.001% is
throwing money away
15. Answering the questions ain’t easy
Data is large, and growing need technology at scale
It’s called different things need to “normalize” data
Data interactions are complex need sophisticated models
Mix of goals (upper/lower funnel) need flexible methodology
Supply & demand constantly changing need to remodel often
Clients need to understand need intuitive, transparent output
It’s all in real-time (100ms) need speed without latency
Only a machine-learning algorithmic approach can handle
the size, variability, complexity, and speed required
16. Question #1 – A simple exercise
What is the right bid for each impression?
$1 prize
Flip a coin to
win 1 dollar
50% chance $0.50
Bid Price
Goal Value x Action Rate = Bid Price
17. Question #1 – The real thing
What is the right bid for each impression?
Goal Value x Action Rate = Bid Price
1% chance consumer
takes desired action
(purchase)
$50 value to
advertiser
(CPA)
Bid for an
RTB ad
$0.50 bid
price
(breakeven)
YOUR AD HERE
18. Question #1 – The Goal Value
(Input) (Prediction) (Output)
Goal Value
x Action Rate = Bid Price
The goal can be anything at all:
• Branding: positive survey response (awareness, intent, etc.)
• Engagement: site visit, site action (locate store, post comment)
• Conversion: signup, application, purchase, etc.
• Retention: repeat purchase, renewal, upsell
If it can be measured, it can be made better by math
19. Question #1 – The Action Rate
(Input) (Prediction) (Output)
Goal Value x Action Rate
= Bid Price
Predictive modeling: the process by which a mathematical model is created to
predict the probability of an outcome, usually based on historical input data
The model should base the prediction on all available data:
• User: site activity (1p), interests & behaviors (3p), geo, TOD, DOW, etc.
• Media: channel, publisher, page, ad size, above/below fold, etc.
• Creative: image, offer, call to action, etc.
20. Answering question #1
Video
Publisher: YouTube
Unit: 15 sec pre roll
Time: 16.46 – 17.00
Age: 25-34
Gender: Male
Price: $15.76 CPM
Social
Publisher: FBX
Unit: Newsfeed
Day: Tuesday
Time: 5.00pm – 5.15pm
Price: $2.30 CPM
Display
Publisher: Rubicon
Data: Rakuten Male
Location: Tokyo
Creative size: 160 x 600
Price: $0.63 CPM
A different model for every creative in every
campaign of every advertiser – all in real time!
21. Question #2 – Which ones to bid on?
Optimization: the process of making
the best choice among a set of
options to achieve a desired goal,
usually under some constraints
Example – Shopping for food
Constraints: fixed budget,
nothing artificial
Goal: Most mass of food?
Most volume of food?
Healthiest mix?
22. Question #2 – Two important concepts
1) Bid Price: How much the impression is worth to the buyer
• Depends on who the publisher is and who the advertiser is
• Is a measure of quality (i.e., what it’s worth to the buyer)
2) Market Price: The price the impression will clear for
• Depends on the entire marketplace
• Also obtained through predictive modeling
23. Question #2 – A meaty example
Bid Price:
$30
Bid Price:
$30
$30
High
(good quality)
Bid Price
Low
(poor quality)
Bid Price:
$2
Bid Price:
$2
$2
YES!
Market Price
High
Eh, OK
(not a deal)
Low
(a deal!)
NO!
Eh, OK
$30
Selling for:
$30
Selling for:
$30
$2
Selling for:
$2
Selling for:
$2
24. Answering question #2 – Which to bid on?
III IV
Quality-driven
performance
<10% of impressions
I II
performance
40-70% of impressions
Value-driven
performance
<5% of impressions
Cost-driven
performance
20-50% of impressions
Relative Value
Low High
Non
Low High
Bid Price
25. Putting it all together
1) Use a predictive model
to determine what each
impression is worth
2) Use optimization to
determine which
impressions to bid for
What is the right bid for each impression?
Which impressions should I buy?
26. So where do I get me some of those?
Find a partner who:
Leverages robust technology – ask to see the scale & speed
Has proven results – across verticals, geographies, over time
Will expose the “black box” – transparency & insights are key!
Has cross-channel capabilities – display, video, social, mobile, premium, BYO
Has broad integrations – 3p data, surveys, viewability, attribution, etc.
Can incorporate 1st party offline data – increasingly important
Develops custom solutions – to suit your unique business needs
Makes it easy – execution, workflow, reporting, testing, etc.
Provides thought partnership & great service – it’s not just machines!
(machines just enable people to do the REAL value-added work)
27. Forrester DSP Wave: MediaMath is #1
“MediaMath boasts excellent algorithmic
optimization capabilities (including a
multifaceted view of the decisioning engine’s
output), and its multichannel media and data
access is both broad and deep.”
“MediaMath is a great all-around choice
for buyers in market for a DSP.”
“Its large employee base and diverse, well-tenured
management team also provide the
necessary foundation for it to execute
effectively on its strategic vision: to
empower marketing professionals with a
flexible, easy-to-use, multichannel platform.”