Evaluate User Loss Risk from Product Changes
Alienation Analysis in Insights helps you assess the risk of losing current users due to product changes by comparing Overall Liking (Liking + Purchase Intent) between products. Available under the Analysis tab once surveys are closed and all responses are collected, this tool is designed for Alienation Assessor research projects.
Accessing Alienation Analysis
Alienation Analysis becomes available after your surveys close and fielding ends, ensuring all responses are in. It’s exclusive to Alienation Assessor research, which requires a specific recruit: all participants must review both the control (the existing or baseline product) and the test (the new or modified product), and they must be current users of the control. To start, select which product is the control and which is the test in the analysis settings.
Defining Overall Liking
Overall Liking scores determine the categories:
- High Overall Liking: Indicates liking and purchase intent (e.g., top 4 box for a 9-point scale, top 3 box for 7-point, top 2 box for 5-point).
- Low Overall Liking: Indicates dislike and no purchase intent (e.g., bottom 5 box for 9-point, bottom 4 box for 7-point, bottom 3 box for 5-point).
These thresholds help identify which users are satisfied or dissatisfied with each product.
Understanding the Analysis
Alienation Analysis uses Overall Liking scores to compare user satisfaction between the control and test products. It categorizes respondents into four groups based on their overall liking scores:
- Liked: Both control and test are above neutral.
- Disliked: Both control and test are neutral or below.
- Alienated: Control is above neutral, test is neutral or below.
- Noise: Control is neutral or below, test is above neutral.
The analysis focuses on the Alienated and Noise groups to gauge risk. A higher Alienated percentage compared to Noise indicates potential user loss, while a lower percentage suggests the change is safer.
Interpreting the Table
After selecting the control and test, you’ll see a table breaking down the four categories. The table will show counts and proportions for Liked, Disliked, Alienated, and Noise groups. There is also a total column that displays the total population size for context.
The table helps you see how many users fall into the Alienated group, those who like the control but not the test, compared to the Noise group, which reflects natural variability. This comparison drives the risk assessment.