Applying ROI to Prioritization

I’ve been working through a few updates on a prioritization framework for use in a feature and sprint planning session. The very nature of the sessions are to align on what a team is going to be working on with ensuring it’s appropriated towards the highest priority items. After a few recent chats with other PM’s, there was a needed sense of balance between business needs, product fit, and cost.

Here’s the latest update that aims to provide that sense of balance:

  • ROI – Return on investment of fitting product theme fit, churn
    potential, and roadmap fit relayed to a weighted average against t-shirt
    size.
  • Cost – These are the t-shirt sizes that engineering generally comes up with to assess viability of a themed story or issue.
  • Legend – It’s an updated map or legend that provides PM’s and Engineers the ability to map back specific priorities to the ROI number.

Feel free to use the template; if you find it useful, let me know. You can reach me via Twitter @aakashhdesai or Linkedin @aakashhdesai.

Thought Experiment — Public Service Matching

I’ve been on a brief hiatus for a couple of months
and have been thinking about the next endeavor. After some personal time off (even now), there was one idea that’d come to mind. The hard part about it is getting feedback from a product perspective. How do you do that in a way that garners enough attention, brings in interested individuals, and siphons the idea into something usable?

So, I’m writing this as a thought experiment to see what feedback I could garner for an app idea (and prototype). Namely, with public service infrastructure, the hardest part about getting engaged with an institution is how do you reach them and about what? There’s some movements in the Civic Tech space with Open 311, but how do you talk about moments of epiphany about positive comments or questions?

Summary

  • 3M residents in silicon valley; 1M in San Jose
  • 50 requests incoming through mobile apps; 1,000’s coming through phone
  • Inefficiency issues in getting the right feedback
  • Inefficiency in the right departments receiving the feedback

Users

  • Customer Service Directors — Government employees who run day-to-day operations for customer service departments within departments in a municipality (both city and county). They need to quickly triage and propagate work incoming requests in an efficient and effective manner.
  • Community Relations Directors — Government employees who directly interface with the public in regards to social media and email. They need to ensure the department and municipalities are directly interfacing with the needs of their constituents and are kept happy with the service provided. They want to provide greater engagement and quality interactions with citizens and department staff on feedback and requests.
  • Residents — Citizens and individuals within specific geographic regions that depend on public infrastructure to live and perform tasks day-to-day. They need to have infrastructure to run efficiently and effectively. They want not have to interface with their public works departments, but understand that it takes feedback in order to populate how well the infrastructure is running.

Problems

  • Community Relations Directors: Customer service responses don’t go to the right departments.
  • Customer Service Directors: Customer service requests don’t have enough information to be serviceable.
  • Residents: It’s difficult to send customer service requests and feedback to public agencies.

Product Offering

Provide a mobile app experience that adds a description, location, and photo. Using a matching algorithm, provide recommendations on departments to send the request to and sends a customer service request to individual public service departments. Each request requires a verified e-mail address and, potentially, a physical address.

Mockups

App (prototyped on Ionic Framework)

Distribution Strategy

First, target individual geographic regions, such as Silicon Valley, to improve on ways to match filters to recommended departments. Stick to regions with a distribution strategy of tech-savvy, civic oriented residents and focus on garnering normative samples of early adopters. Build up regions supported based off of data-informed recommendations from feedback from the user base. Then, expand using volunteer groups and customer service departments as advocates of a free mobile app
across iOS and Android.