Case Studies That Actually Win Grants

Most charity case studies do not win grants. They are included in applications because funders ask for them, but they rarely do any persuasive work because they follow the same pattern: a sympathetic person was struggling, the charity helped, and now things are better. There is no measurable change, no link to the programme's wider outcomes, and nothing that distinguishes the story from hundreds of others the funder will read that month.

The difference between a case study that fills a box and one that strengthens an application is evidence. Sector research has found that the majority of grant assessors say case studies are "important" or "very important" in their funding decisions — but only a minority describe the case studies they typically receive as "strong." That gap represents an enormous missed opportunity for charities that are doing genuinely transformative work but cannot demonstrate it on paper.

This guide covers what funders actually look for in case studies, why most charity case studies fall short, and how to produce stories that include the measurable outcomes, distance travelled, and programme-level context that turn a nice anecdote into compelling evidence. It also introduces an approach that is changing how charities produce case studies: generating them directly from the data already in your systems.

What you will learn:

• What makes a case study persuasive to grant assessors (and what does not)
• The five elements every funder-ready case study needs
• How to quantify "distance travelled" in a way that is credible and compelling
• Why most case studies fail — and the structural reasons behind it
• How data-driven case studies differ from traditional narratives
• A practical template for case studies that win grants

Who this is for: Fundraisers, programme managers, monitoring and evaluation leads, and charity directors who need to produce case studies for grant applications, funder reports, or impact communications — and want them to do genuine persuasive work.

What Do Grant Assessors Actually Look For in a Case Study?

Grant assessors read case studies differently from the general public. They are not looking for emotional impact alone — they are looking for evidence that your programme works. Understanding what assessors value helps you collect the right information and frame it effectively.

Sector guidance from organisations including the National Lottery Community Fund, IVAR, and the Directory of Social Change consistently highlights five elements that grant assessors consider most important:

1. Baseline: What was the person's situation before they engaged with the programme?
2. Specificity: What exactly did the charity provide — not a general service description, but what this person received?
3. Measurable change: What is different now, and how do you know?
4. Voice: Does the beneficiary's own perspective come through?
5. Connection to programme outcomes: Does the individual story reflect the programme's intended theory of change?

Most charity case studies include elements 1, 2, and 4 — a before situation, a description of help, and a quote. Far fewer include elements 3 and 5, which are precisely the elements that distinguish strong case studies from weak ones.

A good case study does not just tell the assessor that someone's life improved. It shows the starting point, the journey, and the measurable distance between the two. That is what gives funders confidence that the programme will produce results if funded again — because the evidence shows it already has.

Why Do Most Charity Case Studies Fall Short?

The problem is rarely a lack of impact. Charities delivering frontline services see lives change every week. The problem is structural: the information needed to write a strong case study is scattered across multiple systems, or is not collected at all.

The data is siloed

A typical charity might record attendance in one spreadsheet, case notes in another, outcome measurements in a third (if they exist), and beneficiary quotes in a Word document or email chain. Assembling a case study means manually pulling information from four or five sources, cross-referencing it, and hoping the records are complete and up to date.

Outcomes are not measured at individual level

Many charities collect programme-level outcomes (number of people who gained employment, for example) but do not systematically track individual journeys. Without a baseline measurement and a follow-up for the specific person in the case study, you cannot demonstrate distance travelled — and that is the single most persuasive element for assessors.

Frontline staff are not trained to collect stories

Many frontline charity workers feel uncomfortable or unprepared when asked to collect a case study. They are skilled at delivering services, not at conducting structured interviews or writing narratives for external audiences.

The process is reactive, not systematic

Case studies are typically produced under deadline pressure — a grant report is due, someone needs a story by Friday. This reactive approach means you are working with whoever is available, not whoever has the most compelling journey. In practice, most charities only collect case studies when specifically required by a funder, rather than building a continuous pipeline.

What Does a Winning Case Study Look Like Compared to a Weak One?

The difference is not about writing quality — it is about what information is included. Here is the same beneficiary story told two ways:

The strong version is not longer or harder to read. It simply includes the evidence. The challenge is that producing the strong version requires access to attendance data, baseline and follow-up measurements, and programme-level outcome data — information that most charities have, but not in one place.

How Do You Measure and Present Distance Travelled?

Distance travelled is the most powerful element in a case study because it proves change happened, not just that someone felt better. But it needs to be presented carefully to be credible.

Choose appropriate measurement tools

The tool you use should match the outcomes you are measuring. Common validated tools used in the UK charity sector include:

Using validated tools is important because funders trust them. A bespoke "how are you feeling on a scale of 1–10" question is better than nothing, but it carries far less weight than a recognised instrument. Funders consistently report that grant applications using validated outcome measures are assessed more favourably than those relying on bespoke or unvalidated tools.

Present the numbers in context

Raw scores mean little to a reader unfamiliar with the tool. Always include:

• The score range (e.g., "scored 14 out of 70")
• What the score means in plain language (e.g., "placing her in the 'very low' category")
• The change in context (e.g., "a 34-point increase, moving from 'very low' to 'average'")
• How the individual compares to the programme average (e.g., "the programme average improvement was 22 points; Sarah's improvement of 34 points was in the top quartile")

Link individual change to programme data

This is what separates a case study from an anecdote. When you can say "Sarah's experience reflects a programme where 78% of participants showed clinically significant improvement," you are using the individual story to validate programme-level evidence, and vice versa. Each makes the other more credible.

Plinth's impact reporting automatically tracks distance travelled for each beneficiary using whatever outcome tools you choose, and aggregates individual results into programme-level data — so when you generate a case study, the link between individual journey and programme outcomes is built in.

How Do You Build a Continuous Case Study Pipeline?

Waiting until a grant report is due to collect case studies is like waiting until you need a reference to build professional relationships. The best time to collect a case study is when the evidence is fresh and the beneficiary is engaged — not three months later when they may have moved on.

Embed collection into your programme workflow

Identify natural collection points in your programme cycle. For most programmes, the end of an intervention (final session, programme completion, or exit) is the ideal moment. The beneficiary is still engaged, the outcomes data has just been collected, and the experience is recent.

Use recorded conversations, not written interviews

Frontline workers produce higher-quality case study material when they record a short conversation (5–10 minutes) rather than trying to take notes and write it up afterwards. The recording captures the beneficiary's actual language and emotional tone, which are lost in paraphrased write-ups. A typical written case study takes 2–4 hours to produce when done manually. A recorded conversation, transcribed and structured by AI, takes 10–15 minutes of staff time.

Maintain a consent-ready process

Consent should be part of your standard intake process, not an afterthought. At registration, explain that you may ask to share their story (anonymised unless they choose otherwise) and get broad consent. At the point of collection, confirm specific consent for the particular story. This two-stage approach, recommended by the Charity Commission's safeguarding guidance, means you are never scrambling for consent documentation after the fact.

Plinth's AI case notes let frontline staff record a short conversation with a beneficiary, then automatically transcribe and generate a structured case study that includes attendance data, outcome measurements, and distance travelled pulled directly from the system. The result is a funder-ready case study produced in minutes, not hours.

How Do Data-Driven Case Studies Differ From Traditional Narratives?

The fundamental difference is provenance. A traditional case study is written from memory and notes. A data-driven case study is generated from recorded evidence — attendance records, outcome measurements, session notes, and the beneficiary's own words — with AI structuring the narrative.

This does not mean data-driven case studies lack warmth or humanity. The beneficiary's voice — captured through recorded conversation — remains central. What changes is that the evidence framework around that voice is accurate, complete, and automatically linked to programme-level data.

The difference is immediately apparent in practice. A data-driven case study draft includes attendance figures, wellbeing scores, and exact quotes from exit conversations — details that are more accurate than anything a worker could reconstruct from memory days later. What used to take three hours of writing takes five minutes of review and light editing.

What Is the Best Template for a Grant-Winning Case Study?

Drawing on guidance from major UK funders including the National Lottery Community Fund, Comic Relief, and the Tudor Trust, as well as sector resources from the Directory of Social Change, the most effective case study structure follows a five-part framework:

1. Context (2–3 sentences)

Establish who the person is (anonymised appropriately), their key challenge, and their baseline measurement. This sets the starting point for the distance-travelled narrative.

Example: "When James joined the programme in September 2025, he had been unemployed for 14 months and scored 2 out of 10 on our employment readiness assessment. He had no recent work references and described his confidence as 'rock bottom.'"

2. Intervention (3–4 sentences)

Describe specifically what the person received — not a general description of your programme, but what this individual engaged with. Include attendance data where possible.

Example: "Over 12 weeks, James attended all 12 employability workshops, completed a three-day work placement at a local social enterprise, and had four individual sessions with an employment coach focused on interview preparation and CV development."

3. Outcome (3–4 sentences)

State what changed, using measurable data where available. Include distance travelled and any qualitative changes.

Example: "At programme completion, James scored 8 out of 10 on the employment readiness assessment — a six-point increase. Within four weeks of completing the programme, he secured a part-time role at a retail charity. He now describes his confidence as '7 out of 10, up from about 2.'"

4. Voice (1–2 sentences)

A direct quote from the beneficiary that captures their experience in their own words. The best quotes are specific, not generic.

Example: "The mock interviews were what changed things for me. I had not sat in front of someone and answered questions in over a year. By the fourth one, I actually started to believe I could do it."

5. Programme connection (1–2 sentences)

Link the individual story to the programme's wider outcomes. This transforms the case study from an anecdote into evidence.

Example: "James's experience reflects the programme's overall outcomes: 71% of participants showed a significant improvement in employment readiness scores, and 43% secured employment within three months of completing the programme."

Total length: 300–500 words. Concise, evidence-based, and directly relevant to the funder's assessment criteria.

How Many Case Studies Should You Include in a Grant Application?

Grant assessors consistently report that applications with 2–3 well-evidenced case studies score higher than those with 5–6 weaker ones. Quality dominates quantity.

Match case studies to your theory of change

If your programme aims to achieve three primary outcomes (for example, improved wellbeing, increased social connections, and progression to employment), select one case study that demonstrates each outcome strongly, rather than three case studies that cover all outcomes weakly.

Choose diversity over similarity

Select case studies that show your programme works for different types of beneficiary — different ages, backgrounds, and starting points. This demonstrates range and reduces the risk that assessors view your evidence as cherry-picked.

Reference programme data alongside individual stories

A strong application weaves together individual case studies and aggregate data. After each case study, a sentence linking back to programme-level outcomes reinforces the evidence: "Maria's experience is consistent with the programme's overall results, where 82% of participants reported improved wellbeing at follow-up."

Plinth's survey and outcome tools collect structured data from every beneficiary in your programme, so you always have up-to-date aggregate figures to cite alongside individual stories.

Frequently Asked Questions

Do funders prefer named case studies or anonymous ones?

Most funders accept anonymised case studies provided the outcomes data is real and verifiable. The National Lottery Community Fund's guidance states that anonymisation is expected where beneficiaries are vulnerable or the story involves sensitive circumstances. What matters is not the name but the specificity and evidence. "Sarah, aged 34" is no more persuasive than "A 34-year-old woman" if both include the same outcome data.

How long should a case study be for a grant application?

Between 300 and 600 words for most applications. Longer case studies are rarely read in full by assessors working through large volumes of applications. If your application allows appendices, you can include a longer version there and provide a 300-word summary in the main body.

Can we use the same case study in multiple applications?

Yes, but adapt it for each funder's priorities. A case study demonstrating employment outcomes can be reframed to emphasise wellbeing for a health-focused funder, or community integration for a social cohesion programme. The underlying evidence stays the same; the emphasis shifts.

What if our beneficiaries are hard to reach or reluctant to share their stories?

Focus on making it easy and safe. Short recorded conversations (5 minutes) with a familiar worker are far less intimidating than formal interviews. Offer full anonymisation. Explain exactly how the story will be used and who will see it. The Charity Commission's guidance emphasises that consent must be informed and specific — and that participants must know they can withdraw at any time.

How do we handle case studies where the outcome was not entirely positive?

Honest case studies are often more persuasive than perfect ones. If a beneficiary made significant progress but did not achieve the ultimate goal (for example, improved in wellbeing but did not secure employment), that story still demonstrates value. Funders understand that not every journey ends in a headline outcome. What matters is that change was measurable and attributable to the programme.

Should we hire a professional writer for case studies?

For annual reports, a professional writer can add polish. For grant applications, it is usually unnecessary — assessors can tell when a case study has been written by a communications professional rather than drawn from genuine programme records. Authenticity matters more than eloquence.

How often should we refresh our case study bank?

Aim to add at least one new case study per programme per quarter. Case studies older than 18 months lose relevance for grant applications — assessors want to see evidence from your current programme, not a story from three years ago. With a continuous collection pipeline, this becomes routine rather than an additional task.

Can AI-generated case studies be trusted?

When the AI is generating from real system data — attendance records, outcome measurements, recorded conversations — the output is typically more accurate than a case study written from memory weeks after the event. The key is that AI is structuring and presenting real evidence, not fabricating content. The human review step (checking the draft before use) remains essential, but it takes 5 minutes rather than the hours required to write from scratch.

Recommended Next Pages

• How to Collect Charity Case Studies Without the Pain — A practical guide to the collection process, from consent to AI-assisted write-up
• Grant Applications That Get Funded — What makes the difference in competitive grant applications
• How to Prove Charity Impact to Funders — Combining quantitative and qualitative evidence effectively
• Why Charities Struggle to Collect Impact Data — Understanding the systemic barriers to evidencing outcomes

Last updated: February 2026