CoinMinutes Approach to Gathering Insights

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CoinMinutes' Approach to Gathering Insights from Crypto Audiences

The​‍​‌‍​‍‌ cryptocurrency world is inundated with information for users but seldom provides content that actually caters to their needs. Every day, several thousand articles vie for the readers' attention, but the majority of them do not engage the readers in a way that is significant. The lack of communication here is not due to the scarcity of writing talents, but rather due to the ignorance of the crypto audiences' wants and needs.

At CoinMinutes, we have invented a method to get audience insights which totally changes our content creation process. This method was not a sudden result - it was an evolution through looking, testing, and a pledge of putting the readers first.

Research and Insight Methods



Measuring data, understanding the audience

Our system for gathering insights is a combination of metric-based data collection and feedback research aimed at getting a thorough understanding of the audience's needs. This method is a way of converting raw data into valuable ​‍​‌‍​‍‌insights.

Number-Based​‍​‌‍​‍‌ Approaches

The different ways through which we monitor content effectiveness entail a Matomo analytics configuration along with a Hotjar heat mapping that is not limited to page views. We look at:

Time spent on pages (normalized for content length and difficulty)

Scroll depth patterns revealing the pages where users disengage

Click-through rates on internal links, indicating topic connections

Social sharing figures by platform and audience type

Content-led conversions to advanced guides

Completion rates have also been part of our tracking across various content formats. Our findings revealed that long-form technical guides have a higher completion rate (62%) than short market updates (47%) despite the difference in length - thus, they are going against the trend of expected attention spans.

Useful Reference: https://www.bandlab.com/coinminutes

Feedback ​‍​‌‍​‍‌Approaches

Numbers tell us what happens, but feedback tells us why. Our research includes:

Quarterly reader surveys with rotating focus areas

Community feedback threads with prompts

User testing sessions for content prototypes (a process we borrowed from software development)

Social listening across Discord, Twitter, and Reddit (we use a version of Brand24 for this)

Interviews with high-engagement readers (initially monthly, now weekly)

These are the ways through which we get to know the reasons behind the numbers. When our data indicated that people were less interested in market analysis pieces, our surveys unveiled that readers wanted technical examination instead of price predictions.

We also check these different sources of information against each other to be sure of what we are seeing before we make a decision, thus we lessen the risk of following the opinion or trend of an outlier.

From Data to ​‍​‌‍​‍‌Insights

Raw data only matters when we can act on it. Our analysis team includes content creators, data analysts, and subject matter experts. They meet weekly to make sense of the feedback.

This team works through several steps:

Identify patterns across data sources

Tell the difference between temporary interest spikes and sustained needs

Sort​‍​‌‍​‍‌ insights by audience segment and knowledge level

Rank findings based on impact potential and resource requirements

We utilize a 5-point system in our "Reader Intent Framework" that categorizes learner needs into Foundational Learning, Problem Solving, Skill Development, Market Navigation, and Advanced Development. Any content piece that we produce has to fulfill at least one of these reader intentions.

Our content ranking model considers four factors: search demand (30%), community request frequency (25%), technical complexity (25%), and implementation urgency (20%). With the help of this framework, we can assess the most pressing content priorities that are in competition with each other.

Coinminutes Cryptocurrency focus on the actual needs of the readers rather than on what they say they want. For instance, when readers requested "more NFT content" last year, the deeper investigation revealed that what they truly wanted was technical guides on how to evaluate collection utility and how to avoid scams - not market commentary.

This method caused a lot of disagreement among the members of our editorial team. Some of them were in favor of the argument that technical depth should be the focus while others thought that accessibility should be the primary concern. Rather than trying to serve both audiences with the same articles, we decided to create two different content streams although we are still working on this approach.

Content Strategy Implementation



From core lessons to community deep dives

Our audience teaches us what to put in our editorial calendar. We do this through a three-tier system:

Tier 1: Core curriculum content – Topics of sustained interest, updated quarterly

Tier 2: Emerging trend coverage – New changes that require explanation

Tier 3: Community-requested deep dives – Topics with dedicated audience segments

By using this method, the team balances the need to be responsive to the news while also doing some planning work. Besides every breaking news article that they release, they also come up with an educational piece that offers the needed background.

One of the examples is what we did when Ethereum was getting ready for its Shanghai upgrade. In fact, the hard stuff was merely a small part of the story. Our audience data revealed that people were very confused about what this would mean for the staked ETH. Therefore, we issued three pieces which had very different outcomes:

First: A brief news report on the timeline and specifications (traffic is high, but the level of interaction is low)

Then: An explanatory piece that breaks down the technical details (traffic is moderate, but the level of interaction is high)

Finally: A guide for stakers (traffic is lower, but interaction and sharing are at their highest)

We tried to separate content by experience level, but this led to an 8% drop in engagement, so we had to rethink our strategy. Instead of producing different pieces for various knowledge levels, we now incorporate progressive disclosure within our articles.

This multiple-piece strategy enables us to serve various sections of our audience while simultaneously putting together a thorough knowledge base.

Feedback Systems and Technology

The Community Feedback Loop

In order for insights gathering to be effective, audiences have to feel that their input is reflected in the content. We do not hide our feelings and stay clear through:

Monthly "Content Roadmap" updates that give a glimpse of the topics in store

"Developed From Your Feedback" labels on articles that are the result of requests

Threads where editors justify content decisions (it can be a bit controversial when we decide to go against the most requests)

Recognition of those readers whose suggestions have led to the pieces published

The existence of this response loop encourages readers to provide us with better feedback since they see that their feedback really counts.

Rather than treating audience input as a separate activity, we've built feedback collection into the reading experience itself. The questions are placed at the most important points of the articles, thus, we get more detailed feedback instead of general feedback from ​‍​‌‍​‍‌surveys.

Coinminutes​‍​‌‍​‍‌ have gone through three iterations of our feedback collection system. Our first version, a survey tool, barely got any engagement. The second attempt with email-based interviews yielded better results but was still not scalable. Now, we are using multiple contact points to get comprehensive feedback.

Technical Infrastructure

We use a mix of off-the-shelf solutions and custom-built tools for insight gathering:

A stripped-down version of Matomo for privacy-compliant analytics

Feedback widgets in the article powered by our WordPress framework

A tagging system that links reader questions with the next ​‍​‌‍​‍‌content

We stick to data minimization principles, collecting only information that helps us improve content.

We​‍​‌‍​‍‌ have a system that makes it possible for people to have a personalized experience and at the same time keep their identity anonymous. For example, users who interact with content about sophisticated trading will receive additional content of a similar nature, however, this preference-based adjustment operates without the need for an individual to be ​‍​‌‍​‍‌identified.

Useful Reference: CoinMinutes' Philosophy on Responsible Crypto Information Sharing