Prioritizing features based on user testing and feedback. 
Research Objectives
The purpose of this research was to evaluate the validity of previous research on the prioritized features within our product roadmap. The product team's Four Pillars of personal cloud needs for consumers included the four areas shown above.
With shuffling business objectives and product requirements, I sought to partner with product managers, data analysts, and designers to reach consensus among stakeholders on which features to prioritize. This meant I needed to find answers to what features users find to be the most important, and which factors are likely to lead to conversion.
With this study being about evaluating our existing solutions and validating decisions, the desired outcome was to reach a go or no-go decision point on proposed features, concepts, and designs.
With existing knowledge in hand, I first conducted secondary research and reviewed prior studies on product feature prioritization. Based on the research and business goals, I determined that a survey and closed cart sort were fitting, as I needed to capture attitudinal data and to create a prioritization matrix.
This made sense as we needed to reach a consensus quickly to decide which features to prioritize and de-prioritize, as well as gathering cloud user feedback on which features to de-emphasize.
Findings & Implications
Many features and jobs-to-be-done tasks under the Four Pillars were deemed important, desirable, and likely to be used by the participants. Protect & Preserve performed the best all across the board, with Unleash Creative Potential and Celebrate Memories being more scattered.
The results validated what we knew up to this point, and aligned with our product features roadmap.
The implications that arose from this research led me to conclude that additional research and dissection of data across the segments to be necessary to further synthesize action items.
Impact & Reflection
The findings from this feature prioritization matrix study directly influenced the teams to strategic and tactical decisions for not just the upcoming release, but for long term planning.
With the teams' assumptions, decisions, and knowledge being supported by the findings and implications from this study, I was able to embed confidence in product decision making.
As the research was quite well received by the teams, I've continued to periodically conduct this type of study to record feature insights over time. With this methodology, the teams were able boost collaboration as well, alongside improved resource allocation and collaboration.

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