# Case Study Result Baseline Packet

Canonical page: https://waitlist.crany.ai/marketing-templates/case-study-result-baseline-packet

By Tangenix. Published 2026-10-24. Category: Customer proof.

Prepare comparable before-and-after evidence for a customer story, preserving task scope, assistance and uncertainty before writing an outcome headline.

## Task

Determine whether a proposed case-study result has a defensible baseline and comparable outcome evidence.

## When to use

Use this when a real customer story proposes a specific measured improvement. Reconstruct the comparison before creating a headline, quote card or chart.

## Choose another approach when

You have only a favorable anecdote, need to decide whether a pilot should continue, or want to attribute every change after adoption to the software.

## Inputs

- Actual before-and-after task records with dates and definitions.
- The customer context, workload, assistance and other changes during the comparison.
- Permission boundaries and the exact numerical or qualitative claim under consideration.

## Template

    Proposed result wording: [not approved until evidence is reviewed]
    Customer / workflow / observation periods: [private evidence references]
    Baseline definition: [task, unit, start/end and inclusion rules]
    Outcome definition: [same rules or explicitly incompatible]
    Comparable records / missing cases: [include failures and exclusions]
    Workload and task-complexity differences: [material context]
    Manual assistance and other changes: [do not attribute them to automation]
    Calculation inputs / formula / independently checked arithmetic: [actual values]
    Alternative explanation: [what else could account for the difference]
    What this comparison supports / does not support: [bounded conclusion]
    Permission and publication scope: [separate exact-material approval]
    Decision: support narrow statement / qualitative story only / hold
    One customer observation does not establish generally expected results.

## Illustrative example

This is not a Crany customer result. Replace example details with verified facts.

    Fictional example: a customer reports that a recent handoff felt faster. The available earlier record starts when source material was gathered, while the newer record starts after that preparation had finished. Those durations are incompatible, so the packet holds a percentage-reduction headline.
    The story can still explain the actual workflow and the customer's opinion if accurate and approved. Any later measurement needs the same task boundaries and visible assistance.
    The team does not fill missing baseline records with estimates and label them measured. It also does not infer causality from a simple before-and-after sequence. No real customer number or outcome is invented to make this example look successful.

## How to use

1. Define the proposed result and reconstruct both periods using the same task boundaries.
2. Check comparable records, assistance, exclusions and alternative explanations.
3. Approve only a bounded supported statement or hold the quantitative claim.

## Checks

- Baseline and outcome durations measure the same task rather than convenient different stages.
- Missing records and manual assistance have not been hidden.
- A before-and-after comparison is not presented as a controlled causal experiment.

## What to measure

Track result claims narrowed or held because the comparison is incomplete. Review calculation errors and material scope changes. This packet substantiates only its stated evidence; it does not establish typical performance, a universal ROI or legal approval for an advertisement.

## Research and sources

HubSpot supports real customer evidence; Microsoft’s experimentation guidance distinguishes trustworthy measurement from unsupported conclusions. This original comparison packet is not itself a customer study and does not turn an observational case into a causal result.

- [HubSpot: Customer success stories](https://blog.hubspot.com/marketing/customer-success-story) — checked 2026-10-24. Practitioner publishing workflow includes requesting participation and using real customer evidence; not a legal release or proof that a customer approved any particular claim.
- [Microsoft Research: Patterns of Trustworthy Experimentation, Pre-Experiment Stage](https://www.microsoft.com/en-us/research/articles/patterns-of-trustworthy-experimentation-pre-experiment-stage/) — checked 2026-10-24. Clear hypotheses, appropriate measurement, assignment units and data-quality checks before a controlled experiment.

Prepared with AI assistance. Sources support the stated principles; they do not endorse Crany or guarantee performance.

## About Crany

Crany is an AI marketing co-founder in development for software founders and vibe coders. It is designed to strategize, execute approved marketing and improve based on results. These free resources are usable independently of app access. The public waitlist is at https://waitlist.crany.ai/.