Probant vs. Adobe Target
Both tools compared point by point, based on Adobe Target’s public documentation, accessed November 11, 2026. Every statement about Adobe Target links to its source.
What Adobe Target offers.
Adobe Target is the A/B testing and personalization module of Adobe Experience Cloud, for the web, apps, and other channels.1 It is sold as Standard or Premium licenses; users are managed in Adobe’s enterprise Admin Console.23 Its main engine is Welch’s t-test; a Bayesian reading is available as an option.456
Probant is for sites that want A/B test verdicts that hold up, with a plan set before launch and guardrails monitored through rollout. It measures what each conversion is worth: the margin on an order, a qualified or signed lead, a paid subscription.
The differences, point by point.
On the left, what Adobe Target’s public documentation says, each sentence with its source. On the right, what Probant does. “Not mentioned” means nothing was found in that documentation, which does not prove a feature is missing.
| Criterion | Adobe Target | Probant |
|---|---|---|
| Statistical method | Manual A/B tests use a two-sided Welch’s t-test with a fixed sample size; a Bayesian method can be chosen per activity, but only when the primary goal is a conversion (revenue goals stay on Welch). Auto-Allocate uses its own intervals (Bernstein inequality).456 | Frequentist, with a sequential plan set before launch: target effect, duration, and checkpoints. The verdict fits in one sentence, with a 95% range. Where to check |
| Peeking | Welch’s test has a fixed horizon: Adobe recommends setting the sample size before launch and not concluding earlier (“no peeking”). The Bayesian mode (conversion goals) is presented as readable at any time, and Auto-Allocate accounts for repeated evaluations through the Bernstein inequality; no frequentist sequential test is documented.789 | Decisions are only made at planned checkpoints, with O’Brien-Fleming boundaries: very strict at first, close to the usual threshold at the end. During a rollout, ranges are always valid (mSPRT), so you can check them at any time. Where to check |
| Traffic split (SRM) | An automatic sample ratio mismatch (SRM) check is not mentioned in their public documentation (as of November 11, 2026). | The traffic split is checked with a chi-square test every time the report is read. If the split is off, the test moves to “blocked” status and no winner is declared. Where to check |
| Guardrails | Target lets you add success metrics on top of the primary one (except with A4T).10 Guardrails that stop a test on their own are not mentioned in their public documentation (as of November 11, 2026). | Guardrail metrics are tracked with their range; a guardrail is only breached if the whole range crosses the limit. A breached guardrail blocks the test’s verdict, stops a running test on its own, and during a rollout moves visitors back to the original automatically. Where to check |
| Gradual rollouts | A stepped rollout with automatic rollback is not mentioned in their public documentation (as of November 11, 2026). | Steps of 25% → 50% → 100% by default, 10% → 25% → 50% → 100% in cautious mode. Automatic rollback when a guardrail is breached; an exposed visitor stays exposed in the following steps. Where to check |
| Multi-armed bandit | Auto-Allocate is a multi-armed bandit: 80% of new visitors go to the best-performing experiences and 20% are assigned at random, with updates at least hourly. Auto-Target (Premium) also allocates per visitor.111213 | Optimization test: Thompson sampling, reallocated at most once a day, with a minimum share per variant. It maximizes a conversion rate, or an amount per visitor such as revenue: purchase probability and average order value drawn from their distributions, with values capped at a percentile as in an A/B test. Optionally, shares per device (mobile, tablet, desktop): one draw per device, each with its own learning and floor. A visitor always keeps their variant: a new split only applies to visitors who arrive after it. The screen says so: it maximizes conversions (or the amount per visitor) during the test, without proving that a variant is better. The difference is read period by period (and device by device), with no declared winner; the CSV export gives the same reading. Where to check |
| Revenue and margin per visitor | Revenue per visitor (RPV) is analyzed with Welch’s t-test, with a 95% confidence interval on the mean and on the lift (delta method). In A4T, average order value (AOV) has no lift or confidence.141516 The estimated financial gain is based on revenue per visit.17 Margin per visitor is not mentioned in their public documentation (as of November 11, 2026). | Revenue and margin per visitor, each with its range; the gain is stated in euros. Margin is calculated on the server from reconciled orders, the cost of each product (Shopify or CSV), and store fees; no margin verdict below 90% of revenue covered by known costs. Where to check |
| Winner’s curse | A winner’s curse correction is not mentioned in their public documentation (as of November 11, 2026). | The reported gain is corrected for the winner’s curse by Bayesian shrinkage toward the effects of the store’s past tests. Where to check |
| Bayesian mode | Bayesian mode per activity: decision threshold, credible interval, “Chance to Beat Control,” and an optional prior drawn from past tests. Not available for revenue or engagement goals; no expected loss is documented.181920 | Optional, off by default: a Bayesian reading next to the verdict, with the probability that the variant does better, the credible interval, and the expected loss of each choice. The prior is centered on zero and as wide as the effects measured by the store’s completed tests; while there are too few of them to estimate it, the Bayesian reading is not shown. The decision always follows the sealed plan; when the two readings disagree, the report explains why. Where to check |
| Multiple comparisons | The Bonferroni correction is an option in the sample size calculator (more than two offers) and is recommended to apply yourself; the confidence computed in A4T does not apply it. Auto-Allocate compares all experiences with each other.212223 | Segment comparisons are corrected with the Holm method. With several variants, the error risk is split across comparisons (Bonferroni). Where to check |
| Multivariate testing | Multivariate tests use a full factorial design; the documentation describes measuring each element’s influence (main effect) and interaction effects. A partial factorial design (Taguchi) is only possible through an offline spreadsheet.242526 | Full factorial design generated by Probant: two to four sections, each with two to four levels, up to sixteen combinations, each served as a variant. Main effects of each section across all traffic and interactions between two sections, corrected with the Holm method on the comparisons stated in the plan. Based on the workspace’s measured traffic, the plan says whether the test can reach a conclusion and suggests removing a level or a section; no best combination is declared without a check. Where to check |
| Variance reduction (CUPED) | Variance reduction (CUPED) is not mentioned in their public documentation (as of November 11, 2026). | CUPED applied to live test reports, using each visitor’s history before their first exposure; without history, the difference stays unadjusted and the report says so. Where to check |
| Visual editor | Visual Experience Composer (VEC) in the Target interface, with HTML/JavaScript editing and a Chrome extension that helps load sites; a form-based composer covers cases without the VEC. The VEC does not work in Safari.27282930 | Two ways to edit, producing the same changes: a page preview loaded by Probant, without its scripts, or the live page on the site, scripts included. On the site, the editor is loaded by the script already installed, through a short-lived signed link for one draft and one variant, with no browser extension. It only sends the chosen changes to Probant. Where to check |
| Widgets | Only display templates (designs) for product recommendations (Premium) are documented; a widget library (banner, countdown, pop-in, social proof) is not mentioned.31 | Banner, countdown, pop-up, badge, reassurance, action bar, delivery date, social proof, promo code, free shipping, and survey, set up without code in the editor and saved in the workspace library for other tests. A widget is just a set of ordinary changes: escaped text, links over https or to the site, and styles applied without a style attribute, so a strict CSP does not need loosening. The countdown follows the site’s time zone and disappears at the deadline; the pop-in opens on arrival, on exit intent, or after scrolling, closes with the keyboard, and does not reappear during the visit if consent allows. Their small modules only load for visitors in the variant. The delivery date is calculated in the visitor’s browser (cutoff time, business days, public holidays); social proof shows the exact number of purchases received over a rolling week, and only above a threshold. The survey asks up to 8 questions (closed answers, recommendation scale, open question) with conditional logic, on the page or through a link outside the site (email, QR code), based on 70 templates (recommendation, satisfaction, effort, checkout blockers, cancellation, concept test, multi-question surveys…); open answers are redacted before sending (email addresses, phone numbers, cards, IBANs), kept for 180 days, and grouped by theme; each question is read by variant, with its range and response rate, and comparisons across all questions are corrected together; a response through a link is attached to the variant the visitor saw, recalculated by Probant, or stays outside the test. Where to check |
| Script and flicker | at.js (or the Web SDK) can load asynchronously; to prevent flicker, it hides the whole body (opacity 0) until Target responds, with a prehiding snippet whose example timeout is 3000 ms. A newer synchronous Prehide SDK is available. Script size is not documented.32333435 | Script under 7 KB compressed, loaded asynchronously, that does not hide the page while it loads. Server-side assignment available, for a display with no flicker at all. Where to check |
| Granular permissions | Observer, Editor, Approver, and Publisher roles; assignment by workspace and by property (site, channel, environment) is limited to Target Premium. No custom role is documented.363738 | Four fixed roles, plus custom roles the owner builds from a catalog of named permissions: prepare, launch and stop, roll out, approve a launch, write variant JavaScript, manage flags, metrics, margin, members, and keys. No privilege escalation: billing, roles, transfer, and deletion of the workspace stay with the owner; no one can grant more than their own permissions, and an API key cannot exceed its author’s. Where to check |
| Data hosting | Hosted on AWS: processing in a predetermined central cluster (Oregon, Dublin, or Singapore), visitor profiles stored on the edge cluster closest to the visitor, and the Recommendations service hosted in Oregon (United States).394041 | Database and compute in Amsterdam, with Scaleway, in the European Union. Scaleway, which hosts the database and compute, is a French company. Published subprocessor list: Scaleway (Amsterdam, Netherlands (nl-ams region), in the European Union), Brevo (European Union: servers in France and Germany, encrypted backups in Belgium (Google Cloud), according to Brevo), Mistral AI (European Union by default (European API endpoint), according to Mistral), ClickHouse (option not used), Stripe (Contract with Stripe Payments Europe, Ltd. (Ireland); data transferred to Stripe, LLC (United States), according to Stripe), Sentry (European Union: Frankfurt (Germany), including backups, according to Sentry). Depending on the feature used, Mistral may temporarily transfer data outside the European Union to the subprocessors it publishes; its data processing agreement limits these transfers to countries the EU recognizes as adequate, or covers them with standard contractual clauses. Stripe transfers payment data to Stripe, LLC, in the United States, under the Data Privacy Framework and, failing that, standard contractual clauses. Whatever region is chosen, Sentry may keep the data of Probant’s Sentry account (members, settings, audit log, project descriptions) in the United States; reported errors stay in Frankfurt. Where to check |
| Consent by default | By default (optinEnabled is false), at.js fires without waiting for consent; an opt-in mode through Adobe Experience Platform tags holds Target until consent. Adobe states that Target does not manage consent. The mbox cookie lifetime differs between Adobe pages (two years or two weeks) and cannot be changed.4243444546 | Consent required by default: no cookie, no assignment, and nothing sent before the visitor agrees. Consent given mid-page: the page does not change in front of the visitor; their variant starts on the next page. First-party cookie, lasting 390 days at most, never extended. Where to check |
| Pricing model | Target is sold as Standard or Premium licenses; Adobe’s partner page lists page-view volume growth among upsell triggers.347 Public pricing is not mentioned in their public documentation (as of November 11, 2026). | Creating a Probant workspace is free; paid plans cost €590 to €1,990 per month depending on the number of tested visitors (2 months free with annual billing), with no overage charges. Agency plan: 1,000,000 tested visitors per month, shared across client workspaces, from 3 workspaces, for €1,490 per month or €14,900 per year. Where to check |
What Adobe Target also does.
Probant shares these points with Adobe Target.
Revenue per visitor, with its range
Revenue per visitor (RPV) is analyzed with Welch’s t-test, with a 95% confidence interval on the mean and on the lift (delta method). In A4T, average order value (AOV) has no lift or confidence.141516
Dynamic traffic allocation
Auto-Allocate is a multi-armed bandit: 80% of new visitors go to the best-performing experiences and 20% are assigned at random, with updates at least hourly. Auto-Target (Premium) also allocates per visitor.111213
Multivariate testing
Multivariate tests use a full factorial design; the documentation describes measuring each element’s influence (main effect) and interaction effects. A partial factorial design (Taguchi) is only possible through an offline spreadsheet.242526
A visual editor
Visual Experience Composer (VEC) in the Target interface, with HTML/JavaScript editing and a Chrome extension that helps load sites; a form-based composer covers cases without the VEC. The VEC does not work in Safari.27282930
When to choose Adobe Target over Probant.
Based on Adobe Target’s public documentation.
You already use Adobe Experience Cloud
Target is part of Adobe Experience Cloud, and its users are managed in Adobe’s Admin Console.12
You want personalization chosen by machine learning
Rule-based targeting (Experience Targeting); Auto-Target and Automated Personalization (Premium license) personalize with a Random Forest model and hold out a control group by default to measure lift.484950
You want server SDKs in Java or .NET
Server SDKs for Node.js, Java, .NET, and Python, a delivery API, and in-memory on-device decisioning from a rules artifact served by the Akamai CDN. Edge execution (Akamai EdgeWorkers, Lambda@Edge) is only described in Adobe tech blog posts; Cloudflare and Vercel are not mentioned.515253
What sets Probant apart.
These points are not in Adobe Target’s public documentation, accessed November 11, 2026. A missing mention does not prove a feature is missing; if these points matter to you, ask Adobe Target.
Margin per visitor
Probant measures revenue and margin per visitor, each with its range.
Adobe TargetThe estimated financial gain is based on revenue per visit.17 Margin per visitor is not mentioned in their public documentation (as of November 11, 2026).
Learn moreWinner’s curse correction
The reported gain is corrected by Bayesian shrinkage toward the effects of the store’s past tests.
Adobe TargetA winner’s curse correction is not mentioned in their public documentation (as of November 11, 2026).
Learn moreSRM that blocks the verdict
A traffic split imbalance moves the test to “blocked” status, and no winner is declared.
Adobe TargetAn automatic sample ratio mismatch (SRM) check is not mentioned in their public documentation (as of November 11, 2026).
Consent required by default
No cookie, no assignment, and nothing sent before the visitor agrees.
Adobe TargetBy default (optinEnabled is false), at.js fires without waiting for consent; an opt-in mode through Adobe Experience Platform tags holds Target until consent. Adobe states that Target does not manage consent. The mbox cookie lifetime differs between Adobe pages (two years or two weeks) and cannot be changed.4243444546 Consent required by default is not mentioned there (as of November 11, 2026).
Frequently asked questions.
How much does Adobe Target cost?
Target is sold as Standard or Premium licenses; Adobe’s partner page lists page-view volume growth among upsell triggers.347 Public pricing is not mentioned in their public documentation (as of November 11, 2026). Creating a Probant workspace is free; paid plans cost €590 to €1,990 per month depending on the number of tested visitors (2 months free with annual billing), with no overage charges.
Where does Adobe Target host data?
Hosted on AWS: processing in a predetermined central cluster (Oregon, Dublin, or Singapore), visitor profiles stored on the edge cluster closest to the visitor, and the Recommendations service hosted in Oregon (United States).394041 Probant hosts its database and application in Amsterdam, with Scaleway.
Does Adobe Target measure margin?
The estimated financial gain is based on revenue per visit.17 Margin per visitor is not mentioned in their public documentation (as of November 11, 2026). Probant measures revenue and margin per visitor, each with its range, from orders and the cost of each product.
Can you look at Adobe Target results while a test is running?
Welch’s test has a fixed horizon: Adobe recommends setting the sample size before launch and not concluding earlier (“no peeking”). The Bayesian mode (conversion goals) is presented as readable at any time, and Auto-Allocate accounts for repeated evaluations through the Bernstein inequality; no frequentist sequential test is documented.789 Probant sets its checkpoints before launch (O’Brien-Fleming) and uses always-valid ranges during rollouts.
Sources.
Official pages from Adobe Target, accessed November 11, 2026. Quotes are reproduced in their original language.
- Source 1: experienceleague.adobe.com/en/docs/target/using/introduction/intro
Adobe Target, part of the Adobe Experience Cloud, offers comprehensive tools to personalize customer experiences across web, mobile sites, apps, social media, and other digital channels.
- Source 2: experienceleague.adobe.com/en/docs/target/using/administer/manage-users/users/user-management
All user management must be performed in the Adobe Admin Console for Enterprise.
- Source 3: experienceleague.adobe.com/en/docs/target/using/introduction/intro
Target helps maximize revenue and can be licensed as Target Standard or Target Premium.
- Source 4: experienceleague.adobe.com/en/docs/target/using/reports/statistical-methodology/statistical-calculations
An A/B Test (Manual) activity supports two statistical methodologies, selected per activity in Goals & Settings:
- Source 5: experienceleague.adobe.com/en/docs/target/using/reports/statistical-methodology/statistical-calculations
Welch’s t-test: a frequentist methodology that reports a Confidence percentage and confidence interval, based on a fixed-sample-size hypothesis test.
- Source 6: experienceleague.adobe.com/en/docs/target/using/activities/abtest/create/ab-goals-and-settings
This setting is only available for activities whose primary goal metric is Conversion, activities with a Revenue or Engagement primary goal always use Welch’s t-test.
- Source 7: experienceleague.adobe.com/en/docs/target/using/activities/abtest/sample-size-determination
Simply monitoring the activity until statistical significance is achieved causes the confidence interval to be vastly underestimated, making the test unreliable.
- Source 8: experienceleague.adobe.com/en/docs/target/using/reports/statistical-methodology/statistical-calculations
This makes it safe to monitor a Bayesian report continuously, since there is no statistical penalty for checking results before a fixed sample size is reached, and it can converge faster on smaller samples than Welch’s t-test.
- Source 9: experienceleague.adobe.com/en/docs/target/using/activities/auto-allocate/automated-traffic-allocation
Normally this process could produce false positives, but Auto-Allocate uses confidence intervals based on the Bernstein Inequality that compensates for repeated evaluations.
- Source 10: experienceleague.adobe.com/en/docs/target/using/activities/abtest/create/ab-goals-and-settings
Create additional success metrics. This setting is not available if the reporting solution is set to Analytics.
- Source 11: experienceleague.adobe.com/en/docs/target/using/activities/auto-allocate/automated-traffic-allocation
An Auto-Allocate activity in Adobe Target identifies a winner among two or more experiences and automatically reallocates more traffic to the winner to increase conversions while the test continues to run and learn.
- Source 12: experienceleague.adobe.com/en/docs/target/using/activities/auto-allocate/automated-traffic-allocation
80% of visitors are allocated using the intelligent logic described below.
- Source 13: experienceleague.adobe.com/en/docs/target/using/activities/auto-allocate/automated-traffic-allocation
These models update at least once an hour to ensure that the model reacts to the latest data.
- Source 14: experienceleague.adobe.com/en/docs/target/using/reports/statistical-methodology/statistical-calculations
Welch’s t-test: a frequentist methodology that reports a Confidence percentage and confidence interval, based on a fixed-sample-size hypothesis test. Used for activities with a Revenue or Engagement primary goal.
- Source 15: experienceleague.adobe.com/en/docs/target/using/reports/statistical-methodology/statistical-calculations
Target always reports a 95% confidence interval, which means that in the end, 95% of confidence intervals calculated include the true conversion rate of the experience.
- Source 16: experienceleague.adobe.com/en/docs/target/using/integrate/a4t/a4t-faq/a4t-faq-lift-and-confidence
AOV is not supported because the numerator is a calculated metric.
- Source 17: experienceleague.adobe.com/en/docs/target/using/administer/reporting/estimating-lift-in-revenue
The estimated lift in revenue is calculated based on the Revenue Per Visit (RPV) obtained from the Primary Goal metric.
- Source 18: experienceleague.adobe.com/en/docs/target/using/activities/abtest/create/ab-goals-and-settings
Use prior knowledge from past tests?: Toggle this option on to provide prior knowledge from past performance to inform the Bayesian calculation.
- Source 19: experienceleague.adobe.com/en/docs/target/using/reports/statistical-methodology/statistical-calculations
Chance to Beat Control is the probability that an experience’s goal metric outperforms the Control experience, for example, “92% chance B beats A”.
- Source 20: experienceleague.adobe.com/en/docs/target/using/reports/statistical-methodology/statistical-calculations
The Bayesian methodology is only available for activities whose primary goal metric is Conversion, activities with a Revenue or Engagement primary goal always use Welch’s t-test.
- Source 21: experienceleague.adobe.com/en/docs/target/using/activities/sample-size-calculator
Number of offers: The number of experiences in your experiment, including the control. More than two offers applies a Bonferroni correction, when enabled, to maintain the overall confidence level.
- Source 22: experienceleague.adobe.com/en/docs/target/using/integrate/a4t/a4t-faq/a4t-faq-lift-and-confidence
Also, the confidence calculation does not apply a Bonferroni correction for multiple offers.
- Source 23: experienceleague.adobe.com/en/docs/target/using/activities/auto-allocate/automated-traffic-allocation
Auto-Allocate performs all implicit comparisons across experiences and produces a “true” winner.
- Source 24: experienceleague.adobe.com/en/docs/target/using/activities/multivariate-test/multivariate-testing
Target uses full-factorial multivariate tests to help you optimize your content.
- Source 25: experienceleague.adobe.com/en/docs/target/using/activities/multivariate-test/multivariate-testing
Multivariate Test activities also help you find compound effects between two or more elements on a page.
- Source 26: experienceleague.adobe.com/en/docs/target/using/activities/multivariate-test/multivariate-testing
This functionality and testing approach can be applied in Target using this offline spreadsheet.
- Source 27: experienceleague.adobe.com/en/docs/target/using/experiences/vec/visual-experience-composer
The Visual Experience Composer (VEC) in Adobe Target is a WYSIWYG editor for creating and testing personalized experiences directly on websites and mobile web pages without editing code.
- Source 28: experienceleague.adobe.com/en/docs/target/using/experiences/vec/visual-experience-composer
The composer also provides options for editing HTML and JavaScript.
- Source 29: experienceleague.adobe.com/en/docs/target/using/experiences/vec/troubleshoot-composer/vec-helper-browser-extension
The Adobe Target Visual Experience Composer (VEC) Helper browser extension for Google Chrome lets you load websites reliably within the VEC to rapidly author and QA web experiences.
- Source 30: experienceleague.adobe.com/en/docs/target/using/experiences/vec/visual-experience-composer
You can create form-based activities in Safari and use all functionality, except the VEC and the Enhanced Experience Composer (EEC).
- Source 31: experienceleague.adobe.com/en/docs/target/using/recommendations/recommendations-design/create-design
You can create a Recommendations design using a default design or by creating a custom design.
- Source 32: experienceleague.adobe.com/en/docs/target-dev/developer/client-side/at-js-implementation/at-js/manage-flicker-with-atjs
When at.js loads, it will change the opacity setting of the <body> element to “0”, making the page initially invisible to visitors.
- Source 33: experienceleague.adobe.com/en/docs/target-dev/developer/client-side/at-js-implementation/at-js/manage-flicker-with-atjs
You can avoid flicker by using a pre-hiding snippet that will be visible after the relevant HTML elements are personalized by Target.
- Source 34: experienceleague.adobe.com/en/docs/target-dev/developer/client-side/at-js-implementation/at-js/manage-flicker-with-atjs
}(window, document, "body {opacity: 0 !important}", 3000));
- Source 35: experienceleague.adobe.com/en/docs/target-dev/developer/client-side/prehide-sdk
Do not add async or defer to the Prehide SDK script tag.
- Source 36: experienceleague.adobe.com/en/docs/target/using/administer/manage-users/enterprise/property-channel
You can assign users the roles of Observer, Editor, Approver, or Publisher.
- Source 37: experienceleague.adobe.com/en/docs/target/using/administer/manage-users/enterprise/property-channel
Properties and Permissions functionality is available as part of the Target Premium solution.
- Source 38: experienceleague.adobe.com/en/docs/target/using/administer/manage-users/enterprise/property-channel
The new Target Permissions model has the same three permission roles (Observer, Editor, and Approver); however, you can assign a user’s permissions roles separately for individual pages, properties, or sites, as shown below:
- Source 39: experienceleague.adobe.com/en/docs/target/using/introduction/how-target-works
This data is then directed to a pre-determined Central Cluster destination (Oregon, Dublin, or Singapore) for processing. Visitor profile data is stored on the Edge Cluster closest to the site visitor.
- Source 40: experienceleague.adobe.com/en/docs/target/using/introduction/how-target-works
Target Central Clusters, hosted on Amazon Web Services (AWS), include:
- Source 41: experienceleague.adobe.com/en/docs/target/using/introduction/how-target-works
The Target Recommendations service is hosted in an Adobe data center in Oregon.
- Source 42: experienceleague.adobe.com/en/docs/target-dev/developer/client-side/functions-overview/targetglobalsettings
To enable the ability to use Opt-In in the Target at.js library, add the optinEnabled=true setting.
- Source 43: experienceleague.adobe.com/en/docs/target-dev/developer/implementation/privacy/cmp-privacy-and-general-data-protection-regulation
The Target tag is NOT pre-approved and bodyHidingEnabled is FALSE: The Target tag fires only after consent is collected from the customer.
- Source 44: experienceleague.adobe.com/en/docs/target-dev/developer/implementation/privacy/cmp-privacy-and-general-data-protection-regulation
Consent management isn’t supported by and shouldn’t be achieved via Target for GDPR and CCPA.
- Source 45: experienceleague.adobe.com/en/docs/core-services/interface/data-collection/cookies/target
Cookie duration: The cookie remains on the visitor’s browser two years from the last login. You cannot change the cookie duration.
- Source 46: experienceleague.adobe.com/en/docs/target-dev/developer/implementation/privacy/cookie-behavior
The cookie remains on the visitor’s browser two weeks from the last login. You cannot change the cookie duration.
- Source 47: partners.adobe.com/digitalexperience/salesplays/tactics/target
Upsell to customers who want to optimize for LLM traffic and expand and mature Target activities on new website and mobile application experiences, want to upgrade from Standard to Premium, or have organic growth in page view volume.
- Source 48: experienceleague.adobe.com/en/docs/target/using/activities/auto-target/auto-target-to-optimize
Like in Auto Personalization, Auto-Target, by default, reserves part of the activity’s traffic as a control group to measure lift.
- Source 49: experienceleague.adobe.com/en/docs/target/using/activities/auto-target/auto-target-to-optimize
Auto-Target is available as part of the Target Premium solution.
- Source 50: experienceleague.adobe.com/en/docs/target/using/activities/automated-personalization/automated-personalization
Automated Personalization is available as part of the Target Premium solution.
- Source 51: experienceleague.adobe.com/en/docs/target-dev/developer/server-side/getting-started
In order to get up and running, we encourage you to create your first on-device decisioning feature flag activity in the language of your choice: Node.js Java .NET Python
- Source 52: experienceleague.adobe.com/en/docs/target-dev/developer/server-side/on-device-decisioning/overview
When you install and initialize an Adobe Target SDK with on-device decisioning enabled, a rule artifact is downloaded and cached locally on your server, from the Akamai CDN closest to your server.
- Source 53: experienceleague.adobe.com/en/docs/target-dev/developer/server-side/on-device-decisioning/overview
Adobe Tech Blog - Part 1: Run Adobe Target NodeJS SDK for experimentation and personalization on edge platforms (Akamai Edge Workers)
Migrate from Adobe Target
Your Adobe Target history can be imported as a generic CSV (visitors and conversions per variant) and is reread by Probant’s engine: every verdict is recalculated from visitors and conversions.
- Open demo, no sign-up
- Free to start, no overage charges
- Database and compute in Amsterdam, with Scaleway