Adobe Target

Hi @markcol, you're in the right place! Feel free to post any of your Target Recommendations related questions here in the thread and our team of experts will follow up with you by replying to your question here in the thread.

Hi Jyoti! These would be unique single value parameters which are classified internally with an organization and mapped to not just products but could be content components such as movies, articles, etc and predefined for each 'entity.id' attribute value (e.g. unique numeric values)

@jingle77  you would need to build out a custom algorithm and send it into Target. This is quite possible, but nothing in the box in Adobe Target.  

Hi @jingle77  Thank you for your question! Based on custom templates / scripts, you could use your python algorithm and upload the output in custom criteria configuration as a .csv. You can customized all the OOTB options in the criteria menu to create your own criteria configuration. Note - Custom criteria replace the “offline training” portion of Item-Based recommendations, but behave similarly to Item-Based recommendation algorithms during the online content delivery phase, in that a single key is used for retrieval of recommendations and business rules/filters are then applied.

https://experienceleague.adobe.com/docs/target/using/recommendations/criteria/recommendations-csv.ht...

One way to tackle this would to filter out recommendations based on profile attributes assuming that the profile stores information you need to filter recommendations on. Some more details about this type of filtering can be found on experience league page: https://experienceleague.adobe.com/docs/target/using/recommendations/criteria/dynamic-static/profile... 

Thank you @BryanSt3  for your question, traffic for a recommendation would depend on your test time to confidence using a statistical calculator. If you have low traffic and cannot meet confidence, I would look at prior data of recommendation interaction and make a trade off as long as your analytics team agrees. Your best bet to understand granularity would be to store profile attributes and to create more of an affinity approach. In addition, you can layer several stacking segments in a recommendation test depending on your use case.

Hi @hagarwal thank you for your question! Models to building times within AI activities will correlate to traffic to selected activity locations and conversion rates associated with those success metrics.

For Auto-Target specifically, the personalization activity does not attempt to build a personalized model for a given experience until there are least 50 conversions for that experience. If the model built is of insufficient quality (as determined by offline evaluation on hold-out “test” data, using a metric known as AUC), the model is not used to serve traffic in a personalized manner.

Additional points on model building:

• After an activity is live, Auto-Target considers up to the last 45 days of randomly served data when attempting to build models. For example, control traffic, plus some extra randomly served data held out by the algorithm.
• When Revenue per Visit is your success metric, these activities typically require more data to build models due to the higher data variance that typically exists in visit-revenue compared to conversion rate.
• Because models are built on a per-experience basis, replacing one experience with another experience means that sufficient traffic (at least 50 conversions) must be collected for the new experience before personalized models can be rebuilt.

@AmsaPa  thank you for your question I highly suggest a read through of https://experienceleague.adobe.com/docs/target/using/recommendations/introduction-to-recommendations....

Adobe Target recommendations can be used for so many capabilities than products - Travel, Gaming, Videos, Publish, B2B Sales, and more. It will open up so many possibilities on how to utilize recommendations. 

Now how you optimize, you have to be willing to take risks on different UI, featuring reviews, and also testing by different locations WW. You may find what works for one country does not work for another. 

Thank you @Sharma_Manu . Recommendation algorithms could be configured to use dynamic inclusion rules based on user profile attributes or on page context similar to category affinity filtering. Using dynamic rules brings more engagement. More details about available filtering rules could be found on Experience League: https://experienceleague.adobe.com/docs/target/using/recommendations/criteria/dynamic-static/use-dyn... 

Thank you @Sharma_Manu  appreciate your question. There are a luxury of algorithms from Adobe - I suggest you look into affinity.  Target https://experienceleague.adobe.com/docs/target/using/recommendations/criteria/base-the-recommendatio...

If you are out of a product, you can have out of stock filters out of the box in Adobe Target. 

Thank you for your great question and that would be a great new feature of Target and / or Target + Analytics. So far, the Target reporting data as well as the algorithms co-occurrence scores are not available. Please use the Adobe Target Community Ideas to post this as a feature request and have other community members vote for this feature.

@Ramesh_N yes you would use a profile attribute to signify the visitor has added this item to cart before. If they signed in you could identify the hash as well to be cross device. This would allow for a more personalized algorithm to complimentary items. 

@RobBa2  thank you for your question. Yes, what you would need is a custom out of the box algorithm to tie in accessories to products that would depend on a profile scripting activation of the product add to cart to tie into that recommendation spot. This way you can test multiple accessory algorithms and features.

Great question, thank you. Would you be able to provide more details on the assumptions you're working with it when trying to add additional parameters or even weight change? While this is not possible at this point with recommendations algorithms, you might want to combine recommendations with Auto-Target activities. For the configurable recommended for you algorithm, please submit a community idea. 

@Ramesh_N this is very possible in the box in Target. You would have to send that data into target and use a set of filters. By doing this, you would be ready to have personalization recommendations by top discount. 

I suggest a friendly read of this as well to help give some background on the potential of feeds. Its where so much creativity can be reached with recommendations - https://experienceleague.adobe.com/docs/target/using/recommendations/entities/feeds.html?lang=en