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Challenge

Automated GTM Funnel Metrics & Revenue Alignment Auditor for Fintech SaaS

Aligning product usage metrics with Go-To-Market (GTM) strategy is critical for B2B fintech SaaS providers scale sales pipelines without increasing churn. Misalignment between customer acquisition cost (CAC), net revenue retention (NRR), and platform API consumption leads to mispriced enterprise contracts. In this challenge, you will implement a GTM alignment auditor using LangChain. The agent will analyze product telemetry alongside customer billing records to detect pricing leakage, underutilized contract tiers, and churn risk indicators.

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Challenge brief

What you are building

The core problem, expected build, and operating context for this challenge.

Build a LangChain agent to analyze B2B fintech SaaS revenue metrics and identify GTM misalignment.

Delivery guide

How work is evaluated

Evaluation

Evaluates calculation accuracy for SaaS metrics and alignment recommendations.

Datasets

Challenge datasets

Public datasets, plus the private datasets you have access to.

Evaluation rubric

How submissions are scored

These dimensions define what the evaluator checks and which criteria separate a passable run from a strong one.

Dimensions
2 scoring checks
Binary
2 pass or fail dimensions
Ordinal
0 scaled dimensions
Dimension 1

nrr_calculation_exact

Verifies NRR calculation matches formula exactly

Binary check

This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.

Dimension 2

math_precision_error

Mean absolute percentage error on numerical metric estimations • target: 0 • range: 0-100

Binary check

This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.

Learning goals

What you should walk away with

  • Construct a LangChain agent with custom python analytical tools

  • Ingest customer API usage records and SaaS subscription terms

  • Calculate key metrics including NRR, LTV/CAC ratio, and usage-tier delta

  • Generate automated executive recommendations for pricing alignment

Resources and assets

Reference links and supporting material

Dataset notes

Synthetic B2B SaaS cohort telemetry data including monthly API call volumes and subscription billings.

Challenge setup

Evaluates calculation accuracy for SaaS metrics and alignment recommendations.

Challenge input

JSON containing customer cohorts, ARR start, expansions, and contractions

LangChain

Standard Python framework for language model agents and tool orchestration.

Evaluated output

JSON containing calculated_nrr_percentage, churn_risk_count, and strategy_recommendation

Evaluation checks
  • Verifies NRR calculation matches formula exactly
  • Mean absolute percentage error on numerical metric estimations • target: 0 • range: 0-100
Configured evaluation evidence
  • Benchmark: FinSaaS-Analytics-Bench
  • Math Precision Error target: 0
  • 1 public reference case
Configured runtime
  • Python execution harness
  • Python sandbox (unavailable on Versalist)

Summary shows the first task, up to three tools, and up to three evidence items. Open the technical recipe to view all configured items.

View technical recipe

Configured tools

Action Space
  • Langchain · Required
  • LangChain · Optional
Orchestration
  • Langchain · Required
  • LangChain · Optional

Evaluation contract

  • nrr_calculation_exact · Weight 1
  • math_precision_error · Weight 1

Metrics

  • math_precision_error · Target 0

Benchmarks

  • FinSaaS-Analytics-Bench
  • general

Reference cases

  • 1 public reference case

Workflow tasks

  1. Task 1

    Input: JSON containing customer cohorts, ARR start, expansions, and contractions

    Output: JSON containing calculated_nrr_percentage, churn_risk_count, and strategy_recommendation

    Execution harness: Python

Evaluation tools

  • LangChain · Agent Framework

Recipe freeze marker

No recipe freeze time is recorded.

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