OKRs and Dual Track Agility

After having inspirational back and forth conversations with Marty Cagan and Tim Herbig in the past couple of weeks, my mind started dwelling on the relationship between a framework like OKR and the operational functions of Dual Track Agile – discovery and delivery. Further inspiration was drawn from Teresa Torres’ Opportunity Solution tree.

The following mental model, which I call Dual Track Single Flow is what resulted:

Dual Track Single Flow

The Dual Track Single Flow mental model is helpful in identifying which user problems are worth solving and which ideas are worth pursuing. Following this mental model works best when you are working as a product team that’s empowered to solve customer problems, but also want to see how those customer problems contribute towards the business objectives. As you can potentially find lots of potential user problems to solve, focus on the ones that feed into your organization’s objectives (using OKR terminology). For user problems that don’t currently fit into the organizational objectives, keep them in a backlog or discard them for now. You can potentially use them to review your company objectives and see if they make sense.

In the image above, step 1 is the problem space where you learn more about the user problem. You want to find out more about the customer needs before thinking about potential solutions. In step 2, you want to see whether solving these problems fit into your business objectives. If it then makes sense, you proceed to step 3, where you explore the problem further and build a potential solution. There are qualitative and quantitative ways to do this, including JTBD interviews, which is a personal favorite. Using a What-Why framework here could be a good approach. Once you’ve discovered user problems, brainstorm 1-3 key opportunities for your product or service to solve this problem. Validate, test and iterate these low-fidelity experiments in the solution space. This is where the core of product strategy occurs. Two companies faced with the same customer problem can choose different approaches, experiments and solutions for it. This is where organizations gain or lose competitive advantage. The success of the experiments should be tracked with key results, KPIs, and metrics relevant to your business objectives. If the experiment(s) move the needle for the outcome you want to achieve, it can be considered a potential solution that can then be iterated to a higher fidelity, based on the need.

The reason the dual track terminology is still valid here is because you can have multiple (1-3 suggested) discovery and delivery approaches running in parallel while you are in the step 3 cycle. This allows you to remain focused on solving the customer problem that fits into your objectives, while prioritizing your low-fidelity experiments.

This mental model helps validate assumptions about:

  1. Which user problems to focus on in the solution space.
  2. How that user problem aligns to the greater business goals of the organization.

Let’s look at a scenario where you’re building a purely online bank. Start by assessing some user problems, then think about how solving those problems might help a business objective or metric for you. Finally, think about some experiments you can try to help you solve those problems while achieving your business metrics.

Sample DTSF model

In the example above, you decide to tackle the user problem of a user being able to check their live bank account online whenever they would like, connect it to some business metrics that are included in your strategy, and think about potential experiments to try and validate your assumptions about the potential benefits of using your mobile banking app.

If there are certain user problems that you can’t assign business goals or metrics to, as in the last column above, that is not something that you should prioritize to work on right now.

Want to continue the conversation? Connect with me on LinkedIn and let’s chat about how I can help you and your organization out.

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