Written by: Abigail Mann, University of South Carolina

In November 2015, the Prospect Research Department at the University of South Carolina received a request from one of our professors based overseas. They were planning an alumni event for the coming year and wanted a list of university alumni living in France and Germany. As our team’s international analyst, I was handed an early Christmas present.

My initial plan was to conduct a search on LinkedIn (LI) filtered by school and location and compile these results. The search was successful, but there was a catch – I was working with a basic account, which meant that nearly all of the alumni in my search results were named “LinkedIn Member.” So what’s an analyst to do with a deadline looming and a project budget of free resources only? Below is a methodology as well as tips, tricks, and work-arounds I discovered as a result of this project. It may not solve all of your problems, but you’ll still be surprised by what you do find along the way.

Start with What You Have

My first step was to regroup and start closer to home – specifically, what did our CRM have to tell us. If your CRM is anything like ours, then you tend to take your international data with a grain of salt. Foreign regulations and privacy laws make it difficult to verify and update contact information, screen for capacity, etc., so this data isn’t always as current as what we have on file for our domestic alumni. I also knew that there were going to be gaps in my data. For example, my LI search for alumni in France returned over 300 results while our CRM listed only 140. A smaller dataset for each country actually ended up being an advantage, since I was going to have to do some research on each constituent individually. (Short of being tied to my desk chair and put on a caffeine drip, there was no way I was going to be able to reverse engineer the results for the 300+ alumni in France, not to mention the nearly 500 LI said were in Germany).

Utilizing our CRM’s dashboard, I exported spreadsheets containing education and address information for alumni in each country and combined the data via an Access database. Once I had my master list, constituents were checked against three main data points – address/country of residence, e-mail address, and social media.

Verify Your Data

In addition to designations for home, business, etc., addresses in our CRM are further coded as “Good”, “Bad”, and “Deceased”. Deceased individuals had been excluded from the dataset, so I was dealing only with “Good” and “Bad” addresses. Regardless of whether an address was coded good or bad, there was little I could do concerning the postal address itself. I could, however, reasonably verify that they were still in the country in question via social media. For the sake of convenience, I utilized LI rather than other social media platforms, since it was most likely to have additional education and employment information that might be missing from our constituents’ records. I ran searches for each name on the lists, applying additional filters for education and country/location as necessary. Though this took time, it proved to be a helpful work-around as it let me find through individual searches profiles for constituents who displayed anonymously in my initial master search. Additionally, if a constituent had an e-mail address on file, these were checked to see if they were valid (mailtester.com is a great free resource for this).

By this point, my spreadsheet was a festive sea of color coding that would have made catalogers at libraries everywhere jump for joy. There were constituents with good postal addresses (so far as we knew), valid e-mail addresses, and who followed the university on LI, constituents with bad postal address but good e-mail addresses and no social media presence, and constituents for whom all we had was a bad address, as well as every other combination of these criteria and a partridge in a pear tree. From these results, I refined the datasets for each country down to those alumni who, to the best of our knowledge, we had a viable means of contacting.

Constituents were included in the final lists based on the presence of one or more of the following criteria:

• ·         A good postal address
• ·         A valid e-mail address
• ·         A LI profile wherein they referenced attending and/or followed the University of South Carolina
• ·         Any combination of the above

Those who only met the social media criterion were included based on the rationale that the professor could make arrangements to promote the event through this platform. As followers of the university they, as well as those alumni who got lost in the “LinkedIn Member” shuffle, could still be made aware of the event. Rows for these constituents were highlighted on the spreadsheet while all other contact and education information was displayed in columns.

Data Integrity and Data Mining

An unexpected result of this project was that we were able to improve the integrity of the data we had on file for a number of these international alumni. In some cases, we discovered that our “Good” addresses weren’t so good after all. For example, a constituent we had every reason to believe was living and working in Paris had since relocated to Oslo, Norway. Other constituents had been promoted within their company or now worked for a different company altogether. This meant that some of our e-mail addresses were likely to be invalid now, since they were business e-mail addresses reflecting a past employer. Any necessary updates were made to constituents’ records and static URLs to their LI profiles were added as well. Again, this took time, but we are now able to point to collection of international records in our database and say that the information is reliable.

Additionally, it is our hope to use this data to help find international proactive leads for our fundraisers.

While fundraising strategies and donor appeal vary by country, the profiles of several alumni on our lists showed some of the characteristics we commonly look for in domestic donors – affinity for the university, interest in/support of particular causes or initiatives, and job titles suggestive of disposable income. We are curious to see if these factors hold true across the Atlantic as well.