We know it’s been a while since you’ve heard from us here at CharityCAN, but that’s because we’ve had our heads down working on three new exciting features for you: Federal Corporation data, Obituary data, and a new and faster way to search our donation records.
We’ve added data from Corporations Canada on all federally registered corporations and their directors. This data includes information on not only privately held corporations but also not-for-profits, co-operatives and boards of trade from all across the country.
We think this data will be a boon to prospect researchers all across Canada, but especially those in smaller cities and towns looking for local business leaders. Altogether this data covers more than 1.1M corporations and almost 2M director positions and will be updated with more data monthly.
We’re already hard at work incorporating the new connections that this director information will provide into our relationship maps. We’re not quite there but the corporation data on its own was too exciting to keep back until the relationship maps were ready.
Obituaries have been on our users’ data wish lists for quite some time. We’ve finally been able to team up with our new friends at Canada Deceased List to offer obituary data from their ObitScan and Canada Bereavement Registry products. With their help we’ll be providing thousands of recent Canadian obituaries from public online sources every month.
Improved Donation Records Search
As our database of almost 15M Canadian donation records continues to grow, it has started to put enough of a strain on our database that we decided we needed to make things better. We found that certain searches were starting to take a few seconds to deliver results, and so it was time to speed things up.
We’ve completely rewritten our Donation Records search engine so that results are now consistently delivered within milliseconds of your query, no matter the complexity. We look forward to moving more of our searches to this new search engine in the future!
More On The Way
As I mentioned earlier, work continues on getting these new datasets into our prospect profiles. For the moment both datasets are searchable on their own or through our Integrated Search. Stay tuned for more developments, and as always, contact us if you’d like to see these new features in action.
As we start 2022 here in CharityCAN’s home base of Waterloo, Ontario, in some ways it feels eerily like the start of 2021: working from home with limited contact with friends, family and coworkers, with lockdown measures in place to stop another COVID wave from crashing over our healthcare system.
In this Groundhog Day of a January it’s been too easy for me lately to feel down and sometimes hard to concentrate on anything that’s not just getting through the next day of home confinement, including our work here at CharityCAN.
That’s why it’s been nice for me today to take a look back at everything our fantastic team has done in 2021 to put things in perspective and to get me excited about the things we have coming up in 2022.
In company news, 2021 saw us implement a blind interview selection process that we’ve used to select our co-op students. We still have a pretty small sample size, but anecdotally the process has led to a diverse group of students selected as candidates.
Like many companies, we also experimented with reduced working hours over the summer. Feedback from employees was positive and we didn’t see any sort of difference in our business operations, so it’s something I hope we’ll do again this summer!
Last but certainly not least, two employees (myself included!) welcomed new additions to their families.
Of course the other thing we love to look back on here is what we accomplished with our CharityCAN family of products over the year.
This year we saw the addition of some new data sets, including Canada’s private aircraft and marine craft registries and public sector salaries from Manitoba. What’s especially exciting about these new features for me is that they were created internally by an employee who took on the challenge of learning to code on the job – these were their first additions to our platform!
Besides the new data sets, we also added some major new features. After doing a lot of work behind the scenes, we introduced Donor Discovery, a new way to do prospect identification based on relationship and donation data.
We also launched Avenue Donor Data, a separate add-in for Raiser’s Edge NXT that allows you to view our CharityCAN Household Data alongside donor constituent records.
We added new features to our prospect profiles, like letting you export a customized PDF version of the profile, and letting you easily connect profiles to your organization so you can use their connections in our relationship path searches.
Through my volunteer experience this last year with Apra Canada (become a member if you’re not already!) and from participating in a study that found a significant return on investment from prospect research in the fundraising world, it feels like prospect research in Canada is still a rapidly growing field. We’re excited to be able to play a part in that growing world as more fundraisers start using data to make better fundraising decisions.
Here’s to 2022 and all the new challenges and successes it will bring!
After months of work behind the scenes, today we’re announcing a new way to do prospect identification in CharityCAN: a new feature we’re calling Donor Discovery.
Donor Discovery uses our donation record dataset with our relationship graph to give you a customizeable way to find new potential donors.
Set up filters like geographic region, donation amount, connections, donor type (individual, foundation or corporate) and cause to cast as wide or narrow a net as you’d like. We’ll show you donors who have given gifts according to your filters but who haven’t (yet!) made a recorded donation to your chosen organization.
For example, a university looking for funding for a new medical school building might look for health care donors in their geographic region with a connection to the university’s board of directors. A smaller local charity might look for individual donors to their cause giving amounts of under $1,000.
Once the results are generated we’ll show you how much the donor has given in the past and how they’re connected to your organization, with a link to the donor’s profile so you can quickly qualify your prospect.
We hope that by using relationship data as well as past donation data, we can surface prospects with a higher affinity to your organization and give you a path to connecting with them.
This summer at CharityCAN, we’re trying something a little different. We’re taking a break.
Well, that’s not quite true – we’re taking a lot of little breaks. 8 of them, to be exact.
This summer, we’re turning every weekend into a long weekend for our employees. Every Monday that isn’t already a holiday between Canada Day and Labour Day becomes a paid day off. No time to make up during the week, just a long weekend for every weekend of the summer.
I’ve been toying with the idea of a summer of long weekends ever since I read It Doesn’t Have To Be Crazy At Work by the folks at Basecamp. You can read up a little more on their four day summer weeks here.
But to be honest, I was a little frightened as the owner and operator of a small business. Would we really have time to finish all the things we need to get done? Would we fall behind somehow?
This summer though, the math has changed. We’re all tired and worn out after living through more than a year of a global pandemic. As we slowly emerge from lockdowns to summer weather and safer outdoor conditions, we need spend more time seeing and reconnect with the people we’ve been separated from for so long.
It’s still not going to be perfect – our co-op students have to work 35 hours a week to get a co-op credit, so they’ll have to work 45 extra minutes on each of the four working days (or flex those hours however they wish), but it’s as close as we can get for now.
I’m hoping that if the experiment goes well this summer it’s something we can look forward to here every summer from now on.
We here at CharityCAN hope you have a great summer, however you spend it.
And if you happen to email any of us on a summer Monday starting this week, here’s what you’ll get back:
Thanks for your email! This summer is a “Pandemic Recovery Summer” here at CharityCAN, which means that we’re taking every Monday off from Canada Day to Labour Day.
I’ll respond to your email when I’m back at my desk tomorrow.
Avenue Donor Data is a new application available in the Blackbaud Marketplace that creates an add-in tile in your constituent pages to display a donor’s net worth, annual income, dwelling value, and annual donations, based on their postal code.
We think this application will be useful for any Raiser’s Edge user or fundraiser that either doesn’t have a team of prospect researchers behind them to create donor profiles – or a fundraiser who might be supported by prospect research but who needs data now and at their fingertips.
Imagine a time when we get back to galas and golf tournaments and a fundraiser meets a donor over cocktails (full disclosure – I just got my second COVID vaccine dose and so this actually seems like more than a hypothetical right now!) and wants to find a little bit more about them and if they might be a major gift prospect.
Since Raiser’s Edge NXT and Avenue are mobile friendly, the fundraiser can log in and quickly view Avenue’s data to qualify the donor on the spot using their smartphone.
Avenue Donor Data is just a little window into some of the data we have in our main CharityCAN prospect research tool. If you’re already a CharityCAN user, you have access to the same data in Avenue via our Household Data Search.
If you want the same kinds of data that Avenue has to offer in your donor management system, then you might be interested in doing a donor screening with us. We can append similar data (plus a whole lot more) to your donor file to help you qualify your whole database at once.
If you’re already a CharityCAN user and a Raiser’s Edge NXT user, you might also be interested in CharityCAN for Raiser’s Edge. It includes the same constituent add-in for household data, plus add-ins for relationship and donation data too.
And if you’re interested in something totally different, get in touch directly. We love to hear what ideas for projects you have and see if we can help you bring them to fruition.
Back in the summer of 2020, with Black Lives Matter marches happening in every major city in North America, I was challenged to take a look at the diversity, equity and inclusion (DEI) practices at our little company. As anyone who works at a small business can attest to, there aren’t often official practices or procedures put in place until something goes wrong and forces you to create something to adhere to.
As a company that straddles the fundraising and software industries, we’re in a double whammy of fields that are predominantly white (in the case of the former) and predominantly white and male (in the latter). Our current staff is split 50/50 between genders, but we only have one team member who is racialized (or 12% of the company).
With that in mind, I contacted Lunaria, a local company that helps companies with their DEI practices. While taking me through some things to consider, Lunaria suggested hiring practices as one place a small company could look to reduce unconscious bias and make sure we’re finding the best candidates regardless of race or gender. While we don’t have any open job positions on the immediate horizon, we do hire a co-op student every four months to help on the software development team. I wasn’t sure how we would do it, but this term I made it a goal to use anonymous hiring, or blind recruitment – stripping away any identifiable information from job applications to reduce bias – while selecting co-op students to interview.
To start out, I was curious to see if I could pull together information about the co-op students from the University of Waterloo (UW) (where we hire our co-op students from). This would let me see whether or not we were attracting and selecting students that were more or less in line with the race and gender of the overall student body.
Using the Common University Data Set from UW , I was able to get a breakdown of students along gender lines from the programs we hire our co-ops from, Engineering and Computer Science (CS):
The closest thing I could track down was a demographic survey on the /r/uWaterloo subreddit. Despite all of the obvious issues with a self-reported survey from a small internet community, let’s take a look at the reported racial breakdown in the Math and Engineering faculties (note that this is slightly different than the Engineering/CS breakdown above – CS is only one part of the Math faculty at UW, but it still gives us some idea). The survey creators also posted a breakdown of the subreddit’s reported race vs. Ontario demographics in general, if that helps give some idea of who may be under/over-reported in the results.
The Anonymous Selection Process
Now let’s take a look at our hiring process! Every four month term, we submit a job posting to the University of Waterloo’s co-op job portal, Waterloo Works. Students who are interested in the position post a PDF copy of their resume, and the university includes a student grade report and past co-op employer evaluations. I review these application packages and select students for the next stage, the in-person interview.
Following another of Lunaria’s suggestions, I asked our current UW co-op to help with the anonymization experiment, and they took the time to black out identifying student information in the applications (names, addresses, emails, etc.), leaving only their student numbers behind for reference.
Next came the selection process. I was surprised at how much my old (non-anonymous) process relied on names until they were gone! My brain was apparently trained to use a student’s name as a placeholder in my head, and with student numbers any sort of personality I might have built up completely disappeared. It made remembering which resumes I had already read a little difficult, but it’s easy to see how unconscious bias seeps in without you even thinking about it.
Something else I noticed was that it’s probably not enough just to scrub names and email addresses – next time I’ll probably scrub any “interests” from the resumes as well. They made it too easy to make a gender (e.g. “mixed martial arts” vs. “figure skating”) or race (“Chinese Student Association” vs. “Minor League Hockey Referee”) assumption.
Other than that, it was no trouble to whittle down the resumes based on the anonymized data.
Here are the results of the anonymous selection process broken down by gender and race (these are just guesses – the only way for me to identify student race and gender was to use names and LinkedIn profiles from applications). Out of 48 initial candidates, I selected 7 students for one-on-one interviews. Here’s how that looks:
# Students Applied
# Students Selected
Assumed Race or Ethnic Background
# Students Applied
# Students Selected
So in the end I ended up with a fairly diverse group of students. If you look along gender lines, the anonymous process selected a number of candidates that matched up with the applicant pool.
But after all that I still ended up with an over-representation of white students! This is a small sample size, so maybe it doesn’t mean anything, but I wonder if some unconscious bias was still happening – through the interests section of the resumes, or the format of the resumes. Maybe the fact that these students are white means they have had better co-op jobs or were evaluated on those jobs better in the past? I also ended up with a severe under-representation of Asian students. Again, I’ll have to see if this is some sort of bias or whether we’re just looking at a small sample issue.
When I compare our applicant pool to the student body breakdown, it seems like our applicants are more or less as racially diverse as the general student population, but it doesn’t look like we’re attracting quite as many female candidates as are in the general student population. We did end up with a pretty good representation in our selected candidates, however. Maybe we can update our future job descriptions to make them more inclusive.
In the end, the anonymous selection added negligible overhead and seems to have worked out so far! I’m looking forward to using it again next term and finding other ways to improve our process.