# A Data Scientist's Guide To Measuring Product Success

DevFeed: [A Data Scientist's Guide To Measuring Product Success](<https://devfeed.tech/articles/a-data-scientist-s-guide-to-measuring-product-success-1281.md>)

Original publisher: [Read original article](<https://shopify.engineering/a-data-scientist-s-guide-to-measuring-product-success>)

Author: Willie Costello

Published: 2022-03-23T20:27:11Z

Content type: tutorial

Language: en

Sources: [Shopify Engineering](<https://devfeed.tech/sources/shopify-engineering.md>), [Shopify Engineering - Shopify Engineering](<https://devfeed.tech/sources/shopify-engineering-shopify-engineering.md>)

Topics: [data](<https://devfeed.tech/topics/data.md>), [benchmarking](<https://devfeed.tech/topics/benchmarking.md>), [Shopify](<https://devfeed.tech/topics/shopify.md>)

Tags: [data](<https://devfeed.tech/tags/data.md>), [guide](<https://devfeed.tech/tags/guide.md>), [measurement](<https://devfeed.tech/tags/measurement.md>), [metrics](<https://devfeed.tech/tags/metrics.md>), [performance](<https://devfeed.tech/tags/performance.md>), [shopify](<https://devfeed.tech/tags/shopify.md>)

## AI overview

This guide presents a framework for data scientists to measure product success after release. It distinguishes product performance metrics, such as data freshness, pipeline speed, and model accuracy, from measures of a product's impact on its users. The article recommends starting with end-user goals and organizing them into main goals and supporting subgoals.

## Source excerpt

If you're a data scientist on a product team, much of your work involves getting a product ready for release. You may conduct exploratory data analyses to understand your product's market, or build the data models and pipelines needed to power a new product feature, or design a machine learning model to unlock new product functionality. But your work doesn't end once a product goes live. After a product is released, it's your job to help identify if your product is a success.