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Salesforce’s inability to create reports across objects without direct lookup relationships affects most complex business analysis scenarios. Coefficient specifically solves this challenge through flexible data import and spreadsheet-based relationship building using business logic rather than database constraints.
Here’s how to connect unrelated objects and create the cross-object analysis that’s impossible with native Salesforce capabilities.
Build custom relationships between unrelated Salesforce objects
Common scenarios like connecting Contacts with Product Usage data, Leads with Support Cases, or Campaigns with Support Tickets can’t be reported on natively because these objects lack direct relationships. Spreadsheet-based reporting eliminates this limitation.
How to make it work
Step 1. Import unrelated objects independently.
Use Coefficient to import data from each object separately – Contacts, Product Usage, Leads, Support Cases, Campaigns, Custom Objects – without dependency on pre-existing Salesforce relationship structures. This gives you access to all fields regardless of relationship status.
Step 2. Identify common identifiers for custom relationship building.
Look for shared fields that can logically connect your unrelated objects: email addresses for contact-centric analysis, account names for account-focused connections, phone numbers for lead matching, or external IDs for third-party system integration.
Step 3. Create business logic connections using advanced lookup formulas.
Use XLOOKUP to connect unrelated data based on your identified common fields. For example: =XLOOKUP(A2,’Product Usage’!B:B,’Product Usage’!C:E) connects contact emails with usage data, creating relationships that don’t exist in Salesforce’s database structure.
Step 4. Build time-based relationships for activity correlation.
Connect objects based on date ranges and activity periods when direct field matching isn’t possible. Match campaign activities with support ticket creation dates to analyze marketing impact on support volume, or connect lead creation with product usage patterns.
Step 5. Handle fuzzy matching for near-duplicate data.
Use approximate matching techniques for data that doesn’t match exactly. Combine SEARCH and XLOOKUP functions to connect records with similar but not identical company names, or use LEFT functions to match partial email domains.
Step 6. Create comprehensive cross-object analysis dashboards.
Build pivot tables and charts that analyze your custom relationships. Create unified customer profiles combining Contact engagement with Product Usage metrics, or analyze Lead quality by connecting lead sources with eventual support case volume.
Start cross-object reporting today
This approach enables cross-object reporting that’s impossible with native Salesforce capabilities, providing business insights previously requiring expensive data warehouse solutions. You can connect any objects using business logic that makes sense for your analysis. Build the relationships your business needs to see the complete picture.
