Actionable Dashboard: Unveiling the Path to Data-Driven Insights
As Data professionals and Product Managers, we navigate a complex landscape, striving to make informed decisions that drive our products towards success. In this pursuit, data plays a pivotal role, and dashboards are our window into the performance of our products.
However, the vast amount of data available can be overwhelming, making it challenging to extract meaningful insights that lead to actionable outcomes. This is where the concept of "Metric Hierarchy" steps in to empower us with the right tools for effective data analysis and decision-making.
Understanding Metric Hierarchy : The Layers of Insight
Metric hierarchy is a structured framework that organizes key performance indicators (KPIs) into layers, offering different levels of granularity and relevance to the product's goals. It goes beyond just monitoring metrics, diving deep into the cause-and-effect relationships that govern product performance. A well-defined metric hierarchy typically consists of three main layers:
- Outcome Metrics
These are the top-level metrics that directly reflect the success of the product. They align with high-level business objectives and provide a holistic view of the product's performance. Examples include overall revenue, customer retention rate, and customer lifetime value (CLV). - Driver Metrics
Sitting beneath the outcome metrics, driver metrics are the factors that influence the outcome metrics. They serve as the intermediate indicators that help us understand the specific drivers of success or failure. For instance, the number of sign-ups, user engagement, and conversion rates are common driver metrics. - Actionable Metrics
At the granular level, actionable metrics are the most specific and actionable indicators. They highlight the specific actions or improvements needed to influence the driver metrics positively. Examples include the click-through rate (CTR) on a particular call-to-action button or the bounce rate on a landing page.
Why Metric Hierarchy is Crucial for PMs and Data Professionals
1. Informed Decision-Making
Metric hierarchy provides a clear roadmap for product managers and data professionals to analyze data effectively. By understanding the cause-and-effect relationships between metrics, we can make informed decisions that directly impact the product's success.
2. Prioritizing Metrics that Matter
With an overwhelming amount of data available, metric hierarchy helps us focus on the metrics that have the most significant impact on the product's success. By prioritizing and drilling down into actionable metrics, we can channel our efforts towards the areas that truly matter.
Real-Life Application: Optimizing a Music Streaming App
Let's consider a real-life example to illustrate the benefits of metric hierarchy for both product managers and data professionals.
Imagine being a product manager for a music streaming app, and you collaborate with data analysts to optimize user engagement. Your outcome metric is the Weekly Active Users (WAU), indicating how engaged users are with your app.
To understand and influence this metric, you explore the driver metrics, such as onboarding rate and key user actions like saving songs and sharing music. By diving deeper into actionable metrics, like the number of sessions per user and the number of songs saved, the data analysts uncover insights about user behavior.
Armed with this knowledge, both the product manager and data professionals can collaborate to focus on improving the onboarding experience and encouraging users to save more songs, thus driving up the WAU.
4 Steps for Setting Up a Solid Metric Hierarchy
- Identify Business Objectives
Start by aligning the metric hierarchy with the overarching business objectives. Ensure that each outcome metric reflects a specific goal that the product aims to achieve. - Map Cause-and-Effect Relationships
Understand the connections between outcome, driver, and actionable metrics. This mapping will help both product managers and data professionals see the bigger picture and identify the most impactful levers for success. - Drill Down with Data
Leverage data analytics tools to analyze the metrics at different levels. Use visualizations to identify trends and anomalies, uncovering insights that drive meaningful actions. - Iterate and Refine
As the product evolves, continuously review and refine your metric hierarchy. Business objectives may change, and new metrics may become relevant. Stay adaptable and adjust the hierarchy accordingly.
“So, how to make an actionable dashboard?”
Finally, Introducing the Actionable Dashboard — your gateway to deciphering the movement of metrics and gaining actionable insights. To understand why metrics change, follow these two essential steps:
Step 1. Dissect the Outcome Metrics
First, dissect the Outcome Metrics that reflect your business’s success from three perspectives:
- Acquisition Metrics
Examples include Free Trial Starts with a focus on the percentage of pricing page conversions. - Retention Metrics
Observe Day X Retention, along with metrics like Page Views, Searches, and Saved items to understand user engagement. - Monetization Metrics
Recurring Revenue, Conversion Percentage, Average Revenue Per User (ARPU), and the number of Paying Users to grasp financial success.
Mapping the Hierarchy of Outcome Metrics is the next crucial step. This ensures you identify the most critical indicators that influence the Outcome Metrics, enabling you to access actionable insights on your dashboard.
Step 2. Delve into each Outcome Metrics
Now, let’s delve into each Outcome Metric from different angles: channels, user characteristics, and user behavior.
Acquisition Metrics
For example, Weekly Signups can be divided into3 different channels:
- Channel 1 (e.g. Referral)
Examine user invitations based on geographical locations, platforms, and user personas. Work on enhancing the invitation process to boost sign-up conversion rates. - Channel 2 (e.g. Paid)
Evaluate impressions, click-through rates (CTR), and signup conversion rates. Optimize ad campaigns for improved results. - Channel 3 (e.g. SEO)
Analyze SEO traffic and signup conversion rates. Refine SEO strategies for increased organic sign-ups.
✅ Example
Product Managers can compare and optimize different channels for customer acquisition, leading to better results.
Monetization Metrics
Break down Revenue into two essential concepts — breadth and depth:
- Breadth (e.g. Paying Subscribers)
1. Existing Subscribers
2. New Subscribers : Total users * % trial CVR * % paid user CVR
3. Churn rates : voluntary churn through Net Promoter Score (NPS) and involuntary churn with the percentage of failed auto-renewals. - Depth
Average Revenue Per User (ARPU) : $ per plan * % of subscribers on plan
✅ Example:
For Product Managers aiming to improve purchase conversion rates, focus on reducing friction at the end of the trial version.
Retention Metrics
Weekly Active Users can be divided into four groups: New, Active, Dormant, and Recurring Users.
- New Active Users
Observe key stages of service usage
1. setup moment (% sign up to login)
2. aha moment (% login to use service)
3. habit moment (% use service to regularly). - Active Users
Focus on Key Actions (# of sessions, # of saving, # of sharing) - Dormant Users
Understand factors contributing to dormant behavior. - Recurring Users
Examine users who consistently return to the service.
✅ Example
By analyzing the user groups, Product Managers can find ideas to enhance retention and user engagement.
Comprehensive, efficient, and actionable — that’s the power of the Actionable Dashboard. It’s time to make decisions that are backed by metrics in a meaningful way. Its visual representation makes it easy to absorb information.
Taking full advantage of this tool could become a defining factor for the success of your business. Follow me on Medium for data-driven insights, data visualization tips, and more about the powerful BI tool Tableau!
