Product Discovery & UX Strategy Framework

Learning Document - Aurora Learning Systems Case

A practical example of product discovery, prioritization, and roadmap development

1. Executive Summary

Context

Aurora Learning Systems launched an AI-powered learning management platform in early 2025 with promising initial adoption. However, customer feedback reveals significant friction in usability, onboarding clarity, and feature discoverability. The analytics dashboard presents both UX challenges and technical debt that limits maintainability.

The organization operates with constrained engineering resources and requires external expertise to accelerate product improvements while establishing sustainable agile workflows. Security, compliance, and collaborative iteration are core requirements.

Core Problem Statement

Inconsistent UX patterns and weak feature discovery are creating adoption barriers that prevent users from realizing the platform's full value, threatening retention and growth.

High-Level Opportunities

Establish a unified design system to eliminate UX inconsistencies across primary workflows
Implement structured onboarding to reduce time-to-value for new users
Refactor analytics architecture for scalability and maintainability
Improve feature discoverability through contextual guidance and improved information architecture
Introduce agile processes tailored to current team structure and constraints

Success Metrics

Onboarding Completion

Target: 75% of new users complete core setup flow (estimated baseline: 45-50%)

Feature Adoption

Target: 40% increase in usage of underutilized core features within 90 days

Time to First Value

Target: Reduce from estimated 3-5 days to under 24 hours

Support Ticket Volume

Target: 30% reduction in UX-related inquiries

User Satisfaction (CSAT)

Target: Improve from current state by 25+ percentage points

Development Velocity

Target: 20% improvement in story points delivered per sprint after process stabilization

Note: Baseline metrics are estimated pending formal discovery validation. Phase 0 will establish accurate measurement benchmarks.

2. Discovery Findings

Users & Stakeholders

Primary User Segments:

Educational administrators: Need oversight, reporting, and compliance tracking
Instructors/facilitators: Require content creation tools and learner progress monitoring
End learners: Seek intuitive navigation, progress clarity, and AI-driven personalization

Internal Stakeholders:

Engineering team: Limited bandwidth; concerned about technical debt in analytics module
Product leadership: Focused on retention, competitive differentiation, and sustainable growth
Customer success: Reporting high volume of onboarding support requests

Current Workflow / Journey

New User Journey (Instructors):

Account creation via email invitation
Minimal onboarding: single welcome screen with no guided setup
Dashboard presents full feature set without context or prioritization
Users must discover course creation, AI features, and analytics independently
Many abandon initial setup or submit support tickets

Feature Discovery Path:

Navigation structure lacks hierarchy; critical features buried in menus
No in-app tooltips, progressive disclosure, or contextual help
AI-powered capabilities are not surfaced proactively

Observations

Design inconsistency: Button styles, form layouts, and navigation patterns vary across modules, suggesting organic growth without governance
Cognitive overload: Users face all options simultaneously without guidance toward high-value actions
Analytics complexity: Dashboard requires multiple clicks to access basic insights; performance degrades with larger datasets
Mobile experience: Responsive design issues; critical workflows difficult on tablets and phones
Feedback loops: No systematic mechanism for capturing and prioritizing user feature requests

Pain Points

User-Facing:

New users cannot quickly assess platform value or complete initial setup independently
Instructors overlook AI content assistance features that could save significant time
Administrators struggle to generate compliance reports due to unintuitive analytics interface
Learners experience inconsistent interaction patterns that erode trust

Internal:

Engineering team spends excessive time maintaining fragile analytics codebase
Customer success team overwhelmed with preventable support inquiries
Absence of agile rituals creates planning uncertainty and delivery unpredictability

Risks & Constraints

Resource constraints: Limited engineering capacity requires careful prioritization; initiatives must deliver measurable ROI
Technical debt: Analytics module refactoring carries regression risk and requires careful testing strategy
Market timing: Competitive pressure to improve UX while maintaining feature velocity
Change management: Introducing agile workflows requires team buy-in and gradual adoption
Security and compliance: Any changes must maintain data protection standards and audit trail integrity
Remote collaboration: Distributed team structure requires asynchronous-first communication patterns

3. Opportunities Identified

Opportunity 1: Design System Foundation

Statement: Establish a lightweight design system with reusable components, tokens, and usage guidelines to ensure consistency across all user-facing surfaces.

Expected Impact:

Business: Reduce design and development time for new features by 25-30%; decrease QA cycles by eliminating inconsistency bugs
User: Build confidence through predictable interactions; reduce learning curve when exploring new features

Evidence / Rationale: Inconsistent UI patterns directly correlate with user confusion reports. Design systems are proven to accelerate delivery in resource-constrained teams by eliminating repeated decisions.

Dependencies / Assumptions: Requires design and front-end engineering collaboration; assumes leadership commitment to enforcing standards. May require brief learning period for adoption.

Opportunity 2: Contextual Onboarding Experience

Statement: Implement a role-based onboarding flow that guides users through essential setup steps and surfaces high-value features contextually.

Expected Impact:

Business: Increase activation rate (estimated +30-40%); reduce support ticket volume related to initial setup
User: Achieve first meaningful outcome within first session; understand platform value proposition clearly

Evidence / Rationale: Customer success data indicates onboarding friction as primary support driver. Research shows guided onboarding significantly improves activation metrics across SaaS products.

Dependencies / Assumptions: Requires user research to validate optimal onboarding sequences per role; assumes ability to track completion milestones in analytics.

Opportunity 3: Feature Discovery Framework

Statement: Redesign information architecture and implement progressive disclosure patterns with in-app guidance to improve discoverability of underutilized features.

Expected Impact:

Business: Increase utilization of AI-powered features by 40-50%; strengthen competitive positioning; reduce churn from perceived lack of value
User: Discover capabilities aligned with current tasks; experience platform as intelligent assistant rather than static tool

Evidence / Rationale: Client feedback specifically mentions overlooked core functionalities. Usage analytics would likely reveal significant feature underutilization.

Dependencies / Assumptions: Requires navigation restructuring and potentially tooltips/tours; assumes instrumentation to measure feature adoption changes. Assumption: requires validation through usage analytics during Phase 0.

Opportunity 4: Analytics Dashboard Refactor

Statement: Rebuild analytics module with modern architecture, improved query performance, and simplified UX focused on actionable insights.

Expected Impact:

Business: Reduce maintenance burden by 40-50%; enable faster feature iteration; improve scalability for enterprise customers
User: Access critical insights 3-5x faster; generate compliance reports without frustration; trust data accuracy

Evidence / Rationale: Technical debt explicitly called out by client; performance issues confirmed. Administrators depend on analytics for compliance and decision-making.

Dependencies / Assumptions: Requires significant engineering effort; must maintain backward compatibility or provide migration path; Python expertise needed. Assumption: requires proof-of-concept to validate performance gains before full commitment.

Opportunity 5: Agile Process Implementation

Statement: Introduce tailored agile workflows including sprint planning, retrospectives, and iterative delivery cadence aligned with team structure and constraints.

Expected Impact:

Business: Improve delivery predictability; enable data-driven prioritization; create sustainable pace for continuous improvement
User: Indirect benefit through faster response to feedback and more frequent incremental improvements

Evidence / Rationale: Client explicitly requests agile support. Structured workflows critical for managing multiple parallel initiatives with limited resources.

Dependencies / Assumptions: Requires team training and cultural adaptation; assumes leadership support for protecting sprint commitments; gradual adoption recommended.

Opportunity 6: Mobile Experience Optimization

Statement: Audit and redesign responsive breakpoints and touch interactions to ensure core workflows function seamlessly on tablets and mobile devices.

Expected Impact:

Business: Expand addressable market to mobile-first users; reduce platform switching friction
User: Access platform from any device; instructors can monitor progress on-the-go; learners engage from preferred devices

Evidence / Rationale: Observations revealed responsive design issues. Mobile usage continues growing across education technology.

Dependencies / Assumptions: Should follow design system implementation for consistency; requires device testing infrastructure.

Opportunity 7: Feedback Loop Systematization

Statement: Implement structured mechanisms for collecting, categorizing, and prioritizing user feedback integrated with product roadmap planning.

Expected Impact:

Business: Build customer-centric culture; prioritize high-ROI improvements; demonstrate responsiveness to customer needs
User: Feel heard and valued; gain visibility into roadmap; see their suggestions implemented

Evidence / Rationale: No current systematic approach mentioned. Feedback loops essential for retention and continuous product-market fit refinement.

Dependencies / Assumptions: Requires lightweight tooling and process discipline; customer success team involvement critical.

Opportunity 8: Compliance & Security UX

Statement: Ensure all UX improvements maintain and enhance security posture and audit trail integrity, with clear user communication about data protection.

Expected Impact:

Business: Maintain compliance certifications; enable enterprise sales; avoid regulatory risk
User: Trust platform with sensitive educational data; understand privacy controls clearly

Evidence / Rationale: Client requirement explicitly stated. Education sector has specific compliance obligations.

Dependencies / Assumptions: Must be integrated into all design and development activities; requires security review at key milestones.

4. Prioritization Framework

Framework Selection: RICE

Rationale: RICE (Reach, Impact, Confidence, Effort) was selected because it balances user impact with resource constraints, making it ideal for teams with limited engineering capacity. It forces explicit discussion of confidence levels, which is critical when working with incomplete data during early discovery. The quantitative scoring enables transparent prioritization conversations with stakeholders.

Scoring Guidance:

Reach: Number of users affected per quarter
Impact: 3 = Massive, 2 = High, 1 = Medium, 0.5 = Low
Confidence: Percentage (100% = high certainty, 50% = low data)
Effort: Person-weeks required
Score: (Reach × Impact × Confidence) / Effort

Initiative	Reach	Impact	Confidence	Effort (weeks)	RICE Score	Rationale
Contextual Onboarding	1200	3	80%	4	720	Affects all new users; directly addresses activation friction; validated pattern with strong industry precedent
Design System Foundation	2000	2	90%	6	600	Multiplier for all future work; affects entire user base; proven ROI for consistency improvements
Feature Discovery Framework	1500	2	70%	5	420	High user reach; addresses stated problem; confidence reduced due to need for analytics validation
Agile Process Implementation	15	2	80%	3	8	Internal team impact; indirect user benefit; essential for sustainable delivery but lower immediate user reach
Analytics Dashboard Refactor	300	3	60%	8	68	Critical admin segment; high effort; confidence limited without PoC validation; prioritize after foundation work Assumption: requires validation PoC
Mobile Optimization	800	1.5	75%	5	180	Growing user need; medium impact; should follow design system for consistency; defer to Phase 3-4
Feedback Loop System	2000	1	85%	2	850	Lightweight effort with broad impact; enables continuous prioritization refinement; low complexity
Compliance & Security UX	2000	1	90%	3	600	Table stakes requirement; integrated throughout all phases rather than standalone initiative

Prioritization Notes

Uncertainty explicitly acknowledged: Confidence scores reflect data limitations. Analytics Dashboard scored at 60% pending technical PoC.
Dependencies mapped: Design System positioned early as force multiplier; Feature Discovery depends on improved instrumentation.
Resource reality: Scores adjusted for constrained engineering capacity; high-effort initiatives deferred unless critical.
Phased approach: Top priorities establish foundation for subsequent initiatives while delivering immediate user value.

5. Roadmap

Phase 0: Discovery Alignment & PoC Weeks 1-2

Duration: 2 weeks

Key Deliverables:

Validated user journey maps and pain point inventory
Analytics instrumentation audit and baseline metrics establishment
Technical architecture review for analytics module
Analytics refactor proof-of-concept (limited scope)
Stakeholder alignment workshop and roadmap validation
Risk assessment and mitigation strategy

Outcomes / Success Criteria:

Shared understanding of user needs validated through data
Technical feasibility confirmed for analytics refactor with measurable performance benchmarks
Agreement on success metrics and measurement approach
Phase 1 scope finalized with stakeholder sign-off

Key Risks: Discovery may reveal additional complexity requiring scope adjustment; stakeholder availability for workshops may delay decisions.

Phase 1: Requirements & Solution Definition Weeks 3-5

Duration: 3 weeks

Key Deliverables:

Design system specification (tokens, components, patterns)
Onboarding flow requirements and user stories per role
Feedback collection system specification
Information architecture proposal for improved feature discovery
Agile workflow framework customized for team structure
Detailed functional specifications with acceptance criteria

Outcomes / Success Criteria:

Documented requirements with engineering team sign-off
Clear definition of done for each initiative
Identified edge cases and technical constraints
Sprint planning ceremony conducted successfully

Key Risks: Scope creep during requirements definition; technical constraints may require creative solutions; remote collaboration may extend iteration cycles.

Phase 2: UX/UI Design Weeks 6-9

Duration: 4 weeks

Key Deliverables:

Design system component library (Figma/Sketch + coded components)
High-fidelity onboarding flow designs with micro-interactions
Redesigned navigation and information architecture
Feature discovery patterns and contextual guidance designs
Analytics dashboard wireframes and visual design
Usability testing plan and execution (5-8 users per segment)
Design QA and accessibility audit

Outcomes / Success Criteria:

Designs validated through user testing with 80%+ task success rate
Engineering team confirms technical feasibility of all interactions
Accessibility standards met (WCAG 2.1 AA minimum)
Design handoff documentation complete with redlines and specifications

Key Risks: User testing recruitment delays; design revisions based on feedback may extend timeline; accessibility requirements may require design iterations.

Phase 3: Build MVP Weeks 10-17

Duration: 8 weeks

Key Deliverables:

Design system components implemented in codebase
Onboarding flows for all three user roles (admin, instructor, learner)
Feedback collection mechanism integrated
Improved navigation and feature discovery patterns deployed
Analytics dashboard refactor (Phase 1: core reporting)
Unit and integration test coverage
Internal staging environment testing

Outcomes / Success Criteria:

All acceptance criteria met for prioritized user stories
Zero critical bugs in staging environment
Performance benchmarks achieved (page load under 2s, analytics queries under 1s)
Security review passed with no high-severity findings
Design system adoption documented with usage examples

Key Risks: Technical complexity may require additional engineering time; integration challenges with existing codebase; resource constraints may limit parallel workstreams.

Phase 4: QA & Launch Weeks 18-20

Duration: 3 weeks

Key Deliverables:

Comprehensive QA testing (functional, regression, cross-browser, device)
Beta release to select customer cohort (50-100 users)
User acceptance testing and feedback incorporation
Production deployment plan and rollback procedures
Support team training and documentation
Launch communication and change management materials
Monitoring and alerting configuration

Outcomes / Success Criteria:

Beta users report 80%+ satisfaction with improvements
Zero critical production incidents during rollout
Support team prepared to handle inquiries
Analytics instrumentation capturing all defined success metrics
Rollout completed to 100% of user base

Key Risks: Unexpected production issues requiring rollback; user resistance to change requiring additional communication; beta feedback may reveal refinements needed before full launch.

Phase 5: Iterate & Scale Weeks 21-28

Duration: 8 weeks (ongoing)

Key Deliverables:

Post-launch metrics analysis and insights report
Rapid iteration sprints addressing user feedback (2-week cycles)
Analytics dashboard Phase 2 features (advanced reporting)
Mobile optimization initiative
Continuous design system expansion and refinement
Quarterly roadmap planning based on feedback loop data
Knowledge transfer and team capability building

Outcomes / Success Criteria:

Success metrics trending toward targets (review at 30, 60, 90 days)
Feedback loop generating prioritized improvement backlog
Internal team autonomously managing agile ceremonies
Design system adopted as standard for all new development
Continuous deployment cadence established (weekly or bi-weekly releases)

Key Risks: Metric improvements may require longer timeframe to materialize; competing priorities may dilute focus; team capacity constraints may limit iteration speed.

6. Resources & Budget

Team Roles

Role	Responsibility	Engagement Level
Product Strategist	Discovery facilitation, prioritization, roadmap oversight, stakeholder alignment	Weeks 1-28 (10-15 hrs/week)
UX Researcher	User interviews, usability testing, analytics analysis, insights synthesis	Weeks 1-2, 6-9 (20 hrs/week during active phases)
UX/UI Designer	Design system creation, interface design, prototyping, design QA	Weeks 3-17 (30-40 hrs/week)
Front-End Engineer	Design system implementation, UI development, component library	Weeks 6-20 (40 hrs/week)
Back-End Engineer	Analytics refactor, API development, onboarding logic, integrations	Weeks 10-20 (40 hrs/week)
QA Engineer	Test planning, execution, automation, regression coverage	Weeks 14-20 (30-40 hrs/week)
Agile Coach	Process design, team training, ceremony facilitation, continuous improvement	Weeks 3-28 (5-10 hrs/week)

Effort Ranges

Phase	Duration	Effort Range (person-weeks)	Key Contributors
Phase 0: Discovery Alignment & PoC	2 weeks	6-8 person-weeks	Product Strategist, UX Researcher, Back-End Engineer
Phase 1: Requirements & Solution Definition	3 weeks	8-10 person-weeks	Product Strategist, UX Designer, Agile Coach
Phase 2: UX/UI Design	4 weeks	14-18 person-weeks	UX Designer, UX Researcher, Product Strategist
Phase 3: Build MVP	8 weeks	30-36 person-weeks	Front-End Engineer, Back-End Engineer, UX Designer, Product Strategist
Phase 4: QA & Launch	3 weeks	12-15 person-weeks	QA Engineer, Engineers, Product Strategist
Phase 5: Iterate & Scale	8 weeks	20-28 person-weeks	Full team rotation based on priorities
Total Engagement		90-115 person-weeks

Budget Ranges (USD)

Phase	Duration	Budget Range	Primary Deliverables
Phase 0	2 weeks	$12,000 - $16,000	Discovery validation, analytics PoC, roadmap alignment
Phase 1	3 weeks	$16,000 - $20,000	Requirements, design system spec, agile framework
Phase 2	4 weeks	$28,000 - $36,000	Design system, onboarding designs, usability testing
Phase 3	8 weeks	$60,000 - $72,000	Development, analytics refactor, design system implementation
Phase 4	3 weeks	$24,000 - $30,000	QA, beta testing, production launch
Phase 5	8 weeks	$40,000 - $52,000	Iteration, mobile optimization, knowledge transfer
Total Investment		$180,000 - $226,000

Note: Budget ranges reflect variable scope and resource allocation based on Phase 0 findings. Client's stated budget of $8,000-$12,000/month aligns with approximately $56,000-$84,000 over 7 months, which would require phased approach with prioritization of critical initiatives (Phases 0-2 + select Phase 3 components).

Budget Assumptions

Blended rate: Calculations assume $150-$200/hour blended rate across roles
Remote collaboration: No travel expenses included; assumes distributed tooling and async workflows
Client infrastructure: Assumes client provides development environments, hosting, and existing tooling
Scope flexibility: Ranges accommodate discovery-driven adjustments; formal change control for scope additions
Knowledge transfer: Includes documentation and team enablement; excludes long-term staff augmentation
Third-party costs: Excludes licenses for design tools, testing platforms, or user research incentives (estimated $3,000-$5,000 additional)

Budget Optimization Options

To align with client's stated budget expectations, the following phased approach is recommended:

Foundation Phase (Months 1-3): Phases 0-2 + Design System implementation = $56,000-$72,000
Core Implementation (Months 4-6): Prioritized Phase 3 components (onboarding + feature discovery) = $40,000-$50,000
Analytics & Scale (Months 7+): Analytics refactor + Phase 5 iterations = flexible based on outcomes

This staged investment allows early value realization and data-driven decisions about subsequent phases.

7. Engagement Model

Work Structure

The engagement is designed for transparency, flexibility, and sustainable collaboration within resource constraints:

Iterative delivery: Two-week sprint cycles with demonstrable incremental progress
Asynchronous-first: Written documentation and recorded demos accommodate distributed schedules; synchronous meetings reserved for decisions and workshops
Shared visibility: Project management dashboard (Jira/Linear/Notion) with real-time status updates
Decision gates: Formal approval points at phase transitions prevent downstream churn
Risk management: Weekly risk register review; escalation protocols for blockers
Quality assurance: Built-in review cycles at requirements, design, and code levels

Key Deliverables

Strategic Artifacts:

Product discovery report with validated insights
Prioritization framework and scored opportunity backlog
Phased roadmap with success metrics and KPIs
Quarterly planning framework and feedback loop process

Design Artifacts:

Design system documentation and component library
High-fidelity mockups and interactive prototypes
Usability testing reports and recommendations
Design handoff specifications with annotations

Technical Artifacts:

Technical architecture documentation
Implemented features with test coverage
API documentation and integration guides
Performance benchmarks and monitoring dashboards

Process Artifacts:

Agile workflow documentation tailored to team
Sprint retrospective insights and continuous improvement log
Team training materials and knowledge transfer documentation

Decision Gates

Formal approval required at these checkpoints to ensure alignment and prevent rework:

Gate 1 (End of Phase 0): Validated discovery findings, confirmed scope and roadmap, budget approval for Phase 1-2
Gate 2 (End of Phase 1): Requirements sign-off, technical feasibility confirmation, design direction approval
Gate 3 (End of Phase 2): Final designs approved, usability testing validated, development ready
Gate 4 (Mid Phase 3): Sprint review at 50% completion, scope adjustment if needed, risk mitigation plan
Gate 5 (End of Phase 4): Launch approval, production readiness checklist complete, support team trained

Responsibilities Matrix

Activity	Client Responsibilities	Consultant Responsibilities
Discovery & Research	Provide access to users for interviews Share existing analytics and feedback data Identify stakeholders for workshops Review and validate findings	Facilitate user research sessions Conduct analytics audit and analysis Synthesize insights and recommendations Present findings and facilitate alignment
Requirements & Planning	Clarify business requirements and constraints Participate in prioritization discussions Approve roadmap and phasing decisions Communicate technical limitations early	Document functional specifications Lead prioritization framework application Create detailed roadmap and timelines Recommend technical approach and architecture
Design	Provide brand guidelines and assets Review design iterations and provide feedback Participate in usability testing sessions Approve final designs at Gate 3	Create design system and component library Design all user-facing interfaces Conduct usability testing and iterate Provide design QA throughout implementation
Development	Provide development and staging environments Grant necessary system access and credentials Conduct code reviews (optional collaboration) Allocate internal engineering for integrations	Implement designed features and components Write unit and integration tests Document code and technical decisions Coordinate with client engineering team
QA & Launch	Conduct user acceptance testing Provide production environment access Coordinate internal change management Monitor production stability post-launch	Execute comprehensive QA testing Coordinate beta release and gather feedback Support production deployment Provide post-launch monitoring and support
Iteration & Knowledge Transfer	Track and report on success metrics Participate in retrospectives and planning Internalize agile processes and design system Own product roadmap after engagement	Facilitate continuous improvement cycles Train team on processes and tools Document all systems and decisions Transition ownership smoothly

Communication Cadence

Daily: Asynchronous updates via shared workspace (Slack/Teams channel)
Weekly: Sprint planning and review (60-90 minutes); separate stakeholder sync (30 minutes)
Bi-weekly: Sprint retrospective and continuous improvement discussion (45 minutes)
Monthly: Executive steering committee update with metrics dashboard review (60 minutes)
Ad-hoc: Decision-making workshops as needed at phase transitions

Response time expectations: Non-urgent questions within 24 hours; blockers escalated immediately with 4-hour response target.

Learning Reflection

This document demonstrates how product strategy, UX research, and prioritization frameworks come together to transform ambiguous customer feedback into an actionable roadmap. The Aurora Learning Systems case illustrates several critical principles:

Discovery drives decisions: Investment in Phase 0 prevents costly assumptions and creates shared understanding
Uncertainty must be explicit: RICE confidence scores and PoC requirements acknowledge what we don't yet know
Constraints are opportunities: Limited resources force ruthless prioritization that often yields better outcomes than unlimited budgets
Process enables velocity: Agile frameworks and design systems are investments that compound over time
Measurement matters: Without clear success metrics and instrumentation, improvement becomes guesswork

The frameworks and approaches shown here are adaptable across industries and organization sizes. The key is matching the rigor of the method to the complexity of the problem and the constraints of the context.

See these frameworks applied to a complex multi-stakeholder product challenge

ROI Detective Agency Case Study

This document is a learning artifact demonstrating product strategy methodologies. All company details are illustrative.