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Marketing analytics

B2B activation and retention cohort analysis from product usage data

Analyze account and user cohorts by acquisition source, segment, onboarding behavior, activation milestone, retention, and expansion signal.

Output
professional marketing deliverable
Inputs
8 required
Prompt depth
747 words

Copy-paste prompt

Replace the bracketed inputs with your real material.

You are a senior B2B marketing analytics lead. Produce a practitioner-ready deliverable for: B2B activation and retention cohort analysis from product usage data.

Objective
Analyze account and user cohorts by acquisition source, segment, onboarding behavior, activation milestone, retention, and expansion signal.

Required practitioner depth
The work connects marketing acquisition to product behavior and account retention. The prompt requires cohort eligibility rules, denominator checks, account-versus-user separation, and actionable lifecycle interventions.
Do not collapse this assignment into a generic summary, brainstorm, or list of...

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What you provide

  • Company context
  • Analysis period
  • Source exports
  • Metric and stage definitions
  • Segment definitions
  • Known data issues
  • Decision thresholds
  • Task-specific evidence

What you receive

  • Primary deliverable that fully executes this job: Analyze account and user cohorts by acquisition source, segment, onboarding behavior, activation milestone, retention, and expansion signal.
  • Practitioner-depth requirements: The work connects marketing acquisition to product behavior and account retention. The prompt requires cohort eligibility rules, denominator checks, account-versus-user separation, and actionable lifecycle interventions.
  • Decision-safe analysis table with definitions, denominators, confidence, and limitations.
  • Action plan with owners, leading indicators, and validation checks.
  • Executive readout separating supported decisions from unsupported conclusions.

Quality checks

  • Recalculate totals and denominators before interpreting performance.
  • Keep account, contact, lead, opportunity, and revenue grains separate.
  • Mark every causal statement that the data cannot prove.
  • Do not create benchmarks that were not supplied.
  • Missing or conflicting inputs are surfaced rather than silently resolved.
  • Every material recommendation has evidence, confidence, owner, and a validation or reversal condition.