It's using analytics and AI to build data-backed journey maps—from first touch to support—so teams see where users succeed, where they drop, and what to fix first.
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Experience mapping with AI turns clickstreams, sessions, surveys, and support conversations into clear journey maps. We cluster behavior, surface drop-, and root causes, and highlight the steps that matter most across web, app, and service channels.
We combine event analytics, heatmaps/session replay, and AI-assisted research to spot patterns people miss. The output is a ranked list of issues and opportunities—paired with wireframes, content updates, and a test plan to validate impact quickly.
A prioritized backlog with owners and KPIs; journey maps and opportunity trees; tagging plans; and experiment briefs. Early wins focus on onboarding, search/browse, forms, and help-in-flow to lift conversion and reduce time-to-value.
Privacy and accessibility are first-class: consented data only, role-based access, and WCAG-aligned design. We document assumptions and decisions so changes are auditable and easy to scale across teams.
It's using analytics and AI to build data-backed journey maps—from first touch to support—so teams see where users succeed, where they drop, and what to fix first.
AI clusters behavior and language patterns, linking events, sessions, and feedback. It reveals hidden bottlenecks and predicts where guidance or personalization will help most.
Yes. The method adapts to self-serve PLG flows and high-touch enterprise journeys across industries like SaaS, retail, healthcare, and industrial.
Clickstream events, session replay, search logs, form errors, surveys/interviews, CRM/support data—only with consent and least-privilege access.
Not necessarily. We start with your analytics and research stack and recommend gaps only if required to answer specific questions.
Quarterly is typical; update sooner when you ship significant changes or see KPI shifts. Treat maps as living assets tied to dashboards and experiments.
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