Outcomes Over Output · Josh Seiden · 2019

I’ve heard many product managers say "outcomes over output," but few can clearly define what they mean by outcome. Josh defines an outcome as a change in behaviour that drives business results. This builds on the idea of value exchange between the customers and the business. The author does a good job of extrapolating this idea into a way to do product management.

Key Highlights

An outcome is a change in behaviour that drives business results. It's important to focus on outcomes and changing human behaviour, rather than just outputs. Features don't automatically create value, so they shouldn't be used as the centre of the planning process. Managing outputs means telling a team what to build, but features don't always deliver value. Managing impact involves telling a team to target some high-level value, like growing revenue, but that's not specific enough. Managing outcomes, on the other hand, means asking teams to create a specific human behaviour that drives business results. This approach gives them room to find the right solution while keeping them focused on delivering value.

Agile methodologies don't explicitly define what 'value' means. However, when you combine outcome-based targets with a process based on running experiments, you really start to unlock the power of agile approaches. Setting outcome goals gives teams room to try different approaches and experiment. Think of an MVP as the smallest thing you can do or make to learn if your hypothesis is correct.

According to Jared Spool, there are only five things executives care about, which are impact-level metrics: increasing revenue, decreasing costs, increasing new business and market share, increasing revenue from existing customers, and increasing shareholder value.

To find the right outcome, consider the following questions:

What are the customer behaviours that will drive business results?
What are the things customers do that help us predict the outcomes we care about?

Because outcomes are observable and measurable human behaviours, they are suitable for use as a management tool. Understanding what your customers are doing that drives the results you care about is key.

Leading indicators are things people are doing (human behaviours) that predict the success we're seeking. In other words, leading indicators are outcomes. There's uncertainty when creating outcomes, such as whether the output will create the outcome and whether the outcome will contribute to the desired impact. It's essential to treat ideas like assumptions, express assumptions as hypotheses, and run experiments to test these hypotheses. A hypothesis outlines what we believe and the evidence we're seeking to know if we're right or not.

The magic questions to ask are:

What are the user and customer behaviours that drive business results?
How can we get people to do more of those behaviours?
How do we know that we're right?

Tracking progress is easier when teams are working on well-defined outcomes and making their hypotheses clear. Outcomes are measurable, so you can determine if customer behaviours are changing.

Outcomes help you write better OKRs by first considering the business result you're trying to achieve and expressing that in easy-to-measure terms of customer behaviour. Think about your system of outcomes and consider creating outcome-based roadmaps.

Visualising the customer journey can help identify the behaviours that predict success, satisfaction, failure, and dissatisfaction at each step. Overlay success factors (boosters) and failure factors (blockers), and consider how you might encourage positive behaviours and eliminate issues. Frame your ideas as hypotheses:

"We believe that if we increase the rate at which buyers and sellers meet early in the process, it will lead to more successful transactions (as measured by X) and higher user satisfaction (as measured by NPS).
We think we can increase the rate of early meetings with [this idea], [this idea], and [this idea]. We will work on testing these ideas in Q1 of the coming year."

Organisations are often set up around products or channels rather than behaviours or customer journeys, which favours outputs over outcomes. Teams should be clear about the value they are trying to create by specifying the outcome they are seeking for the customer or user and the outcome they are seeking for the business. If the user outcome is created, it should deliver the desired business outcome. Product managers should have dedicated teams to avoid waiting for team allocation before starting work. If stakeholders have to wait a long time for project approval, they may bloat their feature requirements and force in everything they need.

Companies may need to re-engineer their work processes to implement an outcome-based approach. Consider how employee behaviour can be changed to generate business results. Apply an outcome-based approach to transformation by treating colleagues as customers, framing everything as an outcome, and treating everything as an experiment. Take a customer-centric approach with colleagues, understanding their goals and the value you can offer them to get them to "buy" the change you are selling. Frame organisational change initiatives in terms of outcomes, focusing on the new behaviours you want to create and what people will be doing differently when your change program is successful.

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While clinicians are remarkable, they are constrained by the limits of individual human cognition and experience. Pairing clinicians with AI assistants could significantly improve patient outcomes. They could provide real-time insights, support decision making, and help deliver world-class, personalised care at scale. Realising this potential will require thoughtful design and rigorous validation to ensure safety, efficacy, and clinician acceptance. But if done well, the partnership between human practitioners and artificial intelligence could revolutionise healthcare quality and accessibility.

I’m super excited by the research that’s going on at Google in this space. Their latest Med-Gemini models look like the start of something special · Paper

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Pioneer Advantage: Marketing Logic or Marketing Legend? Golder and Tellis · 1993

Several studies have shown that pioneers have long-lived market share advantages and are likely to be market leaders in their product categories. However, that research has potential limitations: the reliance on a few established databases, the exclusion of nonsurvivors, and the use of single-informant self-reports for data collection.

Marketing legend. Contrary to popular belief, being the first company to enter a market doesn't guarantee enduring success. Pioneering advantages such as higher market share, brand recognition, and customer loyalty may not result from timing but from superior marketing and strategic execution. Other factors like technology, brand appeal, or positioning could allow later market entrants to overtake pioneers.

Research involving around 500 brands across 50 product categories revealed that pioneers average only about a 10% market share, significantly lower than previously reported. Nearly half of market leaders were not pioneers, and a pioneer's market share advantages tend to diminish over time. These findings suggest that while a pioneer advantage exists, it may be less significant and less permanent than previously assumed.

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