Unlocking the power of Salesforce Data Modeling: Techniques and Best Practices

RevOps professionals rely heavily on Salesforce, but occasionally they can face frustrations with getting the platform to work for them.

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In our latest webinar, we sat down with experts Demar Amacker (Senior Director of Business Operations at Zift Solutions) and Nati Beeri (VP of Data at Sightfull), who explain why Salesforce data models are so important and recommend three suitable models for your revenue operations.

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While Salesforce is an incredible tool, poor initial setup can lead to unreliable data for growth and insufficient reporting. In a worst-case scenario, your board may question the accuracy of your data.

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What is a data model?

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“Everybody has a data model, whether or not they’ve thought about it”

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A well-designed data model facilitates data capture, storage, and enhancement, thereby enhancing your business and sales teams’ daily activities, and establishing the foundation for advanced analysis. 

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Demar Amacker of Zift Solutions suggests selecting the appropriate model while documenting the build for future upgrades.

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“If you're not documenting your data model, you’re navigating blind.”

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3 Recommended Salesforce Data Models

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Sightfull recommends choosing one of three Salesforce data models, depending on your specific needs: 

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1. Opportunity model 
2. Product line model 
3. Contract model

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As companies expand, they progress through these models, with the contract model being the most intricate and advanced. Each alternative has its advantages and drawbacks.

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Here’s some key points of these three models:

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The opportunity model

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The opportunity model is simple and easy to set up and maintain. Data is centered around the opportunity object and requires these fields: 

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• Start date of contract/service (not to be confused with the close date of the deal)
• Contract length or end date/renewal date
• Recurring value (not TCV, exclude non-recurring fees like deployment costs)

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This model is easy to get up and running, supports high level SaaS metrics, and has quick “time to value”. Its drawbacks include limited support for multi-product and ramped deals. It also isn’t built to maintain continuity when co-terms or early renewals are in play.

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The product line model

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The product line model is centered around line items that break down the total deal amount in closed-won opportunities. The focus is on product lines rather than opportunities. Each product line includes: 

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• Total price per specific component/line item
• Product ID for each associated product
• Recurring value, if any
• Start date and contract length/end date (helpful for deals that ramp up)

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This model supports advanced deal structures, offers analytics by product, and allows for expansion in ramp up deals. The tradeoffs are increased overhead (for setup and maintenance), more sensitivity to sales team inputs, and more complicated reports.

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The contract model

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The contract model is centered around accounts and is well suited for enterprise businesses. The focus is on managing engagement with customers as well as co-term opportunities and renewals. Components include: 

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• Renewal date or expiration/when the new contract starts
• Renewal contract/opportunity that specifies what will replace the current contract. This mitigates overlaps and gaps.
• All co-termed opportunities should point to the same contract 

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This date model supports account level continuity, sales workflows, and sales alignment with legal and finance teams. It’s very effective for measuring net dollar retention (NDR). On the other hand, it's more complex and requires additional tools like CPQ and creates day to day overhead while being susceptible to data hygiene issues. 

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“Getting your data model right is the first step towards an effective analytics engine”

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