Most contributors skip research entirely. They create images they think look good, upload them, and wait. Months pass. Downloads stay flat.
The problem is never the images. It’s that nobody searched for what they shot.
Adobe Stock is a search engine with 350 million assets. Uploading without researching buyer demand is like opening a restaurant without checking if anyone in the neighborhood eats that cuisine. You need to know what buyers are searching for before you create anything.
This guide walks through the exact research process, step by step. No theory, no motivational fluff. Just the method.
Start With Buyers, Not With Stock Platforms
The biggest research mistake contributors make: they open Adobe Stock, browse what’s there, and try to fill gaps in the catalog. That’s backwards. A gap in the catalog might exist because nobody wants those images.
Instead, start with the people who buy stock images. Ask one question: what content is being published right now that needs an image?
Stock photos sell because a blogger needed a header, a marketer needed a campaign visual, or a designer needed a landing page hero. Every download traces back to someone publishing something.
Here’s how to map that demand:
- Pick a topic area you want to shoot (cybersecurity, wellness, remote work, whatever).
- Search Google for “[topic] + blog” or “[topic] + guide”. How many recent results appear? Topics with hundreds of fresh articles per month mean hundreds of people searching for stock images this month.
- Read 5-10 of those articles. Look at the header images they’re using. Are they generic and dated? Are they clearly stock photos from 2019? That’s your opening.
- Name the buyer. “SaaS marketers writing product comparison posts” is useful. “Marketers” is not. The more specific your buyer, the more specific your images, the better they sell.
A practical example: therapy and mental health. Wellness publishers put out new content weekly. Each post needs a header. Search Adobe Stock for “therapy session” and you’ll find the same staged couch-and-clipboard shots from five years ago. The demand is constant, the supply is stale. That’s a signal.
Mine Adobe Stock’s Search Bar for Demand Signals
Adobe Stock’s autocomplete is one of the most underused research tools available. When you type a keyword into the search bar, the suggestions that appear are real buyer searches. Adobe surfaces them because people actually type them, frequently enough to matter.
Here’s how to extract the most from it:
- Type your root keyword (e.g., “cybersecurity”).
- Note every autocomplete suggestion. These are what buyers search for.
- Now type your root keyword + each letter of the alphabet: “cybersecurity a”, “cybersecurity b”, “cybersecurity c”, all the way through z.
- Record every suggestion for every letter.
This takes about 10 minutes per keyword. What you get back is a map of actual buyer intent, not your assumptions about what people want.
Pay special attention to suggestions that are specific and scenario-based. “cybersecurity team meeting” tells you more than “cybersecurity abstract.” The specific query reveals a buyer who has a concrete need. The abstract query is a designer browsing.
Compare autocomplete across platforms. Run the same process on Freepik or Shutterstock. Suggestions that appear on multiple platforms confirm strong demand. Suggestions that appear on only one platform might indicate a platform-specific opportunity.
Check If the Market Exists (Kill Triggers)
Before investing serious research time, run three binary checks. If a concept fails any one of them, drop it and move on.
1. Does demand exist? Type your concept keywords into Adobe Stock’s search bar. If zero autocomplete suggestions appear on both Adobe Stock and Freepik, buyers aren’t searching for it. Kill it.
2. Is there an existing market? Check the total result count. Fewer than 50 results across both platforms means the market is too niche. You’d be trying to create demand instead of serving it. Creating markets is expensive and slow. Serving existing demand is where the money is.
3. Is the market oversaturated? If there are 50,000+ results and the top results are recent, high-quality, and specific to your exact concept, the space is too crowded for a newcomer to get visibility. Your images will be buried on page 47.
Most concepts die at gate 1. That’s good. Killing bad ideas fast means you spend production time on ideas that can actually earn.
Scan the Top 20 Results
Total result count tells you almost nothing useful. What matters is the quality of what appears on page 1 and page 2, because that’s what buyers see.
Search your concept on Adobe Stock and examine the top 20 results across four dimensions:
Quality. Can you match or beat the technical quality of these images? If the top results are gorgeous high-end editorial photography and you’re generating AI images, be honest about whether you can compete.
Freshness. When were the top results uploaded? If the best-ranking images look like they were shot in 2018 with outdated styling, that’s a refresh opportunity. Modern execution beats dated content even if the concept is the same.
Specificity. Are the results generic stock clichés, or are they targeted to specific scenarios? If “remote work” returns 20 identical laptop-on-desk shots, there’s room for specific situations: a parent on a video call with a child in the background, someone working from a coffee shop patio, a team standup on a screen.
AI saturation. How many of the top 20 results are already AI-generated? If AI has flooded the niche, adding more AI images has diminishing returns. Your content needs to look meaningfully different from what’s already there.
The patterns to watch for:
- Generic results dominating = opportunity for specificity
- Dated top results = refresh opportunity with contemporary execution
- High AI saturation = marginal value is low, move on
- Few results but strong autocomplete = underserved demand, prioritize this
Use the Sort Toggle (Downloads vs. Relevance)
Most contributors only look at default relevance sorting. Switch to “Most Downloads” and you get a completely different picture.
Downloads sort shows you what actually sells. These are the proven winners. Study them closely. What subjects appear repeatedly? What compositions? What color palettes? This is your benchmark for commercial viability.
Relevance sort shows you what the algorithm considers the best match for a query right now. Newer uploads with strong metadata can appear here even without many downloads yet. This is where you see what’s currently competing.
Compare the two. When the downloads sort shows older, dated images but the relevance sort shows newer content, the market is in transition. Buyers want fresh options but the proven sellers haven’t been replaced yet. That’s your window.
Estimate Demand With Content Velocity
You can’t see exact download numbers on Adobe Stock, but you can estimate demand through a proxy: how much content is being published about the topic.
Search Google News for your niche keyword. Count recent articles. The more articles published per week, the more header images needed per week, the more stock image searches happening right now.
A rough scale:
- 50+ articles per week on Google News = high demand, consistent licensing potential
- 10-50 per week = moderate demand, worth pursuing if competition is manageable
- Fewer than 10 = niche demand, only worth it if supply is very thin
Cross-reference this with Google Trends. You’re looking for topics with steady or growing interest, not one-time spikes. A topic that trends for two weeks then disappears will generate a burst of downloads and then nothing. Evergreen topics, like workplace safety, financial planning, or mental health, generate consistent licensing month after month.
Build a Keyword Gap Map
This is where research turns into a production plan.
Take the autocomplete suggestions you collected earlier (the demand signals). Now compare them against the actual supply on Adobe Stock:
- High autocomplete frequency + thin supply = strongest opportunity. Buyers search for it, but the results are weak or sparse. Produce here first.
- High autocomplete frequency + strong supply = competitive market. You need meaningfully different content to break through.
- Low autocomplete frequency + thin supply = probably not worth it. Low demand and low supply usually means low interest, period.
Organize your gaps into tiers:
Tier 1: Produce first. Strong buyer demand, weak existing supply, and your production method handles it well. This is where you allocate the most images.
Tier 2: Produce second. Solid opportunity, but one dimension is weaker. Maybe the demand is clear but there’s already decent supply. Or the demand is moderate but almost nothing exists.
Tier 3: Test with 2-3 images. Speculative. You think there’s demand based on adjacent signals, but you can’t confirm it yet. Small batch, wait for data.
Know Where AI Wins (and Where It Doesn’t)
If you’re using AI generation tools, research should also tell you where AI has a structural advantage over photography:
AI wins on:
- Technology and data visualization concepts (impossible to photograph)
- Abstract and minimalist design (infinite color control, zero set cost)
- Access-gated environments like medical facilities, factories, laboratories (photography requires expensive location access, AI does not)
- Conceptual business and finance imagery (generic photography is declining here)
AI loses on:
- Authentic lifestyle with real people (buyers increasingly detect and avoid AI people shots)
- Cultural specificity requiring genuine environments
- Anything needing precise text, specific hand poses, or complex human interactions
Research isn’t just about finding demand. It’s about finding demand you can serve better than what’s already there.
The 30-Minute Version
If you don’t have two hours for a full research pass, here’s the compressed version:
- Pick a topic (2 min). Start with what you know or what’s trending in the news.
- Autocomplete mining (10 min). Type the root keyword + a through z into Adobe Stock search. Record every suggestion.
- Kill check (3 min). Does autocomplete exist? Are there 50+ results? Are there fewer than 50K high-quality recent results? If yes to all three, proceed.
- Scan top 20 (10 min). Sort by downloads, then by relevance. Note quality, freshness, specificity, AI saturation.
- Decision (5 min). Can you place in the top 20? Is there a clear gap between what buyers search for and what exists? If yes, produce. If not, pick the next keyword from your autocomplete list and repeat from step 3.
That’s the research loop. Run it before every production session and you’ll stop creating images nobody searches for.
Where This Connects to Keywording
Research and keywording are two sides of the same problem. Research tells you what to create. Keywording tells you how to describe it so buyers find it.
The autocomplete suggestions you collected during research? Those become your keywords. The buyer language you identified? That goes into your titles. The gap you found? Your metadata should target exactly that gap.
If you haven’t read our keywording guide yet, start there after your first research session. The research process gives you the raw material. The keywording process turns it into metadata that ranks.
The Mistake That Costs Contributors the Most
The most expensive mistake isn’t bad keywords or weak images. It’s producing 50 images in a niche nobody searches for.
Two hours of research before a production session saves weeks of wasted effort. Every successful stock contributor does some version of this process. The ones who don’t are the ones posting in forums asking why their portfolio isn’t earning.
Research first. Create second. Keyword third. That’s the order.