Stickiness metric is a dashboard widget to help you measure customer engagement with your product. This widget will visualize engagement in terms of percentages so you can correlate customers who realized value with the higher average percentage over a period of time.
How Do I Add this Widget to My Dashboard?
To add this widget, navigate to the dashboard and click on the “+ Add Widget” button in the top-right corner. Then, choose "Stickiness Metric" as your option.
Then, you will see the following configuration options:
- Name - Add a custom name as desired
- Metric - Choose the type of metric you want to use
- Date Range - Choose which date range the widget should use
- Segment - Select your custom segment the widget should use
- App - If you have multiple apps, choose which app the widget should use
The acronyms in these options stand for:
- DAU - Daily Active Users
- WAU - Weekly Active Users
- MAU - Monthly Active Users
So in the metric dropdown, you will be asked to choose one of the following options:
- DAU/WAU - Daily Active Users compared to Weekly Active Users
- DAU/MAU - Daily Active Users compared to Monthly Active Users
- WAU/MAU - Weekly Active Users compared to Monthly Active Users
Each option will display the overall average percentage of eligible visitors within your selected timeframe. You will also see a line graph accompany the average percentage based on the selected date range. Hover over each data point to see the percentage for that timeframe.
Daily/Weekly/Monthly Active Users Calculation
Daily Active Users are defined as the total number of unique users who visit your product everyday. In Pendo, if a visitor has one event, then he/she is counted in our daily unique visitors bucket for the day.
Similarly, Weekly Active Users are all the unique users who visit your product in the last 7 days. Monthly Active Users are all unique visitors within the last 30 days.
Using these definitions, we can derive any ratio that you want for every single day. This is what you see in the graph. For example, if your 3-day range has the following DAU, WAU and MAU values:
Using these example daily values, you can derive the following ratios for every day:
Depending on what metric you chose when you setup your widget, these are the values you will see in the graph. Hover over the points in your line graph to see the average ratio. You can use this to see how every date range is performing compared to the average.
Why Does This Metric Use Percentages?
Percentages are used because B2B (business-to-business) companies typically have a product that is seasonal in nature.
For example, imagine a product that caters to the education market in the United States. Based on when Schools are in session, you will have more activity in the Fall than the Summer. To illustrate, if this product shows 9,000 Daily Active Users (DAUs) and 10,000 Monthly Active Users (MAUs). When using the DAU/MAU metric, your percentage is 90%.
Summer is usually when students have a long vacation period unless schools have Summer sessions. In the Summer, this product shows DAUs drop to 90 and MAUs drop to 100. When using the DAU/MAU metric, your percentage is still 90%. Using percentages show that your product is still valuable in the Summer, even if you have less traffic.
When Would I Use It?
Through this widget, you can understand if your product is used daily, weekly or monthly by users. You can set goals within your company to increase the ‘stickiness’ of your product by seeing more users become Daily Users.
Use this widget in the following example scenario:
Product and Engineering can ensure that they deliver value to the customers by influencing the DAUs for each day. Whenever you add new or updated features that add value to the customers, you can easily see that your product becomes more sticky by watching the percentages increase.
Customer Success can see if their adoption campaigns are resonating with their customers by seeing an increase of daily or weekly active users in the product.
- Sales and Marketing can see the impact of their demand generation campaigns by introducing new users from converted trials. You can see a spike(s) in your graph due to the new users so you can see how it affects your overall average.