User Loyalty Analysis

Background

User loyalty refers to the degree of dependence and repeat consumption expectations that result from a combination of user stickiness, trust, and positive experiences with a brand or product. It is an important indicator for understanding the health of a product.

To understand whether the team's continuous improvement on the product boosts user loyalty, Zhang Ming from the product team plans to create a user loyalty dashboard to observe the loyalty trends.

Analysis Method

You can calculate the distribution of days for users using the product in a week. For example, you can calculate how many users are using the product for 2 days or 7 consecutive days a week.

You can calculate the trend of the number of users who log in more than 2 days a week over time. The more users who log in more than 2 days a week, the healthier the product is.

You can classify and view the loyalty of users by examining their status of Collaboration, Follow-up, and Potentiality.

Online preview link: User Loyalty Analysis. You can click the link and save the dashboard to view the internal editing steps or practice on your own.

Analysis Result

From the first component, you can see that the number of users who log in more than 2 days a week from week 05 to week 10 continues to increase. Combining this with the line chart on the right, it's evident that potential users show the most significant increase. The feedback from user loyalty indicates that the activities or product improvement during this period have a positive impact, especially for potential users.

The number of users who only log in once a week comprises 74.83%. Therefore, overall, the user loyalty is to be enhanced.

Obtaining Data

You can download the sample data: User Login Information.xlsx.

Data Preparation

1. Create an analysis subject and upload the sample data User Login Information.

2. Add data and select all the fields in User Login Information, as shown in the following figure.

3. Click Group Summary, as shown in the following figure. (The following steps are to deduplicate the data. If a user logs in multiple times a day, you only need to retain one record of that user.)

4. Click Formula Column to add a count column to mark each row of deduplicated data with 1, as shown in the following figure.

5. Click Group Summary, drag fields into Group and Summary, click the  icon next to the field Login Date, and select Number of Weeks in Year from the drop-down list to calculate the number of logins per user per week.

6. Click Save and Update.

Component Creation

Viewing the Trend of the Number of Users who Log in more than Twice a Week

1. Click the  icon next to the field Mobile and select Convert to Indicator from the drop-down list to obtain the deduplicated count on the Mobile data (namely the total number of users).

2. Perform Indicator Condition to the field Mobile (after the deduplicated count) to obtain the number of users who log in more than once a week.

3. Set Chart Type to Custom Chart, drag the field Login Date into Horizontal Axis, and drag the field Mobile into Vertical Axis to create a trend area chart.

You have successfully created the trend area chart showing the number of users who log in more than twice a week.

Viewing the Trend of the Number of Users in Different Status who Log in more than Twice a Week

Copy the component created in section "Viewing the Trend of the Number of Users who Log in more than Twice a Week", drag the field User Status into Color, and change Area to Line.

You have obtained the trend of the number of users in different status who log in more than twice a week.

For other components, you can save the dashboard shown in section "Analysis Method" to view the concrete steps.

Effect Display

For details, see section "Analysis Method."