How to make your product prioritization more data-driven

by Vanessa Hojda

Product prioritization is a task that can make or break your product. For product managers, in particular, it’s important to make prioritization decisions that are data-driven so that they yield the greatest impact on the product.

What are the benefits of making more data-driven prioritization decisions?

  • You can use the data to see overarching trends
  • It fosters a data-curious and data-driven mindset in the team
  • You’ll more confident about your decisions
  • You’ll achieve smoother alignment and buy-in processes
  • You can use data to filter out random requests that aren’t based on evidence
  • Data helps you hold less emotional product discussions

There’s no way we can tell you how to prioritize for your specific company. But we can give you some tools to rethink how you approach your prioritization process and make it more data-driven. For the purposes of this article, we’re categorizing data into two buckets:

  1. Quantitative, structured data
  2. Qualitative, unstructured data: This is made up of customer feedback, surveys, polls, interviews and team and stakeholder input

This article will be about that elusive quantitative data. This type of data will give you an added layer of insight into how you can improve your product and your users' experiences.

Here are some tips for making sure that you’re not making decisions based on opinion and gut feeling (not the same as a PM’s instinct—that one is very valuable, but it should be informed by things like KPIs and metrics). Instead, you want to make sure you’re basing your decisions on the data that matters the most—to the product, to the team and to the company.

Build reliable product KPIs and metrics

You might be thinking: “Yes, I have KPIs and metrics in place. Duh!” but are you using them when it’s time to prioritize features? During the prioritization process, you should be asking yourself: “Is this decision going to bring me closer to our vision and goals?” If this isn’t part of your prioritization process, you need to redefine your KPIs and metrics so they’re more relevant and actionable.

More importantly, every relevant decision-maker and stakeholder should have access to those KPIs. They should be able to see how they evolve over each phase of the product’s lifecycle (we’ll get into building a centralized data hub for your team later).

This is great in theory, after all, anyone can rationalize their ideas to fit a vague product KPI. The key is to implement a system of detailed and relevant metrics and KPIs that get into every aspect of your product. Good and specific metrics allow you to see how your product is performing, monitor how every aspect of the product is progressing, and have a baseline for when it’s time to improve one of those aspects. On top of that, having good KPIs will give you an incredible breadth of data to access whenever you have to make a prioritization decision.

That’s where you, the product manager, come in. Defining strong KPIs and metrics across all aspects and stages of your product is essential for ensuring that your prioritization decisions are not dependent on personal biases or stakeholder politics. By now, you probably already have buckets or categories for your KPIs. Generally, you should be tracking KPIs that fall under each of these:

  • Business metrics: cost and revenue values
  • Customer metrics: user success and frequency of usage values
  • Product metrics: feature and functionality-specific metrics

This isn’t a comprehensive list of KPIs. The idea here is for you to think about how you can make sure you’re measuring, tracking, storing and analyzing the KPIs and metrics that will facilitate your prioritization process when it’s time to make a decision.

Organize those product KPIs and metrics

You can then take then organize your metrics and KPIs using some kind of system that’s intuitive and easy to understand by everyone. These are your product analytic reports: funnel analysis, cohort analysis, retention and segmentation reports.

For this article, we’ll get into one type of funnel analysis; Dave McClure’s pirate metrics.

Funnel analysis

You can use a funnel analysis report model like Dave McClure's pirate metrics to visualize how strategic your KPIs are along each stage of the sales funnel (you can also have a read over our handy deep-dive blog post into SaaS product metrics. A lot of the metrics presented in that post are equally valuable for other types of businesses).

McClure’s model presents 5 KPI categories, using the acronym AARRR (yep, just like a pirate), that allow you to see where you stand and if you’re missing anything. The categories are:

  • Acquisition
  • Activation
  • Revenue
  • Retention
  • Referral

The idea is that you should be doing feature prioritization based on increasing and improving conversion rates through each category and for each type of metric (business, customer and product).

Centralize those product KPIs and metrics

The key to prioritization is data, but more importantly, it’s having a team that feels motivated to make data-based decisions. The first step to achieving a more data-oriented team is to make your KPIs and metrics accessible for everyone to access in one place.

On top of that, your centralized KPI dashboard should be kept in the same place as customer feedback data like emails, customer support messages, surveys and polls. When you have all this data in one place, your team can then use it when it’s time to hold a prioritization meeting.

Consider investing in one or more analytics platform tool that offers intuitive interfaces (like Kissmetrics, Amplitude or Heap). They will allow anyone throughout the company to access the relevant data they need to assess a decision.

But we can’t name specific analytics platform tools—that will depend on your type of business and the number of metrics you’re tracking. But there are some things you can keep in mind when choosing an analytics platform tool for your KPIs and metrics:

  • Integrations: Do you need it to offer pre-built integrations? Accessible APIs?
  • Features: How important is support and training? Are there any features that need to comply with internal and external data regulations? How much control do you need to have over data collection and storage? Access to raw data?
  • Implementation: What resources do you need to successfully implement an analytics platform tool? How much will it cost in resources?

Remember that being data-informed instead of data-driven means taking the time to analyze results and really think about how they match up with the KPIs and metrics you defined. Data-informed teams try to understand the elements that contributed to the results they see in the data. It’s important not to get too carried away with data (Analysis Paralysis, anyone?), but you should always be thinking about the different ways you can align your team around the metrics that matter.