Improved examination of the influence of product attributes on customer retention
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In the realm of product analysis, understanding the retention of features is crucial. A recent study has shed light on the distribution of product features based on usage and retention. Here's how a well-designed scatter plot can help visualise this data.
The scatter plot in question combines two essential metrics: the percentage of returned users (a qualitative metric) and the number of users (a quantitative metric). To ensure clarity, follow these guidelines:
- Appropriate Axis Assignments: Place the truly quantitative variable (number of users) on the X-axis and the percentage returned users (a ratio/percentage, which can be treated as a continuous variable) on the Y-axis.
- Color Coding or Distinct Markers: If the "qualitative" aspect refers to categories or user segments within returned users, differentiate these groups using different colors or symbols to add clarity and enable visual separation of segments.
- Clear, Descriptive Title, Axis Labels, and Legends: This helps viewers immediately understand what each axis represents and what the colors or markers signify.
- Add Trendlines or Smoothing Lines: To show the overall relationship or trend between percent returned users and number of users, a trendline can clarify patterns in retention effectiveness.
- Highlight Outliers or Notable Points: For example, if certain features have exceptionally high retention but low user count, size the points differently or annotate these to draw attention.
- Simplify and Declutter: Remove unnecessary gridlines, limit color palette to a few distinguishable hues, and keep labels concise to reduce cognitive load.
- Consider Additional Visual Encoding If Needed: Sometimes adding point size to represent a third variable or adding small multiple plots (facets) can help interpret complex multi-metric data.
After applying 50/80 percentile rules, 9 clusters of product features were identified in the scatter plot. The X-axis measures the % users, representing the popularity of a product feature, while the Y-axis measures the % returned users, representing the value of a product feature.
The first two product features with the highest % returned users have a modest volume in terms of users. These are the "Niche" features, a special subset that brings enormous value to a limited number of users (11.3%).
On the other hand, "Power2" features bring as much value as core features but are less popular. These features have a percentage of users within the 50-80th percentile and a high percentage of returned users.
The "Core" features, a small number (2-3), are used by more than 80% of users and have a high percentage of returned users.
"Casual1" features are used from time to time, with a usage percentage within the 50-80th percentile and a usage percentage of returned users within the same range.
Lastly, there's the "Set-up" features, a unique subset designed to set up a product for convenient usage. These features are used by a lot of users, but this usage typically happens only once, during the onboarding phase.
This problem is common in many analyses, where a quality measure is presented without a quantity measure. Combining both qualitative and quantitative metrics into one chart can help fix this issue, providing a more comprehensive understanding of product feature retention.
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