Ask or search…
K
Links

Prospecting Clean Rooms

Build lookalike models to increase campaign addressability

What are Prospecting Clean Rooms?

Prospecting clean rooms enable Optable DCN customers to join their first-party identity graphs with an existing DCN partner or a flash partner. These operations are privacy preserving and the underlying audience IDs are never learned by the partners.
They enable the receiving partner to build a lookalike model on their own identity graph, using the private set intersection, computed by the clean room, as the seed.

Initiating a Prospecting Clean Room

In order to initiate a prospecting clean room, you first need to be partnered with at least one or more DCN partner(s) as described in the partnership section.
A prospecting clean room involves two partners. There is a sender, requesting a prospecting model from their DCN partner, and a receiver which will configure and run the model for its partner.

Configuration - Sender

As a sender, you must to select either a source audience or all available data, and request a prospecting model from one or more partner(s). A distinct clean room is created for each selected partner.

Configuration - Receiver

You will receive notification(s) for each prospecting request, which you can accept or reject.
An example of prospecting request notification
Upon acceptance, you will be required to select an audience to score (evaluation audience) or use all available data. You will then need to select traits to use in the prospecting model.
The trait picker will show you all traits available for the chosen audience, along with their coverage within that audience.
Traits selection example
As you select more traits, the Current Prospecting Modelling Success Chance indicator will change from null to Low, Medium or High. The indicated success chance is based on the coverage of the traits you selected.
Any combination of traits that yields an estimated overall coverage of 100% will be deemed as having High success chances. Low or Medium indicators means that the clean room will not be able to score all clusters within the selected audience, and may return no result.
Even though selecting all traits usually boosts model performance, exceptions exist. For instance, if your partner's clients all live in Florida but your identity graph covers the entire USA, you might exclude geographic traits. This way, the model focuses on more relevant socio-demographic and attitudinal traits. You can later create an audience that uses the clean room result, and exclude non-Florida residents from it.
An example of configured prospecting clean room
Once the configuration is complete, the model will begin to run. This may take approximately five minutes.

Reports

Once you receive a notification confirming the model's completion, two reports will be accessible for you to review:

Distribution

Although the clean room calculates an exact score for each individual cluster, it does not disclose this information to preserve privacy. Instead, it categorizes clusters into percentiles, with the first percentile representing clusters most similar to your partner's audience. This report aids you in determining the number of percentiles necessary to construct a prospecting audience sizable enough for a campaign, yet still performant. An audience can be directly created from this report.
An example of cluster distribution

Top Contributing Features

The clean room calculates a precise value for each feature (selected trait values or their absence in the model seeds), but doesn't reveal these details to preserve privacy.
Instead, it provides up to ten absolute Top Contributing Features, as determined by the model. These features can represent either very strong positive signals or very strong negative signals. Clusters with these positive features are likely to be in the top percentiles, whereas clusters with negative features will probably fall into the bottom percentiles. The features are displayed in alphabetical order as a mechanism to preserve privacy.
An example of top contributing features report
Because of the absolute nature of Top Contributing Features, you could get all three features Male, Female, Non-Binary in the top 10. This means Gender is a very important trait in the model, as one feature is likely overwhelmingly positive while the other two are negative.

Audience Building

Once the clean room successfully generates results, you will be able to create audiences out of them, either by navigating to the Audience Builder, directly from the Prospecting clean room, or through the Optable CLI. You can build as many simple (e.g. percentiles 10 to 20) or composite (e.g. Top 15% AND State=Florida) audiences from them.
An example of prospecting audience building

Privacy

Prospecting clean rooms integrate with Optable's differential privacy budgeting system, allowing DCN customers to monitor privacy protected clean room activity with its partners, as well as track its rate of collaboration versus a system assigned and replenished privacy budget. With the built in notifications, a DCN customer can better assess and decide its level of risk tolerance for re-identification resulting from clean rooms, on a partner by partner basis.
To learn more about privacy budget, visit our differential privacy section
In addition, multiple mechanisms are built in the prospecting clean room to ensure privacy.
  • Clean room will refuse to output results if the intersection between the two audiences is too low
  • Clean room will refuse to output results if the match rate is too high
  • Clean room will refuse to output results if the count of scored clusters (containing at least one feature from the model) is too low
  • Clean room does not evaluate features that are too rare
  • Only the top 10 contributing features are exposed in reports
  • Top contributing features are ordered alphabetically and not in order of importance
  • Top contributing features does not tell whether it is very positive or negative
  • Feature importance scores are not exposed
  • Cluster similarity scores are not exposed
  • Clusters are grouped in percentiles
  • Most dissimilar clusters are not included in percentiles grouping
  • Bottom percentiles are grouped together in percentile ranges
  • The intersection of the two audiences are not removed from the percentiles
Last modified 1d ago