OKR Examples

/

Engineering

OKRs for Engineering Teams

Focus

Acquisition
Activation
Retention
Referral
Revenue

Key Questions

?

How efficient is our development process?

?

How good is the quality of the releases?

?

Are we producing too much debt?

?

Is our development team able to do their best work?

Product teams are cross-functional groups that blend a mix of product managers, designers, data scientists, copywriters... and developers. So should Engineering OKRs exist if we already have Product OKRs?

Yes. Engineering OKRs focus on the quality of the work, while Product OKRs focus on delivering the right value. It's a bit of a simplified framing, but it highlights the two sides of the coin. In a lot of cases, you'll need great dev work to improve the user experience. But there are also many things specific to Engineering that Product goals will miss.

Example of Engineering OKRs

You'll find below an example built around a fictitious company. Your OKRs should move from quarter to quarter and map to your company's reality – that's why we thought it's best to illustrate things as a case study that you can take inspiration from.

Scenario

Askawoof is a startup building a platform to help companies run customer satisfaction surveys. They have been under pressure to release a lot of features quickly, and the quality of the work has suffered as a result. The Engineering team has decided to focus on quality with their next set of OKRs.

General advice

Don't start with the metrics. It's far more important to have a shared understanding of the Objectives, rather than nitpicking what the percentage of improvement should be.

Some Engineering metrics might be hard to understand for outsiders, which is why your Objectives need to be easy to grasp. It'll help others make sure that they're aligning properly with the rest of the organization.

Their OKRs

Objective

Tackle technical debt generated by feature rush

Key result

Fix the top 10 existing issues

Key result

Reduce contact rate by 30%

Objective

Reduce future development errors through automation

Key result

Increase code coverage from 50% to 75%

Key result

Reduce build time from 20mins to 5mins

Key result

Reduce cycle time from 8 days to 24h

Best practices for tracking OKRs

#1 Make it part of the team rituals

OKRs won't be of much help if you're not keeping an eye on them. Staying focused and aligned starts by adopting a simple routine with the team.

  • Monday: review progress on OKRs as a team, then look at your roadmap.
  • Monday-Friday: work on projects.
  • Friday: demos.

Start your Monday by looking at outcomes first (OKRs) and then outputs (roadmap). This will make sure that roadmaps discussions are centered around the most pressing issues.

#2 Make sure everyone can see trends

A common mistake for tracking OKRs is to use a table where you replace values in cells with the most recent update.

Not seeing trends can give you a false sense of security. You may be above the target line today, but the overall trend might be going the wrong way. So make sure that you have a simple way to understand if you're getting off track.

A simple progress chart can do wonders to help you understand if you're getting off track.

#3 Automate your OKRs process

OKRs will most likely cause friction as you expand their use within your organization:

  • Team leads have to send reminders every week.
  • People have to scramble through spreadsheets to find their goals.
  • Reports need to be handcrafted for leadership.
  • There's a general lack of consistency in implementation.

You can greatly simplify things by adopting a platform like Tability that will automate most of the OKRs tracking and make progress easy to see.

Tability can help you automate OKRs
Turning OKRs into a collaboration process with Tability

What other Engineering metrics can you use?

If you’re looking for some inspiration, here are some example of metrics that can be relevant for your Key Results.

Cycle time

How long it takes to go from code committed to code released to customers.

Deploy volume

How many deployments are performed every day/week/month.

Open/closed rate

Number of issues opened divided by the number of issues closed.

Number of incidents

How many incidents happened in production, ideally categorized by severity.

Code coverage

What's the percentage of the code that is tested by automated tests.

Build time

How long it takes to complete a build on average. Indicates how long it takes for a developer to know if their changes broke the application.

Review to merge time (RTMT)

How long it takes for a code review to be completed and merged.