Client engagement · Case study
Imgry.com
Image automation for marketing teams that are not design experts
Product Designer
Overview
Imgry is an emerging image automation, design, and publishing tool. This B2B product helps businesses boost marketing by creating image-based content from well-curated pre-made templates.
Challenge
There were almost no direct competitors for this exact product. Existing image automation tools (Canva-adjacent players like Bannerbear, Pixelied, Dynapictures, and others) had a steep learning curve and felt too technical. We had to balance simplicity for low-tech-savvy marketers with enough fidelity for intermediate designers and developers.
Outcomes
- Validated demand through user research, market analysis, and discovery workshops
- Designed an MVP that stayed intuitive without stripping power
- Applied Jakob’s Law so onboarding matched familiar market patterns
- Iterated flows through unit and usability testing with strong task success signals
Preliminary research
Since the product was new, user research tested whether there was substantial demand. Market analysis mapped what platforms target users already relied on, and discovery workshops with the client finalized MVP scope, layouts, and intuition bets.
Competitor analysis & challenges
Competitor analysis covered direct and indirect players. Most existing automation tools were too technical — a UX and design challenge that pushed the team toward an interface that felt approachable first.
The design philosophy leaned on standard UX principles, especially Jakob’s Law: users prefer your product to work like the sites they already know. Niches, moods, and template charts were extracted from survey and competitive data.
Product surfaces
From landing and auth through the editor, dashboard, and pricing — each surface was designed to keep automation powerful without feeling like a developer tool.
Usability testing
Based on different use cases, prototypes and flows were tested with questionnaires. Responses shaped the next iteration — including frequency-of-use patterns across daily, weekly, and monthly cohorts.
Next
Let's design the next chapter.