Guest Post by: Christopher Davis (Mentee, Session 4, The Product Mentor) [Paired with Mentor, Jonathan Berg]
As a product manager, you’ll often find yourself with a growing backlog of user stories and product defects that need grooming and scheduling. But which ones should come first, and why? A robust ranking framework is key to answering these questions.
Sometime after beginning my work as a product manager at Bandsintown, I was introduced by my product mentor, Jonathan Berg, to a framework that allows me to confidently justify my decision-making to key company stakeholders and to align my goals and priorities to those of the broader business. Because every team’s needs may be a little different, the framework can be adapted to many situations.
With this framework, you will be able to promote more effective communication with your development team, reduce re-work, and prioritize which defects to work on, ultimately allowing your team to achieve a much faster time-to-market. Following successful implementation of this framework, you should feel that you’ve improved your operational product management skills, focused your day-to-day activities, and increased your team’s productivity.
USER STORY PRIORITIZATION
To prioritize our user stories, my team implemented a simple story ranking system adapted from Michael Lant, founder of projectyap.com. In theory, we assign both urgency and business value a separate number from one to five, then we multiply the two numbers to determine a story’s final weight. This weight is mapped on a two-vector matrix to help us visualize and prioritize the story in our upcoming roadmap.
In practice, I plug each user story’s rankings into a scorecard inside Aha!, a wonderful product roadmapping software tool my team uses in conjunction with JIRA. Before joining The Product Mentor program, I had already toyed with Aha!’s default scorecard system in an attempt to implement fibonacci sequence story sizing with my engineering team. Ultimately, I found it daunting, causing me to ignore the feature and us to return to time-based story sizing instead of point-based sizing.
Below is an example of Aha!’s default scorecard:
After my mentor introduced Lant’s system to me, I revisited Aha!’s scorecards and decided to create a custom scorecard for user story management. This is what it looked like at first:
The values for urgency relate to immediate impact, dependency of other stories, and timeliness of delivery. For example, a story with an urgency ranking of 1 might have no time constraint and very little impact, while a story with an urgency ranking of 5 might be extremely time constrained, have many dependencies, and must be completed immediately to have any meaningful impact.
The table below provides Lant’s example wording for ranking a story’s urgency:
5 |
Extremely time constrained. Extreme level of dependency of other items on the completion of this task If not completed immediately there is little or no value to doing it |
4 |
Highly time constrained High level of dependency of other items on the completion of this task Important to go into the next sprint because of customer or contractual requirements |
3 |
Moderately time constrained Moderate dependency of other items on the completion of this task Desirable to complete in the next one or two sprints |
2 |
Minimally time constrained Minimal dependency of other items on the completion of this task Completion in the next two or three sprints is adequate |
1 |
Not time constrained No dependencies Little or no impact |
The numbers for business value relate to the level of competitive advantage, impact on brand/reputation and the number of customers the story is important to. A story with a business value of 1 might be important to very few customers with little impact, while a 5 might be important to every single customer and/or critical to the survival of the business.
The table below provides Lant’s example wording for ranking a story’s business value:
5 |
Extremely important to most or all customers Extreme impact on brand or reputation Critical to the success of the business |
4 |
Important to many customers Significant impact on brand or reputation Significant competitive advantage |
3 |
Important to a moderate number of customers Moderate significant impact on brand or reputation Moderate important competitive advantage |
2 |
Important to only few customers Minor impact on brand or reputation Minor competitive advantage |
1 |
Important to only a few or even no customers Little or no impact on brand or reputation Little or no competitive advantage |
Now, how do you make the work actionable once you’ve assigned each story its numbers? That’s where priority comes in. Below you’ll see Lant’s original story priority matrix:
After mapping your story across the priority matrix, you need a color-coded key to help you decide when to take action. Below is Lant’s original story priority key:
After creating my original Aha scorecard, I began using this color-coded matrix and actionable color key with my own team. Due to resource constraints, I quickly realized that my boss and organization in general is a bit more comfortable with addressing 6’s, 8’s and 9’s at a later point on the roadmap than Lant suggests, so we adjusted the story priority matrix to look like this:
DEFECT PRIORITIZATION
After my team had a system in place for prioritizing user stories, we began struggling to work in defects. “Can’t we use the same framework?” I wondered, and as it turns out, we could!
Leveraging another article from Michael Lant, we created a matrix to work from that was similar to our story matrix, but with different criteria that made a little more sense when addressing a defect (also known as a bug, or whatever you want to call it).
To prioritize our defects, we changed what’s on the X and Y axis of the two-vector matrix. Instead of urgency, we use scope, and instead of business value, we use severity. For each, we still assign a number from one to five, then we multiply the two numbers to determine a story’s final weight.
I went to add a second scorecard in Aha! after settling on these criteria and I realized that the software only allows you to assign a single scorecard per product. This was a problem. My solution was to combine both defect and story prioritization into one scorecard.
View an example of the combined scorecard below:
For defects, the values for scope relate to the amount of users affected and the amount of system functionality affected. For example, a defect with an scope ranking of 1 might affect a very small set of users and/or a tiny piece of system functionality, while a defect with a scope ranking of 5 might be affecting all users of the product and/or most system functionality.
The table below provides Lant’s example wording for ranking a defect’s scope:
5 |
Affects most or all users and/or a very large range of system functionality |
4 |
Affects a large set of users and/or large range of system functionality |
3 |
Affects a moderate set of users and/or moderate range of system functionality |
2 |
Affects a small set of users and/or a small range of system functionality |
1 |
Affects a minimal set of users and/or a very small range of system functionality |
The values for severity relate to how easy it is to get around the defect. For example, a defect with a severity ranking of 1 might only be a typo or some small cosmetic issue, while a story with a severity ranking of 5 might be corrupted or lost data, or a system that is entirely unavailable.
The table below provides Lant’s example wording for ranking a defect’s severity:
5 |
Data loss, data corruption or system unavailable |
4 |
Important functionality is unavailable with no workaround |
3 |
Important functionality is unavailable but has a reasonable workaround |
2 |
Secondary functionality is unavailable but has a reasonable workaround |
1 |
Cosmetic issues or some functionality unavailable but has a simple workaround |
Once again, we need a color-coded matrix and associated key to help us address each priority color. Below you will find Lant’s original color matrix:
Similar to our story matrix, we adjusted the defect priority matrix colors to look like this:
And here is Lant’s decision-making key for defects:
As a result of these new ranking frameworks, my team was able to work our way through a long, seemingly never-ending list of defects that were rarely being addressed. I’m now feeling better about the quality of my product from both a defect management and a new feature prioritization perspective. Our process is much more streamlined, and I am able to quickly throw ideas and defects into unranked buckets before I properly rank and prioritize them. This way, I never lose an idea if I am too busy working on something else.
And that’s it! I’m still iterating on this process myself, so I would love to hear if you are able to apply a similar process to your own team using new methods or tools other than the ones mentioned here.
Good luck!
About Christopher DavisChris is a Product Manager at Bandsintown, Product Expert in Residence at General Assembly, and a writer and editor for music magazines including DJ Times, ClubWorld and The Music & Sound Retailer. Additionally, Chris is a life-long percussionist, DJ, and a music producer that has performed in wind symphonies, the Atlanta Falcons drumline, and various bands. He is also a graduate of The University of Georgia and is a huge college football fan. Find him on Twitter and across the internet at @chriskdavis.
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