Asana Ticketing Upgrade: Choosing a Primary Metric, Guardrails and a Test Design

Quick Overview

A product analytics interview question asking how to tell whether an improvement to Asana's ticketing and request-tracking workflow succeeded. It tests picking a primary success metric with adoption signals and guardrails, and designing a measurement plan that attributes the change to the feature.

Asana Ticketing Upgrade: Choosing a Primary Metric, Guardrails and a Test Design

Company: Asana

Role: Product Manager

Category: Analytics & Experimentation

Difficulty: medium

Interview Round: Other

Asana has shipped an improvement to one of the features in its ticketing system. As the PM who owns that area, how would you measure whether the improvement succeeded? ```hint Pin down the change before the metric Name the specific improvement and the person it was meant to help: the requester, the assignee working the queue, or the admin who set the queue up. Your success metric should follow from what is now supposed to be different for that person. ``` ### Clarifying Questions - Which feature was improved: how requests are submitted, how they are triaged and assigned, or how requesters are kept informed? If that is left open, may I pick one and state it? - Is the ticketing system the request-tracking workflow Asana offers to customer teams, or the internal desk Asana's own support team uses to answer customers? - Has the improvement already gone out to everyone, or can we still hold back a group of teams or queues for comparison? - Was there a stated goal behind the change, such as faster resolution, less manual triage or wider use of request intake? ### What a Strong Answer Covers - Which improvement is being measured, which user it serves, and the goal it was meant to move - One primary success metric with a precise definition (unit, time window, denominator), plus adoption signals showing whether teams actually use the feature - Guardrails that would catch speed bought at the cost of resolution quality, assignee overload or wider Asana engagement - A measurement design that attributes the change to the improvement despite opt-in adoption, shared queues and requests still open at readout time - A decision rule, and the segments (team function, queue volume, plan) where the result could differ ### Follow-up Questions - Your primary metric improved, but reopened requests went up. Did the launch succeed, and what would you do next? - The improvement went to every customer at once with no holdout. How would you still estimate its effect? - Which single number would you report to leadership each week for this area, and why that one?

Overview: A product analytics interview question asking how to tell whether an improvement to Asana's ticketing and request-tracking workflow succeeded. It tests picking a primary success metric with adoption signals and guardrails, and designing a measurement plan that attributes the change to the feature.

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Asana
Dec 3, 2024
mediumProduct ManagerOtherAnalytics & Experimentation
0
0

Asana has shipped an improvement to one of the features in its ticketing system. As the PM who owns that area, how would you measure whether the improvement succeeded?

Clarifying Questions Guidance

  • Which feature was improved: how requests are submitted, how they are triaged and assigned, or how requesters are kept informed? If that is left open, may I pick one and state it?
  • Is the ticketing system the request-tracking workflow Asana offers to customer teams, or the internal desk Asana's own support team uses to answer customers?
  • Has the improvement already gone out to everyone, or can we still hold back a group of teams or queues for comparison?
  • Was there a stated goal behind the change, such as faster resolution, less manual triage or wider use of request intake?

What a Strong Answer Covers Guidance

  • Which improvement is being measured, which user it serves, and the goal it was meant to move
  • One primary success metric with a precise definition (unit, time window, denominator), plus adoption signals showing whether teams actually use the feature
  • Guardrails that would catch speed bought at the cost of resolution quality, assignee overload or wider Asana engagement
  • A measurement design that attributes the change to the improvement despite opt-in adoption, shared queues and requests still open at readout time
  • A decision rule, and the segments (team function, queue volume, plan) where the result could differ

Follow-up Questions Guidance

  • Your primary metric improved, but reopened requests went up. Did the launch succeed, and what would you do next?
  • The improvement went to every customer at once with no holdout. How would you still estimate its effect?
  • Which single number would you report to leadership each week for this area, and why that one?