This week, we learn how IT supports data science and ways storytelling and personalized marketing automation can be used together for greater efficacy. We consider how machine learning (ML) helps us define fairness and what the next-generation applications of ML and artificial intelligence (AI) look like. And finally, we share a ground-breaking donor loyalty study.
by Mary E. Shacklett, contributing writer for InformationWeek.com
Data science specialists need to work hand-in-hand with their IT counterparts. But for many organizations, this just isn’t the case. But without IT’s expertise of knowing where the data is and how to access and aggregate it, analytics and data science engineers would be challenged to arrive at accurate insights that can benefit the business. IT support of the data science operation is a key pillar of corporate analytics success, and that is why it must be a partnership.
by Pat Farrell for NonProfitPro.com
For years the nonprofit industry has relied on benchmark studies to establish trendlines for fundraising effectiveness. But there’s never been a study to establish what donor loyalty means and how it should be measured across sectors, channels, sources, and giving levels… until now. The Donor Loyalty Benchmarking Study by Pursuant provides a baseline and framework that your organization can use to elevate the loyalty and commitment of your donors by improving retention and increasing their lifetime value.
by Ryan Taft, contributing writer for MarTechAdvisor.com
A big challenge these days is delivering the right message at the right time to the right person. Reach is now a commodity and the average consumer is hit with thousands of marketing messages a day. How can your brand be heard and seen in all the clutter and distraction? Personalized, compelling digital stories powered by marketing automation that will capture attention, build loyalty, and drive sales in a saturated marketplace. Learn how to pair storytelling and personalized marketing.
by Dr. Manjiri Bakre, contributing writer for HBR.org
Bias is inadvertently built into machine learning’s essence—the system learns from data and picks up the human biases that the data represents. Yet machine learning’s very nature may also be making us think about fairness in new and productive ways. It can help us in discussing fairness and relevant distinction because it requires us to instruct it in highly precise ways about what outcomes are ethically acceptable. It gives us the tools to have these discussions in clearer and more productive ways. Learn how and get examples in this interesting article.
by Chithrai Mani, contributing writer for Forbes.com
AI and ML applications are all around us. From Alexa to Netflix to chatbots, companies across many industries are implementing this technology and people are interacting with it every day. But it doesn’t stop here. Looking ahead, there are some interesting next-generation applications of AI and ML. This article highlights three examples.
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