The food industry moves quickly, and research can't keep up.
Consumer interest can shift overnight. Viral products, recipes, and ingredients often gain traction across social platforms long before traditional market research can identify them.
At Weee!, an online grocery platform serving diverse Asian and multicultural communities, marketing teams needed a faster way to discover trends relevant to specific ethnic audiences and translate those insights into engaging content.
As both a marketer and designer, I saw an opportunity to use AI to reduce the time spent researching trends and brainstorming campaign ideas, so teams could focus more on execution and strategy.
Marketing teams couldn't move at the speed of food culture.
Marketing teams relied heavily on manual research across TikTok, Instagram, Reddit, and other social platforms to identify emerging food trends. That created several challenges:
- Slow, inconsistent discovery. Trend research was time-consuming and varied widely between marketers.
- Late identification. Viral products were often spotted after peak engagement had already passed.
- One-size-fits-all monitoring. Different ethnic markets follow unique trend cycles, so universal monitoring missed the signal.
- Manual content ideation. Generating culturally relevant social content ideas required significant manual brainstorming.
- Scattered insights. Valuable trend data lived across multiple platforms and was difficult to track over time.
“How might we help marketing teams discover emerging food trends earlier — and transform those insights into culturally relevant content opportunities faster?”
Understanding current workflows.
I interviewed and observed marketers to understand how trend research was currently being conducted. A few patterns came back over and over:
- Platform switching. Teams spent considerable time hopping between platforms to validate a single trend.
- Intuition over data. Most trend identification leaned on gut feel rather than structured data.
- Momentum, not popularity. Marketers wanted visibility into how fast a trend was growing, not just how big it already was.
- Disconnected ideation. Content brainstorming kicked off after trend discovery — a second workflow that added friction.
Trends move differently across communities.
Through market analysis across multiple ethnic audiences, I found that food trends emerge differently across cultural communities. The same product may trend for different reasons depending on the audience, and certain trends gain traction weeks earlier within specific communities before reaching mainstream.
This highlighted the need for segmentation rather than a universal trend dashboard.
What existing tools were missing.
I reviewed existing social listening and trend-monitoring platforms. Common gaps included broad consumer insights with limited ethnic-market specificity, a lack of actionable content recommendations, and high complexity that put them out of reach for non-technical marketing teams.
Three principles guided the design.
- Surface trends earlier. Give visibility into emerging trends before they go mainstream by continuously monitoring multiple social platforms.
- Organize insights by audience. Segment trends by ethnicity and market so teams can discover culturally relevant opportunities faster.
- Turn insights into action. Close the gap between trend discovery and content creation by automatically generating campaign and social content ideas.
An AI-powered trend intelligence platform.
I designed an internal platform that combines automated data collection, trend analysis, and content ideation into a single workflow — so marketers can go from signal to social post without switching tools.
Emerging food conversations, in one place.
The dashboard aggregates data from social channels through automated scraping pipelines and continuously tracks emerging food conversations.
- Trending products and ingredients.
- Trend growth indicators.
- Historical trend tracking.
- Cross-platform trend visibility.
Insights organized by audience segment.
Rather than treating trends as universal, the dashboard organizes insights by audience segment — letting marketers compare trends across communities, identify culturally specific opportunities, and prioritize products that map to their target market.
From trend to campaign in one place.
Once a trend is identified, marketers can instantly generate social media content concepts, video topic ideas, campaign angles, and audience-specific messaging directions — collapsing the gap between research and execution.
Three calls that shaped how it works.
- Signal over volume. Instead of showing every trend, I surfaced the ones with meaningful momentum — cutting information overload and lifting decision confidence.
- Discovery and ideation as one workflow. Research showed marketers treated trend discovery and content planning as one continuous loop. Integrating them killed the context switching.
- Built for non-technical users. The platform leans on AI, scraping infrastructure, and a database under the hood, but the UI is intentionally simple so marketers can act on insights without technical expertise.
What it unlocked for the team.
While still in its early stages, the platform showed real operational gains for the marketing org:
- Trend research time dropped from ~2 hours per session to ~15 minutes.
- Content ideation produced 50+ campaign-ready ideas weekly, up from a manual handful.
- 4 ethnic market teams now share a centralized source of trend intelligence.
- Culturally specific visibility across ethnic audiences, instead of one universal feed.
- A scalable foundation for future AI-assisted marketing initiatives at Weee!.
The value of AI products is rarely the AI itself.
The real opportunity is reducing friction inside an existing workflow. I started this project framing it as a trend-discovery problem, but research kept pointing me somewhere else: the bigger issue was the disconnect between spotting opportunities and acting on them.
By combining trend intelligence with content ideation, I designed a workflow that helped marketers move from insight to execution more efficiently. The project sharpened how I design AI-assisted workflows, translate operational pain into product opportunities, balance automation with human judgment, and build products that connect business goals with user needs.