[AT Community Q&A Coffee Break] 7/8: Rob Hornick, Adobe Target Product Manager

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@evidana wrote:

@rhornick what are the odds of being able to expand the window of conversion to more than 30 minutes? Financial planning events have a much longer conversion time than e-commerce events.

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Eric, thanks for your question! I can't comment on the likelihood of adding features that aren't on our publicly committed roadmap, but we don't have an immediate plan to add a longer window to Adobe Target-defined conversion metrics. You're welcome to submit this (and any other feature requests) in our Ideas forum: https://experienceleaguecommunities.adobe.com/t5/adobe-target-ideas/idb-p/adobe-target-ideas

In the meantime, you may be able to accomplish your goal using our Analytics for Target (A4T) integration along with a conversion metric defined in Adobe Analytics with a longer attribution lookback window. Adobe Analytics conversion metrics are supported for A/B and XT activities, and with our June release, we added A4T support for Auto-Allocate activities. Later this summer, we'll release A4T support for Auto-Target activities.

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@evidana wrote:

@rhornick do you have any advice on hooking up internal decisioning (algos) to Adobe Target as the last mile of determining eligibility for running experiments?

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Hi Eric, there are a few options you might consider here for bringing external decisioning data into Adobe Target.

1. For product and content recommendations based on a key value (like customer ID or current product ID) that can be pre-computed, consider the "Custom Criteria" feature of Target Recommendations, which allows you to upload custom recommendations for each key value. See: https://docs.adobe.com/content/help/en/target/using/recommendations/criteria/recommendations-csv.html 
2. For real-time rules-based targeting based on an external signal, you can call your external system prior to calling Adobe Target, then pass the signal to Adobe Target as a profile or mbox parameter.
3. You can use Target's Data Providers capability to ingest external data sources: https://docs.adobe.com/content/help/en/target-learn/tutorials/integrations/use-data-providers-to-integrate-third-party-data.html

This is an area of active investment for us. We're currently working on a feature, "Auto-Target with Custom Model", that will enable Target Premium users to bring their own model into Target's Auto-Target feature. If participating in a beta of this feature is of interest to you, please reach out through your Adobe CSM.

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@nicolas_swisscom wrote:

@Rob_Hornick why is AP and AT not an activity type of it's own i.e. and currently "inside" the A/B Test flow?

What is the difference between AP, AT and Recommended for you? Are all 3 using the same algorithm?

How would I be ablecable to use AT or AP together with a propensity score (hopefully from AA)? Any examples or tips how to achieve this?

Can you share some examples of websites that sucessfully use AP, AT or Recs.?

How do I use Recs. within a Single Page Application? Trigger Views?

Merci beacoup!

Nicolas

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Nicolas!  Great to connect with you and hope all is well in your neck of the globe!  Automated Personalization (AP) is its own activity type, but Auto-Target (AT) is embedded within the testing activity workflow for ease of "one-click" personalization, evaluating each individual's profile for determining the next best experience to deliver.  It makes it easier to consider utilizing personalization when considering traffic allocation to different experiences (when you are in the 2nd step of our 3-step testing activity workflow), and to consider leveraging our algorithms for dynamically decisioning for each individual (equivalent of taking action off of hundreds of tests in a single moment) 

A good reference for the underlying algorithms is our Automation Infographic: https://wwwimages2.adobe.com/content/dam/acom/en/marketing-cloud/target/pdf/54658.en.target.infographic.automation-sensei-update-2017-summit.pdf

Here is more information on our Recommended For You algorithm and use cases, written by Rob: https://theblog.adobe.com/delivering-dynamic-personalized-experiences-with-adobe-targets-new-user-based-recommendations-algorithm/  

AT.js, our javascript library, is built for integrations within Single Page Applications.  This enables leveraging triggerviews for delivering experiences discretely within an SPA experience.  

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@activemitchell wrote:

Hello - how can I get into the Conversation? first timer here - already registered, not seeing the link.  thanks.

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You are in the conversation/discussion thread - post your question and we'll respond as quickly as we can!

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@surebee wrote:

Hi @rhornick, this question was posted in the community by @anils5920589:

 

Had a question on how Target derives reward probabilities for the MAB algorithms implemented for Auto Allocate, Auto Target and Automated Personalization activities.

Was going through your docs and found out that there are three ways of feeding data into Target:

• mbox parameters
• Profile parameters/attributes
• Server side APIs for profile updates.

Since MAB algorithms need reward probabilities of each experience/variant as an input which change over time as more visitors participate in an activity, does Target derive the reward probability from the data supplied using the above methods ?

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Hi @anils5920589! 

The Auto-Allocate multi-armed bandit feature is a non-contextual bandit so does not leverage the aforementioned attributes to derive a reward probability; instead it solely examines prior behavior at the aggregate level.

The Auto-Target and Automated Personalization feature act as contextual bandits and, as you've correctly inferred, leverage the provided profile and contextual (mbox) parameters to derive an estimated probability of conversion (per experience for Auto-Target and per offer for Automated Personalization). To learn more, see: https://docs.adobe.com/content/help/en/target/using/activities/automated-personalization/ap-data.html and https://docs.adobe.com/content/help/en/target/using/activities/automated-personalization/uploading-data-for-the-target-personalization-algorithms.html .

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@amelia_waliany wrote:

Hi @rhornick this question was previously posted in the Community by @ambikatewari_atci: 

 

Hi Experts,

As per help doc, Entity attribute values expire after 61 days. This means that you should ensure that the latest value of each entity attribute is passed to Target Recommendations at least once per month for each item in your catalog.

does this mean if the same value for an entity attribute is updated then attribute will not expire. ? or should it have new value for entity attribute not to expire?

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@ambikatewari_atci (love the user name!), you do not need to change the value. Sending the same value will still let us know that the product data should not expire.

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@surebee wrote:

Hi @rhornick, this question was posted in the community by @dsu50:

 

Do Adobe Target Auto-Target and Auto-Allocate options use Bayesian or frequentist statistics? Is there a documentation that describes the statistical methods these options use? 

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Hi @dsu50 . In both Auto-Allocate and Auto-Target we start with a prior probability of conversion for each experience and then update the estimate over time, so both use Bayesian methods. More info about Auto-Allocate is available here: https://docs.adobe.com/content/help/en/target/using/activities/auto-allocate/automated-traffic-allocation.html

More info about Auto-Target's model is available here (note the same model powers Automated Personalization): https://docs.adobe.com/content/help/en/target/using/activities/automated-personalization/automated-personalization.html