Maximize the ROI of your MarTech stack with this six-step checklist
In today’s MarTech landscape, there is no shortage of tools you can use to engage with your customers and deliver personalization. But adopting tools alone by no means guarantees improved business outcomes. Here is a simple checklist you can use to increase the ROI of your MarTech stack.
BigQuery vs. Redshift: Which cloud data warehouse is right for you?
The data warehouse is the source of truth from your business's data set. Choosing the right solution is critical. This article explains how BigQuery and Redshift compare in factors such as performance, security, and cost so that you can select the right warehouse for your needs.
Kinesis vs. Kafka: Comparing performance, features, and cost
In this article, we compare two leading streaming solutions, Kinesis and Kafka. We focus on how they match up in performance, deployment time, fault tolerance, monitoring, and cost, so that you can identify the right solution for your streaming needs.
What the heck is reverse ETL?
Reverse ETL is a process in which data is delivered from a data warehouse to the business applications where non-technical teams can put it to use. By piping data from a data warehouse to downstream business systems, reverse ETL tools fill the gap between data storage and activation.
Snowflake vs. Redshift: Which Data Warehouse Is Better for You?
Learn how popular data warehouse providers Snowflake and Redshift compare in maintenance requirements, pricing, structure, and security so that you can understand which solution is right for your team.
Snowflake vs. BigQuery: What are the key differences?
Learn more about the differences between two popular data warehouse solutions, Snowflake and Google BigQuery, and understand how to identify which is right for your team.
How we improved performance and scalability by migrating to Apache Pulsar
We recently made a significant investment in the scalability and performance of our platform by adopting Apache Pulsar as the streaming engine that powers our core features. Thanks to the time and effort we spent on this project, our mission-critical services now rest on a more flexible, scalable, and reliable data pipeline.
Transform customer data into predictive insights with an AI CDP
You don’t need an army of data scientists to generate predictive models from your customer data, or complicated pipelines to translate this intelligence into business value. mParticle is democratizing AI adoption, and turning what was once a pipe dream into a catalyst for your pipeline.
mParticle is now available for purchase on AWS Marketplace
mParticle is now available on AWS Marketplace, which means it is easier than ever for organizations to transform cross-channel, first-party data into deeper insights and better outcomes by connecting mParticle with 300+ integration partners, including Amazon Kinesis, RedShift, Mixpanel, Zendesk and Braze.
mParticle democratizes access to its enterprise grade customer data infrastructure product
The combined offering will help teams integrate customer data directly from their data warehouse and improve customer journey exploration.
What is Data Chaos? And how to solve it
As you successfully drive growth, the complexity of your data landscape increases significantly. Unpredictable changes, both internal and external, make it difficult to execute your customer data strategy at scale. This is data chaos, and to solve it you need to be able to adapt to the changes.
How to choose the right foundation for your data stack
If you’re relying on downstream activation tools to combine data events into profiles, don’t. You’ll end up with fragmented and redundant datasets across systems. Enriching each data point before it is forwarded downstream will prevent this problem, but not all customer data infrastructure solutions deliver this capability.
How to build a data-driven culture
Many business have data, but few are actually able to use their data to increase customer value. This post describes the technology and processes you can implement to build a data-driven culture that transcends individual teams and differing levels of technical understanding.
Sollten Sie eine Kundendatenplattform aufbauen oder kaufen?
Kundendatenplattformen sind ein wichtiger Bestandteil der modernen Dateninfrastruktur. Erfahren Sie, was für den Aufbau einer Kundendatenplattform erforderlich ist und wie Sie feststellen können, ob der Aufbau einer eigenen Lösung oder die Zusammenarbeit mit einem führenden Anbieter der richtige Weg für Ihr Unternehmen ist.
mParticle Acquires Customer Journey Analytics Provider Indicative to Help Teams Accelerate their Customer Data Strategy
The combined offering will help teams integrate customer data directly from their data warehouse and improve customer journey exploration.
How to use a CDP with your data warehouse
Data warehouses and CDPs are two pillars of the modern data stack. Recently, a perception has emerged that companies need to choose one system or the other as a “source of truth” for their data. This article poses a counter perspective, and demonstrates how when used together, a CDP and a data warehouse can form a dynamic duo at the core of your data infrastructure.
Why I’m Here — I’ve joined mParticle to build the future of customer relationships
Access to accurate customer data, and the ability to turn it into actions that benefit customers, is the future of customer relationships. In this post, Katie Cerar discusses how she'll be helping brands build that future as Head of Product Strategy at mParticle.
How to implement an mParticle data plan in an eCommerce app
This sample application allows you to see mParticle data events and attributes displayed in an eCommerce UI as you perform them, and experiment with implementing an mParticle data plan yourself.
Should you be buying or building your data pipelines?
With demand for data increasing across the business, data engineers are inundated with requests for new data pipelines. With few cycles to spare, engineers are often forced to decide between implementing third-party solutions and building custom pipelines in-house. This article discusses when it makes sense to buy, and when it makes sense to build.
When to use a data lake vs data warehouse
Enabling teams with access to high-quality data is important for business success. The way in which this data is stored impacts on cost, scalability, data availability, and more. This article breaks down the difference between data lakes and data warehouses, and provides tips on how to decide which to use for data storage.
How to assemble a cross-functional data quality team
Smash your data silos and improve data quality across your organization by assembling a cross-functional team to own data planning.
mParticle's Series E: The rise of Customer Data Infrastructure
mParticle CEO Michael Katz discusses the company's latest funding round, sharing the company's growth thus far and future trajectory.
How to prepare for your Customer Data Platform implementation
Implementing a Customer Data Platform at the infrastructure layer of your tech stack can transform the way in which you work with customer data—if you do it right. This article breaks down the steps you can take to prepare for your Customer Data Platform implementation and make the most of your investment.
Power first-party advertising with mParticle's Yahoo! integration
Yahoo! sees roughly 200b daily cross-screen data signals from search, mail, commerce, and content. With mParticle’s new integration with Yahoo!, connecting those data signals just got a lot easier.
Everything you need to know about data integrations
Data integrations are ubiquitous throughout the SaaS ecosystem. But not all data integrations are created equal. This article walks through the different types of integrations commonly available and provides tips on how to choose the right integration types for your use cases.
Introducing the new Certified Solutions Partner Program (beta)
Today, mParticle is excited to announce the beta launch of our new Certified Solutions Partner Program and portal, where partners can access certification tracks and educational materials, create co-branded sales and marketing collateral, refer opportunities, and request co-marketing activities, as well as earn and redeem Partner Growth Funds.
How to set up your Customer Data Platform team
In part two of our two-part series on making the most of your Customer Data Platform, we discuss how you can set up a cross-functional team of 'customer data excellence' to operate your CDP.
Python and SQL: Complementary tools for complex challenges in data science
While Data Scientists today have an ever-expanding list of toolkits, languages, libraries and platforms at their disposal, two mainstays––Python and SQL––are likely to remain staples of data analysis for years to come. Here, we’ll look at the role these languages play in the rapidly evolving field of Data Science.
Data-informed Decision Making: What does it mean, and should you be doing it?
Like data-driven decision making, being data-informed entails relying heavily on raw, measurable information to guide an organization’s direction. Data-informed strategies leave more room for opinions and past experiences, however, and recognize the limitations of using data alone to make every decision.
What is a Customer Data Platform?
Learn more about what a Customer Data Platform (CDP) is and how you can make sense of the rapidly evolving Customer Data Platform market.
What is data orchestration
Data orchestration is an automated process in which a software solution combines, cleanses, and organizes data from multiple sources, then directs it to downstream services where various internal teams can put it to use. The purpose of data orchestration is to help a company make its data as useful and versatile as possible.
CDPs vs. Data Lakes: What’s the difference, and can you use both?
CDPs and Data Lakes differ in the insights they surface, the users they serve, and the overall value they deliver. Though when used together, they are a powerful duo that can help your organization leverage historical and real-time customer data to the fullest extent.
CDP vs Data Warehouse: What's the difference?
Data warehouses enable critical insights, and speed of data collection and stability of warehousing are important to their performance. Learn the differences between a CDP vs data warehouse and how you can use both to improve functionality and take action on business intelligence.
A single customer view is not the goal (it's the result)
Companies have long tried to establish a single customer view, but few have been able to put a solution into place that addresses the cross-functional needs of stakeholders. The problem is that a single customer view is often seen as the goal of processes, rather than the result. Learn how to create a single view of the customer by enforcing an organization-wide commitment to data quality and collaboration catalyzed by a sound data design process.
Generic data infrastructure isn’t enough
Can your data stack keep up with the personalization, measurement, and customer support demands of your team? Download this free customer data infrastructure guide to learn how you can use a CDI to support scalable customer engagements across every touchpoint, channel, and device.
Should you build or buy a Customer Data Platform?
Customer Data Platforms are a critical piece of the modern data infrastructure. Learn what it takes to build a Customer Data Platform and how to determine whether building a solution or working with a leading vendor is the right path for your organization.
Seamless first- and third-party data enrichment
The newly debuted Data Partners Program is a group of enrichment data partners that meet the highest standard of data integration with mParticle’s CDP; these integrations allow third-party data to be collected and connected to the first-party persistent customer profiles existing within mParticle to provide a complete, real-time view of the customer which can then be used to seamlessly connect insights to a growing ecosystem of 250+ outbound integrations.
How JetBlue improved their mobile customer experience
Learn how JetBlue uses mParticle to understand how customers experience the app on an individual basis, identify points of friction that affect customers' satisfaction, and test and deploy tools efficiently without adding third-party code that could impact end-user functionality
Real-time event processing with Kafka
Learn how mParticle's Kafka integration can help you stream customer data to systems and applications with event data forwarding, advanced filtering and compliance, distributed event notification, and event sourcing.
Load data to Snowflake data warehouse
Learn how mParticle's Snowflake integration can help you warehouse your customer data more efficiently and securely at scale with automated data exports, advanced filtering and compliance, and easier querying.
10 Critical data infrastructure capabilities
Instead of focusing on core data management challenges, many Customer Data Platforms are focused on the application of data. Learn about the 10 critical components of modern data infrastructure.
The new consumer marketing
How a new generation of direct-to-consumer brands is reimagining relationship management and transforming the marketing world.
How to plan your integrated data layer
Learn how to develop an integrated data layer that aligns with your business and techical goals and provides you with a centralized source of clean customer data.
CDP vs. DMP: What’s the difference and which one should you use?
Create a dynamic martech stack capable of collecting, transforming, and activating customer data by using a Customer Data Platform to augment your Data Management Platform. Learn how you can leverage these technologies to help you turn your customer data into a growth asset.
How to assemble a best in class tech stack
How to implement a best in class stack...the right way
ScyllaDB Migration: How to design high throughput and low latency NoSQL deployments
Yuan Ren, Head of Data Science at mParticle, discusses our ScyllaDB migration and how to process 50 billion monthly messages via NoSQL deployments.
Announcing our integration with Google Cloud Storage
The new Google Cloud Storage integration allows mParticle customers to seamlessly onboard customer data to Google’s Cloud Platform without requiring code updates and ongoing maintenance.
14 eCommerce audiences marketers need to succeed
Reaching these 14 eCommerce audiences with the right content can make all the difference in terms of engagement, conversion, and satisfaction.
15 questions to evaluate CDP data preparation capabilities
Marketing technology analyst David Raab explains how customer data platforms can enable data transformations and how to ensure the right fit for your needs.
Tag managers vs CDPs: What’s the difference?
Both tag managers and CDPs help marketers collect customer data without relying on engineering, but that's where the similarity ends.
CDP ROI: Risk management
This blog is part three of a five-part series detailing how using a customer data layer can help brands comply with data privacy standards and mitigate risk across their portfolio of martech investments.
Announcing our Feeds integration with Salesforce Marketing Cloud!
We are excited to announce our latest Feeds integration with Salesforce Marketing Cloud (SFMC), making it easier for brands to…
Behind the script: Building a Roku SDK
mParticle's Sam Dozor walks us through building our open source Roku SDK and the many eccentricities of the platform that made the experience so unique.
Marketing data as a core asset
Data isn’t what matters, what you do with it is the important part. Conceive of and manage your data properly from the start to get the most out of your marketing data.