Blog

customer-data-platform-tracking-plan

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.

Glenn Vanderlinden – June 29, 2021
tech-for-black-founders

Company

Celebrating one year of Tech for Black Founders

This Juneteenth marks the one-year anniversary of Tech for Black Founders. We wanted to take this opportunity to highlight what the program has helped Black-led companies achieve thus far and celebrate the program's mission for the future.

Joey Colvin – June 17, 2021
probabilistic-vs-deterministic

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.

Joey Colvin – June 16, 2021

Growth

How to leverage user data to improve customer service

Great customer service is an important driver of retention. Learn how you can connect user data to your support system in order to provide a better support experience and prevent churn.

Joey Colvin – May 19, 2021

Engineering

How we improved our core web vitals by migrating to Gatsby

By migrating the architecture of this website to Gatsby, we were able to double key core web vitals, increase our accessibility rating by 50%, and boost our SEO scores from 80 to 100

Sean Ryan – May 18, 2021

Engineering

What is Gatsby?

Gatsby is an open-source framework that combines functionality from React, GraphQL and Webpack into a single tool for building static websites and apps. Owing to the fast performance of the sites it powers, impressive out-of-the-box features like code splitting, and friendly developer experience, Gatsby is fast becoming a staple of modern web development.

May 11, 2021
mParticle’s new integration with Zendesk gives you a low-cost way to improve your CS experience by taking all the user data you’re already collecting for your marketing and product teams and making the most relevant data points easily available to your CS team as context for each support ticket.

mParticle Product

Personalize Support with mParticle and Zendesk

You wouldn’t expect your Marketing or Product teams to function without data. Show your support team some love and help them provide personalized service, using the same data points you’re already collecting.

Kale Bogdanovs – May 06, 2021

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.

Sean Ryan – April 29, 2021
iOS 14.5 will bring significant changes to the way iOS apps handle privacy and user tracking transparency.

mParticle Product

Get ready for Apple’s App Tracking Transparency Framework

This week’s release of iOS 14.5 marks the beginning of enforcement of Apple’s App Tracking Transparency (ATT) Framework, announced last year. The ATT brings significant changes to the way iOS apps handle privacy and user tracking transparency. mParticle is continuing to roll out updates to help customers adhere to the new framework, as well as tools you can use to build a future-proof data strategy based on first-party customer data infrastructure.

Kale Bogdanovs – April 27, 2021

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.

Joey Colvin – April 19, 2021
What is data engineering?

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.

Sean Ryan – April 08, 2021
Comparing SQL and Python for Data Analysis use cases.

Engineering

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.

Sean Ryan – March 31, 2021