Alienation Analysis

Overview

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. You can analyze your full audience or focus on specific segments for more targeted insights.

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 access the feature, navigate to Insights > Analysis > Alienation Analysis. In the Settings sidebar, you'll configure your analysis using the following controls:

• Control: Select the baseline product (the current or original product that serves as the baseline for comparison)
• Test: Select the product you're evaluating (the new or modified product to see how it might affect user preferences and purchase intent)
• Filter: Optionally select a segment to focus the analysis on a specific audience subset

Filtering by Segment

The segment filter enables you to focus your alienation risk assessment on specific audience subsets. This is particularly useful when projects contain multiple research types or diverse user populations.

To apply a segment filter:

1. In the Settings sidebar, locate the Filter dropdown
2. Select a segment from the list to filter responses
3. The analysis automatically refreshes to show results for only that audience subset
4. The sample size (n) and all statistics update to reflect the segmented population
5. Clear the filter to return to analyzing the full population

Tip: Create and manage segments via Insights > Segments. You can also click Create Segments below the filter dropdown to navigate directly to segment management.

When no segment is selected, the analysis includes all eligible responses. When a segment is selected, only responses from users matching the segment criteria are included.

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 shows counts and proportions for Liked, Disliked, Alienated, and Noise groups, along with a total column displaying 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.

When a segment is applied, all values in the table reflect only the segmented population. This allows you to compare alienation risk across different audience groups by running separate analyses for each segment.

Interpreting the Verdict

Alienation Analysis provides a clear verdict based on the comparison between Alienated and Noise groups:

• High Potential for User Loss: A higher percentage of users are alienated than fall into the noise category, indicating the test product may cause user loss
• Low Potential for User Loss: The alienated percentage is lower than or equal to the noise, suggesting the product change is safer to implement

When a segment is selected, the verdict is calculated based only on the segmented population. A segment showing "High Potential for User Loss" means more users within that segment specifically were alienated than benefited from the change.

Applying the Insights

Use Alienation Analysis to evaluate if a new product risks losing loyal users. A higher Alienated percentage compared to Noise suggests caution, as more users may reject the test product. A lower percentage indicates the change is safer to implement.

Example use cases for segment filtering:

• Assess alienation risk specifically among "Early Adopters" or "Power Users"
• Evaluate product changes separately for different demographic segments
• Focus analysis on users who match specific behavioral criteria
• Compare alienation across segments to identify which user groups are most impacted

For complementary analysis, consider using Penalty Analysis to identify which specific attributes might be driving alienation.

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