Properties are one of the most powerful concepts in FirstQuadrant. When used correctly, they allow you to organize your sales data in a structured way, segment your audience with precision, and enable the AI to personalize its behavior—both in how it crafts messages and in how it makes decisions.

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What are properties?

A property is any structured data point attached to either a contact or a company in FirstQuadrant. Examples include job title, company name, headcount, location, industries, or language—but you can also create your own custom properties.

All the information shown in the right-hand context panel (when viewing a contact or company) consists of properties.

Properties are useful in several ways:

  • Segmentation and filtering: You can use properties to create views and filter your data precisely. Views are covered in a separate helpdesk article which explains how to use them to build targeted segments for campaigns, exports, and personalization.
  • Personalization: The AI uses these properties when generating emails or making decisions.
  • Enrichment: Properties can be populated manually or enriched automatically via internal or external data sources.

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Creating a new property

To create a property, navigate to any contact or company view. You’ll see Add property appear separately in both the contact section and the company section of the context panel.

You’ll first see a list of default properties. At the bottom, click New property to create a custom one.

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Step 1: Select the property type

You can choose from:

  • Text
  • Checkbox (Boolean)
  • Number
  • Date
  • Single select
  • Multi-select

Choose the format that fits the kind of data you want to store.

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Step 2: Name the property

Give your property a clear and specific name. This name is also what the AI will use to understand what the property means—especially important if you plan to enable AI enrichment.

Example: Latest funding round, Y Combinator funded, or Lost reason.

Next to the name, you’ll see an icon to control visibility. By default, the property is shown in the context panel. You can toggle it to keep it hidden.

Info: Only properties with a value are shown in the context panel. If a property is empty for a specific contact or company, it won’t appear.

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Enriching properties with AI

You can manually fill in property values—or use FirstQuadrant’s AI enrichment. This saves time, scales your workflows, and makes your data far more actionable.

After creating the property, toggle Enrich with AI. You’ll then choose:

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1. Scope: All companies or selected views

Although the property is technically attached to all contacts/companies, enrichment can be scoped down to a specific view.

This allows you to:

  • Conserve AI credits
  • Target enrichment only where it makes sense

Example: If you already have a view for VC-backed companies, and you create a new property called Latest funding round, you may want to enrich this only for VC-backed companies, as others are unlikely to have this data.

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2. Source: Perplexity vs Internal data

There are two enrichment methods:

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a) Enrich using Perplexity

This uses Perplexity’s AI search engine to fetch public data from the web.

Ideal for properties like:

  • Latest funding round
  • Total headcount
  • Number of offices
  • SOC2 compliance 
  • Best-selling product

Tip: Before running a large enrichment, test Perplexity manually: Go to perplexity.ai. Ask: _“What was the latest funding round of [company name]?”. _Try this with 2–3 companies. If results are accurate, you can proceed to enrich in FirstQuadrant.

Tip: To ensure enrichment logic is working as expected before using it broadly, you can alternatively create a small test view with just a few records and apply the enrichment to that view first. This allows you to verify the results and avoid wasting AI credits on a full dataset if something doesn’t work as intended.

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b) Enrich using internal data

This pulls from:

  • Emails
  • Notes
  • Other properties
  • Any synced communication or internal record in FirstQuadrant

It’s best for properties that reflect:

  • Past conversations
  • Qualitative context
  • Historical interactions

Examples:

Example 1: Lost reason

Create a property Lost reason (as multi- or single-select). When someone declines to work with you, you send a follow-up email asking for the reason. Once the prospect replies, FirstQuadrant can automatically extract the answer and populate the property.

Example 2: Interest in a feature

Let’s say some leads have expressed interest in an upcoming feature. You may have recorded that in meeting notes or emails. You can:

  • Create a checkbox property Interested in Feature A
  • Select enrichment from internal data
  • FirstQuadrant will analyze past conversations and auto-tag contacts accordingly

You can then build a view based on this property and use it to run a personalized campaign.

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Advanced instructions for enrichment

When creating a property, you can click Advanced to open a description box. This allows you to add context or special instructions that the AI should use when enriching the data. This is especially helpful if you’re running a complex enrichment that depends on nuanced signals, such as interpreting qualitative feedback from emails or notes.