Customer Data & Analytics Blog

What is all this buzz about the Julia Programming Language?

Janet Wagner | 3 minute read

There seems to be a lot of buzz around the Julia programming language lately. After nearly ten years of development, Julia 1.0 was released in August 2018. And according to the KDnuggets software poll published in May 2019, Julia is one of five major analytics, data science, and machine learning tools with a significant increase in usage compared to the 2018 software poll. This post takes a look at the Julia programming language and highlights a few of the advantages and disadvantages of Julia compared to Python.

Topics: data science

How an automated CDP is the foundation for better marketing

Chuck Leddy | 3 minute read

B2B marketers might understand what a customer data platform does, but may struggle with the next-level concept -- connecting what a CDP does with building a smarter, more efficient marketing operation. This post is intended to answer 6 key questions B2B marketers may have about how an automated CDP can make their lives easier and their marketing efforts better. 

Topics: machine learning

How machine learning solves your customer data quality issues

Ariella Brown | 3 minute read

Machine learning helps marketers find potential customers, understand customer purchasing patterns, and even predict and prevent churn. This blog explores where machine learning fits in around customer data quality issues.

Topics: machine learning

Customer experience is the new battlefield

Iqbal Kaur | 3 minute read

It’s one thing to want to please customers. It’s a whole other thing to actually deliver a consistent experience that satisfies customers and keeps them coming back for more. 

Topics: Customer Experience Strategy

Common algorithms in marketing: Decision trees

Janet Wagner | 3 minute read

Organizations that have incorporated machine learning (ML) into their platforms (or plan to) are finding that there are many decisions to be made when it comes to models and algorithms. One of those decisions is which algorithms should be used for which applications. Decision tree algorithms are commonly used in marketing. This post highlights some of the advantages and disadvantages of decision trees. This post also includes a few examples of how decision tree algorithms are used in marketing.

Topics: machine learning