In 2013, Cleveland Clinic was doing a lot of manual work, struggled with resource allocation and had trouble identifying potential major gift prospects. In just a few short years, they have built proactive systems and technologies which help their staff improve productivity, measure performance and identify the next major gift prospect. This session will discuss many of the concepts and tools they used in order to make these drastic changes.

Feverish coding and fancy statistical techniques to generate data is just one aspect of a data scientist's job, constructing a narrative and communicating results to be accessible to non-technical audiences are just as important! This session will explore how to craft a data story through the lens of a LinkedIn analysis evaluating the impact of the recent U.S. government shutdown, which was covered by media such as the Wall Street Journal and Washington Post.

The Kellogg School of Management at Northwestern University has formulated a data-driven fundraising strategy by performing benchmarking analysis. This has enabled The Kellogg School of Management to double down on their strengths while improving on their weaknesses. Learn how your own school can use benchmarking analysis to develop the ultimate campaign strategy.

At Wisconsin Foundation and Alumni Association, we have moved from vendor-purchased models to building our own in-house. This presentation will touch on the history of our models, and what we have learned about the process. Meredith will take time to demonstrate one recent improvement (coefficient change tracking) in R.

Everybody knows that if you’re serious about data science, you’ve supposed to be using R. But then you try to get started and all you seem to be able to do is futz around this with this mysterious mtcars data set. Sure, you tried importing your data by clicking on that “Import Dataset” button, but surely you don’t have to do that every single time your data changes? And now that I’ve done some analysis, how do I get this data back out? We’ll look at a bunch of different ways to push data into and out of R, and how you can set up your own workflows so that you can spend your brainpower on analysis, instead of on the day to day stuff.

Requests of "Repeat this analysis, but for a different timeframe/business unit" or "I'd like these results delivered to me weekly" are likely familiar to those who analyze data. Addressing these and similar issues with a reproducible workflow can save time and effort in the long run, while simultaneously fostering a culture of transparency and trust. This session introduces reproducible methods to best suit users’ skill levels and organizational needs.

A birds-eye view of machine learning that breaks down what it is, cuts through the jargon and gives an overview of some applications in fundraising. The audience will gain exposure to core concepts and applications, and not veer into mathematics or code too deeply.

A significant portion of analytics to support engagement and fundraising is focused on what someone may do: how much they may give, what message, which channel and when? While useful insights, they are simply focused on outcomes. What if we could capture, and understand the “why” of philanthropy and engagement? This session will focus on the field of behavioral data science, which can give us insight into the motivations of our constituents and greater opportunity to positively impact before they give. Getting started may seem like a daunting task, but when it comes to philanthropy, plugging in to data science can and should be an integral part of every organization. This session is designed for practitioners at all levels as we delve into behavioral data science trends and implications for fundraising and donor engagement.

How do you become a fundraising data scientist? Many of us come into fundraising analytics from other careers and degrees -- and the field of fundraising data science is a maturing field, growing in its methods and career pathways. In short, there are many routes to mastery. Choosing projects, deciding what additional skills you might need, and practicing those skills on the job are all part of the fun juggling act we encounter. The good news is that there are folks who are paving the way and can offer advice -- hear from three data craftspeople discuss their approaches to learning, talk shop about tools, and discuss what they're practicing now.

Data is a valuable asset and deriving insights from the right data points -- and the right time -- expands our prospect pools and fundraising ambitions. So how do we enhance the ROI of our data at our organizations? At St. Thomas, our newly created Constituent Intelligence team focuses on putting "data into action" and expanding our view of alumni using data science. Throughout this process, we reduced the time and effort dedicated to manual work and increased the richness of our relationship, employment, and other data points. In this presentation, we will walk through this team transformation and share specific ways to tap into your existing data for new information.