User Stickiness Analysis

Background

User stickiness refers to the degree of dependence and likelihood of repeat consumption that result from a combination of user stickiness, trust, and positive experiences of a brand or product. It serves as an important indicator for assessing product health.

To understand whether a team's continuous improvement on the product boosts user stickiness, you (from the product team) plan to create a user stickiness dashboard to observe the stickiness trends.

Analysis Method

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

You can calculate the trend in the number of users logging in on at least two days within a week. The more users logging in more than 2 days a week, the healthier the product is.

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

Analysis Result

• The chart in the first component shows that the number of users logging in on more than 2 days in a week from Week 05 to Week 10 continues to increase. Combined with the line chart component on the right, the most significant rise is seen among potential users.The user stickiness indicates that the activities or product improvement during this period have a positive impact, especially for potential users.

• Users logging in on only one day in a week takes up 74.83%. From an overall perspective, user stickiness still requires improvement.

Example data

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

Data Processing

1. Create an analysis subject, and add the dataset User Login Information. For details, see Getting Started with FineBI.

2. Click Group Summary and drag the corresponding fields into the Group and Summary bars, respectively, 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.)

3. Click Formula Column, name the column Count, and mark each row of deduplicated data with 1, as shown in the following figure.

4. Click Group Summary, drag the corresponding fields into Group and Summary bars, respectively, click the icon next to the field Login Date, and select Number of Weeks in Year from the drop-down list to calculate the login days per user per week.

5. Click Save and Update.

Component Creation

Viewing the Trend of the Number of Users Logging in on More than Two Days in 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 of the Mobile data (namely, the total number of users), as shown in the following figure.

2. Add a condition to the field Mobile (deduplicated) for filtering to obtain the number of users logging in on more than one day in a week, as shown in the following figure.

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, as shown in the following figure.

In this case, you have successfully created the trend area chart showing the number of users logging in on more than two days in a week.

Viewing the Trend of the Number of Users Logging in on More than Two Days in a Week by User Status

Copy the component created in the "Viewing the Trend of the Number of Users Logging in on More than Two Days in a Week" section, drag the field User Status into the Color bar, and change Area to Line, as shown in the following figure.

In this case, you can obtain the trend of the number of users logging in on more than two days in a week by user status.

The remaining components are not introduced in this document.

Effect Display

For details, see the "Analysis Method" section.