Premium

Auto-Target overview

Last update: 2025-02-04

Auto-Target activities in Adobe Target use advanced machine learning to select from multiple high-performing, marketer-defined experiences to personalize content and drive conversions. Auto-Target serves the most tailored experience to each visitor based on the individual customer profile and the behavior of previous visitors with similar profiles.

NOTE

  • Auto-Target is available as part of the Target Premium solution. This feature is not available in Target Standard without a Target Premium license. For more information about the advanced features this license provides, see Target Premium.

  • Analytics for Target (A4T) supports Auto-Target activities. For more information, see A4T support for Auto-Allocate and Auto-Target activities.

Real-world success story using Auto-Target

A major clothing retailer recently used an Auto-Target activity with ten product category-based experiences (plus randomized control) to deliver the right content to each visitor. “Add to Cart” was chosen as the primary optimization metric. The targeted experiences had an average lift of 29.09%. After building the Auto-Target models, the activity was set to 90% personalized experiences.

In just ten days, more than $1,700,000 in lift was achieved.

Keep reading to learn how to use Auto-Target to increase lift and revenue for your organization.

Overview

While creating an A/B activity using the three-step guided workflow, choose the Auto-Target for personalized experiences option on the Targeting page (step 2).

Traffic Allocation Method settings

The Auto-Target option within the A/B activity flow lets you harness machine-learning to personalize based on a set of marketer-defined experiences in one click. Auto-Target is designed to deliver maximum optimization, compared to traditional A/B testing or Auto Allocate, by determining which experience to display for each visitor. Unlike an A/B activity in which the objective is to find a single winner, Auto-Target automatically determines the best experience for a given visitor. The best experience is based on the visitor’s profile and other contextual information to deliver a highly personalized experience.

Similarly to Automated Personalization, Auto-Target uses a Random Forest algorithm, a leading data science ensemble method, to determine the best experience to show to a visitor. Because Auto-Target can adapt to changes in visitor behavior, it can run perpetually to provide lift. This method is sometimes referred to as “always-on” mode.

Unlike an A/B activity in which the experience allocation for a given visitor is sticky, Auto-Target optimizes the specified business goal over each visit. Like in Auto Personalization, Auto-Target, by default, reserves part of the activity’s traffic as a control group to measure lift. Visitors in the control group are served a random experience in the activity.

Considerations

There are a few important considerations to keep in mind when using Auto-Target:

  • You cannot switch a specific activity from Auto-Target to Automated Personalization, and the opposite way.

  • You cannot switch from Manual traffic allocation (traditional A/B Test) to Auto-Target, and the opposite way after an activity is saved as draft.

  • One model is built to identify the performance of the personalized strategy versus randomly served traffic versus sending all traffic to the overall winning experience. This model considers hits and conversions in the default environment only.

    Traffic from a second set of models is built for each modeling group (AP) or experience (AT). For each of these models, hits and conversions across all environments are considered.

    Requests are served with the same model, regardless of environment. However, the plurality of traffic should come from the default environment to ensure that the identified overall winning experience is consistent with real-world behavior.

  • Use a minimum of two experiences.

Terminology

The following terms are useful when discussing Auto-Target:

Term Definition
Multi-armed bandit A multi-armed bandit approach to optimization balances exploratory learning and exploitation of that learning.
Random Forest Random Forest is a leading machine learning approach. In data-science speak, it is an ensemble classification, or regression method, that works by constructing many decision trees based on visitor and visit attributes. Within Target, Random Forest is used to determine which experience is expected to have the highest likelihood of conversion (or highest revenue per visit) for each specific visitor.
Thompson Sampling The goal of Thompson Sampling is to determine which experience is the best overall (non-personalized), while minimizing the “cost” of finding that experience. Thompson sampling always picks a winner, even if there is no statistical difference between two experiences.

How Auto-Target Works

Learn more about the data and algorithms underlying Auto-Target and Automated Personalization at the links below:

Term Details
Random Forest Algorithm Target’s main personalization algorithm used in both Auto-Target and Automated Personalization is Random Forest. Ensemble methods, such as Random Forest, use multiple learning algorithms to obtain better predictive performance than could be obtained from any of the constituent learning algorithms. The Random Forest algorithm in the Automated Personalization and Auto-Target activities is a classification, or regression method, that operates by constructing a multitude of decision trees at training time.
Uploading Data For Target’s Personalization Algorithms There are several ways to input data for Auto-Target and Automated Personalization models.
Data Collection for Target’s Personalization Algorithms Target’s personalization algorithms automatically collect various data.

Determining Traffic Allocation

Depending on the goal of your activity, you might choose a different traffic allocation between control and personalized experiences. Best practice is to determine this goal before you make your activity live.

The Custom Allocation drop-down list lets you choose from the following options:

  • Evaluate Personalization Algorithm (50/50)
  • Maximize Personalization Traffic (90/10)
  • Custom Allocation

Allocation Goal drop-down list

The following table explains the three options:

Activity Goal Suggested Traffic Allocation Tradeoffs
Evaluate Personalization Algorithm (50/50): If your goal is to test the algorithm, use a 50/50 percent split of visitors between the control and the targeted algorithm. This split gives the most accurate estimate of the lift. Suggested for use with “random experiences” as your control. 50% Control / 50% Personalized Experience split
  • Maximizes accuracy of lift between control and personalized
  • Relatively fewer visitors have a personalized experience
Maximize Personalization Traffic (90/10): If your goal is to create an “always on” activity, put 10% of the visitors into the control to ensure that there is enough data for the algorithms to continue learning over time. The tradeoff here is that in exchange for personalizing a larger proportion of your traffic, you have less precision in what the exact lift is. No matter your goal, this is the recommended traffic split when using a specific experience as the control. Best practice is to use a 10% - 30% Control / 70% - 90% Personalized Experience split
  • Maximizes the number of visitors who have a personalized experience
  • Maximizes lift
  • Less accuracy as to what the lift is for the activity
Custom Allocation Manually split the percentage as desired.
  • You might not achieve the desired results. If you are unsure, follow the suggestions for either of the preceding options

To adjust the Control percentage, click Experiences in the Traffic Allocation pane, then adjust the percentages as desired. You cannot decrease the control group to less than 10%.

You can select a specific experience to use as control or you can use the Random experience option.

When should you choose Auto-Target over Automated Personalization?

There are several scenarios in which you might prefer to use Auto-Target over Automated Personalization:

  • If you want to define the entire experience rather than individual offers that are automatically combined to form an experience.
  • If you want to use the full set of Visual Experience Composer (VEC) features not supported by Auto Personalization: the custom code editor, multiple experience audiences, and more.
  • If you want to make structural changes to your page in different experiences. For example, if you want to rearrange elements on your home page, Auto-Target is more appropriate to use than Automated Personalization.

What does Auto-Target have in common with Automated Personalization?

The algorithm optimizes for a favorable outcome for each visit.

  • The algorithm predicts a visitor’s propensity for conversion (or estimated revenue from conversion) to serve the best experience.
  • A visitor is eligible for a new experience upon the end of an existing session (unless the visitor is in the control group, in which case the experience that the visitor is assigned on the first visit remains the same for subsequent visits).
  • Within a session, the prediction doesn’t change, to maintain visual consistency.

The algorithm adapts to changes in visitor behavior.

  • The multi-arm bandit ensures that the model is always “spending” a small fraction traffic to continue to learn throughout the life of the activity learning and to prevent over-exploitation of previously learned trends.
  • The underlying models are rebuilt every 24 hours using the latest visitor behavior data to ensure that Target is always exploiting changing visitor preferences.
  • If the algorithm can’t determine winning experiences for individuals, it automatically switches to showing the overall best-performing experience while still continuing to look for personalized winners. The best-performing experience is found using Thompson sampling.

The algorithm continually optimizes for a single goal metric.

  • This metric could be conversion-based or revenue-based (more specifically Revenue per Visit).

Target automatically collects information about visitors to build the personalization models.

Target automatically uses all Adobe Experience Cloud shared audiences to build the personalization models.

  • You don’t have to do anything specific to add audiences to the model. For information about using Experience Cloud Audiences with Target, see Experience Cloud Audiences.

Marketers can upload offline data, propensity scores, or other custom data to build personalization models.

How Does Auto-Target differ from Automated Personalization?

Auto-Target frequently requires less traffic than Automated Personalization for a personalized model to build.

Although the amount of traffic per experience required for Auto-Target or Auto Personalization models to build are the same, there are usually more experiences in an Automated Personalization activity than an Auto-Target activity.

For example, if you had an Auto Personalization activity in which you’ve created two offers per location with two locations, there would be four (2 = 4) total experiences included in the activity (with no exclusions). Using Auto-Target, you could set experience 1 to include offer 1 in location 1 and offer 2 in location 2, and experience 2 to include offer 1 in location 1 and offer 2 in location 2. Because Auto-Target allows you could choose to have multiple changes within one experience, you can reduce the number of total experiences in your activity.

For Auto-Target, simple rules of thumb can be used to understand traffic requirements:

  • When Conversion is your success metric: 1,000 visits and at least 50 conversions per day per experience, and in addition the activity must have at least 7,000 visits and 350 conversions.
  • When Revenue per Visit is your success metric: 1,000 visits and at least 50 conversions per day per experience, and in addition the activity must have at least 1,000 conversions per experience. RPV usually requires more data to build models due to the higher data variance that typically exists in visit revenue compared to conversion rate.

Auto-Target has full-fledged setup functionality.

  • Because Auto-Target is embedded in the A/B activity workflow, Auto-Target benefits from the more mature and full-fledged Visual Experience Composer (VEC). You can also use QA links with Auto-Target.

Auto-Target provides an extensive online testing framework.

  • The multi-arm bandit is part of a larger online-testing framework that allows Adobe data-scientists and researchers to understand the benefits of their continual improvements in real-world conditions.
  • In the future, this test bed will allow us to open the Adobe machine learning platform to data-savvy clients so that they can bring in their own models to augment the Target models.

Reporting and Auto-Target

For more information, see Reporting and Auto-Target.

Previous page
Next page

On this page