This is lesson thirty-eight. This is towards one of our missions. Education. You’ll learn everything about marketing - from the basics to the most advanced strategies - for free, thanks to VellumWorks.
Collecting data is easy.
Using it well is the hard part.
Data collection and analysis are the processes that turn raw information into insight, and insight into better decisions. For charities, this isn’t about dashboards or vanity metrics.
It’s about understanding people, improving experiences, and protecting trust.
Without analysis, data is just noise.
Without good collection, analysis is misleading.
What Is Data Collection?
Data collection is the process of gathering information in a structured, intentional way to answer specific questions.
Data can come from:
supporters
donors
volunteers
beneficiaries
digital behaviour
external sources
The keyword is intentional.
Collecting data “just in case” leads to clutter, risk, and confusion.
Good data collection starts with a question - not a tool.
Types of Data You Collect
1. Quantitative Data (Numbers)
This tells you what is happening and how often.
Examples:
donation amounts
conversion rates
email opens and clicks
retention rates
attendance numbers
response times
Strengths:
scalable
comparable
trackable over time
Limitations:
rarely explains why
2. Qualitative Data (Meaning)
This tells you why things happen and how people feel.
Examples:
open-ended survey responses
interview notes
feedback emails
comments and complaints
stories and testimonials
Strengths:
depth
emotional insight
language discovery
Limitations:
smaller samples
requires interpretation
The strongest insights come from combining both.
Structured vs Unstructured Data
Structured Data
spreadsheets
survey responses
databases
CRM fields
Easy to analyse, but limited in nuance.
Unstructured Data
free-text responses
emails
interviews
social comments
Richer, but requires thoughtful analysis.
Charities often ignore unstructured data, even though it’s where the most valuable insight lives.
Ethical Data Collection (Non-Negotiable)
Because charities work with trust, vulnerability, and sensitive contexts, ethics matter more than optimisation.
Always ensure:
clear consent
transparency about purpose
data minimisation (only collect what you need)
secure storage
respectful language
the ability to opt out
Ask yourself:
Would I feel comfortable if this data were about me?
If the answer is no, don’t collect it.
What Is Data Analysis?
Data analysis is the process of:
cleaning data
organising it
identifying patterns
interpreting meaning
drawing conclusions
informing decisions
Analysis turns:
numbers → insight
feedback → themes
behaviour → understanding
The goal is clarity, not complexity.
Quantitative Analysis (Making Sense of Numbers)
Key techniques include:
Descriptive Analysis
What’s happening right now?
totals
averages
percentages
trends over time
Example:
donation volume by month
retention rate year-on-year
Comparative Analysis
How do things differ?
campaign A vs campaign B
new supporters vs existing
email subject lines
before vs after changes
Trend Analysis
What’s changing over time?
growth or decline
seasonal patterns
long-term shifts
Trend > snapshot.
Qualitative Analysis (Making Sense of Meaning)
This is where many organisations struggle, but it’s also where insight lives.
Thematic Analysis
Group feedback into themes.
Example themes:
confusion
trust
gratitude
frustration
motivation
Count how often themes appear, but don’t ignore emotional weight.
Language Analysis
Pay attention to:
words people repeat
emotional phrases
metaphors
hesitations
This language should feed directly into:
messaging
content
donation pages
FAQs
Use people’s words. Not internal jargon.
Common Analysis Mistakes
chasing vanity metrics
ignoring small but consistent signals
analysing without context
confirmation bias (“seeing what you want to see”)
collecting more data instead of acting
reporting without deciding
Insight without action is wasted effort.
From Insight to Action (The Critical Step)
After analysis, always answer:
What did we learn?
What surprised us?
What assumption was wrong?
What decision does this inform?
What will we change now?
Then communicate: “You said X. We changed Y.”
This closes the trust loop.
A Simple Data Workflow for Charities
You don’t need sophistication, you need consistency.
A healthy loop:
Ask a clear question
Collect only relevant data
Analyse simply
Decide one action
Implement
Review impact
Repeat.
Data Analysis Is a Skill, Not a Tool
Dashboards don’t create insight.
People do.
The most valuable analysts:
understand context
ask better questions
listen carefully
combine data with empathy
resist over-confidence
Data should inform judgment, not replace it.
10-Minute Exercise: Analyse One Thing Properly
Pick one dataset you already have:
a survey
email analytics
donation data
feedback emails
Ask:
What’s the main pattern?
What does it not explain?
What’s one change we can test?
Make one improvement this week.
That’s real analysis.
Why is this important to know?
Data doesn’t make decisions, people do.
Understanding how to collect and analyse data properly helps charities avoid assumptions, respect the people they serve, and make decisions that are evidence-based, ethical, and effective.
When data is handled well, it strengthens trust instead of eroding it.
At VellumWorks, we believe knowledge should be free. That’s why this series will guide you, step by step, through everything from the basics to the most advanced strategies in marketing: no jargon, no gatekeeping, just education that empowers.
