TARS: A Product Metric Game Changer




The first stage of the TARS framework focuses on this by first encouraging us to quantify the target audience by asking, “What percentage of all my product’s users have this specific problem my feature aims to solve?” For example, if we wanted to build an Export Button for our search results page, we may know from user interviews that around 10% of all users need to use their search results in another format to perform various offline tasks. In this case, our target audience would be 10%. (View Highlight)review

One excellent thing about measuring feature adoption is that it encourages product teams to align around what a good outcome looks like. I’m constantly surprised by how differently some people interpret what success looks like for a product, so setting expectations with a framework like TARS can help save many headaches later down the line. (View Highlight)review

The initial logarithmic decline from initial adoption occurs because after users adopt a feature, it still requires an effort to build a habit around using the feature. We want a flattened retention curve based on your user’s acquisition action — stable retention. Once the curve’s flattening is identified, we can establish your feature’s natural frequency use and benchmark if the expected number of users use the same feature weeks or months later. (View Highlight)review

According to Gartner research, 96% of customers who identify a product or service experience as high effort become disloyal, compared to only 9% who have a low effort experience. (View Highlight)review

The CES survey is a standard methodology that effectively asks some variation of the question, “How easy or difficult was it to complete a task?” Users can then respond on a scale of 1–5, from “more difficult than expected” to “much easier than expected”. Anything that scores 3–5, i.e. “as expected” to “much easier than expected”, should be considered a “satisfied” user experience. For CES to be most effective, our surveys should target only our retained users. This allows us to understand if there are any hidden problems not reflected in the features retention while focusing on improving retention independently. (View Highlight)review