Function: Customer marketing

AI Workflow for Case Study Candidate Selection

Deployment Brief

Start with a candidate list that includes result, story angle, evidence strength, customer fit, permission status, and owner.

Related Field Report

AI customer health scoring workflow: A field report on customer risk, retention signals, owner review, and measurable follow-up.

Quick Answer

An AI workflow for case study candidate selection ranks customers by outcome evidence, story angle, fit, permission status, sensitivity, and sales usefulness. It can assemble a proof packet, but marketing and account owners approve outreach, claims, public details, and whether the customer should be asked at all.

TL;DR

A case study candidate needs more than a happy customer. You need a clear story, usable evidence, permission, and sales relevance.

What is case study candidate selection?

Case study candidate selection is the process of identifying customers who have enough evidence, fit, story, and permission potential to become a useful customer story.

Who is this workflow for?

Agencies, consultants, SaaS companies, service firms, and professional businesses that need stronger proof for sales and SEO.
Marketing teams that need customer stories but do not want to ask the wrong customer.
Account owners who know which customers are happy but need a structured proof review.

What breaks in the manual process?

The manual process fails when case studies are chosen by whoever is easiest to ask. The resulting story may be vague, unsupported, or not useful for the sales conversations that need proof.

How does the AI-enabled process work?

The workflow reviews outcomes, feedback, segment, use case, proof gap, relationship context, permission status, and sensitivity. It prepares a ranked candidate list and proof packet for review.

What does this look like in practice?

Example scenario: A professional service client completes an onboarding automation project with clear before/after process data. The workflow assembles the proof, flags permission needs, suggests a story angle around missed inquiry reduction, and routes the candidate to marketing and the account owner.

What decision rules should govern this workflow?

Require a clear before-and-after story.
Score evidence strength before outreach.
Check permission and sensitivity before drafting public claims.
Match candidates to sales proof gaps.
Route all outreach and publication decisions to marketing and account owner review.

What are the implementation steps?

1. Trigger: A customer shows strong results, gives positive feedback, renews, expands, completes a successful project, or matches a strategic proof gap. 2. Inputs collected: customer outcome evidence, before and after context, customer segment and fit, story angle, sales proof gap, permission and sensitivity status, account relationship notes, marketing review rules. 3. AI/system action: The system checks source evidence, prepares the proof or feedback output, and flags permission, claim, context, or owner-review requirements. 4. Human review point: Marketing and account owners review candidate fit, evidence, customer sensitivity, claim strength, permission, outreach, and publication plan. 5. Output delivered: case study candidate list, proof packet, story angle recommendation, permission and sensitivity flag, outreach draft, measurement event for case study pipeline. 6. Measurement logged: Track candidates identified, candidates approved, outreach sent, interviews booked, case studies published, permissions granted, proof gaps filled, and sales use.

Required inputs

customer outcome evidence
before and after context
customer segment and fit
story angle
sales proof gap
permission and sensitivity status
account relationship notes
marketing review rules

Expected outputs

case study candidate list
proof packet
story angle recommendation
permission and sensitivity flag
outreach draft
measurement event for case study pipeline

Human review point

Marketing and account owners review candidate fit, evidence, customer sensitivity, claim strength, permission, outreach, and publication plan.

Risks and stop rules

customer asked without enough proof
sensitive details exposed
case study based on weak or unsupported claims
sales usefulness ignored

Stop the workflow when permission is missing, claims are unsupported, customer issues are unresolved, sensitive details are involved, or the next action would create a public proof, customer ask, or relationship-sensitive message without approval.

Best first version

Create a candidate list with result, story angle, evidence strength, customer fit, permission status, and owner.

Advanced version

The advanced version maps candidates by industry, buyer persona, workflow type, keyword target, proof gap, renewal status, and public-use restrictions.

Related workflows

Measurement plan

Track candidates identified, candidates approved, outreach sent, interviews booked, case studies published, permissions granted, proof gaps filled, and sales use.

What not to automate

Do not automate customer outreach, public claims, permission assumptions, sensitive detail publication, or case study approval without human review.

FAQ

What is case study candidate selection?

It is the process of identifying customers with enough evidence, fit, story, and permission potential to become a useful customer story.

What can AI prepare?

AI can prepare candidate rankings, proof packets, story angles, sensitivity flags, and outreach drafts.

What should stay under human review?

Candidate fit, evidence strength, claims, permission, outreach, sensitivity, and publication plan should stay under marketing and account review.

What is the simplest first version?

Create a candidate list with result, story angle, evidence strength, customer fit, permission status, and owner.

How should this workflow be measured?

Measure candidates, approvals, outreach, interviews, published stories, permissions, proof gaps filled, and sales use.