Landscapes: Innovation trend analysis
Mintel Landscapes; a web-based analytics tool inside Mintel’s client platform. It helps innovation teams quickly identify what’s emerging, established, and likely to grow next by visualising product-launch data across different views (e.g. flavours, ingredients, claims, formats/textures) and showing forecasting alongside the current landscape.
ROLE
PROBLEM
Innovation teams couldn’t quickly see what’s emerging vs established in a market. Finding growth opportunities meant stitching together separate tools or doing manual analysis so we needed a single, trustworthy market overview
SCOPE
Led the UI and interaction design
Merge previously separate tools into one cohesive experience
Designed the navigation model so users can switch between lenses without losing their place.
Overview
Mintel builds products that help companies make better decisions about innovation and growth.
This project proves my ability to bring clarity to complex, data-heavy products by designing an interface that helps users interpret sophisticated analytics quickly and confidently.
The core value isn’t the number of features, it’s the speed at which users can understand a market and move from overview to evidence. The landscape chart is designed as that starting point: a snapshot that helps users separate signal from noise and then drill into forecasting and supporting detail when needed.
The problem
Before this work, three major analytics experiences lived as separate tools, one focused on flavours, one on ingredients and one focused on claims. That split created friction: users had to decide where to start, learn different patterns, and mentally connect insights across tools.
The product direction was to unify those experiences into a single tool, expand it to cover additional lenses (claims and formats/textures), and add forecasting so the tool supports forward-looking decisions rather than only hindsight.
This created a classic design challenge: how do you add breadth (more views, more audiences) without increasing cognitive load? In analytics, if the interface feels heavy or inconsistent, trust drops quickly.
There were also practical constraints that affected UX decisions. For example, some navigation elements were controlled by a shared platform framework rather than the product itself, limiting what could be moved within the app.
My role and collaboration
I led the UI and interaction design for the unified Mintel Landscapes experience including navigation, layout hierarchy, and reusable patterns.
I worked closely with product and engineering throughout, especially where UI ambition needed to be balanced against feasibility and performance (e.g. forecasting at scale) and platform constraints.
Rather than a one-time handoff, I partnered through refinements: identifying issues most likely to erode trust (layout breaks at zoom, unclear hierarchy, tooltips clipping, contrast) and converting them into implementable improvements.
Design decisions
Our goals included:
Making the chart instantly readable: users should understand what they’re looking at within seconds.
Clarity over density: The first screen should be scannable and teach the pattern quickly, pushing complexity into the drill-down.
Stable context: Switching views or filters shouldn’t feel like starting again.
Progressive disclosure: Deeper detail (definitions, forecasting, examples) should appear when users ask for it.
Scalable patterns: Design for additional views and future evolution without one-off UI.
Making the landscape chart the front door
As a product team we chose a scatterplot for the landscape overview because the core job of the tool is to show two signals at once; how established an attribute is (Prevalence) and whether it’s accelerating or declining (Growth Momentum). Plotting Prevalence on the x axis and Growth Momentum on the y axis lets users interpret the market in a single glance; niche vs dominant, rising vs falling, before deciding where to drill in.
In dense markets, many attributes can share similar values, which causes points to overlap and reduces the chart’s usefulness. To preserve readability, I reduced point fill opacity so overlapping clusters become visible, and paired this with small positional jitter to separate coincident points without changing the underlying story of the data. Where lowering opacity risked weakening visibility, I reinforced the points with a subtle stroke so they remained legible while still reducing visual noise.
Finally, we intentionally kept the scatterplot visual consistent while improving the underlying methodology and the supporting UI around it. This avoided retraining users on a new chart type, preserved the familiar “market snapshot” mental model, and allowed us to focus change where it added most value (interpretation, drill down, forecasting).
Unifying separate tools into one coherent experience
I created a single product entry point with internal navigation between lenses, instead of making users jump between separate tools.
This reduced the “where do I go?” anxiety and encouraged cross-attribute exploration, which is where the insight value lives.
Designing the navigation flow for first-time and returning users
A key navigation decision was to balance guidance for first-time users with continuity for returning ones.
On first use, the experience adapts to the user by pre-selecting filters based on their industry, giving them a relevant starting point without requiring upfront setup. From there, the system remembers their most recent view and filter context, so future visits feel continuous rather than reset.
My intent was to reduce early friction while building familiarity over time. For first-time users, a guided starting point removes decision load. For returning users, restoring their previous state helps them pick up where they left off making the product feel intuitive and reliable from the first few seconds.
A guided starting point for new users, with state persistence for returning users.
Filters as the anchor for context and exploration
The broader goal was a unified interface that lets users switch view/tab seamlessly rather than treating each view like a separate product.
I kept the filter area stable and prominent so the user’s context (category/market) is preserved while switching lenses.
This helped users think “what market and category am I exploring?” first. Keeping that stable makes exploration feel fast and reversible.
Overview → drill-down → forecast: progressive disclosure of complexity
Landscapes and forecasts are designed to work together. The chart helps users identify what matters, forecasts help validate whether a trend looks sustainable or short-lived.
This is why the UI hierarchy matters. The overview should remain scannable, while forecasting and supporting detail appear in drill-down where users have already signalled intent by selecting an attribute
Progressive disclosure: overview first, then forecasting depth on selection
Outcome
Early feedback shared described clients “absolutely LOVING” the new ways to leverage the data, while also surfacing enhancement ideas, which is a useful signal that the core value and interaction model landed.
The unified experience also created a clearer foundation for onboarding and for routing users from a conversational interface into the right visual analysis page.
Learnings / reflection
Unification is mostly about removing invisible friction: Clear defaults, stable anchors, and predictable navigation make complex analytics feel approachable.
In data products, trust is built through small details: Tooltip behaviour, grid discipline, and contrast aren’t polish, they’re part of the product’s credibility.
New capabilities only work when the hierarchy is deliberate: Keeping a strong overview → drill-down structure prevented forecasting and added lenses from turning into noise as scope expanded.