The Feature Adoption dashboard widget provides insight on how much your visitors use your product. It also helps you answer which areas of your product that users find the most valuable.
Add the widget to the dashboard
- Navigate to a dashboard, then select + Add Widget in the top-right corner of the page.
- Select Feature Adoption from the list of available widgets.
- Update each field to your preference.
- Name. Enter a display name of the widget as it appears on your dashboard.
- Date Range. Select the time period where event usage is measured.
- Segment. Select the users included in the analysis for usage data. Larger segments generally result in less variable data.
- App. Select the application queried for event selection and product usage data.
- Benchmark. Configure the benchmark percentage to identify Features generating a significant portion of your click volume within the chosen timeframe. The default is set to 80%, but you can customize it between 1% and 99% to suit your analysis needs.
- Metric. Choose either Percent of Feature Clicks or Average Percent of Daily Feature Clicks to align the benchmark calculation method with your preferences.
- Select Save to add the widget to your dashboard.
For more information on general dashboard and widget functionality, see Dashboards.
Interpret the widget data
The Feature Adoption widget employs the Pareto principle, where 80% of the output is typically generated by 20% of the input. This principle guides the widget's functionality, helping you identify which features drive a significant portion of user engagement within your selected timeframe.
Percentage of Feature clicks
When you select Percent of Feature Clicks in the widget configuration, this calculation reveals the percentage of usage for each Feature relative to the total Feature usage within the specified date range.
To illustrate this calculation, consider the following example scenario represented in the following table. In this example:
- There are four Features tagged.
- The date range spans three days.
- The benchmark is set at 80%.
| Day 1 | Day 2 | Day 3 | Total | |||||
|
Feature |
Clicks | Volume | Clicks | Volume | Clicks | Volume | Clicks | Volume |
| A | 4,000 | 20% | 5,000 | 22.7% | 6,000 | 24% | 15,000 | 22.4% |
| B | 12,000 | 60% | 12,500 | 56.8% | 13,000 | 52% | 37,500 | 56.0% |
| C | 1,000 | 5% | 1,500 | 6.8% | 2,000 | 8% | 4,500 | 6.7% |
| D | 3,000 | 15% | 3,000 | 13.6% | 4,000 | 16% | 10,000 | 14.9% |
| Total | 20,000 | 22,000 | 25,000 | 67,000 | ||||
In this table, you can see each Feature's engagement levels within the specified timeframe:
- Each Feature's click volume and corresponding percentage are shown for each day.
- The Total column aggregates the clicks and volumes across all days.
- The Volume percentage represents each Feature's contribution to the total click volume.
Average percentage of daily Feature clicks
When you choose Average Percent of Daily Feature Clicks in the widget configuration, it calculates the average percentage of clicks per Feature based on the usage for each Feature per active day within the chosen date range. This calculation then normalizes the average percentages per Feature to identify the top Features that make up the 80% (or your specified benchmark).
To illustrate this calculation, consider the following example scenario:
- There are four Features tagged.
- The date range spans three days.
- The benchmark is set at 80%.
On the first day, the percentage of click volume is calculated by dividing the total number of clicks from each Feature by the total click volume, resulting in the percentage of clicks for each Feature.
| Feature | Clicks | Volume |
| A | 4,000 | (4,000/20,000) x 100 = 20% |
| B | 12,000 | (12,000/20,000) x 100 = 60% |
| C | 3,000 | (3,000/20,000) x 100 = 15% |
| D | 1,000 | (1,000/20,000) x 100 = 5% |
| Total | 20,000 | 100% |
This calculation is repeated for each day within the date range. The normalized average of the percentages for each Feature across all days is then calculated: (Average/Average Total) x 100.
|
Feature |
Day 1 | Day 2 | Day 3 | Average | Normalized average |
| A | 20% | 22.7% | 24% | 22.2% | (22.2/99.9) x 100 = 22.2% |
| B | 60% | 56.8% | 52% | 56.3% | (56.3/99.9) x 100 = 56.4% |
| C | 15% | 6.8% | 8% | 9.9% | (9.9/99.9) x 100 = 9.9% |
| D | 5% | 13.6% | 16% | 11.5% | (11.5/99.9) x 100 = 11.5% |
| Total | 20,000 | 22,000 | 99.9% | 100% |
Feature list
The list of Features on the right side of the widget is generated based on the percentage of all clicks in descending order. Using the same example scenarios mentioned earlier for both metrics (percentage of Feature clicks and average percentage of daily Feature clicks), your top Features list comprises the Features that collectively contribute to to 80% (or your specified benchmark) of the total click volume
Percent of Feature Clicks
In the following table, Features B and A account for 80% of the click volume:
| Feature | Click percentage |
| B | 56.0% |
| A | 22.4% |
| D | 14.9% |
| C |
6.7% |
| Total | 100% |
Average Percent of Daily Feature Clicks
Similarly, in the following table, Features B and A represent 80% of the click volume, where the normalized average = percentage sum / number of active days:
|
Feature |
Day 1 | Day 2 | Day 3 | Normalized average |
| B | 60% | 56.8% | 52% | 56.4% |
| A | 20% | 22.7% | 24% | 22.2% |
| D | 5% | 13.6% | 16% | 11.5% |
| C | 15% | 6.8% | 8% | 9.9% |
| Total | 100% |
If the Features don't exactly sum up to 80% (or your specified benchmark percentage), Pendo identifies the Features that contribute volume as close to 80% as possible.
Tip: Quickly discern trends by reviewing the Product Areas assigned to your Features. This can help you understand which parts of your product users find more valuable than others at a glance.