Data Quality

Growth

How SoFi powers personalized email campaigns with high-quality customer data

By partnering with mParticle, SoFi has been able to improve data quality across their martech stack and deliver more relevant email experiences to their members.

Growth

Most important data skills for marketers to master in 2023 (spoiler: none of them are SQL)

In today’s data-driven environment, marketers need to be adept at certain data skills and concepts. This doesn’t mean they need to learn SQL and the ins and outs of querying a database, however. Rather, many marketers would be beter served by acquiring skills that augment their core competency: maximizing customer value.

mParticle Product

Event Connection Health delivers enhanced visibility, control, and adaptability for developers

Event Connection Health is the first release in an upcoming series of features that give data teams more visibility and control over all their customer data with one integration. Developers can now save engineering hours by quickly troubleshooting most connection issues without having to open a support ticket.

Growth

How ivee built a universal source of truth for customer data

Learn how ivee established a cross-functional data planning process and improved customer data quality across their marketing stack with mParticle.

Engineering

Enhancements to mParticle’s developer tools make it easier to collect data and ensure quality at the source

mParticle makes it easy for engineers to accurately collect customer data by translating data schemas into production-ready code.

Growth

How to assess your organization's customer data maturity

Successful personalization at scale requires intentional planning around customer data management, process, and tooling. Learn more about customer data maturity, and why it’s important for increasing the ROI of your MarTech stack.

Engineering

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.

Engineering

What does good data validation look like?

Data engineers should add data validation processes in various stages throughout ETL pipelines to ensure that data remains accurate and consistent throughout its lifecycle. This article outlines strategies and best practices for doing this effectively.

Engineering

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.

Engineering

Ask an mParticle Solutions Consultant: What is data quality?

In this video, Andy Wong, a senior leader on mParticle’s Solutions Consulting team, discusses what data quality means, why it is important prioritize, and the benefits of creating a centralized data planning team to oversee data quality.

Engineering

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.

Engineering

How Reverb optimized their data workflows at scale and gave users the rockstar treatment

With mParticle at the heart of their data stack, the world’s largest online music marketplace said goodbye to burdensome ETL pipelines, slashed their data maintenance workload, and unlocked new opportunities to build data-driven features into their product.

Engineering

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.

Engineering

What is data integrity and why does it matter for customer data?

Integrity is a good quality. Just like you want the people around you to have integrity, you also want the data on which you base strategic decisions to be of high integrity as well. That sounds good, but what does it mean for data to have integrity, and why is this so important? In this post, we’ll explore this broad and nuanced concept, define what it means in the context of customer data, and learn a strategy to ensure your customer data maintains high integrity throughout its lifecycle.

Engineering

Debug customer event collection in real time

If you are responsible for implementing data tracking plans across your apps and websites, you’re probably familiar with how tedious and time consuming it can be to track down data collection bugs when they pop up. This video walks through how you can use mParticle’s Live Stream to simplify your team’s testing and debugging cycles.

Engineering

How do CDPs benefit engineers?

Customer Data Platforms (CDPs) have traditionally been thought of as tools that benefit marketers and product managers. But from simplifying data collection to enabling data-driven feature development, CDPs have far-reaching value for engineers as well. Learn more about the benefits of CDPs for technical teams.

Engineering

What is a data plan, and why is it important to have one?

"Wait, why do we need this data again?" "Was that attribute supposed to use snake or camel case?" Data tracking plans keep everyone in your organization aligned on your data efforts, from the high-level strategy to the nittiest, grittiest details.

Data engineering

Leverage mParticle’s data quality developer tools to create, implement, and QA a data plan

mParticle makes it easy to create a robust data plan, implement it with ease, and seamlessly check incoming events to prevent bad data from making its way to downstream systems. Here, we walk through all three steps in a sample application.

Data engineering

How EPIX leverages mParticle to simplify data collection and unlock use cases

In this installment of the Digital Anarchist web series, Sam Dozor, VP of Engineering at mParticle, and Sacha Stanton, SVP and Chief Technology Officer at EPIX, discuss the many wins the streaming innovator has realized as a result of placing mParticle at the heart of their data infrastructure.

Engineering

What is a UUID?

The challenge of identifying data shared between systems dates back to the advent of networked computing. One of the earliest solutions to this problem, the Universally Unique Identifier (UUID), is still in wide use today. Here, we’ll explore this ever-present data identifier in detail.

Growth

6 tips for building a Customer Data Platform tracking plan

The value of your Customer Data Platform depends on the quality of data that you get into it. Here are six implementation tips you can follow to set yourself up for success.

Growth

Probabilistic vs deterministic: Which method should you be using for identity resolution?

The way in which you build your customer profiles can have big consequences on marketing strategy, data privacy, and customer relationships. Learn about the difference between probabilistic and deterministic identity models and how to determine which method you should be using.

mParticle Product

Smartype Custom Receivers

mParticle’s Smartype is an open-source tool that delivers data quality benefits to any engineering team, whether or not you’re working with an mParticle Data Plan. Here, we’ll review how to automatically generate typesafe libraries with Smartype, and learn how Smartype can send user events to any third-party database or library.

Growth

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.

Engineering

What is data engineering?

The quantity and complexity of the data that companies deal with is constantly increasing. While Data Scientists analyze and generate actionable insights from data, they cannot do this effectively with data that suffers from poor quality. Data Engineering roles exist in companies to build data pipelines, transform data into useful formats and structures, and ensure quality and completeness in data sets.

Engineering

The value of a universal customer ID across your tech stack

Teams across industries are striving to create a 360-degree customer view. But if that view isn't seamlessly integrated with the tools and systems throughout the tech stack in real time, growth teams aren't able to use it to drive results. Learn more about how you can implement a universal ID and make it available across the stack.

Engineering

Data enrichment and machine learning: Maximizing the value of your data insights

Data enrichment and machine learning are two techniques that can enhance the ability of your customer data to drive personalized experiences. While there is some overlap in the end goal of both approaches to enhancing data value, there are significant differences in the time, resources, and overhead they each require.

Growth

How to manage data across your tech stack based on customer consent

Great customer experiences are built on trust. Learn more about the tooling that can help you collect customer consent and manage how data flows between systems based on customer consent.

Engineering

How to stop endless data shipping cycles

Engineers should ship products, not data. Product managers and marketers should experiment with data, increase personalization, and improve experiences. With a permanent data infrastructure, these goals are not mutually exclusive.

Engineering

Capture page navigation events in a React Application

In a single-page application, understanding which pages your customers visit and the journeys they take through your website can be challenging. Here, we’ll look at a scalable and maintainable strategy for tracking page navigation events in a React application.

Engineering

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.

Engineering

Smartype Generate: Translate any JSON schema into data collection libraries for web, iOS and Android

mParticle’s Smartype is a platform-agnostic tool that can help every engineering team ensure data quality and consistency. Learn how to use Smartype to translate any JSON schema into custom data collection libraries for iOS, Android, and Web platforms.

Engineering

CDP vs Data Warehouse: What's the difference?

Learn the differences between a CDP vs data warehouse, and how you can use both in tandem to configure an architecture that makes sense for your data and business needs.

Growth

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.

Engineering

Future-proof your customer data strategy: Get ready for iOS 14 privacy updates

There are significant changes coming to iOS relating to user privacy, tracking transparency, and specifically the use of the iOS advertising identifier (IDFA). Since the announcement, mParticle has been collaborating with some of the largest consumer brands in the world to holistically achieve a balance between adhering to compliance obligations and ethical data collection policies to protect consumer choice, while also delivering personalized and relevant information to people globally.

mParticle Product

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.

Engineering

Smartype: Proper event collection at run time

Smartype, a data quality product that translates any data model into type-safe code to help developers ensure proper event collection at run time. Smartype generates personalized SDKs, based on any data model, providing automated code completion and improving data collection and quality at scale. Now available in beta.

mParticle Product

Introducing Developer Tools: Linting and CLI

Announcing the beta release of two new open-source data validations tools for developers: CLI and Linting. Learn how these Customer Data Platform open source developer tools can help you ensure proper event collection at run time and introduce instant data quality protection into your integrated development environments (IDE).

Growth

3 data strategies for high-growth companies

As an mParticle CSM, it’s my job to help brands develop and implement a customer data strategy to drive growth. Here’s what I’ve seen work across industries and organizations.

mParticle Product

New platform features: Data infrastructure, quality, governance, and personalization streamlined

Creating an effective, clean data pipeline that helps you create better customer experiences, guides product and business initiatives, and manages your customer data end-to-end while complying with data privacy regulations is no short order. Never fear, new platform features spanning across data infrastructure, quality, governance, and personalization are here to help.

mParticle Product

Empower your team with better access to data

Put your team’s trust back into your data. Create a Data Plan to build and maintain context around your data and involve the right people in data decision-making.

mParticle Product

Data Master 2.0: Create a data flywheel

Introduce total data quality management and enforcement to your data foundation with new Data Master features and Calculated Attributes. With Data Master and Calculated Attributes, establish a source of better data that will create a customer data flywheel, where data quality and data’s impact on the product cycle will continuously improve over time.

Growth

2020 Predictions: Customer data management practices

When it comes to customer data, much has changed in the past decade. With 2020 quickly approaching, learn about the predictions that will set the stage for the coming decade and what you should do to future-proof your business.

Growth

Omnichannel personalization with Profile API

mParticle's Profile API allows you to leverage comprehensive, omnichannel user profiles to create unique customer experiences for your users, anywhere you can make an HTTPS request.

mParticle Product

Data Master 2.0: Ensure data quality

New Data Master features, now in Early Access, provide teams with an easy way to stop bad data at the source with data frameworks, data validation, and data quality enforcement.

Engineering

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

Growth

When identity resolution goes wrong

mParticle CEO and Co-founder, Michael Katz, discusses why identity resolution is so critical for brands today, where mistakes can be made, and how mParticle can help.

mParticle Product

Data is a team sport: Q4 feature announcements

Bad news for the rogue product manager, the lone wolf data scientist, the intransigent head of infosec, and the obstinate engineering manager: Data is a team sport. Learn about the forthcoming releases designed to help your whole team do more with more of your data.

mParticle Product

Google Tag Manager: Scalable web and mobile tag management

The new Google Tag Manager integration from mParticle helps product and marketing teams easily launch pixels and trackers without pre-built connectors across web and mobile applications and collect customer data consistently and compliantly, at scale.

Growth

The rise of big data in retail

The use of big data in Retail and eCommerce has opened up new possibilities for data-driven experiences, but few are leveraging engagement data to its full potential. Learn about the three fundamental data challenges retailers are racing to solve, their impact, and leading experts’ insight into the subject.

Growth

Customer Data Platform Use Cases Guide: Media

Customers expect media brands to create seamless, contextual, and concurrent experiences across every device, which cannot be achieved with legacy systems unable to collect and activate data from every channel. Use these common use cases to help you determine which CDP features are relevant and find the CDP that will help them meet current and future business needs.

Growth

7 ways the top quick-serve restaurant apps engage their users

Quick-serve restaurants lead the pack when it comes to engaging with customers via mobile apps. Read about how seven of the most successful QSR apps do it!

mParticle Product

Sophisticated identity management made easy with IDSync

Cross-device and platform identity management is made easy with IDSync. Define identity resolution logic for a unified identity view catered to your needs.

Growth

Why multi-faceted identity matters

Learn how brands can enhance campaign analytics, expand contextual insights and orchestrate multi-channel engagement with multi-faceted identity.

Growth

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.

Growth

How Venmo increased user engagement by 30%

Engineering

App development: Planning for the long term

Long-term thinking is valuable when trying to meet any goal, and app development is no exception. However, long-term can be as short as a few weeks.