Define success metrics for knowledge search assistant serving local merchants

Problem Statement Description

You are evaluating a knowledge search assistant built for local merchants—such as small retailers, restaurants, service providers, or marketplace sellers—who need quick, trustworthy answers to run their business. The assistant may help them find information about onboarding, account setup, payments, listings, inventory, policies, promotions, customer issues, or operational best practices.

The business goal is activation: determining whether new or newly exposed merchants reach a meaningful first-use milestone that indicates they understand the assistant’s value and are likely to keep using it. The challenge is to define a metric framework that distinguishes shallow usage, such as opening the assistant once, from real activation, such as successfully resolving a merchant need through search or assistant-guided discovery.

Your task is to describe how you would measure success for this product, including the primary activation metric, supporting diagnostic metrics, quality metrics, and guardrails. The metrics should be useful for product decisions, experimentation, and ongoing monitoring across different merchant types and use cases.

The metric framework should consider:

- A clear activation definition, including the user action or outcome that proves the merchant received meaningful value.

- The denominator: which merchants are eligible to be counted, such as new merchants, invited merchants, onboarded merchants, or merchants with a relevant support or setup need.

- Instrumentation needed across the journey, including exposure, query submission, result interaction, assistant response, follow-up action, and task completion.

- Cohorts and segments, such as merchant size, industry, tenure, geography, digital maturity, acquisition channel, and use case category.

- Quality signals for search and assistant responses, including relevance, accuracy, confidence, freshness, and whether the merchant had to reformulate or abandon the query.

- Merchant trust and safety guardrails, especially around incorrect business guidance, policy misinterpretation, payments, compliance, privacy, or AI-generated responses.

- Retention or repeat-use indicators that help validate whether activation predicts longer-term engagement or operational value.

- Decision usefulness: how the metric set would help diagnose whether low activation is caused by awareness, onboarding, query understanding, content gaps, response quality, or merchant workflow fit.

The goal is to present a metrics approach that is specific enough to guide product teams, data teams, and leadership in evaluating whether the assistant is helping local merchants become successfully activated, while avoiding vanity metrics that overstate usage without proving merchant value.