How to Quickly Prioritize Your Product Backlog

Guest Post by: Christopher Davis (Mentee, Session 4, The Product Mentor) [Paired with Mentor, Jonathan Berg]

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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.

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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 Davis
ChrisDavis-headshot_8-3-2013_500x500Chris 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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The Product Guy

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About Jeremy Horn

Jeremy Horn is an award-winning, product management veteran with 2 decades of experience leading and managing product teams. Jeremy has held various executive and advisory roles, from founder of several start-ups to driving diverse organizations in online services, consumer products, and wearables. As founder of The Product Group, he has created the largest product management meetup in the world and hosts the annual awarding of The Best Product Person. Accelerating the next evolution of product management, Jeremy acted as creator and instructor of the 10-week product management course at General Assembly and The New School, and mentoring at Women 2.0 and Lean Startup Machine (where is he also a judge). To see where Jeremy is now check him out at (1) http://linkedin.com/in/TheProductGuy and (2) http://TheProductGuy.com