Business Context

Asana is a B2B SaaS work management product used by teams ranging from 3-person startups to 10,000+ employee enterprises. Assume Asana has ~1.5M monthly active users and sells primarily via self-serve (credit card) for SMB and via sales-assisted contracts for mid-market/enterprise. Leadership is launching a new acquisition push (paid search + partner referrals) expected to add 20,000 new workspaces per month. Finance and Growth want to know how much they can afford to spend to acquire a new customer without hurting payback targets.

Metric Scenario

You are the analytics lead supporting Growth and Monetization. A new onboarding flow and a “Teamwork Starter Pack” template library were launched 6 weeks ago. Early indicators look strong (trial-to-paid conversion is up), but it’s too soon to observe full churn behavior or long-term expansion.

Stakeholders ask:

• CFO: “What is the LTV of a new Asana customer acquired this quarter, and what CAC can we support if we want <12-month payback?”
• VP Growth: “Does LTV differ by acquisition channel (paid search vs organic vs partner) enough to reallocate budget?”
• Head of Sales: “For sales-assisted deals, should we prioritize accounts that show early activation signals, even if initial contract value is smaller?”

You have access to historical cohorts (3+ years) and the last 6 weeks of data for the new onboarding experience. You must propose a practical LTV approach that works with right-censoring (customers not yet churned), expansion revenue, and different billing cadences.

Requirements (what you must produce)

1. Define “customer” and LTV scope: Decide whether LTV is per workspace, per paying account, or per seat; clarify whether you’re estimating gross LTV (revenue) or contribution-margin LTV (revenue minus variable costs like support and payment processing).
2. Choose an estimation approach suitable for a new cohort with limited maturity:
   • Cohort-based realized LTV with censoring adjustments
   • Survival/hazard model for churn + expected expansion
   • Simple heuristic (ARPA × gross margin ÷ churn) and when it is acceptable
3. Decompose LTV into actionable drivers (conversion, retention, expansion, price/plan mix, discounting, margin) and specify which teams can move each driver.
4. Propose how to use early signals (activation within 7/14 days, number of active seats, project creation, integrations) to predict long-run retention/expansion and reduce uncertainty.
5. Recommend decisions: Provide how you would set CAC targets by channel, define payback period, and specify guardrails (e.g., support load, refund rate, delinquency).

Data Available

Constraints:

• You need an answer in 10 business days for budget planning.
• The new onboarding cohort has only 6 weeks of observed retention.
• Annual plans create revenue recognition timing issues; assume you can use cash collected for LTV unless you justify an alternative.