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Recommendations in Discover Explained's Discover Recommendations leverages the power of machine learning to recommend ideal partners you can reach out to and recruit for your program. Our Data Science team has put together a series of well-established machine learning algorithms to analyze millions of data points across the partnership ecosystem, resulting in partner recommendations that you can trust.

See the Recommendations deep-dive section below for a detailed technical explanation of how curates these recommendations.

When in Discover, you can access the recommended partners from the left navigation menu by selecting Recommendations.


By default, all the partners that recommends will be shown. You can filter your recommendations using the Recommendations filter. Select the filter, then select which types of recommended partners you want to view. See the Recommendations filter reference below for a description of the different types of recommended partners.

Recommendations filter reference

Recommendation type


Conversion Path Introducers

These are the top 5 partners (not yet in your program) that play an introducer role to at least 25% of the conversion paths they participate in.

High AOV Potential

These are the top 5 partners (not yet in your program) that generate the highest 90-day average order value across all programs they participate in.

High Revenue Potential

These are the top 5 partners (not yet in your program) that generate the highest 90-day average revenue across all programs they participate in.

Partners from Similar Brands

These are partners (not yet in your program) that our machine learning algorithm sees as most similar to your brand and have the highest propensity to be present in programs from brands like you.

Partners You Will Love

These are the top 10 partners (not yet in your program) that are most likely to have productive partnerships.

Recommendations deep-dive

As mentioned in the introduction, leverages the power of machine learning to recommend partners to you. The inputs of the machine learning algorithm include, but are not limited to:

  • Partner attributes (Location, promotional methods, retail vertical, etc.)

  • Partner audience demographics

  • Partners’ historical performance metrics such as payout history and revenue

  • Brand and program similarity with other brands and programs

  • Brand and program attributes (Location, retail vertical, products, product categories, etc.)

  • Brand’s and program’s target audience

  • Product taxonomy and items sold

These machine learning algorithms are then applied to the entire partner universe so all partners get a propensity score. This score helps us sort and list partners based on how relevant they are to your program.

The relevancy sort order is based on the machine learning propensity score, but is also adjusted by various productive and relevant factors:

  • Productive Rate Given Brand Send—We check how often a partner generates payable actions when a brand proposes a contract to them. Those that are more productive are given a higher relevancy sort order.

  • Partner Size—A re-ranking is given based on the size of the partner, which helps find quality relationships.

  • Low Payout—If the partner has a low payout (less than $200 or less than $1,000 in 90 days), then they are given a lower score.

We hope this deep-dive has strengthened your trust in the recommendations that provides. Our Data Science team is continuously tuning the machine learning model and considering new inputs to further optimize the quality of the recommendations.

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