In our article “How to become a more data-driven organisation”, we touched on Outcomes, and in order to be a more data-driven organisation we need to start at the end – i.e. where do we want to be, and how do we measure our success on that journey.
In the article, we wrote about the need for outcomes, how OKRs (Objectives and Key Results) have become a popular tool over the last few years to drive outcome-based thinking and strategy, and then dug into the different types of metrics that can be used as indicators of meeting those outcomes or OKRs.
We now need to dig a little deeper as there are pitfalls in the journey of being data-driven and outcome focused, and this article will provide clear breakdown and examples of how to define better data-driven outcomes.
Are you objective, outcome, or output driven?
There is a difference between Objectives, Outputs and Outcomes:
- Objective – a desired business result
- Example: increase revenue from e-commerce
- Outcomes – a desired behaviour that drives business results
- Example: increasing average order value or basket size (the amount each customer spends per order)
- Outputs – something you deliver that could influence the change in behaviour that drives business results
- Example: building a new feature in the checkout process that suggests other products the person may like to buy as well
With these, it’s easy to fall into the trap of driving outputs or driving objectives without really thinking about and measuring the desired outcomes we want to achieve that connect it all together.
One of the key functions of the Product Management organisation is using the technique of outcomes and experimentation in this way:
- What value are we trying to drive? (objective)
- What is the metric that determines the value, and to what extent do we want it changed? (outcome)
- What features/enhancements can we deliver and experiment with to see how it affects the metrics and how? (outputs)
Leading and Lagging Indicators
When determining outcomes, it is important to differentiate between leading and lagging indicators.
A lagging metric is something that you would measure from the past or may take a given period of time to collect, which would indicate progress – so last month’s sales for example. A leading metric would be something you can count now, which would demonstrate an impact on the lagging metric – so visitors to the web site for example, i.e. more visitors is likely to lead to more sales.
The reason it’s important to differentiate between them is because relying only on lagging metrics may cause you to notice a problem only after it’s too late, whereas a leading indicator may give you an earlier warning that you can do something about.
Connecting it together with OKRs
OKRs (Objectives and Key Results) have become an effective and popular tool because they help connect your work with the big picture (the objective), and they make sure you’re driving towards an outcome (the key result).
While OKRs sound like a good idea, if not structured right, they can be false indicators of success and give an incorrect sense of accomplishment, or worse drive failure by focusing on the wrong things.
So, how do you write a good OKR? Think of the key results as Outcomes. In our example above, increasing average order value or basket size (the amount each customer spends per order). Outcomes help you write better OKRs as they allow you to step back from the details and consider the business result you are trying to achieve.
Of course, the more complex and larger the organisations the more complex the objectives and key results that exist for different parts or levels in the organisation, so structuring the hierarchy of OKRs needs careful consideration and planning.
Appropriate consideration also needs to be given to the skills, capabilities and cultural change that need to be developed and supported during the implementation and ongoing support of implementing effective OKRs.
Do you use roadmaps to show outputs or outcomes?
One of the key tools for longer term planning is roadmaps, however often roadmaps are often focused on outputs (i.e. what things will be delivered when) rather than outcomes (i.e. what outcome do we want to achieve and by when).
When implementing OKRs and outcome based thinking, it is important to pivot planning and strategy in the same way, with the appropriate support and development of the people contributing, driving and consuming the strategy and plans.
Similarly, many companies organise their teams around outputs rather than outcomes. The common use of ‘feature teams’ is an example of this. An alternative approach is to organise teams around outcomes, giving teams clear outcome-oriented goals that can be measured, and giving them more empowerment and autonomy to experiment with what will drive the results.