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Adobe Journey Optimizer Experimentation Accelerator

Discovery Questionnaire

Purpose

This questionnaire is designed to assess an organization's experimentation maturity, optimization strategy, operational processes, AI readiness, and implementation requirements for Adobe Journey Optimizer Experimentation Accelerator.

The goal is to understand how experimentation is currently managed, identify opportunities to scale testing programs, evaluate AI-assisted optimization readiness, and determine how Experimentation Accelerator can help automate insights, prioritize growth opportunities, and accelerate business outcomes.

1. Business Objectives & Growth Strategy

Purpose

Understand the strategic business goals driving experimentation and optimization initiatives.

Questions

  1. What business outcomes are driving your investment in experimentation?
  2. What KPIs are most important to your growth organization?
  3. How do you currently measure the success of experimentation programs?
  4. What challenges prevent your teams from running more experiments?
  5. Are you focused primarily on:
  6. Conversion optimization
  7. Revenue growth
  8. Subscription growth
  9. Customer engagement
  10. Retention
  11. Customer lifetime value
  12. How do experimentation results influence business decisions today?
  13. What would define success for an experimentation acceleration initiative?
  14. How important is increasing experimentation velocity across the organization?

Follow-Up Questions

  • Are there executive mandates around experimentation?
  • Is experimentation viewed as a strategic capability or a tactical activity?

2. Experimentation Program Maturity

Purpose

Assess the current state of experimentation practices and operational maturity.

Questions

  1. How mature would you consider your experimentation program today?
  2. How many experiments are typically active at any given time?
  3. How many experiments are launched annually?
  4. Which teams are responsible for experimentation?
  5. Do different business units operate independent testing programs?
  6. How are experimentation ideas generated today?
  7. What percentage of experiments reach statistical significance?
  8. How are experiment learnings documented and shared?
  9. Do teams frequently repeat tests that have already been performed elsewhere?
  10. What are the biggest bottlenecks in your experimentation process?

Follow-Up Questions

  • Is experimentation centralized or decentralized?
  • Are experimentation standards documented?

3. Experimentation Technology Stack

Purpose

Understand current experimentation platforms and supporting technologies.

Questions

  1. What experimentation tools are currently in use?
  2. Are you using:
  3. Adobe Journey Optimizer
  4. Adobe Target
  5. Third-party experimentation platforms
  6. Internal testing solutions
  7. Which teams own each platform?
  8. Are experimentation results integrated with analytics systems?
  9. What reporting tools are used to analyze experiments?
  10. Do you maintain separate experimentation tools across channels?
  11. Are there plans to consolidate experimentation platforms?
  12. How are experimentation workflows managed today?

Follow-Up Questions

  • Are there limitations with current tooling?
  • Is experimentation data accessible across teams?

4. Experiment Design & Hypothesis Development

Purpose

Understand how experimentation opportunities are identified and prioritized.

Questions

  1. How are test hypotheses generated today?
  2. Who is responsible for prioritizing experimentation opportunities?
  3. What criteria are used to prioritize experiments?
  4. How do you determine which experiments have the highest business impact?
  5. How much manual analysis is required before a test is launched?
  6. Are historical experiment results leveraged when creating new tests?
  7. How do teams identify areas of customer friction?
  8. How frequently are experimentation backlogs reviewed?

Follow-Up Questions

  • Are experiment ideas primarily data-driven or intuition-driven?
  • Do teams have difficulty determining what to test next?

5. AI-Powered Experimentation Readiness

Purpose

Evaluate readiness for AI-generated insights, opportunities, and optimization recommendations.

Questions

  1. Are you currently using AI to support experimentation?
  2. How comfortable is your organization with AI-generated recommendations?
  3. What concerns exist regarding AI-generated insights?
  4. Would your teams benefit from automated hypothesis generation?
  5. How much time is currently spent analyzing experiment results?
  6. How valuable would AI-generated test opportunities be?
  7. How important is transparency and explainability in AI recommendations?
  8. Do you have AI governance policies that must be followed?
  9. What level of human review is required before recommendations are acted upon?
  10. Are there business functions that require manual approval before launching tests?

Follow-Up Questions

  • Are AI recommendations currently used in other marketing workflows?
  • Are there internal AI review boards or governance committees?

6. Experiment Insights & Analysis

Purpose

Understand how experiment results are analyzed and operationalized.

Questions

  1. How are experiment results analyzed today?
  2. How long does it take to extract actionable insights from completed tests?
  3. What challenges exist in interpreting results?
  4. Are experiment learnings consistently documented?
  5. How are insights shared across teams?
  6. Do teams struggle to identify why experiments succeeded or failed?
  7. How are customer behaviors incorporated into experiment analysis?
  8. What metrics are most important when evaluating experiment performance?
  9. How frequently are historical experiments revisited?
  10. What reporting gaps exist today?

Follow-Up Questions

  • Do analysts manually produce experiment summaries?
  • Are insights accessible to non-technical stakeholders?

7. AI Experiment Opportunities & Prioritization

Purpose

Assess how future experimentation opportunities are identified and ranked.

Questions

  1. How do teams determine what to test next?
  2. What data sources inform experimentation roadmaps?
  3. How are experimentation opportunities prioritized?
  4. How do you estimate potential business impact before launching a test?
  5. Are there formal prioritization frameworks in place?
  6. Would AI-generated opportunity recommendations improve productivity?
  7. How important is predicted lift when prioritizing experiments?
  8. What evidence would teams need before trusting AI recommendations?
  9. How are competing experimentation priorities resolved?
  10. How frequently are experimentation roadmaps updated?

Follow-Up Questions

  • Are opportunities evaluated consistently across teams?
  • Do teams struggle with experimentation backlog prioritization?

8. Adaptive Experimentation & Optimization Workflows

Purpose

Evaluate readiness for AI Adaptive Experiments and continuous optimization.

Questions

  1. How frequently do active experiments require optimization?
  2. Are test variations manually adjusted during active tests?
  3. What is your current process for refining experiments after launch?
  4. How comfortable are stakeholders with AI-assisted optimization recommendations?
  5. What controls must exist before modifications are applied?
  6. Are there approval workflows for experiment changes?
  7. How important is reducing time-to-insight?
  8. How important is reducing sample size requirements?
  9. How do you balance experimentation speed and statistical rigor?
  10. What optimization challenges would you like to automate?

Follow-Up Questions

  • Are adaptive testing methods currently used?
  • How often are active experiments manually adjusted?

9. Content, Creative & Experience Optimization

Purpose

Understand how experimentation supports content and customer experience optimization.

Questions

  1. What types of experiences are most frequently tested?
  2. What content elements are commonly tested?
  3. Are experiments focused on:
  4. Copy
  5. Headlines
  6. Subject lines
  7. Offers
  8. Calls-to-action
  9. Layouts
  10. Customer journeys
  11. How quickly can new content variations be produced?
  12. Are generative AI tools used for content creation?
  13. How are creative teams involved in experimentation?
  14. How is content approval managed?
  15. How are winning experiences scaled across channels?
  16. How frequently are experiment learnings applied to future content?
  17. What content production bottlenecks exist today?

Follow-Up Questions

  • Are content supply chain processes documented?
  • How does experimentation influence content strategy?

10. Analytics, Reporting & Success Metrics

Purpose

Define how experimentation performance is measured.

Questions

  1. What metrics are used to evaluate experimentation success?
  2. Are Adobe Analytics or Customer Journey Analytics currently deployed?
  3. Which KPIs matter most to stakeholders?
  4. How are revenue impacts measured?
  5. How are conversion improvements measured?
  6. Are executive dashboards currently available?
  7. How frequently are experimentation results reviewed?
  8. How is ROI of experimentation programs calculated?
  9. What reporting challenges exist today?
  10. How are experimentation insights shared with leadership?

Follow-Up Questions

  • Are custom business metrics required?
  • Are stakeholders requesting more experimentation visibility?

11. Experiment Governance & Operational Model

Purpose

Understand governance, approvals, and operational ownership.

Questions

  1. Who owns experimentation strategy?
  2. Who owns experiment execution?
  3. Who approves experiment launches?
  4. How are experimentation standards enforced?
  5. How are successful experiments operationalized?
  6. How are experiment archives maintained?
  7. Are governance committees involved?
  8. How are risks evaluated before testing?
  9. What documentation requirements exist?
  10. How are experimentation best practices shared?

Follow-Up Questions

  • Are experimentation processes standardized?
  • Is there a formal experimentation center of excellence?

12. AI Governance, Privacy & Compliance

Purpose

Assess organizational requirements for responsible AI usage.

Questions

  1. What AI governance policies apply?
  2. Are there restrictions on AI-generated recommendations?
  3. What privacy requirements apply to experimentation data?
  4. Are legal reviews required before deploying AI-enabled capabilities?
  5. What compliance standards must be followed?
  6. How is customer data governed?
  7. What concerns exist regarding AI-generated insights?
  8. How important is explainability?
  9. Are audit trails required?
  10. What internal stakeholders must approve AI-powered workflows?

Follow-Up Questions

  • Has the organization approved GenAI usage?
  • Are there restrictions on automated decision support systems?

13. Implementation Readiness & Adoption Planning

Purpose

Assess readiness to deploy and operationalize Experimentation Accelerator.

Questions

  1. What experimentation use cases are highest priority?
  2. Which teams would be initial adopters?
  3. What capabilities must be available at launch?
  4. What dependencies could impact adoption?
  5. Are there timeline requirements?
  6. What training will be required?
  7. How will success be measured after deployment?
  8. Is executive sponsorship established?
  9. What risks have been identified?
  10. What outcomes are expected at:
    • 90 Days
    • 6 Months
    • 12 Months