Metrics question
You own a customer support agent from first production launch through enterprise-wide expansion. What success metrics would you track in the first 30-60 days versus six months later, and how would you balance business outcomes, customer experience, and operational reliability when those metrics conflict?
- Decagon
- Metrics
- Hard
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What this question tests
Tests defining a metrics framework that evolves over a product's lifecycle, from early adoption to mature operational reliability.
How to approach it
- For the first 30-60 days, track activation and early trust: containment rate, response accuracy, and initial CSAT on handled conversations.
- For the first 30-60 days, also track operational health: latency, uptime, and escalation rate, since early failures shape long-term trust.
- At six months, shift to business outcomes: cost per resolved conversation, deflection rate versus the human-support baseline, and expansion into new intents.
- At six months, add durability metrics: CSAT trend over time, not a single snapshot, and repeat-contact rate as a hidden failure signal.
- When metrics conflict, for example CSAT rising while cost-per-resolution stalls, treat CSAT as the guardrail and investigate the cost metric rather than trading away trust.
- Revisit the metric set explicitly at each stage rather than tracking the same dashboard throughout.
What a strong answer includes
- Distinguishes early trust-building metrics from later business-outcome metrics instead of one static dashboard for the whole lifecycle.
- Names a concrete guardrail rule, CSAT wins over cost when they conflict, showing a real prioritization stance.
- Flags repeat-contact rate as a hidden reliability signal that aggregate CSAT can mask.
Common mistakes
- Using the exact same metric set at launch and at six months.
- Optimizing operational cost metrics at the expense of a declining CSAT trend.
Likely follow-up questions
- How would you decide when a metric has stabilized enough to change your focus?
- What would you do if CSAT and containment moved in opposite directions?
More metrics questions
- A large customer has an AI agent live in production, but adoption is below plan and leadership is hesitating on expansion. What metrics would you review first, how would you isolate whether the issue is workflow selection, agent quality, operational rollout, or stakeholder buy-in, and what actions would you take in the next 30 days to improve adoption and demonstrate business impact?Decagon · Metrics · Hard
- You have inherited a new strategic account and must choose the first customer-support workflows to automate in production. What prioritization framework would you use to decide where the agent goes live first, and which adoption, quality, and business metrics would you require before recommending expansion into additional workflows or channels?Decagon · Metrics · Hard
- A live enterprise agent is generating strong customer demand for expansion, but engineers report unresolved reliability gaps in the current design. How would you decide what to ship next, including what evidence or thresholds you would require to expand safely, what you would defer, and how you would manage the conversation with the customer’s leadership team and internal engineering partners?Decagon · Metrics · Hard
- How would you define a metrics framework for Decagon’s developer experience across APIs, SDKs, and headless deployments? Specify the leading and lagging metrics you’d track from integration start through production launch, and explain how those metrics would change your roadmap priorities.Decagon · Metrics · Hard
- One of Decagon's largest customers has launched an agent, but adoption has plateaued because internal teams will not let it handle higher-value interactions. How would you diagnose whether the bottleneck is model quality, workflow design, integration gaps, or change management, and how would you decide which intervention to make first?Decagon · Metrics · Hard
- A newly launched enterprise agent has lower-than-expected adoption even though the pilot performed well. Walk through how you would diagnose the drop using funnel metrics such as routing, engagement, containment, handoff, CSAT, and resolution; separate product issues from change-management or workflow issues; and prioritize the first 2-3 changes needed to recover adoption and earn expansion.Decagon · Metrics · Hard
More questions from Decagon
Learn the skill behind it
Chapters of the AI PM course that teach what this question tests.
- Chapter 9: Prove it paid off: outcomes, economics, and pricing
- Chapter 2: Data fluency: SQL, logs, and reading the truth yourself
- Chapter 14: Get the job: the AI PM interview loop