It started with an off-hand comment from my husband.
After a weekend trip to the garden centre, he remarked that houseplants were "an expensive hobby." He wasn't wrong. And it got me thinking.
I've been here before. When I was doing my PhD and money was tight, I had a greenhouse and a vegetable garden. I had no spare cash, but I spent a lot of time (and what little I had) cultivating plants.
This project started with that one question: Why are we drawn to this "expensive hobby," especially when budgets are tight?
Houseplants are everywhere—in grocery stores, online stores, all over our feeds. I wanted to understand the real motivations behind the boom.
I'm using this houseplant project as a case study to walk you through my 4-step research framework. It's the same process—and the same tool, Dovetail—that I use for client work.
I'll show how I tested learnings from Abi Awomosu’s course, How to Use AI to Truly Understand Your Market, where AI helped, where it fell short, and how I’d move this personal project to a client-ready strategy.
This is the most important step. Before you can get good answers, you have to ask the right questions.
The goal of the Discovery phase is to define the problem and map out what I actually need to know.
My initial curiosity was about the economic paradox. But as I outlined in my project plan, the research needed to investigate this dual nature: are houseplants a sign of affluence, or are they an accessible creative outlet for people with less control over their circumstances?
Based on that goal, I framed my initial research questions. I wasn't just after what people are buying; I was after the why.
Project overview in Dovetail
You’ll notice I didn’t use AI at this stage.
A common mistake is to throw AI at a problem immediately. At this point, the job was strategic thinking, defining the scope, and framing the questions. That’s human-led work. The AI tools stayed on the shelf.
For the next phase, Insights, I would use a secondary desktop research methodology, systematically gathering and analysing publicly available data from online sources (like Reddit, forums, and trend data) to see what themes emerge.
The "Insights" phase is where the data gathering begins. For a big, broad topic like this, the goal is to get a wide overview and then find specific, honest conversations.
For this project, my toolkit consisted of:
The project started with a "deep research" prompt in Google Gemini. This is the most critical part of using AI—the quality of the output is a direct reflection of the quality of the prompt.
My prompt wasn't simple. It was a deeply considered set of parameters and guardrails. I didn't just ask "why are houseplants popular?" I asked the AI to act as a researcher and investigate the how and why of the surge, looking at specific spending patterns, cultural context, online community behaviour, and economic factors. I specified the need for market reports, consumer data, and geographic focus, with a clear instruction not to invent sources.
Here's the full prompt
Deep Research Prompt: Houseplants as Hobby & Market Trend (2019–2025)
Uncover how and why houseplants have surged in popularity in the past 3–5 years, focusing on spending patterns, cultural/economic context, and online community behaviour.
Search across market reports, consumer data, trend analysis, and community conversations. Focus on Ireland where possible, then EU/US for comparative context. Return findings clearly structured by category. Do not invent sources — if data is unavailable, state so.
Key Research Areas:
Output Format (for clarity)
Organize insights into:
Market & Spending DataI ran this prompt across Gemini, Perplexity, and ChatGPT; the resources I got back from Gemini were significantly better. The AI's first job was to give me a synopsis of the market. Its real value, however, was in acting as a research accelerator. It effectively created a comprehensive bibliography for me, saving days of work.
But the AI overview, while a great starting point, lacked the texture of real, human conversation. This is why the second half of my insights process was purely human-led. I took my research questions to Reddit to find the unfiltered, spontaneous discussions that data summaries always miss. This is where the qualitative "why" behind the "what" started to emerge.
I tracked all of this—the articles, the forum threads, the quotes—in Dovetail, which set the stage for the analysis.
Examples of tagged Reddit quotes in Dovetail
The goal of Analysis isn't just to report what I found. It's to connect the dots and find the "so what."
This is where I applied the techniques from How to Use AI to Truly Understand Your Market.
The core idea I wanted to explore is moving from "data mining" to "data foraging."
Data mining looks for patterns in massive, sterile datasets. It’s what we often default to when conducting research. More is better, right?
Data foraging is different. It’s about digging into small, rich subsets of information to find the human drivers. The goal isn't just to see what people are saying, but to understand the subconscious instincts and emotional trends behind their words. Here, less is more.
We’re looking for the human behind the action. And this applies just as much to B2B as it does to B2C. The person buying software on a Tuesday is the same person buying a houseplant on a Saturday. The same emotional drivers are at play.
To go deeper, I used the frameworks from the course to build a custom GPT trained on specific methodologies.
I didn't just ask it for answers. I built it to be a strategic partner. I prompted it to challenge my premises, spot my biases, ask clarifying questions, and help me select the right framework for the data, based on what we covered in the course. The goal was to use AI to go deeper, layer by layer, looking for unmet needs, buying journeys, and archetypal patterns.
It's an iterative, human-led process. I pushed back, I prompted, I refined.
So what did this process uncover?
My analysis of the market revealed that this multi-billion dollar trend isn't about decoration. It's a convergence of three powerful human drivers.
1. The Therapeutic Outlet: People are seeking tangible tools for mental health management. In a world of digital anxiety, the simple, grounding ritual of plant care provides a sense of purpose and a direct way to manage stress.
2. The Domestic Sanctuary: The pandemic-induced shift to a "home-centric" life was a critical inflection point. People were forced to re-evaluate their domestic spaces and began investing heavily in creating a sense of sanctuary. Plants were the most accessible way to do it.
3. The Search for Connection & Identity: For a generation of Millennials and Gen Z, the "plant parent" identity has become a powerful one. The report highlights that nurturing plants often serves as an "accessible precursor or substitute for traditional life milestones" like homeownership or parenthood, filling a deep need for connection.
Explore the insight and quotes on Dovetail
These three powerful human drivers—Therapy, Sanctuary, and Identity—created more than just a passing trend. They built a new, massive market from the ground up.
The scale of this shift is staggering. This collective search for well-being has fuelled a global indoor plant market projected to approach USD $30 billion by 2029.
To see how profound this is, look at the data from Ireland: in 2020, consumer spending on gardening hit a record-breaking €1.2 billion. That's not just a surge; it's a structural shift, representing a 50% increase from 2018 and 14% higher than the previous peak during the "Celtic Tiger" economic boom.
But the most telling economic proof isn't a dollar amount; it's a behavioural one.
Bord Bia's Value of the Garden Market 2020 reports consumers "clearly shifted their focus to plants rather than cut flowers." This shows a fundamental change in consumer psychology. People were consciously choosing not to buy a "passive, ephemeral decorative item." Instead, they were choosing an "active, long-term investment and an ongoing project" that demands nurturing, learning, and engagement.
Explore the insight and quotes on Dovetail
This maturation from a "boom" to a long-term "hobby" is where the real business insights lie. The market is no longer just about selling plants; it's about serving the dedicated hobbyist.
My analysis points to three clear, forward-looking implications:
Explore the insight and quotes on Dovetail
This case study was a solo sprint. For a client project, the process is far more rigorous and collaborative.
It all starts with a "Discovery" workshop—mirroring Part 1 of this project. But instead of my own curiosity, we would be digging into your business. We'd work together to understand your questions, your customers, and what you truly need to learn from the research. That conversation informs the entire plan.
From there, the data gathering process follows a clear flow: Secondary research first, then Internal, then Primary Qualitative.
For a client, this process would expand to include:
The final step is the most important. A 50-page report that sits in a drawer is useless.
Instead, I deliver an actionable report—backed by data, customer quotes, and clear insights—and then run a collaborative workshop. This is where we work together to turn the research into tangible "next steps" that support the whole company.
This could mean identifying new content pillars, validating a new product idea, finding new campaign angles, or spotting a high-value partnership opportunity.
AI was an incredibly useful partner in this process, but it is not a substitute for research.
It’s a powerful accelerator. It picked up themes I could run with and saved me days of work. But if you're not careful, it can also lead you down the wrong path.
The true skill is no longer just finding information, but interrogating the information the AI gives you.
Before writing my first "deep prompt," I spent time reading so I knew what to ask. If you have a bias in your question, the AI will reflect that bias and take you down a very specific road.
Even with the custom GPTs I spent time building and testing, I found issues.
It flattens data. It has a tendency to misrepresent nuance or force information into an answer it thinks you want.
I still had to spend a significant amount of time verifying its sources, questioning its analysis, and pushing back.
AI is a tool. It's not the strategist.
You cannot build a 12-month business strategy on an AI report alone. You have to combine it with the rigorous qualitative and internal methods I've outlined. And most importantly, you have to know your domain—or work with someone who does—so you can spot the biases and the flaws.
The answer to that first question my husband asked—why we invest in this "expensive hobby" even when money is tight—wasn't economic. It was about a deep, human need for therapy, sanctuary, and identity.
An AI prompt didn't find that answer. A strategic, human-led process did.
This is the 4-step framework I apply to every client project. It’s not about delivering a 50-page report that gathers dust. It’s about finding the human "so what?" and turning that insight into your next strategic move.
If you're ready to find the real "so what" in your market, let's talk about your project.
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