- Define scope and success metrics: identify audiences, signals (awareness, consideration, loyalty), and a 4-point brand score.
- Map user journeys and touchpoints: list interactions (website, signup, docs, support) and rate cross-channel consistency.
- Collect data from multiple sources: surveys with customers and prospects, support transcripts, product reviews, investor decks, partner emails, and analytics.
- Benchmark against 3-5 direct competitors and 2 adjacent startups: compare positioning, tone, visuals, and product messaging.
- Audit visual and verbal identity: logo, color, typography, imagery, voice, and value propositions; validate accessibility and scalability for AI products.
- Assess product alignment: verify naming, onboarding, and docs reflect the brand promise.
- Create a prioritized action plan: categorize gaps by impact and effort; assign owners and milestones.
- Skipping stakeholders: include marketing, product, sales, support, and engineering to avoid bias.
- Overemphasizing visuals at the expense of clarity: ensure messages communicate capabilities and limits of AI.
- Using generic AI descriptors: specify applications, data domains, and outcomes to differentiate.
- Failing to measure brand on product experience: track onboarding drop-offs and API confusion; align docs with branding.
- Start with a 2-week discovery sprint: collect assets, map touchpoints, and define success metrics.
- Use a brand scorecard: score clarity, relevance, trust, and distinctiveness on a 1-5 scale, quarterly updates.
- Prioritize fixes by business impact: align with product roadmap and go-to-market plans.
- Document decisions and rationales: embed changes in product briefs and design systems.
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