Lynne Wester, author of the engaging and insightful Donor Relations Guru blog is conducting her second ever Pulse of donor relations survey.
Your input is needed to create a dense and meaningful distribution of response data. Please click here or on the image above to take the survey.
A fair number of blog posts here are about data analysis and data fluency. If you don’t know me well, you might think my job is all about analyzing tons of data. But that’s far from the truth. I spend more than half my time on prospect research basics – verifying addresses, names, phone numbers, relationships, age and publicly recorded assets.
That’s why this article caught my attention. Chris Hughes, with Ruffalo Noel Levitz, assembled a top-5 list of investments for organizations moving toward the end of our fiscal year (for those of us on the academic calendar).
1. Number one on his list is good old basic research and data enrichment. My favorite. How many of your donors in the last 6-to-12 months are in your system without a good address? Probably more than you think. Check it out! Make sure you can contact your recent donors. Don’t assume the national COA is going to keep your file up to date. If you have too many constituents to look up, then segment by lifetime value, last gift amount, or wealth tier and start there.
2. Second on the list is analytics. This doesn’t have to mean complicated modeling. In fact more than likely, all of us could benefit from knowing more about our donors. What is their age distribution? Where do they live? How many have more than one valid contact method listed in your system? How many fall into more than 1 segment (like alumni, patient, member or ticket subscriber)?
He rounds out the top-5 list with 3 additional worthy ideas, so click on the image above to read the full article.
The days are getting longer and our gardens are exploding with color. I mostly favor perennials myself, but my niece gave me a pretty succulent for my birthday. And it’s blooming! What a treat. Tiny little coral-pink flowers with bright yellow middles.
Ornamental trees, shrubbery and flowering plants all support native wildlife. Let me know if you have any photos from your garden to share on a future edition of Blooming Tuesday 🙂
All of us have a lot of information to share with each other and we’re finding that after someone understands a concept, a question is likely to follow. So we’re learning to assemble clusters of related information that gives a broad picture of a single subject area. Infographics are the go-to resource to fulfill this need.
If you’ve been reading this blog for a while, you know that I’m a huge fan of Canva.com. And just recently they introduced some free infographic templates in their suite of layouts.
Now, let’s just say up front that the templates they offer are purely for illustration, this isn’t a chart building tool. B ut the way Canva works is you can build your chart in the tool of your choice then import the image into the layout. See?
Here’s a good reference article from the Canva blog to get started building infographics. If you want to compare notes, give me a call! I’m still a novice but have been attempting to improve my designs for a while now and am always willing to trade tips for successful infographics!
It’s a given that I’m a fan of data. Precise indicators that give the ability to establish baseline counts and segments. However, in our world we’re often left to make assumptions about whether the numerals have consistent meaning from constituent to constituent. Nothing can replace the rich information we’re able to gather when speaking directly with a constituent. And surveys are a perfect method to engage and inquire, in a very specific way.
The quote above is from a New York Times article by Tim Lahan. He describes how too often we look at big data (such as email clicks, social media likes, etc.) as insightful when true insight can only be gleaned by placing the clicking behavior in context of the user experience.
So instead of assuming that our constituents feel more connected to us as a result of mailing out a big report or sending a video, why not ask? The information could be used to start forming content preference models and we could spend our communications budget delivering content that resonates with the recipients.
Click on the quote above to read Mr. Lahan’s article.
I was reading yet another article about leveraging unstructured text data, like contact reports, notes and comments in our data base. For most of us in this profession, this is our equivalent of internally generated big data.
Boris Evelson, the author of this article, suggested that most organizations only utilize 35% of their available structured data to inform decision making and a mere 25% of unstructured data.
I have to wonder about that. How can I even start to get closer to those numbers?
His article is a succinct how-to for getting started with text analytics. And one of the first steps is to define the use cases for extracting intelligence. What in the world is a use case? It’s just a scenario for where to locate the data and the context of the information you’re looking to find. For every question you have, outline where it might be and what it might look like in that location. In a best case situation, you’d also identify what it wouldn’t look like, as in similar data that isn’t relevant to the question at hand.
For example, if you worked in a college or university setting and wanted to extract information from contact reports about constituents who participated in fraternities or sororities, that would be a description of a possible use case. The output of the search would be your constituent ID number and the name of the fraternity/sorority. Another possibility could be constituents that have told you that they have a second home or vacation home. Another possibility could be constituents who have grandchildren. When mining your text data, if your organization has a tendency to bury relevant lifestyle or affinity details in a big text blob, anything goes.
There are all kinds of use cases that get very complicated, but why not start simple, right? To get familiar with the concepts and terminology, take a look at this article by clicking on the image above.
Qlik published this handy presentation outlining 10 great ideas for making the most of analytics in your organization.
These are my favorites–
#1: Create a plan for what you want to accomplish, why it matters and how you’ll measure success
#3: Team up with a designer when building dashboards! Trust me, this is a must!
#9: Use business intelligence to push the boundaries of the information reports deliver. Instead of columns of dry numbers, add elements of diagnostic discovery in the content by asking “why?”
To read the article, click on the image above.
There are 4 functional analytics methods that the International Institute for Analytics tell us are true business imperatives.
1. Dashboards. Clearly dashboards require thoughtful metric selection and a carefully curated design to be engaging, useful and valuable. However, human brains are designed to detect and understand patterns. Data viz is here to stay.
2. Business analytics. IIA includes a broad spectrum of initiatives in this category – data mining, predictive modeling, and any number of specialized inquiries to pose to your data set.
3. Lifetime value. A key tool for evaluating outcomes, particularly when applying integrated channel communications for solicitation campaigns.
4. Rolling financial forecasts. The next generation of annual budgeting calls for iterative models that consider relevant internal and external influencing components.
To learn more and read IIA’s white paper, click here.
I’m talking about free text data. We all have it and it’s not searchable. All the meaningful intelligence lives in hiding. For the past 3 years I’ve been scouring free text on my own and transferring key words into coded fields we can all use. Finding, interpreting and transforming data is slow and time consuming so I’m also searching for a service provider who can automate the process within an acceptable margin of error.
I recently read this white paper that illustrated the process of analyzing big data, the figuring out how to speed up the process of transforming portions into fielded data that our systems can use. Here’s a diagram that describes the iterative effort.
What can nonprofits do to limit the manual involvement required to get to the insights? First, figure out what frequently occurring patterns exist that have consistently uniform meanings. It makes sense to build automated coded procedures to conduct the searching, selecting and transforming. Here’s an illustration of that process.
Click on either of the images to read the full white paper. It is full of ideas for injecting automation into the work of manipulating and preparing data for analysis.
Another benchmark report comes to us from the International Institute for Analytics. They assessed 33 major health care organizations on their institutional adoption of data analytics. Overall, the participating organizations ranked low on the maturity adoption scale.
What was the number 1 stumbling block to success? Establishing sound data competency practices. Managing internal data quality, effective practices for integrating external data, and building consistent management and analysis practices for interacting with their data.
To download a copy of the report and read IIA’s assessment, click on the image above.