Final Data Story

Shifting Money, Changing Donors: As the Donor Base Gets Younger, Arts and Culture Institutes Must Shift Their Focus

The idea

High Level Summary

This project will explore, through data visualization, changing demographics of non-profits’ donors in the US, their likes and dislikes, their motivations for participating in the arts, and their motivations for donating. It will draw on data from the National Endowment for the Arts and from Culture Track. The target group for this story is anyone who works in fundraising in arts and culture nonprofits in the US.

Project Structure
SET UP

The US population is getting younger

The US population is changing; people age 25-64 are the fastest growing group

CONFLICT

The nonprofit arts/culture industry is reliant on old money, and cultivates prospects according to this, but potential donors are getting younger

Younger generations and older generations differ on their reasons for participating in the arts and donating to the arts.

Why are younger generations attracted to the arts?

What are they participating in?

With the drive online due to COVID, has this changed?

Donating patterns

Why do younger generations donate?

How much are they donating, on average?

RESOLUTION

Connect with this next generation of donors on their level to make a case for funding

Call to Action

Older generations still represent the largest source of funding for nonprofits for now, but younger generations are upcoming. Incorporate new strategies to cultivate new donors from younger generations by appealing to their interests – social interaction – and their motivations for giving (community, equality, child, and health initiatives.) Connect to them using methods they will respond to (social media, text messaging, email).

The datasets I will use for this project come from four sources: Culture Track, the National Endowment for the Arts, Americans for the Arts, and Our World in Data.

Culture Track is an arts and culture tracking survey delivered by arts consulting firm LaPlaca Cohen. The survey focuses on the attitudes and behaviors of cultural consumers in the US. I have sourced two datasets from Culture Track; the first is the result of a survey about trends in arts and culture audiences and was released in 2017. The second provides information on cultural consumers’ responses to the COVID crisis. I will use information from these datasets to compare younger generations to older generations on participating in the arts now and pre-COVID, and on motivations to donating. of Both datasets are publicly accessible and can be found here: https://culturetrack.com/research/reports/

The National Endowment for the arts in a federal agency that supports the arts. They put out a survey periodically to record adult participation in the arts, the Survey of Public Participation in the Arts (SPPA). I will use the information from this source to determine what activities different generations are participating in, and with what frequency. The raw data from the survey is available in a publicly accessible dataset here: https://www.arts.gov/impact/research/publications/us-patterns-arts-participation-full-report-2017-survey-public-participation-arts

A third source I’ll use is Americans for the Arts. Americans for the Arts is a nonprofit dedicated to advancing the arts in the US. They put out a survey to collect perceptions and attitudes about the arts. I will use this source to determine general sentiment toward different art forms per age group, and how this affects motivations to donate. The raw data from this survey is publicly available here: https://www.ipsos.com/sites/default/files/ct/news/documents/2018-09/americans-for-the-arts-report-09-27-2018_0.pdf

Finally, I’ll download a dataset from Our World in Data on population growth in the US, by age group. I will use this at the beginning of my presentation to make the point that the number of people in older generations is shrinking, and younger generations are growing. This information is publicly available here: https://ourworldindata.org/world-population-growth

Other sources

I will use other sources to reference specific statistics for the project. Though these are not in the form of datasets, they provide valuable information. These are an article by Katherine Boyle from the Washington Post, “Cultivating the Next Generation of Ars Donors,” and a page from NonprofitSource.com highlighting important giving statistics for the year 2018.

Method and Medium

The first step of this project will be to process the data into visualizations that tell the story I want to tell. I plan to use a combination of Tableau and Flourish Studio to create the visualizations from the datasets I previously identified. I will also include pictures sourced from open sources which I plan to research using the list provided by the CMU library. I might also use GIMP, a photo manipulation software, to further enhance my visualizations by adding extra elements.

To present my project I plan to use Shorthand. I think that Shorthand is a good choice because it allows creative transitions from one visualization to the next. It also allows for the incorporation of text in a way that flows and doesn’t bore the reader. I’ll probably start with a Shorthand template and adjust slides to fit the flow in my sketched outline.

Design and user research

Initial sketches

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Wireframes and storyboards

This shows the high-fidelity, individual draft visualizations of the critical elements of the story. The storyboard shows the flow of the story, with the wireframes integrated. Part II1

User research protocol and findings

Target Audience

People who work in nonprofits, specifically in fundraising.

Sample group

All three members of the sample group are known to the author as professionals in the field of nonprofit fundraising.

Research Goals
Goal Questions
Evaluate if the message is clear. Who do you think this presentation is for? Can you tell me the problem being conveyed?
Determine if the call to action is clear. Can you tell me the proposed solution to the problem?
Evaluate the success of the data visualizations. Do you feel the data is compelling enough to motivate the viewer to follow the solution? Why or why not?
Prioritize what to fix. Did anything surprise or confuse you? Is there anything you would change or do differently?
Interview script

Intro: “Thank you for agreeing to do this interview with me. I’m a Master’s student at CMU and I’m taking Telling Stories with Data, a class on Data Visualization. For this project we are creating a story with a call to action using data visualizations. I want to see how effective it is, so I just have a few questions for you.”

Main (ask questions): “I’ll give you a few minutes to look over this wireframe of the presentation. This outlines the visuals and flow of the presentation.” (after a few minutes). “Now I just have a few questions:” (ask the following questions)

Wrap Up: “Do you have any follow up questions or suggestions for me?” (if yes, take notes).

Thank the interviewee for their time, end the interview.

Interview findings and planned changes
Question Key Findings Planned changes
Who do you think this presentation is for? All users understood that the presentation was for those involved in fundraising in nonprofits. I think that the target audience is clear, so no changes needed in regard to this.
Can you tell me the problem being conveyed? Users understood that the main idea was that wealth was changing to younger donors. However, one user didn’t make the connection that nonprofits would be reliant on young donors soon. The overall problem is well understood but might need clarification. I can state explicitly that “nonprofits will be reliant on young donors soon.”
Can you tell me the proposed solution to the problem? This was clear for all users. However, one user had trouble making the connection between the last slide listing potential events and the proposed solution. Clarify the final slide by referencing the findings to make clear how they connect to the proposed events. Also clarify that the proposed events are a method to enact the solution.
Do you feel the data is compelling enough to motivate the viewer to follow the solution? Why or why not? One user felt that the visualizations needed to be clarified and lacked context. I plan to re-visualize the second graph as a unit chart to make it clear that these are proportions being represented, not percentages. I’ll also label the graphs with darker colors so that the labels are clearer.
Did anything surprise or confuse you? One user mentioned they were confused at the “formula” used on the final slide (“children x social”). This is something I’ll probably write out rather than trying to shorthand as the message is being misinterpreted as a mathematical formula.
Is there anything you would change or do differently? One user mentioned the importance of keeping the variables the same color. This is something I’ve already been doing and will continue to do.

Final Data Story

As previously mentioned, the audience for this story is people who work in the arts, specifically in fundraising in the arts. The issue presented in the field is something that is talked about sometimes, but never really given focus. Fundraising efforts continue to be directed toward older generations (64+), meaning that younger generations are often passed over for some of the bigger donorship solicitations and appeals. This presentation aims to bring that to light by outlining how younger generations will soon be a major source of funding and showing how fundraisers can successfully solicit donations from younger donors. There were no personas used in the user research process, but all of the interviewees were people who work in nonprofits fundraising. I got some valuable feedback from the users, the summary of which is located in the User Research and Protocol section. The key points that I got from the user research were that the graphs and charts needed to be better labelled, that the reason why switching to younger donors would be more beneficial wasn’t clear, and that the layout of the final slide that listed ideas for events and activities was confusing.  
I suspected that the lack of clarity around why arts organizations should switch to focus on younger donors was due to the fact that users were looking at a storyboard with wireframes rather than seeing the story in its final form. But, just in case, I added in another slide with a stat mentioning how donations from younger people was on the rise. The feedback regarding lack of labelling on the charts and graphs was also due to the fact that users were looking at wireframes, which were just primary renditions of the graphs. Since the users had no issues with the layout of any of the graphs I only made a few changes to the wireframes for my final graphs. I added labels to the y axis of each graph for context. I also gave each graph a header. When doing this, I remembered that Good Charts suggests not simply describing what is in the graphs with the header, but rather using the header to help tell the story. However, for the purpose of my presentation I thought an overly wordy header would be too distracting, so I kept the headers short and simple by using the space to tell the reader whether the graph was about older or younger generations.  
After the changes were made to the content, I created my presentation. I wanted the presentation to flow well, so I used the “transitions” template from Shorthand. The biggest difficulty with this was that it required images, so I had to create all of my charts as PNG images rather than embedding them from Tableau, Flourish Studio, or Infogram. The benefit to this was that I had more flexibility with size and placement than if I’d just used the embed link. In the end I think my presentation worked well for its intended audience and purpose. I learned a lot about creating graphs using different software and about using Shorthand. User research also helped me learn about adjusting the story contents and layout to fit a specific audience. If I were to do something different in future I’d probably try to get more users for user research.  

Final Data Story Link

Final Shorthand presentation 

 
 

References

Boyle, Katherine. “Cultivating the next generation of arts donors.” The Washington Post. 19 October 2019. https://www.washingtonpost.com/lifestyle/style/cultivating-the-next-generation-of-arts-donors/2012/10/18/5a457fa0-f85f-11e1-8b93-c4f4ab1c8d13_story.html

“The Charitable Habits of Generation Z, Millennials, Generation X, Baby Boomers, and Matures.” The Blackbaud Institute. April 2018. https://institute.blackbaud.com/asset/the-next-generation-of-american-giving-2018/

“Culture Track ’17.” La Placa Cohen. New York City:2017. https://culturetrack.com/research/reports/ Roser, Max, Hannah Ritchie, and Esteban Ortiz-Ospina. “World Population Growth.” Our World in Data. May 2019. https://ourworldindata.org/world-population-growth

“The Ultimate List of Charitable Giving Statistics for 2018.” Nonprofit Source. Accessed 19 November 2020. https://nonprofitssource.com/online-giving-statistics/

Images: All background images from Canva.com, for use by subscribers.  
 
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