> ## Documentation Index
> Fetch the complete documentation index at: https://docs.getpg.ai/llms.txt
> Use this file to discover all available pages before exploring further.

# Custom Columns

> Extend territory data with any field you need  -  populated manually, enriched by AI, or auto-refreshed on a schedule.

Custom Columns let you add any data field to your territory's account table. You define the question, choose a data type, and then populate it  -  manually, via AI enrichment, or with location data. AI enrichment researches each account using enriched data, public information, tech stack, jobs, and more to answer your question across all accounts automatically. Custom columns can be monitored and auto-refreshed to stay current as things change.

## Key Capabilities

<CardGroup cols={2}>
  <Card title="Column Definition" icon="pen-to-square">
    Create custom fields on your territory account table. Define column types, names, and what they should contain. Use AI to suggest column structures based on descriptions.
  </Card>

  <Card title="Population Methods" icon="wand-magic">
    Set values manually on individual accounts. Use AI enrichment to research and fill values automatically. Populate geographic data with location enrichment. Update values in bulk.
  </Card>

  <Card title="Monitoring & Refresh" icon="arrows-rotate">
    Scheduled change detection flags when values shift. Ongoing monitoring keeps custom columns current. Automatic refresh for AI-enriched columns. Value normalisation and standardisation.
  </Card>
</CardGroup>

## What Custom Columns Are

Custom columns are new data fields that you add to your territory account table. The key difference from standard enrichment: **you define the question, and PG:AI answers it for every account.**

Want to know each account's primary cloud provider? Their compliance posture? Whether they've been through a recent acquisition? You create a custom column, describe what you want to know, choose a data type (text, number, boolean), and PG:AI's AI researches each account and fills in the answer.

Custom columns can hold:

* **AI-researched answers**  -  PG:AI looks at enrichment data, public information, tech stack, jobs, and more
* **Manual values**  -  data you set yourself on individual accounts
* **Any data type**  -  text, numbers, booleans  -  whatever fits the question

## Viewing Custom Columns

### In the Companies Table

Custom columns appear as additional columns alongside PG:AI Score, firmographic data, and enrichment columns. Each custom column shows the AI-generated (or manually set) value for every account.

### On Account Profiles

Click into any account to see all custom column values, giving you the full picture alongside standard enrichment data.

## Common Workflows

### Getting a Specific Answer About Every Account

This is the core use case. You have a question that matters to your sales strategy, and you want the answer for every account in your territory.

<Steps>
  <Step title="Create a custom column">
    Ask your admin to create a custom column (or create it yourself if you have access).
  </Step>

  <Step title="Define the question clearly">
    E.g., "What is this company's primary cloud infrastructure provider?"
  </Step>

  <Step title="Set the data type">
    Text for open answers, number for quantitative data, boolean for yes/no questions.
  </Step>

  <Step title="Let AI populate the answers">
    PG:AI's AI researches each account and populates the answer.
  </Step>

  <Step title="Use the data">
    The answers appear as a column in your territory table  -  sortable, filterable, and ready to use.
  </Step>
</Steps>

**Examples of questions you can answer:**

| Question                                     | Data type | Example output                      |
| -------------------------------------------- | --------- | ----------------------------------- |
| What is their primary cloud provider?        | Text      | "AWS", "Azure", "GCP"               |
| Have they been through a recent acquisition? | Boolean   | Yes / No                            |
| What compliance frameworks do they follow?   | Text      | "SOC2, GDPR, HIPAA"                 |
| How mature are their DevOps practices?       | Text      | "Early", "Established", "Advanced"  |
| What is their estimated annual IT spend?     | Number    | 5000000                             |
| Are they expanding internationally?          | Boolean   | Yes / No                            |
| What CRM do they use?                        | Text      | "Salesforce", "HubSpot", "Dynamics" |
| What's their primary programming language?   | Text      | "Python", "Java", ".NET"            |

<Info>The only limit is what you can describe clearly enough for AI to research.</Info>

### Using Custom Column Answers for Prioritisation

Once columns are populated:

<Steps>
  <Step title="Sort by custom column">
    Sort the territory by a custom column to see patterns  -  e.g., sort by "Primary Cloud Provider" to group all AWS accounts together.
  </Step>

  <Step title="Filter to specific values">
    Show only accounts where "Recent Acquisition" = Yes.
  </Step>

  <Step title="Combine with PG:AI Score">
    "Show me High-scoring accounts that use AWS and have been through a recent acquisition."
  </Step>
</Steps>

### Segmenting for Campaigns

Custom columns are powerful for campaign targeting:

<Steps>
  <Step title="Create a strategic column">
    Create a column like "DevOps Maturity" or "Digital Transformation Stage".
  </Step>

  <Step title="Let AI populate it">
    AI populates it across all accounts.
  </Step>

  <Step title="Filter for your target segment">
    Filter your territory to accounts at the right stage for your campaign message.
  </Step>

  <Step title="Run targeted playbooks">
    "Early DevOps" gets an education-first approach, "Advanced DevOps" gets a peer comparison approach.
  </Step>
</Steps>

### Adding Your Own Context Alongside AI Answers

Some columns work best as manual additions:

* **Account tier** (Tier 1, 2, 3)  -  your team's strategic classification
* **Outreach status**  -  "Not started", "Sequenced", "Meeting booked"
* **Assigned rep**  -  who owns this account
* **Notes**  -  context that only you have

These sit alongside AI-populated columns, so your territory table combines PG:AI intelligence with your team's knowledge.

### Feeding Custom Columns into Scoring Models

Custom column values can be used as inputs to scoring models:

* If "Primary Cloud Provider = AWS" and you sell an AWS-optimised product, that should boost the score.
* If "Recent Acquisition = Yes", that might indicate budget disruption  -  positive or negative depending on your offering.
* If "DevOps Maturity = Advanced", that could mean they're ready for your product (or already have something similar).

<Tip>Talk to your admin about which custom columns should feed into the scoring model.</Tip>

## How AI Answers Stay Current

Custom columns can be configured to refresh automatically:

* **Scheduled refresh**  -  AI re-researches accounts periodically and updates values.
* **Change detection**  -  flags when an answer changes significantly (e.g., a company switches cloud providers).
* **Manual refresh**  -  re-run AI research on demand for specific accounts.

This means custom column data stays current without manual maintenance. A company that switches from Azure to AWS will have its "Primary Cloud Provider" column updated on the next refresh.

## Tips and Best Practices

<Tip>**Be specific in your questions**  -  "Primary cloud provider" works better than "cloud stuff." The clearer the question, the more accurate the AI answer.</Tip>

<Note>**Choose the right data type**  -  use boolean for yes/no questions, number for quantitative data, text for descriptive answers. The right type makes sorting and filtering work properly.</Note>

* **Start with high-value questions**  -  what data points, if you had them for every account, would most change how you prioritise? Start there.
* **Combine AI columns with manual columns**  -  AI tells you what's true about the account; manual columns track your team's decisions and status. Both are valuable.
* **Review AI answers for accuracy**  -  spot-check a few accounts you know well to validate that the AI is answering correctly. If not, refine the column description.
* **Don't over-create**  -  5–10 well-chosen custom columns are more useful than 30 random ones. Each column should earn its place on the table.
* **Feed important columns into scoring**  -  if a custom column captures a signal that genuinely affects fit, make sure it's weighted in the scoring model.

## Related Modules

<CardGroup cols={3}>
  <Card title="Scoring Models" icon="chart-simple" href="/territory/scoring-models">
    Custom column values can be used as scoring rule inputs.
  </Card>

  <Card title="Territory Management" icon="layer-group" href="/territory/territory-management">
    Custom columns appear in territory account views.
  </Card>

  <Card title="Territory Enrichment" icon="database" href="/territory/territory-enrichment">
    AI enrichment for custom columns is part of the broader enrichment pipeline.
  </Card>

  <Card title="Territory Analytics" icon="chart-pie" href="/territory/territory-analytics">
    Custom column data surfaces in analytics.
  </Card>
</CardGroup>