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.
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.
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.
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).
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.
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, but 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.
The following terms are useful when discussing Auto-Target:
|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.|
Learn more about the data and algorithms underlying Auto-Target and Automated Personalization at the links below:
|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.|
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:
|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||
|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||
|Custom Allocation||Manually split the percentage as desired.||
To adjust the Control percentage, click the icons in the Allocation column. 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.
There are several scenarios in which you might prefer to use Auto-Target over Automated Personalization:
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:
For more information, see Reporting and Auto-Target.
This video explains how to set up an Auto-Target A/B activity.
After completing this training, you should be able to: