Informing Decision-Making with Behavioral Network Data
Case
Advanced User Research
Colin Shiner
Embedded Files
Case
A person's social context influences their choices. By mapping and analyzing these contextual networks, I helped one organization accelerate product adoption.
Synopsis
A fintech company and investment firm had recently switched to a new company-wide project and objectives management tool and wanted to accelerate the adoption process of getting employees to actually use it. I extended their foundational user research with methods from social psychology and network science to develop a data-driven strategy for moving the adoption process forward.
Background
Well-versed in User Experience and Service Design, a group of internal stakeholders had already laid the groundwork for change by creating qualitative user personas for employee archetypes who worked across different areas of the company and demonstrated different levels of openness to adoption. They had conducted training workshops and run informational campaigns, but they had a suspicion that their internal users were not adopting the tool as quickly as anticipated. In one early survey, as many as 70% of respondents had heard of the new tool, yet anecdotal evidence suggested that many people were still on the fence about using it.
Challenge
In initial meetings with our core stakeholders, they expressed a particular interest in measuring potential sources of resistance to adoption for different user types and a way to link those measurements to an actionable strategy to move the adoption process forward.
The graph reads: "In the last month, have you heard about [the project management tool]?" "Yes" responses are recorded on the left in green, and "no" responses are on the right in blue.
Process
Drawing on research techniques from social psychology and sociology, I led a team in conducting a study to collect data on users’ attitudes toward the new tool and their knowledge and use of it. In parallel, I collected data on who they most frequently worked with and the strength of those working relationships with their colleagues around the company. By combining these data sources, my team and I were able to trace the social pathways along which uptake of the new management tool “flowed” and identify points of friction that were slowing greater adoption.
The slide reads: "The Power of Community" Once a certain threshold (~65%) of a user's community had head of the new tool, it became significantly more likely that the user would too.
The slide reads: "Diffusion Strategy" The green points of the social map are where awareness was highest, and the grey points are where awareness was lowest.
Two key outcomes from the study
The study revealed several useful insights. First, it highlighted where in the company structure awareness and adoption of the tool were the most prevalent. This allowed the group of core stakeholders to both track the efficacy of their work and focus their outreach efforts on the places where awareness of the tool was lowest.
Second, it pinpointed where specific teams or influential individuals showed especially strong resistance to adoption. This allowed the core team to discover user groups that had previously been invisible to them and conduct further qualitative research to understand their particular pain points.
Points in red represent people who showed particularly strong resistance to using the new tool. The red lines represent the people they are connected to.
By linking this new layer of data with the existing user research the client had conducted, my team and I were able to provide a deeper, more nuanced perspective into why the adoption process had felt sluggish, despite generally favorable survey results. Ultimately, our analysis enabled the core stakeholder team to update their target timeline for adoption (cutting the projection down from a year to a matter of months), monitor the roll-out in finer detail, and focus their effort where it produced the most impact.
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