The Bottleneck Nobody Names
Experimentation programs stall at the hypothesis stage. A data scientist reviews analytics, identifies a low-performing page, proposes a variant, hands it to a copywriter, the copywriter hands it to an AEM author, and three weeks later you have one test running. AI breaks that chain at every link.
Part 1 – The Experimentation Architecture
Five layers working together:
- Layer 1 – Analytics Input: Adobe Analytics API exports daily page performance data. App Builder scheduler pulls this every morning at 6am.
- Layer 2 – AI Hypothesis Engine: Claude API reads performance data and generates structured hypotheses with confidence scoring. Output is JSON, not prose.
- Layer 3 – AEM Variant Factory: App Builder creates Content Fragment variants in AEM. Each variant maps to an Adobe Target experience in the corresponding activity.
- Layer 4 – Target Orchestration: App Builder calls Adobe Target Admin API to create activities, set traffic splits, and define success metrics.
- Layer 5 – Results and Promotion: Daily App Builder action reads Target reporting API, checks statistical significance, and promotes winners or flags inconclusive tests.
Part 2 – Hypothesis Generation with Claude API
The hypothesis engine reads raw analytics and outputs actionable test plans structured data ready for automated execution. Enable prompt caching for the brand rules block: it is identical across every call and caching reduces hypothesis generation cost by approximately 70%.
Only run experiments on hypotheses the AI rates as medium or high confidence. Low-confidence hypotheses go into a review queue for a human to assess before execution.
Part 3 – Creating AEM Variants Programmatically
Each approved hypothesis triggers Content Fragment creation via the AEM Assets HTTP API. The EDS page uses an experimentation block that reads the CF variants and delegates traffic split to Adobe Target. Authors do not touch the page the system wires up the experiment end-to-end.
Part 4 – Adobe Target Activity Automation
Once variants exist in AEM, App Builder calls the Adobe Target Admin API to create the activity with traffic split, success metrics from the hypothesis JSON, and experience IDs matching the Content Fragment paths.
Part 5 – Statistical Significance Monitoring
Every morning, a second App Builder action reads experiment results from Target Reporting API and asks Claude to interpret them. Claude combines statistical significance data with business context seasonal factors, page traffic tier, experiment age, governance compliance into a single promote/stop/continue decision.
Part 6 – Auto-Promoting Winners
When the analysis returns a promote recommendation, App Builder updates the AEM page’s default Content Fragment to use the winning variant copy, deactivates the Target activity, and republishes via the Replication API. The winning copy is also logged to the Winner Library so the hypothesis engine gets smarter over time.
Part 7 – AEM Sidekick Experiment Panel
Authors see live experiment status on every page via a custom Sidekick v6+ extension: active experiment badge, live conversion rate per variant, estimated days to significance, pause/force-win controls, and a governance status indicator that flags any variant with a brand violation.
Part 8 – Guardrails
Three guardrails run automatically:
- Brand governance check – every variant passes through the content governance scan before Target activation. Zero critical violations required to proceed.
- Experiment cap per page – App Builder refuses to create a second active experiment on any page. One active experiment per URL maximum.
- Stale experiment cleanup – any experiment running more than 21 days is auto-stopped and flagged for review regardless of significance.
Measuring Experimentation Velocity
- Experiments launched per week – baseline current rate; target 10x within 90 days
- Hypothesis-to-live time – from analytics data to active Target activity; target under 4 hours end-to-end
- Win rate – above 30% means the hypothesis engine is well-calibrated
- Cumulative lift – aggregate conversion improvement from all promoted winners across the site
- False positive rate – check promoted variants 30 days later; reversion means the significance threshold needs raising
