Growth·3 questions

QUESTION: Growth: how to choose a North Star metric and set up analytics?

Answer

Choosing the right North Star metric and setting up end-to-end analytics determine the vector of long-term growth and sustainable development of a product company. Regarding growth, it is most effective to start by formulating a key hypothesis and a measurable success metric that reflects the real value of the product for the end user. Such a metric should connect the frequency of service usage, the depth of engagement, and the customer's willingness to recommend the product to others. For example, for a streaming service, this could be the number of music listening hours per week, and for a SaaS platform, the number of successfully completed work tasks.

After defining the North Star metric, you need to configure the analytics system so that you can see not only general traffic indicators, but also the detailed product funnel. Do a quick test using a minimum viable product, a landing page, or a series of interviews to check how changes affect the chosen growth indicator. Make management decisions exclusively based on collected data, rather than the team's intuitive assumptions. It is important to eliminate "vanity metrics" that look great on charts but do not correlate with real revenue and customer retention.

Analytics setup should cover all touchpoints of the user with the product, from the first ad click to subscription payment and regular return to the service. Set up cohort analysis to track the behavior of various user groups over time and understand which specific updates improve retention. Regularly audit the accuracy of data collection, as errors in event tracking can lead to making false strategic decisions and slowing down the overall business growth.

Formulate a growth hypothesis and choose one main North Star metric that reflects the value of the product.
Set up analytics tools to track all stages of the user journey and the product funnel.
Conduct quick tests of changes using an MVP or landing pages to test hypotheses on real data.
Implement cohort analysis to monitor user retention and the effectiveness of product updates.
Make scaling decisions only based on verified analytical data and unit economics metrics.
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