Prioritization is a vital part of a product’s growth that allows teams to narrow down their scopes and build the right things at the right time. There is a wide array of prioritization frameworks that can help teams adapt to their customers’ needs and, as a result, provide them with more value.
While most prioritization approaches do tend to counteract assumptions and help product teams make more calculated decisions in regard to product priorities, they are still prone to bias, stakeholder pressures and gut feel, which often gets reflected in the Confidence dimension within the ICE and RICE frameworks.
In this article, we’ll take a closer look at how your team should quantify Confidence to limit bias and gut feel.
Oh, and if you're feeling curious, feel free to check out this insightful convo we had with Rui Oliveira on effective and collaborative prioritization!
We won’t dwell on definitions for too long, but let’s fall back for a second and briefly explore these two frameworks.
Both of these prioritization methods are fairly similar. ICE stands for Impact, Confidence and Ease, while RICE stands for Reach, Impact, Confidence and Effort.
Their main goal is to help establish a priority-ranked list of tasks that helps introduce more clarity to the product’s growth and focus on the things that matter most to customers.
All the dimensions in ICE and RICE are scored from 1 to 10. For instance, if a proposed task scores 9 on the Impact scale and 8 on the Ease scale, it can be considered a high-impact and easy-to-achieve goal.
These dimensions are generally pretty straightforward to quantify and are fairly reliable. However, that isn’t always the case for Confidence.
It would be fair to note that classifying something as “easy” is pretty subjective, but this is an assessment of a team’s effort that also factors in the members' capabilities, seniority, experience, efficiency, and a wide array of other parameters. Therefore, we can consider it a fairly reliable estimation that also has some room for error. The same can be said about Reach and Impact. They aren’t mathematically precise but are rooted in well-founded evidence.
But when it comes to Confidence, things can get really tricky. This dimension is often the most subjective of them all and it rarely stems from data or objective criteria. Basically speaking, the default way we think about Confidence in ICE and RICE is an estimation of gut feel. Unless you have very clear evidence for confidence—you’re subject to bias.
Should we then just discard Confidence? Absolutely not. Instead, we’re arguing for a data-oriented and less subjective approach to this dimension.
In a nutshell: Assigning a score to Confidence can be very tricky because it’s by far the most subjective and intangible dimension, which makes it susceptible to bias and gut feel.
Confidence levels will open your team up to bias and gut feel, only if you allow it to. Instead of assessing how confident you are, it’s arguably better to establish your Confidence score through data.
Of course, the types of data you have access to will vary considerably depending on the maturity of your project, but that shouldn’t stop you from getting your hands on the best data available to you. While this is not an exhaustive list, a good starting point would be to collect qualitative feedback from your customers, quantitative data, correlational data and experimental evidence. Even if your product is at its early stages, customer feedback and Voice of the Customer data can significantly improve your decision-making during prioritization.
As a result, this approach will allow you to shift your attention from one or a few select stakeholders to users and customers, which will help you remove a considerable amount of bias and gut-feel from the prioritization process and instead source it from the people who your product or service is developed for. Not only does this fine-tune decision-making and facilitate validation, it also ensures a more customer-led approach to building a product.
In a nutshell: You can considerably reduce bias and gut-feel by rooting your Confidence assessment in data.
Now that we’ve arrived at the conclusion that your Confidence levels shouldn’t stem from personal opinion but from data, we should also point out that not all data is equal.
Let’s say that our Confidence level is a spectrum ranging from near-zero to high-conviction—where would different types of data fall on it?
Yes, most frameworks are subject to bias, gut feel, and stakeholder pressure—and there is pretty much no way around it. Our goal shouldn’t be to remove them entirely, but rather reduce their impact to a minimum. Using customer feedback and insights to validate your decisions will help you do just that.
And it’s fair to say that everyone has their preference in terms of prioritization methods, but the key is whether you’re basing your decision on actual data. Unfortunately many of us, consciously and unconsciously, still make arguments up to justify choices rooted in bias.
In a nutshell: Not all data is equal in terms of its effect on confidence. It’s always best to choose the most reliable data you have available, while also removing things like personal opinion and thematic support from your decision-making.
On their own, prioritization frameworks are very useful tools, especially when approached correctly. However, their efficacy can be further increased by having the right mindset.
Your approach to Confidence can make or break your prioritization efforts. Bear in mind that all ICE and RICE dimensions are prone to subjectivity to a certain extent. This has to be factored in and mitigated. Even if you have access to highly-reliable data, it’s still a good idea to be selective about the types of product ideas you’re trying to prioritize.
Tasks that rank high on the Confidence scale can still lack punch in terms of impact—reducing uncertainty is an essential part of the process.
Some may find prioritization frameworks to be too clinical and labor-intensive, but that’s far from true. Yes, they demand time and effort, but in return, you get a much clearer bigger picture. When it comes to building products, playing it by the proverbial “ear” is both harmful and wasteful.
More importantly, a data-based Confidence score will help you learn more about your customers and the market, as well as fuel your vision for the product’s evolution and roadmap.
“You are not your user” is a quote that may appear very simplistic on the surface but carries a lot of valuable wisdom. Products aren’t created for bragging rights; they’re valuable tools that help people solve pressing issues.
When it comes to product decisions or prioritization, it’s important to approach them by being mindful of your bias. This is a valuable exercise that can help you assess whether your decision is driven by gut feel and personal conviction or actual data.
We highly recommend using prioritization frameworks. ICE and RICE are great methods that can help you reduce bias, gut feel, and guesswork from your decision-making, especially when taking a data-oriented approach to establishing Confidence levels. If you use customer feedback and insights to validate your decisions you can reduce that bias to focus on things with actual confidence.
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