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Customer Confidentiality and Crossbeam
Customer Confidentiality and Crossbeam

Customers may have a clause in their contracts that don't allow you to reveal their identity to third parties. Here's what you can do.

Joy Rudnick avatar
Written by Joy Rudnick
Updated over 5 months ago

So, you uploaded your data into Crossbeam and you're ready to build populations and start sharing data with partners so you can find valuable overlaps. But wait! You have a clause in your contracts where you promise not to reveal the identity of some customers to third parties. What now? The following are some options so you can move forward and get the same great value out of Crossbeam. 

1. Exclude certain companies from your populations

If the companies with this confidentiality option are flagged in your CRM or CSV upload, you can exclude them from Crossbeam populations entirely so that they are strictly never revealed to partners. Check out population filtering to learn more. This works best if the number of companies with this option is relatively small.

2. You may be covered by your NDAs with your partners

Depending on the wording of your contract clause about keeping the customer relationship private, you may be comfortable sharing that knowledge with partners under NDAs and treating that knowledge as protected and proprietary. In these cases, the understanding with the partner is that the overlap is only to be used in the context of the partnership and not used for any external communications, including with the customer.

3. Create broad populations 

Some companies opt to create a broader population called "All Relationships" that includes both customers and prospects, without disclosing which is which by not sharing the account status. That way, the identity of customers is protected by obscurity and you can still do account mapping across known entities, sorting out the details on a collaboration-by-collaboration basis.

4. Create strict sharing defaults and settings

Some companies opt to not share data about customer populations outward with partners, but instead make their partners share data inward to them. That way, you can see the overlaps and decide on the best course of action for each, but not have knowledge about the overlap leave in any automated fashion.

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