Analyzing and streamlining acknowledgement process at Ozone House
Aim: To reduce the turn around time of ‘Thank you’ letter from 15 days to 5-7 days.
Duration: 4 month (Fall 2009 | Michigan, US)
Collaborators: Richard Tseng, Beth Ziobro, Megan Hixon, Sara Veltri
Methods Used: Communication Covenant, Project Planning, Scenario Analysis, Personal Interviews, Data Analysis, Data Modeling, Affinity Diagram, System Analysis, On-site Visit.
Project Brief:
As a nonprofit organization, Ozone relies heavily on charitable donations and, to show its deep appreciation of each donation, Ozone sends a letter of acknowledgment thanking donors. Currently, the process by which these letters are generated takes around ten days; Ozone would like to reduce the time it takes for a donation to be received, travel through the Financial and Development Departments, and is delivered to the Executive Director for a signature to five days or less. Recommendations are made with the intent of keeping costs as low as possible and maintaining Ozone's current staffing levels.
Data Analysis (Three Primary Tools):
1. Debriefing Sessions- After each interview, our two-member interview team shared the interview results with our entire team. Collectively, we began to interpret each data set, both as a discrete set and in relation to previously collected data, to determine whether new lines of inquiry were needed.
2. Data Models- We used five distinct frames of reference to model the data collected from each interview. The resulting models offered different perspectives of the process and helped us focus in detail on the sequence of the process, the communication transpiring during the process, any objects or databases used in the process, and how the physical environment and organizational culture affect the process. We made following models
- Communication Flow Model
- Physical Model
- Cultural Model (shown below)
- Sequence Model
- Artifact Model
- Swimming Lane Diagram
3. Affinity Diagram-
Our third primary analysis tool allowed us to reassess our initial interview data. We distilled our notes culled from the interviews into discrete ideas, separating these ideas from their original context by printing each idea on a small, individual piece of paper. We were then able to group and re-group similar ideas until we arrived at a consensus of how ideas fit together. After forming these initial affinity groups, we created a three-level hierarchy to visualize the ways in which groups, and the resulting groups of groups, are linked.
