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Sree Naragaran, CEO, Colligent Sree is a marketing technologist. In the early stages of social networks? evolution, he was fascinated by the idea that consumers, who were not willing to fill out three questions about themselves in a product survey, were willing to put out thirty things about themselves in a public profile in social networks. Aggregating this public data, he envisioned, would make marketing more effective for the common good if the data and relationships could be understood and applied correctly. This led him to found Colligent, an amalgam of ?collectively intelligent,? which has a mission to make marketing effective, benefiting both brands and consumers.
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Prior to joining Acxiom, Phil led the strategy formulation and execution of Google Analytics in his role as group product manager at Google.
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Phil Mui, PhD, Chief Product and Engineering Officer, Acxiom
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George John, CEO, Rocket Fuel George John has built a successful career by consistently generating insights and profits for marketers by analyzing huge amounts of historical data to predict response rates and target messages. Prior to co-founding Rocket Fuel, he led teams that built high-tech systems to optimize marketing at Yahoo!, salesforce.com, Epiphany, and IBM, in roles spanning engineering, marketing, sales, and executive management. As a senior director for behavioral targeting and personalization at Yahoo!, his teams delivered systems to optimize marketing spend and personalize content and ads. At salesforce.com, John led product initiatives in dashboards and analytics that monetized significant deals for the company and sparked community initiatives including what became the successforce user forums. As employee number thirteen at Epiphany, he created a suite of uniquely usable and scalable data-mining tools, helping the company grow from $0 to $127 million in revenue and an IPO within three years. Before Epiphany, John was the first senior data-mining analyst in the Global Business Intelligence Solutions division of IBM,
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Going a step further, the study also determined that marketers who tended to depend almost exclusively on data for decision-making were ?severe underperformers.? Why? The average marketer simply does not have the wherewithal to separate valuable marketing signals from the noise.
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According to the Harvard Business Review,1 marketers use real data to make customerimpacting decisions only 11% of the time.
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Russell Glass, CEO, Bizo Russell is the founder and CEO of Bizo. Bizo gives marketers instant access to the people who sign the checks at work and have the most to spend on life: business professionals. Fueled by proprietary demographic data, the Bizo marketing platform precisely targets more than 100 million professionals around the world, including more than 80% of the U.S. business population.
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She has deep expertise across CRM, loyalty, and digital marketing and is focused on helping Datalogix clients unite those strategies in new and differentiating ways.
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Pamela has more than sixteen years of marketing experience partnering with some of the most recognizable brands in the world. As vice president of marketing, she?s responsible for the Datalogix brand, corporate communications, and advertising
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Bridget Bidlack, VP of Product Management, [x+1] Bridget oversees the development of [x+1]?s Origin platform, including its enterprise DMP, Origin Media DSP, Origin Site for personalized website experiences, and its Funnel R/F attribution analytics tool.
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8. The Future
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7. Audience Targeting, Segmentation, and Lookalike Modeling
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6.2. Types of Data
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6.2.2. Second-Party Data Many marketers consider only two types of data: first-party (owned) data and third-party (purchased) data. Correctly, there are three types, including second-party data, which consists of data that?s from your domain but collected and provided by an outside source. Ad-serving data is one example.
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You need a chef in the kitchen constantly working with the ingredients. This is not a sit-and-forget technology. Vikram Somaya, VP of Global Operations & Audience, Thomson Reuters
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You need a chef in the kitchen constantly working with the ingredients. This is not a sit-and-forget technology.
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6.1. Data and the Customer Funnel
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6. Data Guide
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important that marketers collect data from as many sources as they can, unify these richer data sets and analyze them to create compelling marketing programs. Marketers can create value to their users and convert them into loyalists only via personalized messages. This personalization is feasible only if marketers clearly understand their user profiles by collecting and unifying different data sets. Andy Monfried, Lotame
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important that marketers collect data from as many sources as they can, unify these richer data sets and analyze them to create compelling marketing programs. Marketers can create value to their users and convert them into loyalists only via personalized messages. This personalization is feasible only if marketers clearly understand their user profiles by collecting and unifying different data sets.
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important that marketers collect data from as many sources as they can, unify these richer data sets and analyze them to create compelling marketing programs.
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Marketers can create value to their users and convert them into loyalists only via personalized messages. This personalization is feasible only if marketers clearly understand their user profiles by collecting and unifying different data sets.
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5.1. Data Collection
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5. Data Collection and Storage
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According to Picard, there have been four distinct waves of technology innovation throughout... 6 http://www.adexchanger.com/data-driven-thinking/entering-the-fourth-wave-of-ad-technologyinnovation/
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4.1. Data and Technology Innovation
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Perhaps the most significant sea change in making data widely available at the enterprise level was Salesforce, which put basic CRM data for sales organizations ?in the cloud? and gave every day, non-technical users the ability to update and access data tables. Now, thanks to widely available APIs, deeper levels of data are becoming available in such systems, including detailed transactional data.
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Data management means a lot of different things to different people. In an enterprise environment, there are big differences in the ways IT departments versus sales and marketing organizations view data. For IT, customer and transactional data needs to be safe: stored internally, secured behind firewalls, and only accessible via permissions. For the marketing department, the same data needs to be widely available, configurable, and free to be accessed at will.
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4. What Is Data Management?
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3.1. Section Summary: Marketing?s Big Data Challenge
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Data has simply gotten a lot cheaper to store and is infinitely easier to access, meaning that large datasets that were previously unavailable or too expensive have become available for mining.
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3. Marketing?s Big Data Challenge
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http://www.lumapartners.com/lumascapes/display-ad-tech-lumascape/
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http://www.youtube.com/watch?v=XQu41MVIC9g
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Phil Mui, Chief Product Officer, Acxiom Corporation
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For some, data retention happens just because it?s possible. For other organizations, data is the lifeblood of marketing, and historical customer and purchase data drive key decisions.
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what companies need to solve for is: how to provide a consistent access layer so that a marketer or decision maker doesn?t have to learn the inputs and outputs of different systems nor need to learn those management layers. The future is about making data accessible and usable (insights) when decision makers need it.
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2. Introduction: Data is the Glue of the Advertising Ecosystem
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http://econsultancy.com/reports/best-practices-in-digital-display-advertising
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His last white paper was Best Practices in Digital Display Advertising2 (Econsultancy, March 2012).
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