Quick Snapshot — Who This Conversation Is For

• What single retention or completion problem brought you to this conversation today? Options: First-to-second-year loss, Mid-program stopouts, Low graduation within 4/6 years, Equity gaps for specific cohorts, Unclear — we need help diagnosing, Other
• If you can share one baseline metric (e.g., first-to-second-year retention = X% for cohort Y), what is it and what cohort/timeframe does it cover?
• Who will be the executive sponsor and who will be the day-to-day campus owner for this effort? Options: Provost, VP Enrollment Management, Dean of Students, Director of Student Success/Advising, Registrar, Other
• How urgent is this for your leadership—what’s the realistic timeline to show pilot results that matter to executives? Options: Immediate (1–3 months), Near-term (3–6 months), Medium (6–12 months), Longer-term (>12 months), Unsure
• Which institutional mandates or pressures are driving this work right now? Options: Performance-based funding, Accreditation review, Enrollment targets, Retention-based budgeting, Equity/accountability goals, Reputation concerns, Other

What’s Actually Happening on Campus — and Why It Surprises You

• What if the students slipping away aren’t the ones you expect — are you confident your current data actually finds them? Options: Yes — we believe we capture most at-risk students, Somewhat — we capture some but miss others, No — we suspect big blind spots, Not sure
• Which systems contain the primary student signals we’d need to analyze (select all that apply)? Options: SIS (student records), LMS (Canvas, Blackboard), Financial aid system, CRM/Admissions, Library/learning systems, Student affairs case management, Institutional data warehouse/BI, Other
• How would you describe the completeness and freshness of those data sources today? Options: Near real-time and complete, Nightly/weekly updates with some gaps, Periodic exports with significant lags, Fragmented — difficult to trust, Unsure
• Where do you sense the biggest blind spots (for example: financial holds, attendance, academic probation, student employment) and why do those matter here?
• Tell us about a student who surprised you recently — what signs were missing from your systems that would have helped?

Who's Pulling the Levers — and Who Feels Overwhelmed

• How often do advisors or faculty proactively reach students before a crisis — and what typically causes that cadence to break down? Options: Consistent proactive outreach, Mostly reactive after issues appear, Inconsistent — depends on staff availability, Rarely — outreach is limited
• What are advisor-to-student ratios and how are caseloads assigned? Options: <1:100, 1:100–1:250, 1:250–1:500, >1:500, Varies widely by program
• Describe the primary workflow for an at-risk alert today (how does an alert get created, routed, and closed)? Options: Automated alerts routed to advisors, Manual lists generated weekly, Ad-hoc notifications via email, No formal alert process
• Where do advisors or faculty most often get stuck when trying to intervene, and how long has that been holding back consistent outreach?
• How do advisors feel about current tools and load—do they trust the signals or see them as noise? Options: Trust and use them, Use but distrust accuracy, Ignore due to noise, Mixed across teams

If Predictive Scores Were Real — What Would You Do Differently?

• If we handed you a validated risk score today that reliably flagged students who would stop out, what would be the very first thing you'd want your team to do differently?
• Which interventions are available now and which would require new resourcing (select current capabilities and flag gaps)? Options: One-on-one advising outreach, Proactive tutoring/learning support, Targeted financial aid/emergency funds, Case management from student affairs, Automated nudges/text campaigns, Policy-level changes (e.g., hold removals)
• How are interventions currently tracked and attributed to outcomes (e.g., retained vs. not)? Options: Tracked in SIS/case system with outcomes, Manual spreadsheets, Not tracked systematically, Tracked but not attributed to outcomes
• What practical constraints would limit your ability to scale interventions (people, budget, policy, technology)?
• Which outcome would make campus leadership say ‘this worked’ — a retention percentage, credit completion increase, GPA change, cost per retained student, or closing an equity gap? Options: First-to-second-year retention rate, Semester-to-semester retention, Credit completion per term/year, Average GPA increase, Reduced equity gaps, Cost per retained student

Data & Integration Reality Check — What’s Fast vs. Fragile

• If integrations take months instead of weeks, what systems can realistically be turned on quickly and which will be heavy lifts?
• Do you have API access or scheduled export pipelines for SIS and LMS currently? Options: APIs available for both SIS and LMS, API for one, export for the other, Only manual exports, No reliable exports/APIs
• Who is your technical owner for integrations and what SLA or turnaround do they typically commit to for new data requests?
• Which data elements can you provide today without governance delays (enrollment, grades, LMS activity, financial aid status, caseload notes)? Options: Enrollment/registration, Grades/grades history, LMS engagement (clicks, submissions), Financial aid status, Advising notes/case records, None of the above
• Have you previously completed DSAs/DSARs/IRB approvals for analytics pilots, or would that be a new process? Options: Approvals already in place, Some approvals exist, others needed, No approvals — new process required, Unsure

Politics, Pressure, and People — Who Wins When Things Get Hard

• What happens when a model labels a student ‘at-risk’ and a faculty member or parent objects — who ultimately decides whether outreach proceeds? Options: Student affairs/advising decides, Academic unit/faculty decides, Leadership/Provost decides, Depends on case type, Unsure
• Who on campus must sign off on a pilot and who is most likely to slow or block progress? Options: Provost/Academic Affairs, VP Enrollment/Student Success, Registrar, Legal/Compliance, Faculty Senate, Union/HR, IT/Data Governance, Other
• What political sensitivities or ethical concerns about predictive analytics do we need to anticipate and address upfront?
• Who would be your strongest internal champion for this work and what motivates them?
• How would you like us to demonstrate transparency to students and faculty (e.g., opt-outs, explainable risk factors, joint governance)? Options: Student-facing explanations/opt-out, Faculty briefings and advisory group, Joint governance committee, Regular transparency reports, Other

Success Signals and Acceptance Criteria — What ‘Win’ Actually Looks Like

• If we declared the pilot a success at the end of the evaluation window, what exact numbers and behaviors would have to change?
• Which primary success metrics should we prioritize for pilot acceptance? Options: First-to-second-year retention, Semester continuation rates, Per-term credit completion, Course pass rates in gateway courses, Decrease in stopout numbers, Reduction of equity gaps
• What is the minimum effect size or threshold you would consider a meaningful success (select one)? Options: ≥5 percentage point absolute improvement, 3–5 percentage point absolute improvement, 1–3 percentage point absolute improvement, Improved outcomes for target cohorts even if small overall, Unsure
• What cadence and format of reporting would keep your leadership comfortable during the pilot (dashboards, weekly briefings, monthly reviews)? Options: Real-time dashboards, Weekly summaries, Monthly leadership review, Quarterly strategic review, Combination
• Who will have the final sign-off authority to accept or reject pilot outcomes? Options: Provost, VP Enrollment/Student Success, Pilot Steering Committee, Other

Readiness & Next Steps — What Would Cause You to Pause or Proceed

• What risks or unknowns would cause you to pause after mutual commit (unexpected costs, data limitations, stakeholder opposition, technical failures)? Options: Unexpected costs, Data quality/access issues, Stakeholder opposition, Technical/integration failure, Policy/privacy concerns, Other
• What pilot cohort size and selection approach feels practical and defensible to you (e.g., single program, multiple risk strata, incoming cohort)? Options: Small pilot program (50–200 students), Moderate (200–1,000), Large (>1,000), Program-based pilot, Risk-stratified pilot, All new admits cohort
• What target go-live window would you prefer for the pilot intervention? Options: Next academic term, Next 2–3 months, 3–6 months, 6–12 months, Unsure
• Who on your team will be assigned as program manager and who will be the technical contact?
• What specific support from our team during the first 30, 60, and 90 days would make you feel most confident? Options: Integration support and connectors, Model calibration with historical data, Advisor workflow set-up and training, Weekly progress reviews, Change management with faculty/staff, Other
• On a readiness scale, how prepared is your institution to start a pilot right now? Options: Fully ready, Mostly ready with minor blockers, Partially ready — significant work needed, Not ready

• Pre-Work Alignment & Current-State Confirmation
• Risk Score Validation Workshop — Diagnosis & Proof
• Integration & Data Flow Validation — SIS / LMS Live Tests
• Advisor Workflow Simulation & Role-Play
• Synthesis, Impact Estimation & Pilot Decision Review

• Document UX changes necessary to remove blockers to timely intervention.
• Get mutual sign-off on integration acceptance criteria to enable pilot sequencing.
• IT to correct data mappings for any failed or misformatted fields and re-run ingestion test.
• Seller to provide a data reconciliation report showing differences between SIS/LMS extracts and platform inputs.
• Security team to sign off on PII handling and provide required documentation.
• Agree timeline to close high-priority data issues before pilot kickoff.
• Overview of Advisor Paths & Escalation Rules
• Confirm workflows lead to clear, documented advisor actions for each validated scenario.
• Measure advisor time-to-action and identify staffing implications or workflow tuning needs.
• Agree on advisor acceptance criteria and required training modules before pilot.
• Introductions & Objective
• Capture a prioritized list of UX and workflow adjustments for implementers.
• Define advisor training syllabus and schedule a training pilot session.
• Adjust alert thresholds or routing rules based on time-to-action findings.
• Assign owners to track closed-loop outcome capture for pilot students.
• Recap Confirmed Current State & Consequence
• Obtain mutual agreement on expected retention impact and the assumptions behind it.
• Sign off on pilot scope, success metrics, and acceptance criteria or list outstanding gating issues.
• Assign owners and timeline for remaining high-priority fixes required prior to pilot.
• Finalize and circulate pilot charter with cohort definition, metrics, owners, and timeline.
• Execute any remaining data-access or security paperwork needed before pilot start.
• Seller to schedule model calibration and integration sprints based on agreed fixes.
• Customer to confirm advisor participants and training schedule for pilot readiness.
• Customer confirms a single, explicit current-state sentence to guide the experience.
• Documented consequence (financial, operational, compliance) tied to the problem.
• Agree on a one-sentence future-state outcome that the experience must prove.
• Obtain list of sample student records and data access commitments for live validation.
• Customer provides secure extract or IDs for 8–12 consented student scenarios (including edge cases).
• Customer IT confirms test credentials, data schema, and PII handling for the upcoming sessions.
• Seller drafts the one-sentence current-state and future-state statements for customer sign-off.
• Schedule hands-on validation workshops with required participants (advisors, IT, data leads).
• Reconfirm Current & Future State
• Confirm model identifies the students the customer expected and that contributing features align with institutional context.
• Surface and document all discrepancies between model outputs and institutional knowledge with root-cause hypotheses.
• Agree on immediate calibration or feature engineering actions required before pilot.
• Produce a realistic estimate of retention lift assumptions tied to validated scenarios.
• Seller to produce a scenario validation log listing agreements, mismatches, and root-cause notes.
• Customer to supply missing features or corrections for at least 3 mismatched cases (e.g., grade changes, external aid corrections).
• Model team to schedule a calibration run addressing agreed fixes and share impact estimates.
• Identify two additional edge-case students to validate model robustness.
• Critical Field Mapping Review
• Confirm critical fields are ingested correctly and mapped to model inputs.
• Validate that data latency meets operational needs for timely interventions.
• Document data quality gaps and assign remediation owners and timelines.
• Live Ingestion Demo
• Summary of Validation Findings
• One-sentence Current State
• Role-Play: Alert Triage
• Model Overview (focused)
• Role-Play: Outreach & Intervention Recording
• Consequence Quantification
• Impact Estimation & Assumptions
• Latency & SLA Checks
• Case-by-case Walkthroughs
• Pilot Scope, Acceptance Criteria & Timeline
• Data Quality & Edge-case Reconciliation
• One-sentence Future State
• Calibration & Error Modes
• Measure Time & Cognitive Load
• Sample Student Selection & Consent

• SIS data integration and real-time sync
• LMS engagement data ingestion pipeline
• Financial aid and billing data feed
• Predictive risk-scoring engine deployment
• Early-alert trigger engine activation
• Advisor caseload dashboard deployment
• Intervention tracking and outcome logging
• Student communications automation (email/SMS)
• Role-based access and FERPA controls
• Mobile advisor app deployment
• Advisor hands-on platform training
• Outcome analytics and retention reporting dashboard

SIS data integration and real-time sync

• Which SIS vendor(s) are in use at your institution? Options: Ellucian Banner, PeopleSoft/Oracle Student, Workday Student, Ellucian Colleague, Custom/Other
• Which student record fields are required for the initial integration (e.g., enrollment status, course registrations, majors, demographics, advisor assignments)? List required fields or groups.
• What is the desired sync frequency between SIS and the platform? Options: Real-time/near real-time, Every 15 minutes, Hourly, Daily batch, Weekly batch
• What delivery mechanisms does your SIS provide for data extraction? Options: REST APIs/JSON, SOAP APIs, Flat-file (SFTP), Database replica/ODI access, No APIs / manual export
• Are there student populations or records that must be excluded or masked for privacy (e.g., FERPA holds, restricted programs)? Options: Yes, No
• Who is the institutional owner/technical contact for SIS integration and what team will provide credentials and testing access?
• Approximately how many student records will be in scope for the initial deployment? Options: Less than 5,000, 5,000-20,000, 20,000-100,000, More than 100,000

LMS engagement data ingestion pipeline

• Which LMS vendor(s) do you use? Options: Canvas (Instructure), Blackboard, D2L Brightspace, Moodle, Other/Custom
• Which LMS event types should be ingested initially (e.g., page views, assignment submissions, discussion posts, quiz results, time-on-task)? Options: Page views, Assignment submissions, Discussion posts, Quiz/assessment scores, Time-on-task/engagement duration, Other
• What is the desired latency for LMS events to appear in the platform? Options: Real-time/streaming, Near real-time (minutes), Hourly, Daily
• Does the LMS support Caliper/xAPI or event APIs for streaming engagement data? Options: Caliper available, xAPI available, Proprietary event API, Only nightly exports, Unknown / need help
• How consistent are course identifiers between your SIS and LMS (do they map 1:1)? Options: Yes - clean 1:1 mapping, Partially - requires mapping work, No - significant reconciliation required
• Do you have any policy restrictions on storing or analyzing LMS activity tied to identifiable students? Options: Yes, No
• Which courses, terms, or cohorts should be prioritized for the initial LMS ingest?

Financial aid and billing data feed

• Which systems manage financial aid and billing at your institution? Options: Banner Financial Aid, PeopleSoft Financials/Student, Workday Financials/Student, Colleague, Other/Custom
• Which financial aid/billing elements must be available in the platform (e.g., aid status, disbursement dates, holds, account balance, payment plans)?
• What is the required refresh cadence for financial data (e.g., daily, real-time on disbursement)? Options: Real-time on change, Daily, Weekly, On-demand/manual
• Are there legal or vendor restrictions on sharing financial aid data with third-party analytics providers? Options: Yes, No, Unknown - need contract review
• Do you expect billing holds or financial flags to automatically trigger outreach or advisor alerts? Options: Yes - automatic alerts, No - manual review first, Depends on flag/type
• Who will be the financial aid/billing point of contact for field mapping, test data, and production signoff?

Predictive risk-scoring engine deployment

• Which student outcomes should the model predict initially (select all that apply)? Options: First-to-second-year retention, Stop-out/withdrawal, Course failure (D/F/W), On-time graduation likelihood, Probable financial stop-out
• Do you have historical data (how many years) and an exportable dataset for model training/calibration? Options: Less than 2 years, 2-3 years, 4+ years, Not sure / need assistance
• Approximately how many historical student-term records are available for training? Options: Less than 5,000, 5,000-20,000, 20,000-100,000, More than 100,000
• What level of model explainability do you require (score only, feature-level explanations, or both)? Options: Scores only, Feature-level explanations (e.g., SHAP), Both scores and explanations
• What are the acceptance criteria for model performance (example metrics: AUC, precision@K, lift, calibration targets)? Please specify thresholds if known.
• How frequently should the model be retrained or recalibrated (e.g., continuous learning, quarterly, annually)? Options: Continuous/near real-time, Monthly, Quarterly, Annually, One-time calibration
• Will the model be permitted to use PII/PHI fields for training (e.g., SSN, DOB) or should it be trained on de-identified data? Options: PII/PHI allowed with controls, De-identified only, Need guidance/legal review

Early-alert trigger engine activation

• Which trigger types should be active at launch (e.g., grade drops, low engagement, missed payments, advisor referrals)? Options: Grade drops, Low LMS engagement, Missed payments/billing holds, Advisor-flagged at-risk, Other
• Do you want default risk thresholds provided by the vendor, or custom thresholds defined by your institution? Options: Vendor default thresholds, Institution defines custom thresholds, Hybrid - start vendor then tune
• Who should receive early-alert notifications (roles, teams) and through which channels (email, in-platform, SMS)? Options: Assigned advisor, Advising team lead, Student services, Automated outreach system, Other
• Should alerts include recommended actions or playbooks for advisors, and do you have existing playbooks to import? Options: Yes - include recommendations, No - alerts only, We have playbooks to import
• Do you require an advisor feedback loop to mark false positives/negatives and retrain triggers? Options: Yes - feedback loop required, No - not required initially, Planned for later
• What SLA/timing do you expect between an alert event and advisor notification? Options: Immediate (minutes), Within 1 hour, Same business day, Daily digest
• What acceptance criteria will define a successful trigger configuration for pilot signoff (e.g., alert precision, advisor response rate)?

Advisor caseload dashboard deployment

• Which advisor roles will use the dashboard (e.g., academic advisors, success coaches, financial aid counselors)? Options: Academic advisors, Success coaches, Financial aid counselors, Retention specialists, Other
• What key caseload metrics do you want on the dashboard (e.g., risk distribution, upcoming at-risk students, outreach backlog, recent notes)?
• How should students be grouped for advisors (assigned caseload, program/major, cohort, at-risk segment)? Options: Assigned caseload, Program/Major, Cohort/Entry term, Risk segment, Custom grouping
• Do advisors need the ability to reassign students, add notes, schedule appointments, and record interventions from the dashboard? Options: Yes - full CRUD capabilities, Limited (notes & appointments only), View-only
• How many advisor users and front-line staff will require dashboard access for the pilot? Options: 1-10, 11-50, 51-200, 200+
• Do you require role-based dashboards (different views for advisors vs. managers)? Options: Yes, No
• Are there specific visualizations or export formats managers need (e.g., CSV export, scheduled PDF reports)? Options: CSV/Excel export, Scheduled PDF/Dashboard emails, API access to metrics, Interactive visualizations only

Intervention tracking and outcome logging

• Which intervention types should be tracked at launch (e.g., outreach call, appointment, workshop, financial counseling, academic coaching)? Options: Outreach call, One-on-one appointment, Workshop/classroom intervention, Financial counseling, Tutoring/academic support, Other
• Which fields must be captured for each intervention (e.g., outcome, duration, notes, follow-up date, assigned staff)?
• Who is responsible for logging interventions (advisors, support staff, automated system), and do you need templates to standardize entries? Options: Advisors, Support staff, Automated system / workflows, All of the above
• Do you require linkage between interventions and student outcomes for attribution (so we can measure which interventions moved retention metrics)? Options: Yes - attribution required, No - tracking only, Plan to enable later
• What retention window and follow-up cadence should be used for measuring intervention outcomes (e.g., term-level, year-level)? Options: Within term, First-to-second year, Within academic year, Multiple windows
• Do you have data retention or archival policies for intervention logs that we must follow? Options: Yes, No, Need to confirm policy

Student communications automation (email/SMS)

• Which communication channels are permitted for automated outreach in scope (email, SMS, push notifications)? Options: Email, SMS/text, In-platform push, Other
• Do you have opt-in/opt-out consent requirements for SMS or other channels that must be respected? Options: Yes - strict consent required, No - institutional consent covered, Unknown - need guidance
• Will communications use templates with personalization tokens from SIS/LMS (e.g., name, course, due dates)? Options: Yes - templated with tokens, No - freeform messages, Some templates only
• Do you require campaign scheduling, throttling limits, or send-time optimization? Options: Campaign scheduling, Throttling/ratelimiting, Send-time optimization, None
• Should outbound communications be logged to student records and visible to advisors? Options: Yes - log and surface to advisors, Log only (no advisor view), No automatic logging
• Are there content approval workflows or legal review steps required before messages go live? Options: Yes - approvals required, No, Only for certain message types

Role-based access and FERPA controls

• Which user roles need access and what is the expected number of users per role (e.g., advisors, managers, IT, institutional researchers)?
• Do you require fine-grained FERPA controls such as record-level redaction, restricted attributes, or emergency access procedures? Options: Record-level redaction, Restricted attribute masking, Emergency access auditing, None of the above
• Do you have an identity provider for SSO (e.g., SAML, OIDC) and provisioning (SCIM)? Options: Microsoft/Azure AD, Google Workspace, Okta, Shibboleth/InCommon, No/Other
• Are audit logs and export controls required to satisfy compliance or internal audit teams? Options: Yes - comprehensive audit logs, Basic logging sufficient, No
• Should role permissions be mapped to existing institutional roles or created anew for the pilot? Options: Map to existing roles, Create new custom roles, Hybrid
• Are there any third-party service providers or contractors who should be explicitly excluded from access? Options: Yes, No

Mobile advisor app deployment

• Which mobile platforms must be supported for advisors (iOS, Android, both)? Options: iOS, Android, Both, Mobile web only

• Non-Disclosure Agreement (NDA)
• Master Services Agreement (MSA)
• Statement of Work (SOW)
• Order Form / Commercial Terms
• Data Processing Agreement (DPA)
• Data Access & Security Addendum
• Integration & API Access Agreement
• Pilot Success Criteria & Acceptance
• Project Timeline & Milestone Signoff
• Governance, Roles & Escalation (RACI)
• Service Level Agreement (SLA) & Support
• Change Order & Scope Management
• Termination & Transition Plan
• Security & Compliance Evidence

• Monthly Outcomes Review — Success Metrics & Validation
• Intervention Iteration Workshop — Design & A/B Planning
• Model & Data Health Review
• Shared Backlog & Prioritization — Success Enhancements
• Executive Success Review — Outcomes, ROI & Scale Decision

• Establish an escalation path for critical items affecting compliance or funding.
• Analytics to pre-register analysis plan and upload to shared backlog.
• One-sentence Current Model State
• Decide whether model retraining or recalibration is required based on demonstrated drift and cohort impact.
• Identify and prioritize data fixes that materially affect model performance.
• Define a clear validation-to-deployment pathway with owners and rollback criteria.
• Analytics to produce a retrain proposal including expected lift and required features within 5 business days.
• Data engineering to remediate top 3 data quality issues and update backlog with timelines.
• Product to schedule shadow deployment window and define monitoring alerts for production.
• Review One-sentence Target Outcome for Backlog Prioritization
• Maintain a prioritized backlog that directly ties work items to measurable retention outcomes.
• Ensure clear ownership, timelines, and acceptance criteria for top-priority items.
• One-sentence Current State Confirmation
• Product manager to update the backlog with impact/effort scores and publish prioritized list.
• Assigned owners to provide sprint commitments and acceptance criteria ahead of next meeting.
• Governance lead to circulate escalation workflow and decision SLA documentation.
• Executive One-sentence Situation & Desired Future State
• Secure executive decision on whether to continue current approach, scale, or pivot based on ROI and demonstrated outcomes.
• Obtain executive commitments for any required funding, policy changes, or governance adjustments.
• Ensure executives understand the proven link between proposed actions and retention outcomes (proof not features).
• C-suite sponsor to issue decision memo and approval for requested funding or scope changes.
• Program director to publish an executive one-page that ties the decision to measurable targets and timelines.
• Analytics to prepare a 90-day dashboard for executive visibility on the agreed path forward.
• Confirm current retention outcome relative to targets with explicit consequences for the institution.
• Validate which specific interventions produced measurable change using real student scenarios.
• Agree on immediate corrective actions and owners to adjust programs within the next 30 days.
• Surface any data confidence issues requiring technical follow-up.
• Owner to publish one-sentence current state and consequence summary to stakeholders within 48 hours.
• Analytics lead to deliver anonymized student-case packet for each intervention rated effective/ineffective.
• Operational lead to implement agreed immediate corrective actions and report status in next review.
• Data engineer to log any data quality issues in the shared backlog with severity and ETA for fixes.
• Confirm Target Cohorts & One-sentence Problem
• Agree on at least two measurable intervention variants to test with clear success criteria.
• Establish owners, timeline, and data requirements so the experiment can launch within the agreed window.
• Ensure every proposed variant ties back to the diagnosed problem and expected consequence reduction.
• Program lead to finalize A/B test protocol and publish to governance board within 3 business days.
• IT to provision cohort extract and required LMS engagement signals for the experiment.
• Advising managers to prepare scripting and training materials for the variant outreach.
• Consequence Framing for Cohorts
• Backlog Health & Categorization
• Synthesis of Outcomes vs Targets
• Consequence Snapshot
• Performance Metrics & Calibration
• Feature Stability & Data Quality Findings
• Outcomes Dashboard Walkthrough
• Validated Proof Points & Risks
• Review What’s Been Tried (Diagnosis -> Proof)
• Impact vs Effort Prioritization
• Recommended Next-steps: Scale, Invest, or Pivot
• Model Update Recommendation: Proof and Trade-offs
• Intervention Effectiveness: Proof from Real Cases
• Assign Owners, SLAs, and Sprint Targets
• Brainstorm Variant Interventions
• Root Cause & Data Confidence
• Validation & Deployment Plan
• Governance Escalation Path
• Design A/B Test and Acceptance Criteria
• Decision & Governance Commitments
• Decisions & Immediate Actions
• Assign Owners, Timeline & Data Signals