How to Connect OKRs to OSTs

​Dear Reader,​

​OKRs (Objectives and Key Results) help you measure strategic progress.

​OSTs (Opportunity Solution Trees) are a visual structure for connecting solution and problem spaces to business goals.

Let me break down how the layers of an OST connect to the practices of OKR drafting and execution:

Layer 1: The Outcome

Teresa differentiates between business outcomes, product outcomes, traction metrics to sit in this level. The goal is to provide orientation for the exploration of the problem space.

As defined here, the Desired Outcome best connects to the Key Result you'd find in company- or department-level OKRs:

• Generate $1M additional revenue from EMEA self-service customers
• Increase adoption of the new tool bundle by x% among heavy users

So, if you feel lost in your OSTs, it comes from a lack of strategic specificity in your higher-level OKRs. Remember: Goals are not a strategy. There needs to be a strategic intent to form actionable OKRs.

Other narratives would call this level of metrics also an Impact. Representing a higher-level business interest.

Layer 2: The Opportunities

Opportunities emerge from understanding the problem space a customer experiences. Compared to layer 1, OKRs don't inform the filling of this layer, but are informed by it.

When you understand a specific customer problem space like "Updating our shift planning availability manually creates a high error rate," you can perform what I like to call a positivity flip to describe how customers, whose problem got solved, behave differently, which you can use to design Outcome-focused team-level Key Results:

High error rate from manual data input → Increase shift planning automation by x%; Reduce error rate in shift planning to 1 per customer per week

Layer 3: The Solutions

Solutions are outputs. The activities or artifacts you provide to solve a customer's problem and drive the team-level KRs. The connection between Outputs and OKRs can be two-fold:

1. You link the prioritization and execution of your Outputs to your OKRs: "We work on this feature, because of the evidence for it to drive this Key Result the most" OR "We measure the success of this feature through the metric in Key Result X.
2. If measuring the changes in customer behavior is too lagging (due to sales cycles or development timelines) within an OKR cycle, you CAN measure your progress through Output OKRs: "x% of requirements available on staging" OR "Average team velocity of y"

Note: Output OKRs are a crutch for you to use on your way to steering through measuring changes in customer behavior. They're more useful than staring at lagging metrics without change, but not an indefinite state.

Layer 4: The Experiments

Similar to solutions, experiments are executed to increase the chances of success of your solutions. So, they serve the OKRs, but aren't informed by them. You should align the priority for executing an experiment with the progress you want to make in your OKRs (rather than purely chasing opportunistic ideas). Alternatively, you could temporarily steer experiment-focused behavior through team OKRs to cultivate experimentation: Number of A/B tests run OR Pre-order revenue generated. But the same caveat as for Output OKRs applies.

Next week, I'll translate the fundamentals of this connection to changes you might see in the context of building AI products and using AI to accelerate your work.

Thank you for Practicing Product,

​Tim​

If you consume one thing this week, make it this...

Who is Tim Herbig?

As a Product Management Coach, I guide Product Teams to measure the real progress of their evidence-informed decisions.

I focus on better practices to connect the dots of Product Strategy, Product OKRs, and Product Discovery.