With over 5 million apps in the App Store and Google Play combined, marketers today cannot rely on pure organic discovery of their apps. That's why app owners are realizing that marketing-driven, non-organic installs play an increasingly important role in the marketing mix.
This guide will show you how to get started with mobile attribution. Including the underlying methodology of mobile attribution, post-install marketing analytics, and fraud.
As the digital ecosystem continues to expand and evolve, SaaS technology has become a mission-critical component for effective marketing operations. Marketing and advertising technology falls into numerous complex and overlapping categories, with literally thousands of partners competing for your marketing dollars.
Whether you’re building your marketing tech stack from a mobile-first or web-first perspective, it can be daunting to know where to start as well as when and how to expand. And while adding or changing marketing technology requires significant investment in terms of vetting, training, development, and other resources, adaptability is key to success.
The purpose of this guide is to provide a framework for how to build a solid marketing tech stack—focusing specifically on mobile as the core platform.
With the rise of cord cutting behavior, traditional TV ad spend has been declining in lieu of over-the-top (OTT) and digital video. OTT has made TV advertising more accessible to performance-driven marketers. Beyond the clear synergies between OTT and mobile for app advertising across screens, addressable buying methods offer more sophisticated targeting with lower barriers to entry. OTT also offers deeper insight into attribution, as advertisers are able to definitively measure one-to-one acquisition from TV advertising for the first time.
That being said, OTT is still a grey area for many—with superfluous TV terminology, fragmented options for media buying, and unclear expectations for performance and measurement, it can be tricky to know where to start.
The demand to turn raw data into business insight for today’s companies is now higher than ever. So it’s no surprise that there’s significant interest by data-driven companies for new products and services in a single platform.
An Enterprise Intelligence Platform combines what was traditionally three separate product categories for data and analytics into one seamless experience: data integration, data storage, and processing and analytics designed to meet the needs of both data consumers and data operators. According to 451 Research, 78% of data-driven companies would consider adopting an Enterprise Intelligence Platform.
Financial services institutions lost over $1.45 trillion in combined revenue to financial crimes in 2018, and this is increasing ever rapidly. Today’s leading financial institutions are increasingly relying on data, analytics, machine learning, and AI technologies to capitalize on the information needed to combat financial crime—converting raw data into actionable insights. Here are the top five approaches that have the greatest impact when creating next-generation approaches to fighting financial crime.
Analytics, machine learning, and artificial intelligence are reshaping manufacturing. The most innovative manufacturing companies are applying industrial IoT concepts, coupled with data and analytics, to transform their product development, supply chains, and manufacturing operations. Here are the top five areas where manufacturers are using the power of analytics and machine learning to drive business success.
Customer data and analytics can take many forms, from descriptive analytics on operational data to inferring customer sentiment based on real-time social media feeds. With a variety of analytical tools for streaming, operational and unstructured data, businesses are asking more questions of more data. Yet, deriving and operationalizing the best answers effectively can be a matter of connecting the dots across different analytical workflows and the different teams, from BI analysts to data scientists, that use them.
The webinar includes a live demo and Q&A with our expert panel. We hope to see you there!
Logging Modernization is a holistic approach that unlocks the value of machine generated data by using a comprehensive streaming platform. This platform should include everything from real-time data ingestion, edge processing, transformation, and routing through to descriptive, prescriptive and predictive analytics. All of which should be securely shared across on- premises, public, or hybrid cloud environments.
Apache NiFi is an integrated data logistics and simple event processing platform. It provides an end-to-end platform that can collect, curate, analyze and act on data in real-time, on-premise, or in the cloud with a drag-and-drop visual interface.
This book is a must read.
Cloudera has been named as a Strong Performer in The Forrester WaveTM: Streaming Analytics, Q3 2019. We’re excited to make our debut in this Wave at, what we consider to be, such a strong position. We’ve been named one of “The 11 providers that matter most” in streaming analytics. The report states that analytics prowess, scalability, and deployment freedom are key differentiators in the evaluation across 26 criteria.
Cloudera DataFlow (CDF) is a comprehensive real-time streaming data platform that enables enterprises to address key streaming use cases across a wide range of streaming sources. CDF streamlines the process of collecting, curating, and analyzing real-time streaming data with its integrated set of products.
The Forrester Wave report recognizes Cloudera DataFlow as “more than streaming analytics.” The report states that Cloudera offers “strengths in streaming data platforms, management, security, development, extensibility and deployment.”
Download the report to read in detail about how Forrester has assessed all the vendors on their various strengths.
The growing emphasis on digital transformation is encouraging more organizations to adopt initiatives driven by the Internet of Things (IoT).
While such initiatives enable enterprises to enhance customer experiences, create new business channels, or acquire new partner ecosystems, gaining the insights to realize these benefits can prove to be challenging. Enterprises intend to quickly gather and analyze data and from the devices powering their business. However, the sheer volume of data that these devices generate, the variety of data that comes in, and the velocity in which data is collected creates its own set of challenges in terms of storage, processing power, and analytics for such enterprises.
The growth of IoT adoption has been exponential across all industries, but organizations within each industry face a unique set of challenges along this journey. Enterprises leveraging all the big data generated from IoT devices in their machine learning models are able to use prescriptive and predictive analytics to make well-informed decisions.
In this eBook, we will discuss the challenges of implementing data-driven IoT, and solutions for addressing the challenges across multiple industries.
Original Kafka founding team member, Renu Tewari, discusses how streaming architectures are on the rise and enterprises are struggling to scale up as needed. Apache Kafka has emerged as a winner for now amongst the major stream processing engines. However, Kafka can do wonders for a scalable streaming architecture provided it is set up, configured and implemented appropriately. Watch to learn more.
IT is no longer relegated to the IT group. Lines of business are building new business applications that can drive their business’s top and/or bottom lines. These applications are increasingly stateless -- meaning that they rely on their underlying operational database to manage their state and work with IT to build, deploy and manage the database infrastructure. The application development lifecycle is accelerating with the broad adoption of cloud and the rise of dbPaaS where the database is fully managed and self-optimizes for the applications. In this session, we will show you how the Cloudera Operational Database offers an accelerated on-ramp to app development by offering a modern multi-model database that eliminates infrastructure management.
During this demo, you will learn how to use the Cloudera Operational Database to:
Speakers:
Director Product Management
Cloudera
Technical Marketing
Synopsys helps development teams build secure, high-quality software, minimizing risks while maximizing speed and productivity. Synopsys, a recognized leader in application security, provides static analysis, software composition analysis, and dynamic analysis solutions that enable teams to quickly find and fix vulnerabilities and defects in proprietary code, open source components, and application behavior.
Open source use isn’t risky, but unmanaged use of open source is.
Open source software forms the backbone of nearly every application in every industry. Chances are that includes the applications your company develops as well. If you can’t produce an accurate inventory of the licenses, versions, and patch status of the open source components in your applications, it’s time to assess your open source management policies.
This paper provides insights and recommendations to help organizations and their development and IT teams better manage the open source risk landscape. It covers: