This week, we’ve gathered articles that dive into the concept of Customer Data Management. We also examine the differences between the Netflix subscription and donation subscription models. We consider ways in which automation can bring efficiency to CRM and finally, we share a list of ten free must-read books for machine learning and data science.  

What is Customer Data Management – Part I

By Indrajeet Deshpande, community contributor for

As part of MTA’s MarTech 101 series, the elements of Customer Data Management (CDM) are defined and explored. CDM is the collective processes of acquiring, analyzing, and organizing customer data. CDM tools then centralize this data so that organizations can get a unified view of their customers. Strong CDM practices and processes allow organizations to personalize the customer journey and delivery the best customer experience possible. But doing this correctly and effectively is no small task, as data is often siloed. Take a deep dive into the world of CDM and learn what it is, how to use it, and get some best practices to implement in your own organization.


How Netflix Subscribers Are Different From Monthly Donors

by Erica Waasdorp, contributor for

Subscription services like Netflix make signing up easy. Users sign up for a service, and they’re automatically charged each month without having to lift a finger. This model can be applied to nonprofit donations. In fact, the most recent Blackbaud sustainer report showed that some five to ten percent of monthly donors are now giving automatically from their bank account (EFT/ACH). But just because people and donors are getting more comfortable with “subscription giving,” doesn’t mean everything is the same. There are two major differences between subscription users and monthly donors that have to be taken into account when considering subscription giving. Find out what they are and more in this enlightening article.  


When Automation Brings Efficiency to CRM

by Mark Hill, commentary for

Repetitive manual data entry is a crucial piece of the puzzle to have the best view of customers and clients. But these mundane tasks take away time from more important tasks (and primary job roles) of your team. So why waste their valuable time and frustrate them, rather than adopt modern automation methods such as voice input or data parsing to eliminate the pain. Some of these menial tasks can potentially be handled through automation so that your team can focus on the important, higher-value work. Automation can achieve time savings, increase quality, and scalability. But Sometimes just because you can automate something, doesn’t mean you should. A human element may add a lot of value to a task, or it might not make fiscal sense. It’s important to determine what tasks make sense to automate based on your organization.  


Another 10 Free Must-Read Books for Machine Learning and Data Science

by Matthew Mayo, editor for

We love books… especially when they’re free! In this list, you will find a few books on elementary machine learning, and a few on general machine topics of interest such as feature engineering and model interpretability. You’ll also find an intro to deep learning, a book on Python programming, a pair of data visualizations entrants, and twin reinforcement learning efforts. Happy reading!



Did you see an interesting article in the last week? Share it with us! Send it to astuttle [at]

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