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Rob_Hornick
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08-07-2020


@Amelia_Waliany wrote:

Hi @Rob_Hornick this question was previously posted in the Community by @LordOfTheRings

 

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?


@LordOfTheRings (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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08-07-2020

Hi @Rob_Hornick, 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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Amelia_Waliany
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08-07-2020

Hi @Rob_Hornick, thanks for this insightful coffee break! This Question was previously posted in the Community by @petero52240340 :  

 

I know the general guidelines on the Adobe site around Auto Target are;

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.

How rigid are these, i.e. if I have roughly 960 visits a day and 40 / 35 conversions, is this still viable to run and it will take longer to achieve confidence? Does this work in the same way for say 800 visits? 

I would like to try auto targeting but volume is an issue so I wanted to try and see if we could still run auto targetting.

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Amelia_Waliany
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08-07-2020

Hi @Rob_Hornick, this Question was previously posted in the Community by @Saif :

 

I want to use two different recommendations design in the same recommendations activity and divide the traffic between the 2 designs making it a 50/50 split.

This idea makes it a combination of recommendations and A/B test.

Since recommendations only allows one version apart from control, we are not able to add more designs as different versions in the same activity.

Please let me know how we can achieve something like this

@ryanr79035901

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Rob_Hornick
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08-07-2020


@Amelia_Waliany wrote:

Hi @Rob_Hornick, thanks for this insightful coffee break! This Question was previously posted in the Community by @petero52240340 :  

 

I know the general guidelines on the Adobe site around Auto Target are;

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.

How rigid are these, i.e. if I have roughly 960 visits a day and 40 / 35 conversions, is this still viable to run and it will take longer to achieve confidence? Does this work in the same way for say 800 visits? 

I would like to try auto targeting but volume is an issue so I wanted to try and see if we could still run auto targetting.


Hi @petero52240340, thanks for your question. It's still possible to create an Auto-Target activity with less traffic than the recommended guidelines. However, it will take additional time for Adobe Target to build predictive models, and the predictive models may be lower-quality. Some quick tips to get better performance from Auto-Target activities with less traffic:

  • Direct less traffic (10%) to the control option. This ensures that plenty of traffic is available for exploration and exploitation. (The tradeoff is you may have a less accurate measure of the overall activity's lift.)
  • Use fewer, more distinctive experiences. For example, use 3 very unique, distinct experiences (e.g. headline and hero image and product change) rather than 10 experiences with small differences (e.g. changed background color of product image.)
  • Use a conversion metric rather than an engagement or revenue metric. Binary conversion metrics are easier to model in low traffic scenarios.

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08-07-2020


@Amelia_Waliany wrote:

Hi @Rob_Hornick, this Question was previously posted in the Community by @Saif :

 

I want to use two different recommendations design in the same recommendations activity and divide the traffic between the 2 designs making it a 50/50 split.

This idea makes it a combination of recommendations and A/B test.

Since recommendations only allows one version apart from control, we are not able to add more designs as different versions in the same activity.

Please let me know how we can achieve something like this

@ryanr79035901


Hi @Saif , you have two options:

  1. Set up your test as an A/B test activity and then insert Recommendations offers into each variant. This allows you to test a number of different design/algorithm combinations. For example, you could set up a test with 4 variations based on the number of items to display (4 or 6 items) and the algorithm to use (recently viewed or top sellers). You can then specify the exact traffic split you want (even or otherwise). For more on this option, see: https://docs.adobe.com/content/help/en/target/using/recommendations/recommendations-as-an-offer.html 
  2. To accomplish your goal in a Recommendations activity, select a single Criteria, then select multiple Designs during the "Select a Design template" section of the activity creation workflow before continuing. Recommendations activities allow you to test either multiple Criteria or multiple Designs, but not both at the same time. Recommendations activities also force an even traffic split between all non-control variations. For more on this option, see: https://docs.adobe.com/content/help/en/target/using/recommendations/recommendations-activity/create-... 

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@surebee wrote:

Hi @Rob_Hornick, 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? 


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-alloc...

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-p...