“As designers, we need to accept and embrace the world of metrics and use their amazing powers to change the way we’re doing things.” — Jared Spool

We’ve all found ourselves in situations where we couldn’t predict what users want, even with years of design practice and experience. We relied on best practices and tried to create innovative designs, but it just wasn’t enough. 

Relying solely on best practices and intuition without data-driven insights means you can waste time on ineffective and costly changes. Failing to consider data or, even worse, not using it properly, can have serious implications for a product or a project’s success.

With Napster, we designed on intuition, industry best practices, and extensive data analysis. The data-driven design has helped us achieve results and create fantastic user experiences. Here’s how…

Data-driven design

How does data-driven design help digital products?

When designing any successful product, it’s essential to position oneself in every user experience. Doing so helps product designers become aware of gaps in understanding between user needs and user behavior. This methodology is only half of what can make a product great. The other half addresses the user experience a little differently — with research, rigorous testing, and data analytics.

The best way to evaluate hypotheses during design exploration is with data. Without data, you risk wasting time and money further down the road on product changes that might not bring either monetary value or user satisfaction — or both.

Validating design decisions with data can improve product performance tangibly — the numbers don’t lie about where and what users connect with most. Data helps us understand the depth and complexity of any product and make informed decisions that result in a better user experience.

The role of data analytics in design

Analytics can tailor products to user preferences, thus shaping product design. Introducing data can reveal trends, locate pain points, and provide in-depth user behavior analysis. Designers and data analysts are complementary, and they need to work in tandem to get the most out of any product.

Here are four ways that can influence decision making in product design:

Prove you’re on the right track — or not

You’ve launched a new feature? Great! Are you confident it will attract new users or satisfy existing ones? If a feature performed well before you redesigned it, it doesn’t mean it’ll perform even better, and vice versa. You should regularly check the performance of new and existing features.

To do so, you need to set up the proper analytics tools and strategies. Next, you can establish the baseline for future analyses and decisions. The baseline is a dataset representing the feature’s performance (or any other part of the product that you want to track the changes of). With the right baseline, you can track the feature’s performance against its previous state and see if the change positively or negatively impacted the product.

You can’t prove you’re on the right track without the right baseline. Suppose you’re working on a music platform app where you just launched the ability to create a new playlist. Before assessing the value of the change, you need to look into user data (e.g., % of successfully created playlists) for a few weeks to see how the change impacted users’ behavior.

Reveal future opportunities and pain points

Discovering any user experience problems is essential for any app’s success. Why? If you can identify opportunities and pain points early, minor UX improvements can help get the product and its users back on track.

It’s easy to make assumptions when you’re not tracking your app. Data eliminates guesswork — it tells you what drives users’ attention the most or not at all. Remember, building and maintaining an app is time-consuming and costly. Data can help you make informed choices to save valuable resources later.

Consider the sign-up process, a common pain point: it’s important because it’s the app’s entry point, and you want your users to complete it successfully. Data tells us that it’s also one of the most common spots where users drop off. Just a small UX change can make the sign-up process easier (e.g., logging in with social media accounts, Google, or Apple), which can help bring revenue for your clients.

Discover new user patterns and trends 

Apart from locating pain points, data can find anomalies and patterns in user behavior. Unusual patterns and trends are educational. If a trend is positive, we can investigate the source and find a way to push other users to perform the same actions. If a trend is negative, we need to react quickly to stop further losses.

For example, in a language learning app, we noticed that users who opted to take a language test and start learning at the recommended stayed significantly more active than users who declined the test. Discovering patterns like this can help us improve user experience by removing the option to skip the test — thus guiding users to stay active in the app.

Improve design by making it an iterative process

The previous three approaches can be used as guides to optimize products. Product design should be an iterative process — designers and data analysts should continuously work together. This way, we can optimize our products, keep our users engaged, and meet our business goals. 

What are the steps that make data-driven design?

Discovery (defining the problem, collecting data, brainstorming, and analysing)

Product discovery is about looking at the bigger picture; it collects current problems, data insights, intuition, and the endless drive to make things better. Discovery also is about recognizing patterns and analyzing complex flows to improve the product and the users’ experience. Think about the process like this: 

  1. Collecting the right information:
    • Insights from users – feedback
    • Insights from data – analytics
    • User pain points
    • Competitor analysis
    • Users interviews
  2. Putting it all into context 
  3. Design UI/UX

Design (data-driven explorations) 

After discovery is over, and you’re contextualizing everything, remember that users — with emotions — generate data, not machines. Behavioral data gives us insights into the product (based on your current users and their actions). Remembering this helps interpret the data, create a vision, and work from a path to make better design decisions. The data, empathy, and user stories help designers create a viable vision that is achievable, and in turn, validate the data. And so, the process iterates.

Validation (post-design analysis) 

Post-design analysis is crucial to know if you hit your goals because user behavior can be unpredictable. Like we said, building an app is an iterative process. After a redesign, getting the right insights should tell us which direction to go — not having these insights can lead to wrong conclusions and further actions.

It’s impossible to get the perfect design after the first iteration; one’s audience changes over time, new trends and new competitors are commonplace — it’s critical to keep up with the latest.

Case study – Napster Artist Screen 

Project motivation

Now you can see how we used data and the process above with the Napster mobile app’s Artist screen — a screen that caught our attention for several reasons:

  • Artist discovery is important for new users in the early phase: users who viewed the Artist screen 4x in the first seven days after sign-up are highly predictable for retention in week two, with a correlation score of 0.49. Promoting the Artist screen could help boost retention and reduce churn — the end goal for product improvement.
  • The Artist screen is one of the top five most viewed screens in the mobile app, along with Album, Home, My Music, and Search screens.
  • It’s a median screen for discovering the Album screen, the most viewed screen in the mobile app: 34% of all users’ paths go through the Artist screen while 33% go through Search.

Currently, the Artist screen has low visibility on the app’s main screens; it’s not promoted on the Home Screen, and 71% of all users open the Artist screen through Search. Moreover, once users reached the Artist screen, they were presented with poor content and only three sections: Top Tracks, Albums, and Similar Artists — seven items total.

Our objective was to find opportunities to promote artists and suggest changes to increase the Artist screen’s visibility, the number of artists presented to the user, and the amount of content populated in the library from the Artist screen.

The Artist screen’s present and future

On the left, you can see the current version. On the right is the proposed version.

Here are our design change proposals in detail:

  • Move “Discover fans” to the bottom
  • Move “About Artist” to the bottom
  • Add the “Follow” at the top
  • “Top Tracks” section:
    • Add playback button
    • Increase number of items from 3 to 5
  • Add the following album’s section:
    • “Singles & EP’s”
    • “Compilations containing Artists”
    • “Artist appears on”
  • Add a horizontal scroll to “Album”, “Single & Ep’s”, “Compilations containing Artists”, “Listeners also like”, “Artist appears on”

Making decisions based on data

Here you’ll get to see the data behind our proposed changes. But first, we need to define a metric called CTR, which measures on-screen engagement. We calculated the CTR by dividing the number of clicks on a specific button or section by the number of unique daily Artist screen visitors. You can see the average CTR over the observed period on the screenshot below for the app’s current iteration.

Let’s focus first on the “Top Tracks” section. We proposed the playback button in the new design since this section attracts the most Artist Screen visitors and the idea is to allow users to listen automatically to “Top Tracks”. Conversely, most of that engagement (CTR = 45.9%) is with the See All button because users didn’t find the song they were looking for in the top 3 tracks, so increasing the number of tracks to 5 is suggested. 

Currently, the header section contains three buttons: “Discover Fans” (CTR = 0.3%), “Artist Radio” (CTR = 0.4%), and “About Artist” (CTR = 0.2%) — all of them have low CTR. Due to this, we moved “Discover Fans” and “About Artist” to the bottom, but with an important distinction: direct presentation of content for both sections to reduce additional steps for the user. Also, due to low CTR, “Albums and tracks in Library” was removed.

Our analysis of content action showed that “Add to favorite tracks” was the most performed action by new users in the first six months. The button helps users catalog their content, thus leading to more advanced and desired behavior: creating and adding tracks to the playlist. Could this kind of schema apply to the Artist screen? Yes. That’s why “Follow” was added to the Artist screen, helping users easily track their favorite artists. This placement also improved the content recommendation algorithm, opening up more possibilities in engagement channels (e.g., sending a push to users when a followed artist has a new release).

The “Albums” section is the second most popular spot on the Artist screen, with a CTR of 21.3% on Album Cards and 30.1% on the See All button. We found some interesting behavior: when users clicked one of the two presented albums, 12% returned and clicked See All, meaning they wanted to search for more. When users click See All, they are presented with content divided into four categories: Main Releases, Single & EPs, Compilations, and Others.

Based on observed data, the following changes:

  • Apart from “Albums” on the Artist Screen, two additional sections are added: “Single & EPs” and “Compilations containing Artists”, enriching the Artist screen with more relevant and engaging user content.
  • The horizontal scroll is introduced aiming to reduce the number of clicks users need to make to view desired albums.

Using similar logic, a horizontal scroll is added for the “Similar Artist” section. 

Because our changes won’t be implemented for some time, we can’t give results on how these changes impacted the goals we set at the beginning of the project. We promise we’ll give you an update once we have the results!


Having quantitative and qualitative data gives us better insight into users’ needs and habits. It’s as close as you can get to seeing through your users’ eyes — or at least peaking over their shoulders.

Quantitative data can help create better assumptions, optimize flows, and resolve user pain points more accurately and quickly. It offers insight into areas that might be overlooked or undiscoverable by user testing or standard industry practices. Combining quantitative and qualitative data with intuition and expertise creates quality decisions that give results. 

Using and remembering these principles can help save time and engender the relevant metrics to set up goals, achieve them, and optimize monetization — especially when guided by the SMART goals (Specific, Measurable, Attainable, Relevant, Time-Bound). Implementing these principles is essential for long-term strategies while allowing you to have your finger on the pulse of your user base as you can prioritize user needs while optimizing your revenue. Besides being strategically important, it plays a role in teams’ social dynamics, helps better collaboration, and creates a culture driven by the same goals.

Data-driven design is an investment in the future: putting effort into building an analytics framework and being data and design drive pays off in multiple ROI in the future as it gives you clear insight into the real state of your product and a much clearer guideline. 


Denis Čuljak, Data Analyst

Juraj Anzulović, Product Designer

Mia Galiot, Data Analyst