Methodology

Data Collection Methodology

One important piece of measuring your features impact is data collection. In order to create the TARS metrics, you’ll need to set up a system for collecting that data. This can be through a handful of different product choices. In my mind there are three ways to track this data:

Front-End Click Tracking - Systems like

Heap⁠

Pendo⁠

allow PMs to tag clicks and page loads to track usage. This can be convenient as it requires less engineering effort however depending on the structure of your website or app the data may not be as precise as other options. These solutions are designed to reduce Engineering effort to a minimum.

Front-end Event Tracking - Products like

Amplitude⁠

Google Analytics⁠

also offer tracking on the browser-side however they do require some work on the front-end code. You’ll need to make sure your new features are built with tracking calls built in. Be sure to include the tracking requirements in your stories. These solutions strike a middle ground between Engineering effort, and precision.

Server Logging & Backend Event Tracking - Logging services provide a way at seeing how users are engaging with your services. Many of these services are not built explicitly for this kind of tracking and are usually more suited for Engineering teams but if you’re willing to put the time in you can pull similar metrics from products like

Splunk⁠

and

Elastic⁠

. These solutions can be very precise but can be costly from an engineering effort perspective, and if set up incorrectly can be inaccurate.

Importing Data to Coda and Target Setting

Data can be brought into Coda via a Pack Sync Table or an import from CSV. Once your data is loaded in you can set your ‘Target’ values in the

View of Feature Impact Metrics⁠

table. Once your ‘Target’ column is filled out you can go through each row and set up the math for ‘Actual Calculation’. Each Canvas has a formula that may need to be modified depending on how your data is formatted or what your signals for adoption, retention, and satisfaction look like. Simply click on the value under the column “Actual calculation” to open up the view for editing.

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Retained Users⁠

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Visitor Logins⁠

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Usage of Feature (Last 90)⁠

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CSAT⁠

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