Behavioral Analytics 101: Calculating Your Product Stickiness

Welcome to Indicative’s Behavioral Analytics 101 blog series! Consider these posts your crash course in answering fundamental marketing and product questions in Indicative. You will learn to:

✔ Understand how frequently users are returning to your site or product
✔ Flag issues in your customer journey to quickly mitigate negative effects 
✔ Optimize your customer journey by analyzing A/B test results
✔ Measure the impact of a specific step in your customer journey
✔ Master Dashboard tips and tricks

For your reference, we also created a series of bookmarks that correspond with each of the blog posts goals. You can find them in Indicative, in the left navigation under Bookmarks, in the Behavioral Analytics 101 folder. 

Today’s Goal

Understand product stickiness by calculating how frequently customers are active on your site or product.

What is Product Stickiness?

Product stickiness is when a customer becomes attached to a product that makes it difficult for them to leave.

Business Question

How can I understand how engaged users are with my product?

KPI

Daily Active Users (DAU) over Monthly Active Users (MAU)

Indicative Tool

Segmentation

Bookmark

Behavioral Analytics 101: Calculating Your Product’s Stickiness

Creating a user segment using the segmentation tool with a behavioral analytics product
1. In the Segmentation tool, start by dragging in an event that represents a customer using your site or product. In this case, let’s use Site Visit

2. In the query builder, click on Total count of and change this to Users who performed in the drop-down. The timeframe should be set to Yesterday, which means you are now viewing all unique users who logged in during the last full calendar day.

3. Next, turn this set of users into a User Segment by clicking on any point on the graph. You will see a drop-down menu where you will select Create User Segment. Label this segment Users Active Yesterday, and make the category Active Users. This is your DAU.

4. Be sure to select Dynamic: Update the users in this segment every day so that your segment will be updated daily.

Update your user segment each day using the segmentation tool in a behavioral analytics product
5. Now, click the three stacked dots on the right side of the query row, and click Duplicate Row. You will use a similar formula to create the next part of our equation: users active in the last 30 days (MAU). 

6. Drag in your segment called Users Active Yesterday into the Where section of the first row of your query. This will limit the query to the users active in this timeframe.

7. On the upper-lefthand corner of the graph, click the calendar icon, and change the time frame to Last 30 Days.

8. On the bottom right of each query row, you can name the query. The first row should be named Active Users Yesterday, and the second, Active Users Last 30 Days.

Use the calculator tool in segmentation in a behavioral analytics tool
9. Now for the fun part. Click the calculator icon in the upper right corner of the graph in the gray toolbar. Drag in Active Users Yesterday (DAU) into the numerator, and Active Users Last 30 Days (MAU) into the denominator. Make sure you change the division sign into a percentage sign. Then, press calculate.

10. Finally, change the time interval to Full Range, and choose the Table format. You will see your DAU, MAU, and the DAU:MAU Ratio! Based on this calculation, the ratio is 13.95%.

11. Be sure to click Save to Dashboard in the top right of your screen, so you can reference fresh numbers at a glance at any time.

Next up: Identifying points of friction in your customer journey. As always, check out our Knowledge Base if you have any questions.