Advanced Audience Targeting - beta

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Beta

Advanced Audience Targeting is currently in beta. Apply on the Optimizely beta signup page or contact your Customer Success Manager.

Integration considerations

Before implementing Advanced Audience Targeting, you must decide on what customer data platform (CDP) you want to use. Currently, Advanced Audience Targeting works out of the box with:

• Optimizely Data Platform (ODP)
• Amplitude Audience Sync
• mParticle Segment sync
• Tealium
• Twilio Segment Engage audiences
• Zeotap Segment Sync

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Important

You must understand how Optimizely Data Platform (ODP) handles user data and how real-time segments work before implementing Advanced Audience Targeting in Optimizely Feature Experimentation.

Refer to the ODP support documentation and developer documentation to learn more.

With Advanced Audience Targeting, you can segment your audience based on their behaviors, interactions, and preferences to improve your experimentation efforts, and you can even enable anonymous targeting using a client-side SDK.

By using ODP's real-time segments in Optimizely Feature Experimentation, you can create detailed audiences to target specific users in your flag rules without slowing down your application or increasing the size of the SDK's datafile.

Additionally, using a client-side SDK with Advanced Audience Targeting lets you do anonymous targeting. See VUIDs and anonymous targeting for more.

Implement the Advanced Audience Targeting integration

Prerequisites

To use the Advanced Audience Targeting integration, you must:

• An Optimizely Feature Experimentation account.
• An Optimizely Data Platform account.
  • Have pre-built or custom real-time segments set up in ODP.

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Note

Only the segments in Customers > Real-Time Segments page in ODP are available for use Advanced Audience Targeting.

Enable Advanced Audience Targeting in Optimizely Feature Experimentation

1. In the Optimizely application, select the Optimizely Feature Experimentation project for which you want to enable the integration.
2. Go to Settings > Integrations > Advanced Audience Targeting.
3. Toggle the integration status to On.
4. Click You can find your API key in ODP to go to the APIs page in ODP.
5. Copy the value in the KEY fields on both the Public and Private tabs on the APIs page in ODP.
6. Go back to your Optimizely Feature Experimentation project settings and paste these values into the corresponding fields (ODP Public Key (required) and ODP Private Key (required)).
7. Select your region from the ODP Host (required) drop-down list.
8. Click Save.

Integrate with other CDPs

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Important

If you use ODP as your CDP, do not complete this section. Skip to Create an Optimizely Feature Experimentation audience using ODP's real-time segments.

If you use mParticle or Zeotap as your CDP, configure ODP to automatically create real-time segments based on your identifiers from the CDP:

1. Configure the mParticle or Zeotap integration in ODP:
   • Integrate mParticle Segment Sync (beta).
   • Integrate Zeotap Segment Sync (beta).
2. Follow steps 1-8 in Enable Advanced Audience Targeting in Optimizely Feature Experimentation.

Create an Optimizely Feature Experimentation audience using ODP's real-time segments

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Note

You can create and add an audience using the audience builder (described in this document) or directly in your flag's rule. After creating your audience, you can reuse it in other flags.

To create a Feature Experimentation audience using ODP's real-time segments:

1. Go to Audiences > Create New Audience....

2. Fill in the Name (required) and Description (optional) fields.
3. In the audience condition section, expand Optimizely Data Platform Audience Targeting.

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Important

If you do not see Optimizely Data Platform Audience Targeting as an option, verify that the ODP integration was set up and enabled. See Enable Optimizely Data Platform Audience Targeting.

4. Drag and drop either Custom Segments or Pre-built Segments to the Audience Conditions section.

5. Configure the audience as desired (the real-time segments from ODP display in the Select an audience drop-down list).

6. Once you have configured the audience, click Save Audience.

This creates a new audience you can use to target specific users in your experiments and targeted deliveries.

Integrate Advanced Audience Targeting in your code

After creating an audience using Advanced Audience Targeting and attaching that audience to a flag, the Optimizely Feature Experimentation SDK adds a new section in your datafile namedintegrations. This lets your application query ODP for audience conditions and send event data to the ODP server.

See:

• Implement Advanced Audience Targeting with client-side SDKs
• Implement Advanced Audience Targeting with server-side SDKs

ODP limitations and accuracy

Any customer data platform (CDP) has some latency from when it receives data until it can stitch together or create a new profile for the customer. During this latency period, you may receive out-of-date or stale information about your customers if you query your CDP.

Optimizely Data Platform has the shortest time to accurate results among competing CDPs. ODP usually takes less than 30 seconds, and for 99.999% of the cases, the process takes less than 90 seconds. Many other CDPs take hours or even days to merge data.

It is important to acknowledge this gap in accuracy and account for potentially inaccurate data. If you need your user segments to be 100% accurate 100% of the time, you should manually create your audiences in Optimizely Feature Experimentation and target users through custom user attributes (see Target audiences).

An example of when Advanced Audience Targeting would not be the right use case is when you want to show a purchase confirmation page right after a customer has bought an item. Because it will take at least 30 seconds for the customer to be added to a has_made_purchase audience segment, you cannot use that segment to decide whether to show the confirmation page. Instead, you should use a custom user attribute that can be updated through your local code and queried immediately.

The following table shows different times during the ODP customer profile lifecycle and whether the information coming from ODP will be accurate:

VUIDs and client-side IDs

ODP uses various identifiers unique to a specific customer to track that customer's data. One of these identifiers is called a VUID and is an identifier tied to a specific device.

When you initialize the Optimizely Feature Experimentation client-side SDK, if a VUID does not exist or the SDK cannot find it, the SDK automatically generates one. This VUID is stored in persistent storage, re-used, and shared across all SDK instances within the device. Additionally, the SDK sends the default app and user tracking data to ODP on VUID creation.

You can use this VUID with the userID you already provide to Feature Experimentation to merge customer profiles. For information, refer to Overview of customer identity and resolution in ODP for information on how to use VUIDs to target audiences or make decisions.

VUIDs and anonymous targeting

Anonymous audience targeting refers to a method of targeting users who have not provided personally identifiable information (PII), such as their name or email address, but whose behavior or interactions on a website or app can be tracked and analyzed. With anonymous targeting, you can still deliver personalized content or experiences to these users based on their behavior and preferences without knowing their identity.

In Feature Experimentation, you can conduct anonymous targeting through a VUID, which lets ODP recognize and track the user's data across different sessions or devices without revealing their identity. You can use ODP's customer analytics tools to analyze the data and identify patterns and trends that can inform your targeting strategy. Using Feature Experimentation, you can run tailored experiments on these users without storing or collecting PII.

By using anonymous targeting using a VUID in Optimizely's Data Platform and Feature Experimentation, you can create a personalized, data-driven approach to targeting your audience while protecting their privacy. This approach can help you improve engagement, conversions, and overall user satisfaction.