text: Charitable Impact is a platform that connects donors with charities. We also provide support to companies who offer giving programs and donation matching benefits to their employees. However, to scale this offering, we would need to evaluate our current offering to see what could be improved, as well as uncover the core needs of this user segment.
  Step 1: Evaluative research
  What I wanted to know
  First, I needed to evaluate the current state of our 'company accounts,' which is what we call the account type used for companies, schools, and communities that use Charitable Impact for donation matching. I wanted to know what use cases, pain points, and motivations this segment of users (I called them 'company administrators') had.
  Some of the questions I had included:
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  Why do companies choose Charitable Impact to manage their workplace giving?
  What are users' typical workflow(s) when using Charitable Impact for managing workplace giving?
  In what ways does Charitable Impact fall short for managing your workplace giving programs?
  Planning and recruiting
  When drafting my research plan, I decided that one-on-one interviews paired with users walking us through their typical use of the platform would best answer my questions. These are the rough steps I took to successfully plan and execute interviews:
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  Met with lead designer to understand what questions she needed answered
  Created a semi-structured interview script, with time-boxing to plan for interview flow
  Created a notetaker guide
  Found potential interviewees through internal user list. Validated they met my criteria by looking through behavioural data.
  Recruited users via email and Calendly
  I led the interviews, with the lead designer acting as notetaker and adding questions at the end ​
  Data collection + analysis
  Synthesis + analysis
  ​After transcribing interviews, I analyzed data from transcripts, interview notes, and the above-mentioned data sources. I used a spreadsheet to tally responses and comments linked to specific questions or themes. I also used coding within thematic analysis to spot trends and patterns in the data.
  Recruiting Challenges
  My main challenge involved recruiting good participants. Because we had so few companies using our platform specifically for workplace giving (and had actually stopped promoting it as a service overall), I had a very short list of potential participants. I looked for other sources of data to account for this problem:​
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  I interviewed our customer support team to understand how they were currently interacting with this user type. I asked them to share documentation of specific requests and complaints.
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  Looked through customer support inquiries from potential users who had contacted us to learn about our workplace giving solution​
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  I looked through marketing materials that had previously been used by the Sales team, when we were actively promoting the workplace giving solution
  I managed to recruit users from three companies to participate in hour-long interviews. The interviews were done remotely, over Zoom.
  Telling the story
  Because I had such an amalgamation of qualitative data, I decided to use a journey map to tell the story of how company administrators were using our platform for their workplace giving. ​​
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  The journey map outlined key moments in the customer journey, including: stages, goals, steps, pain points, and opportunities / wish list. I even linked out some of the pain points mentioned directly to related support tickets, to help expand on them.
  This journey map, plus an additional report with further details and analysis, helped my colleague understand what aspects of our company account could be improved from a usability and design perspective. It also helped her identify new features that could improve the overall experience for users.
  Step 2: Generative research
  What I wanted to know
  This is how you know this research case study happened in the real world - the redesign for company accounts ended up being de-prioritized in favour of other projects. Although disappointing, it gave me the opportunity to plan and execute more research.
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  Although speaking with our current users uncovered many usability issues and pointed out use cases we hadn't considered, the data was still slightly biased because our users had to work under the constraints of our platform. Simply put, they couldn't stretch their imagination beyond what our platform was already offering.
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  Because our Business team was still eyeing workplace giving as an avenue for growth in the future, I wanted to be proactive and decided to conduct additional interviews with people who were using other workplace giving solutions.
  Planning and recruiting
  After reading through existing literature (there's so much data out there on workplace giving) I formulated a research question to help guide my exploration:
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  What practices have companies put into place to successfully implement and manage their workplace giving program?
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  I wanted to understand the full journey companies went through in implementing giving programs, and what constraints they faced (as well as what led to success).
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  I decided on interviews once more, to get a rich picture of how these users were currently implementing their giving programs, what tools they were using, and what challenges and constraints they faced.
  Recruiting was less difficult this time around, but I still struggled a bit. I originally thought it would be possible to self-recruit from my network on LinkedIn, as well as from internal customer support inquiries. But because my target segment was very specific (professionals managing workplace giving programs at their company, as part of their job or on the side of their desk) I only ended up with 1 participant. So I tried Respondent.io for the first time, which helped me recruit additional participants of different ages, with varying education, who worked in the private and public sector.
  Synthesis + analysis
  I decided to use the grounded theory approach in analyzing data from the interviews. I used coding to organize the data, more specifically process coding which uses action words to describe and group themes ('fundraising for charity,' 'passing information to payroll department'). Because my research question was looking into key processes and actions these users were taking, this seemed the most logical approach to me.
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  Initially, I used Dovetail to transcribe and code the data to find some initial themes. However, I found myself a little stuck and ended up taking a break from looking at my data for a few days. After that, I used Miro, an online whiteboard, and was able to identify broader themes and motivations by rearranging the data constantly until I could extract more insights.
  I found that arranging the data in this format helped me feel more 'flexible' with the data. With Dovetail, there was more pressure for codes to be 'final' but using Miro helped me view and play around with the data in many different ways - post-it notes, mind maps, charts, etc.
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  I took my key findings and presented it in a report format, which included key insights with user quotes, as well as a journey map outlining how companies usually experienced implementing and managing giving programs. This was a good point of comparison with the earlier journey map I had created for Charitable Impact users.
  The Result
  I presented my findings to a member of the senior leadership team, who is directly involved in strategy for our product and service offerings. Because of this research, they are now seriously considering this area of growth and expansion for our company.
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  I also completed an extensive competitive analysis on corporate social responsibility platforms, requested by the same stakeholder in order to get additional insight into what competitors have done, and how we can utilize findings from my research to brainstorm solutions to the challenges users were having.
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  This work is currently in progress, with talks to reprioritize the redesign of company accounts in the coming year.
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  About Bella Margolles
  I'm a UX Researcher who lives in Vancouver, BC. I'm open to freelance projects, coffee dates, and nerding out about research together.