The goal of prioritisation

We always have more ideas, but never seem to have the resources to execute them. Therefore, it is important (both in private and professional life) to carefully cherry-pick a couple of ideas and go all-in. However, keep in mind: it’s never wrong to pivot.

What are we prioritising?

Depending on the industry you’re in you might be prioritising different things. In my work I prioritise a software backlog, but in my private live I (consciously) prioritise in the way I’m building my house or (unconsciously) the things that intrigue me most. In all of these situations we should ask ourselves: what is the goal that I want to achieve? Without having a clear (and preferably measurable) goal in mind, you cannot prioritise.

If you’re interested in the use of goal-models and how to apply these in requirements engineering, I can highly recommend the article Reasoning About Alternative Requirements Options.


After you’ve defined your goals and what you want to prioritise, you should understand the available frameworks. Note that I didn’t write you should select a framework. I don’t believe there is a one-size-fits-all approach to this. Selecting the right model depends on your personality, way of working, and the context you are prioritising. The UX Collective wrote an article that lists the available prioritisation frameworks and explains the different categories.

Source: UX Collective, How to choose your Product Prioritization Framework

Let me introduce you to two of these frameworks: KANO and RICE. This combination works well for me personally. They are pragmatic, easy to explain, and easy to implement.


RICE was developed by Intercom and is an abbreviation for:

  • Reach: how many of my users will find this thing useful?
  • Impact: how much impact will this thing have on the metrics we have defined that allow us to reach our vision?
  • Confidence: how confident are we on our estimations for reach, impact, and effort?
  • Effort: how much do we need to invest to get the thing in a state that users will start using it?
Source: Productplan, what is the history of the RICE scoring model?

You should define a range for these variables before you assign values o them. Then, you can easily calculate the RICE score by R * I * C / E.

KANO Model

John Vars applied Maslow’s hierarchy of needs to product development. He came up with three layers:

  1. The foundational layer includes functionality that users are not able to live without.
  2. The value proposition layer makes a user successful or it solves a pain.
  3. The growth layer really excited a user by solving a problem that the user wasn’t even aware of. This should set you apart from other solutions as well.
Source: John Vars, the product hierarchy of needs

The KANO model uses a similar approach: it’s goal is find a balance between things that users are disappointed on if they are missing (threshold attributes) and things that excite users (excitement attributes). When excitement attributes mature their implementation depth grows and they slowly move right, into the performance attributes.

Source: Mindtools, Kano Model Analysis

Practically working with RICE and KANO

RICE results in a number. You can easily capture this in a spreadsheet and sort using the highest number. KANO results in a classification. You can easily create a simple spreadsheet to capture the things you want to prioritise. Assign the value and you get an idea of what should be important to you.

Example of the implementation of the RICE and KANO model in a simple spreadsheet.
Example of the implementation of the RICE and KANO model in a simple spreadsheet.

I recommend that you always interpret the output of the models. Ensure the theoretical prioritisation makes sense in practise.

Implementation and validation

These models require a bit of time to understand and apply. I would suggest to implement them in two steps:

  1. Use them based on experience and a couple of explicit assumptions. This immediately gives you an idea of where you stand.
  2. Once you got the hang of it and found a good way of working, start to validate the assumptions and experience with the users of the thing you are creating. This probably lengthens the prioritisation process. You need to decide if this time is worth it.


Taking the time to explain how you came to a decision is just as important as communicating the decision itself. Both RICE and KANO allow you to visually explain how you prioritised the items you are going to work on.

The Pareto principle in Power BI

Product Managers love data. Therefore, being able to create your own reports to track the KPIs relevant for your products is really useful. One of the KPIs you might run into relates to the Pareto principle, for example, 80% of the revenue needs to come from 20% of the customer base.

The Pareto principle

I’m going to refer you to the wikipedia for the extensive explanation. I will give you a couple of examples:

  • 20% of the customers should account for 80% of the revenue.
  • 20% of the richest persons in earth account for 80% of the income.
  • 20% of Italy’s population owned 80% of its land.

The Pareto principle in Power BI

We take a couple of steps to get this visualised in Power BI:

  1. Prepare the data structure.
  2. Adding a rank to the data based on a specific category.

As an example I’m using the KPI that 80% of revenue should come from 20% of the customers.

Data preparations

I recommend you to create a table to put the data you want to visualise on the screen. This makes it easy to validate the results of the measures you create.

Adding a table to a Power BI report.
Adding a table to a Power BI report.

You need two types of data to visualise the Pareto principle in Power BI:

  1. Category: this can be, for example, a customer or country.
  2. A number: this can be, for example, the sales of a customer or country.

Ranking your customers

You first need to find the top 20% of your customers. You should create a measure that calculates the rank of the customer depending on the sales value.

Rank = RANKX(ALL(Sales[Customer]), CALCULATE(SUM(Sales[Sales_Value])))

When you add the Rank measure to your table and sort it by sales value, the customer with the highest amount of sales should have rank 1.

Cumulative sales

Now that we have ranked your customers, we want to calculate the cumulative sales. This measure cumulative sales of the customer with rank 1 is the sum of the sales of customer 1. The cumulative sales of customer 2 is the sum of the sales of customer 1 and customer 2.

(use SHIFT+ENTER to create a new line in DAX).

Cumulative_Sales_Value = 
var currentRank = [Rank]

RETURN SUMX(FILTER(ALL(Sales[Customer]),[Rank]<=currentRank), CALCULATE(SUM(Sales[Sales_Value])))

Total sales

To calculate the stake of the cumulative sales of a customer in the entire revenue you need to calculate the total sales value. The ALL function applies the selected filters.

Total_Sales = CALCULATE(SUM(Sales[Value]), ALL(Sales[Customer]))

The last measure you need to create calculates the cumulative sales (in % of total sales). You will use this measure to create the line in the visualisation of the Pareto principle.

Cumulative_Sales_Perc = [Cumulative_Sales_Value] / [Total_Sales] * 100

Create a line and clustered column chart

Now you are ready to add the measures into a line and clustered column chart.

Adding a line and clustered column chart to your PowerBI report.
Adding a line and clustered column chart to your PowerBI report.
Assigning the Customer, Sales_Value, and Cumulative_Sales_Value_Perc to the line and clustered column chart.
Assigning the Customer, Sales_Value, and Cumulative_Sales_Value_Perc to the line and clustered column chart.

The result:

An example of the result of visualising the Pareto principle in Power BI.
An example of the result of visualising the Pareto principle in Power BI.