# User Stickiness

In this article, we will define what is User Stickiness (i.e. User Retention) in Google Analytics 4 and explain how to track it with Rows.

## What is User Stickiness

User stickiness, or User Retention, is one of the most important, if not THE most relevant, metric in the digital industry. It measures how users "stick" to your product, namely how frequently users return over a given period.

It's a key indicator of user engagement and loyalty, reflecting how habit-forming your product is for your users.

User stickiness is typically expressed as a ratio or percentage, with common formulas including Daily Active Users (DAU) to Monthly Active Users (MAU), DAU to Weekly Active Users (WAU), or WAU to MAU.

The most appropriate measure often depends on the product type and industry. For instance, a B2C social network might focus on daily engagement (DAU/MAU), while a B2B productivity tool might prioritize weekly usage (WAU/MAU). Similarly, an e-commerce platform might track monthly active buyers, while a news app could emphasize daily active readers.

Let's go through a few examples of how to compute User Stickiness.

## How to compute User Stickiness

User stickiness can be computed in several ways, depending on the specific needs of your business and the nature of your product.

Here are some common methods:

1. DAU/MAU Ratio: The most common formula is DAU/MAU

1. For example, if you have 1,000 daily active users and 10,000 monthly active users: User Stickiness = (1,000 / 10,000)
100 = 10%

2. DAU/WAU Ratio: (Daily Active Users / Weekly Active Users)

1. If you have 500 daily active users and 3,000 weekly active users: DAU/WAU = (500 / 3,000)
100 = 16.67%

3. WAU/MAU Ratio: (Weekly Active Users / Monthly Active Users)

1. If you have 3,000 weekly active users and 10,000 monthly active users: WAU/MAU = (3,000 / 10,000)
100 = 30%

4. Returning User Rate: (Number of Returning Users / Total Number of Users)

1. If you have 500 returning users out of 2,000 total users: Returning User Rate = (500 / 2,000)
100 = 25%

5. Cohort Analysis: Cohort analysis is a visual method that lets you group users based on a share characteristic (i.e. signup date) and track them over time to see how many return daily, weekly, or monthly. For instance, out of 1,000 new users in January:

• 800 returned in February (80% stickiness)
• 600 returned in March (60% stickiness)
• 500 returned in April (50% stickiness)
6. Retention Rate: Implied in the above-mentioned cohort analysis is the retention rate, calculated as (Users at End of Period - New Users During Period) / Users at Start of Period

1. If you start with 1,000 users, gain 200 new ones, and end with 900: Retention Rate = (900 - 200) / 1,000
100 = 70%

Choosing the right stickiness metric depends on different factors, including:

• Product Usage Frequency: DAU/MAU is ideal for products used daily (social media, messaging apps). WAU/MAU suits products with expected weekly use (project management tools, fitness apps).

• Business Model: Subscription-based services might focus on WAU/MAU to ensure regular engagement. Ad-supported platforms might prioritize DAU/MAU for daily ad impressions.

• Industry Standards: Choosing the right metric lets you compare yours with industry benchmarks to gauge performance.

• Product Lifecycle: Early-stage products might use shorter-term metrics (DAU/WAU) to quickly iterate, while mature products might use longer-term metrics (WAU/MAU) for stability.

## How to track User Stickiness in GA4

Google Analytics 4 (GA4) provides several ways to track and analyze user stickiness:

1. Engagement Overview Report:

• Navigate to Reports > Engagement > Overview
• This report shows metrics like engaged sessions per user and engagement rate, plus a chart that plots DAU/MAU, DAU/WAU and WAU/MAU ratios over time:

1. Retention Report:

• Go to Reports > Retention
1. Custom Report:

Create a custom report to compare DAU and MAU:

• Metrics: Active Users
• Dimensions: Date
• Set date range to last 30 days
• Create two date comparisons: 1 day and 30 days
1. Explorations:

• You can use the Exploration tool and pick a cohort analysis template or create custom cohort analyses and visualizations of user stickiness metrics.
1. BigQuery Export:
• For more advanced analysis, you can export your GA4 data to BigQuery and create custom queries to calculate the most appropriate user stickiness metric.

## How to use Rows to track User Stickiness

As just seen, User Stickiness can be analyzed using Google Analytics 4 native console, but Rows offers a more flexible and user-friendly approach, that leverage the familiar features of spreadsheets.

Rows is the easiest way to access, transform and share your business data. It works 100% as a spreadsheet, but lets you bring live data from 50+ sources, including GA4's User Stickiness, directly into your spreadsheet, thanks to built-in API integrations.

This makes it extra easy to track the performance of your user acquisition channels, and overall product stickiness.

Rows has a template that makes life extra easy: GA4 Retention Report.

This template provides a pre-built structure that uses GA4 live data for analyzing user retention, saving you time and effort in setting up your own reports.

Here's how the template is composed:

• Full Data page includes two tables that break down DAU/MAU and DAU/WAU by date and First User Default Channel Group and New vs Returning users by date.
• Another table pivots the previous ones offering weekly averages
• Charts plot then the metrics over time, to better spot trends and spikes.

You can further customize this report by

• Clicking on the Full Data page tables option menu "..."
• Click on Edit Data Table, then select Data Request

• Finally customize the data request configuration by selecting new metrics or dimensions

Rows offers other templates specifically designed for marketing analytics, to offer you a head start on several analytics goals, including:

These templates can help you dive deeper into various aspects of user behavior and marketing performance, complementing your user stickiness analysis and providing a more comprehensive view of your marketing efforts.

## Why to use User Stickiness

User stickiness is crucial for several reasons:

1. Indicates Product Value:

High stickiness suggests users find consistent value in your product and use it regularly. This means that you've successfully addressed your users' pain points and met their needs effectively. For example, if a project management tool has high stickiness, it likely means the tool is genuinely helping teams collaborate and manage their work more efficiently.

User stickiness is critical for subscription-based or ad-supported models, where frequent usage is key. High retention supports these models and significantly contribute to make Customer Acquisition Cost (CAC) sustainable. Indeed, sticky users are more likely to renew subscriptions or generate consistent ad revenue, improving the lifetime value (LTV) to CAC ratio.

For example, consider a mobile game with low retention:

CAC: \$5 per user Monthly subscription: \$10 Average user lifetime: 2 months In this case, the LTV is \$20 (2 months * \$10), and the CAC is \$5, resulting in a 4:1 LTV:CAC ratio. While positive, this is barely sustainable, especially considering other operational costs.

If retention improves and the average user lifetime extends to 6 months:

LTV increases to \$60 (6 months * \$10) LTV:CAC ratio becomes 12:1 This improved ratio allows for higher marketing spend, better profitability, and overall business sustainability. Without strong user stickiness, the company might find itself constantly churning through users and struggling to maintain profitability.

1. Predicts Growth: Sticky users are more likely to report their positive experiences to friends and colleagues, ultimately generating word-of-mouth marketing. This organic promotion is the foundation of sustainable growth and may actually generate that kind of exponential growth we experienced with very successful products, like Facebook or LinkedIn. It's often more effective and cost-efficient than paid advertising. For example, popular social media platforms often grow rapidly through word-of-mouth as satisfied users invite their friends to join.

2. Forecasts Revenue: Sticky users are more likely to convert or make repeat purchases. In subscription-based models, they tend to maintain their subscriptions for longer periods. This predictable user behavior allows for more accurate revenue forecasting and helps in planning future business strategies. For instance, an e-commerce platform with high user stickiness can more reliably predict future sales based on historical purchasing patterns of loyal customers.

3. Informs Product Development: Low retention often highlights areas for improvement in your product, providing valuable insights for your product and engineering teams. By analyzing where and why users drop off, you can identify features that need enhancement or new functionalities that should be added. For example, if a fitness app notices a drop in stickiness after users achieve their initial goals, it might indicate a need for more advanced features or long-term goal setting capabilities.

4. Benchmarks Performance: User stickiness allows for comparisons with industry standards or competitors, which is key when evaluating funding opportunities, mergers, acquisitions, etc. Investors and potential business partners often look at user stickiness as a key metric to gauge a product's market fit and potential for long-term success. For instance, a startup with higher user stickiness than the industry average might be more attractive to venture capitalists, as it demonstrates strong product-market fit and potential for sustainable growth.