Adobe Target Business Practitioner Expert cheat sheet

Success metric types: conversion rate, revenue per visitor (RPV), average order value (AOV), engagement, and custom metrics tied to mbox parameters.

Minimum sample size for a two-variant test at 95% confidence and 80% power: n ≈ 16 × (σ/Δ)² per variant, where Δ is the minimum detectable effect.

Statistical significance: p-value < 0.05 rejects the null hypothesis; confidence = (1 − p) × 100%.

Test duration rule of thumb: at least two full business cycles (typically 2 weeks) to capture weekly seasonality.

Activity types: A/B test (single change), A/B/n (multiple), Multivariate (MVT, factorial combinations), Experience Targeting (XT, audience rules), Auto-Target (ML), Auto-Allocate (ML bandit), Recommendations.

Primary goal is chosen at activity creation and cannot be changed after activation; secondary metrics can be added later.

Audience definition belongs in the plan: include rules (geo, device, custom parameters) and exclude rules (existing customers, internal traffic).

Holdout or control group size: keep at least one control experience to measure incremental lift; do not run overlapping tests on the same audience without a collision plan.

at.js is the current library; mbox.js is legacy and deprecated. Adobe Experience Platform Web SDK is the forward path.

Property tokens (at_property) isolate report data and restrict activity visibility across teams; set them in the at.js initialization or Web SDK configuration.

Admin > Users: roles are Approver, Editor, Observer, and (Enterprise) Product Admin / Profile Admin; permissions are scoped per workspace.

Admin > Hosts: only registered hosts can serve activities; the Target edge hostname must be allowlisted in the CNAME/TLS setup.

Response tokens expose visitor attributes (e.g., geo, profile) in the Target response for downstream tools; enable them in Admin > Response Tokens.

Implementations: on-device decisioning (Web SDK) evaluates rules locally for low latency; server-side (Node.js, Java, .NET SDKs) keeps logic off the client.

Audit log and activity approvals: Enterprise accounts can require approval before an activity goes live; changes are recorded with user and timestamp.

Data retention: Target keeps behavioral/offline data per contract terms (commonly 90–180 days); confirm in Admin > Data Retention before relying on long lookbacks.

Visual Experience Composer (VEC) edits the page DOM; Form-Based Composer offers precise mbox/offer control and is required for some server-side or non-HTML responses.

Experiences are assigned traffic allocations that must sum to 100%; a control experience (0% change) is recommended.

Goals: conversion (mbox/click), revenue, engagement; each activity supports one primary goal plus secondary metrics.

Audience targeting uses rules (browser, OS, geo, custom mbox params, profile scripts) and can be combined with AND/OR logic.

Activity QA: use the QA link with ?at_qa_mode=true and the activity token to preview experiences without affecting reports.

Collision handling: Target automatically detects overlapping activities on the same mbox and applies priority; use priority levels (0–999, lower number = higher priority) deliberately.

Scheduling: set start/end dates and time zone; activities can be paused and resumed without losing data.

Auto-Target and Auto-Allocate require sufficient traffic and a primary goal; they adapt allocation based on observed performance.

Lift = (conversion rate of experience − conversion rate of control) / conversion rate of control × 100%.

Confidence interval for a proportion: p̂ ± z × √(p̂(1−p̂)/n); z = 1.96 for 95% confidence.

Two-proportion z-test: z = (p̂₁ − p̂₂) / √(p̂(1−p̂)(1/n₁ + 1/n₂)), where p̂ is the pooled proportion.

RPV = total revenue / number of visitors; AOV = total revenue / number of orders.

Report types: Activity Report (per activity), Audience Report, and A/B Test report with segment breakdowns; use the date range and environment filters.

Segment reports by device, geo, or custom parameters to find heterogeneous treatment effects; small segments may not reach significance.

Statistical significance in Target uses a two-sided test; do not peek repeatedly without adjusting for multiple comparisons.

Auto-Allocate reports show the winning experience and allocation over time; Auto-Target reports show per-experience performance and model confidence.

Activity not showing: verify the visitor qualifies for the audience, the mbox is firing, the activity is active and within schedule, and the host is registered.

Use the Adobe Experience Cloud Debugger / Target Trace to inspect mbox requests, offers, and audience evaluation.

Common causes of no-offer: conflicting higher-priority activity, audience exclusion, QA mode on, or property token mismatch.

Flicker/OFOC (opportunity for content) mitigation: use at.js with async loading, pre-hide snippets, or server-side decisioning.

Optimization levers: increase traffic, reduce number of variants, tighten the primary metric, or extend duration to reach power.

Auto-Allocate is best when variants differ meaningfully and traffic is limited; Auto-Target is best when personalization by visitor profile is the goal.

Validate implementation with the Target API (delivery endpoint) and check response tokens and mbox parameters in the network tab.

When results are flat, inspect segment-level lift before concluding no effect; check for sample ratio mismatch (SRM) across experiences.

Recommendations criteria: popularity, recently viewed, most viewed, top sellers, and custom algorithms based on the catalog schema.

Catalog schema: define entity attributes (id, name, category, message) and behavior attributes (views, purchases); feed via CSV, mbox, or API.

Recommendations requires a behavior data source (mbox or API) and a catalog; both must be populated before the algorithm can serve.

Design types: default design, alternate designs, and include/exclude rules for filtering recommendations.

Experience Targeting (XT) personalizes by audience rules; Auto-Target uses a machine-learning model to choose the best experience per visitor.

Personalization at scale: combine profile attributes (from the Profile API or data layer) with audience rules and recommendations.

Recommendations reporting shows CTR, conversion, and revenue per recommendation; use the entity-level report to tune the catalog.

Exclusions and backup recommendations prevent empty slots when the algorithm has insufficient data.