I kept principles intentionally tight. These acted as a decision filter when trade offs appeared:

• Clarity over density: Prefer structured layouts that reduce cognitive load, especially where data is complex or unfamiliar.

• Progressive disclosure: Put heavy detail (research depth, attribute level scoring) behind deliberate interaction, so the default experience stays approachable.

• Governance without friction: Make enterprise controls (admin editing, brand protection) explicit, but don’t punish non admin users who simply want to generate concepts.

• Trust through transparency: If we show a score, we also show the reasoning structure and the levers users can pull to improve outcomes.

Redesign the landing experience around momentum and re entry

Problem:
Users needed a clearer entry point and a faster way back into relevant exploration, without starting from a blank prompt every time.

Approach:

• Introduced smarter landing page prompts that can be dynamically generated from a user’s conversation history, plus curated defaults for first time users.

• Used the landing page as a stable “home” for Brand Configuration management, so brand setup isn’t buried mid chat.

Why it mattered:
It reduced blank page paralysis, supports returning users, and creates a predictable place for high impact configuration.

First time usage (left) vs returning usage (right)

Make Brand Configuration enterprise safe

Problem:
Brand inputs need careful management because they can be accidentally edited, set up inconsistently, or made visible to the wrong users.

Approach:

• Designed Brand Configuration so users can add/edit/delete brands and attach a logo to influence the concept imagery.

• Implemented a permissions model where clients can choose between open editing vs admin only editing, supporting governance for large organisations while preserving flexibility for smaller teams.

• Kept brand selection optional in the concepting flow, so the tool still works for unbranded exploration.

Why it mattered:
This balances two competing enterprise needs; brand safety and creative iteration speed. Admin only editing reduces the chance of “brand drift” and accidental overwrites, while optional selection avoids blocking ideation when brand setup isn’t ready (a real concern raised in training feedback).

Various viewer vs admin user flows to consider

Brand configuration form for admins on first usage

Force filter confirmation up front to protect relevance and the conversation

Problem:
In a conversational UI, it’s easy for context to drift. If market/category/stage aren’t stable, outputs become inconsistent and users can’t tell what changed.

Approach:

• Added an explicit filter confirmation step at the start of each conversation, capturing Supercategory, Category, Market, Innovation Stage (where applicable), and optional Brand selection.

• Enforced single select category and market (both a technical constraint and a product clarity choice).

• Introduced help content for innovation stages so users understand what they’re choosing.

• Added a toggle to show only Black Swan supported filters (external data source), preventing users from selecting contexts that won’t return the expected analysis.

• Made “start a new conversation to change direction” an explicit workflow rule, keeping each thread coherent.

Why it mattered:
We accept a small amount of upfront structure in exchange for much higher relevance, better explainability (“this score was for this context”), and fewer “why did the tool do that?” moments. It’s also a guardrail against conversational drift, which is a common failure mode in chat based products.

Replace the old quick prompt research flow with auto generated structured research

Problem:
Testing showed users were clicking through prompts without engaging in meaningful research, then generating concepts with shallow context.

Approach:

• Removed the prior quick prompt sequence and instead auto generated a structured research view immediately after filters are confirmed.

• Defined research sections that are scannable and role appropriate.

• Ensured research adjusts to chosen brand and innovation stage, so concepts inherit the right strategic frame.

• Preserved flexibility: users can ask follow ups within research or move straight to “Generate Concepts”.

Why it mattered:
The UI stops pretending users want to do research steps and instead provides the right context by default, while still letting motivated users go deeper. It respects expert users’ time while improving concept quality for everyone.

Designing scores users can understand and trust

Problem:
Scores are only valuable if users can interpret them and act on them. We also saw internal concern about mismatches between tables, roll up scores, and explanatory text; a trust risk.

Approach:

• Added integrated scoring within the concept window, with an overall rating built from three metrics; Demand Momentum, Distinctiveness, Brand Fit.

• Added a Key Data tab to provide transparent, granular support for the score: short metric explanations plus attribute level scores, roles (hero/supporting), and importance weighting.

• Improved concept usability for stakeholder review by enabling expandable concept images (full screen).

Why it mattered:
This uses progressive disclosure to keep the primary concept experience fast and legible, while offering auditability for users who need to defend decisions. It also reduces the risk that scoring becomes a black box, especially important when Spark is positioned as evidence based and grounded in proprietary data.