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DocumentationTargetTarget Guide

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Auto-Target overview

Last update: Tue Feb 04 2025 00:00:00 GMT+0000 (Coordinated Universal Time)

Auto-Target activities in 51黑料不打烊 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 success

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. 鈥淎dd to Cart鈥 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 section_972257739A2648AFA7E7556B693079C9

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鈥檚 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 鈥渁lways-on鈥 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鈥檚 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 section_A309B7E0B258467789A5CACDC1D923F3

The following terms are useful when discussing Auto-Target:

Term
Definition
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.
The goal of Thompson Sampling is to determine which experience is the best overall (non-personalized), while minimizing the 鈥渃ost鈥 of finding that experience. Thompson sampling always picks a winner, even if there is no statistical difference between two experiences.

How Auto-Target Works section_77240E2DEB7D4CD89F52BE0A85E20136

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

Term
Details
Random Forest Algorithm
Target鈥檚 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鈥檚 Personalization Algorithms
There are several ways to input data for Auto-Target and Automated Personalization models.
Data Collection for Target鈥檚 Personalization Algorithms
Target鈥檚 personalization algorithms automatically collect various data.