Lists

Overview

Lists help you organize your prospects by grouping related people and companies in one central location. This powerful organizational tool enhances your prospecting workflow in multiple ways:

Streamline your searches by including or excluding specific lists from results.
Accelerate your outreach by adding entire lists to sequences.
Import external data by uploading CSV files to create new lists.
Export your data by downloading lists as CSV files for external use.
Personalize at scale with list-specific AI Columns for targeted messaging.

The following sections provide detailed guidance on maximizing Graph8's list functionality for your prospecting needs.

Creating a list

For detailed instructions on creating lists, please see our Search article in the Knowledge Base.

Accessing your Lists

Viewing Your Lists Directly

Navigate to Data: From the top navigation bar, click Data.
Open Lists: Select Lists from the dropdown menu.
View All Lists: Your screen will display all your saved lists, showing both contact and company lists.
Check List Details: The list view shows important information including:
- List name
- Type (Contacts or Companies)
- Status
- Type of List (Dynamic or Static)
- Total number of records
- Date created
Access List Contents: Click directly on any list name to view all records within that list.

Using Lists in Search

Open Search: From the top navigation bar select Search from the Data dropdown menu.
Select Target Type: Choose either "People" or "Companies" depending on your needs.
Open Lists Filter: Scroll down the filters panel and click on the Lists dropdown.
Choose Your List: Under "List Name," select one or more lists you want to include in your search results.
Run Search: Click the purple Run Search button to display all records from your selected list(s).
Optional - Exclude Lists: Under "List Name Exclude," select any lists you want to remove from your search results.

List Actions

Graph8 provides three powerful actions you can perform with your lists to enhance your workflow:

1. Download Lists as CSV

Export your list data for use in other applications:

Click the three-dot menu (⋮) at the end of any list row
Select Download from the dropdown menu
The list will be exported as a CSV file with all contact or company data
Use this exported data for reporting, offline analysis, or importing into other tools

2. Query List Criteria

View the exact search parameters used to create the list:

Click the three-dot menu (⋮) next to your desired list
Select Query from the dropdown menu
A popup window will appear showing all search criteria used to build the list, including:
- Contact search parameters (seniority, job departments, etc.)
- Company search parameters (industry, revenue, etc.)
- Any other filters applied when creating the list
This feature helps you understand how lists were created and replicate similar searches

3. Send to Sequence

Add list members to your outreach sequences:

Click the three-dot menu (⋮) next to the list you want to use.
Select Send to Sequence from the dropdown menu.
Follow the prompts to select your desired sequence.
All contacts in the list will be added to your chosen sequence.
This streamlines your outreach process by quickly moving prospects into your engagement workflow.

4. Adding Tags to Lists

Categorize lists with tags for easier organization and feature compatibility:

Double-click the cell in the TAGS column next to your desired list.
Select a tag from the dropdown.
The tag will appear in the TAGS column.
To remove a tag, double-click the cell and click the "X" next to the tag.

Useful Tip: For Sales Coach Compete Mode, lists must have the #SalesCoach tag.

These actions make your lists more versatile and help you move smoothly between prospecting, analysis, and outreach stages.

CSV Uploads

graph8 allows you to import data in bulk using CSV-based files. The upload flow is accessible from the Lists page and supports three different list types:

Contacts
Companies
Suppressions

To begin:

Navigate to Data → Lists
Click Upload List

You will be prompted to select the list type.

Uploading a Contacts List

The Contacts CSV Upload flow enables users to import contact records from external files into graph8.

A Contacts upload is considered valid only when at least one unique identifier is mapped:

Contact Work Email OR
Contact LinkedIn URL

Upload Flow Overview (Contacts)

Step 1 — List Creation

Click Upload List
Select Contacts
Enter a List Name (required)

Step 2 — Upload File

Supported file formats: .csv, .xlsx, .xlsm

Invalid file types are rejected with a validation error.

Step 3 — Preview File

Displays a read-only preview of the raw file data
No transformations or mappings applied yet

Step 4 — Map Columns (Critical Step)

Users map file columns to:

Existing Contact Entity Fields
Newly created Dynamic Columns

Required Mapping Rule

You must map at least one of:

Work Email
LinkedIn URL

A validation banner remains visible until this condition is satisfied.

Mapping Capabilities

Users can:

Map individual fields manually
Add new mapping rows
Map all columns automatically
Create Dynamic Columns
Delete mapped fields

Step 5 — Preview Result

Displays:

Final mapped structure
Actual values after mapping
Pagination & column visibility controls

Step 6 — Finish

After a successful upload:

A confirmation message appears
The page reloads
The list appears in Lists

Mapped fields and Dynamic Columns will contain imported data.

Uploading a Companies List

The Companies CSV Upload flow functions similarly to Contacts but includes import behavior options.

Unique Identifier Requirement

A Companies upload is valid when at least one identifier is mapped:

Company Domain OR
Company LinkedIn URL

Additional Import Options

Before uploading, users choose how graph8 should handle records:

Create and Update Companies
Create New Companies Only
Update Existing Companies Only

Optional:

Overwrite Policy — updates existing properties with new values

Upload Steps

The remaining flow mirrors the Contacts upload:

Upload File
Preview File
Map Columns
Preview Result
Finish

Uploading to the Suppression List

Suppressions behave differently from Contacts and Companies.

graph8 maintains a single global Suppression list, rather than creating multiple lists, Suppression list allows you to exclude specific contacts and companies from ICP searches.

Key Behavior

No list name is required
Uploaded records are appended to the existing Suppression list

Identifier Rules

A suppression record is valid when any ONE of the supported identifiers is provided:

Contact Work Email
Contact LinkedIn URL
Company Domain
Company LinkedIn URL

Only one populated column is sufficient for suppression.

Upload Flow

Click Upload List
Select Suppression
Upload your file
Map available columns (optional but recommended)

After completion:

Records are merged into the Suppression list

Enrichment

The Enrichment allows you to enhance your existing list data by generating new information, validating records, or calculating values using AI, external providers, or formulas.

Enrichment works at the list level, meaning you first choose a list and then configure the columns you want to enrich.

To access enrichment:

Navigate to Data → Lists
Select the list you want to enrich
Open the Enrichment tab

Enrichment Methods

Enrichment in graph8 is column-driven.

You first create a column, then define how values should be generated.

When clicking ➕ Add Column inside the Enrichment tab, you can choose between three enrichment mechanisms:

AI Columns
Waterfall Enrichment
Formula Columns

Each mechanism serves a different type of data generation logic.

Understanding the Enrichment Workspace

Inside the Enrichment tab, your list appears as a grid of records and columns.

At the far right of the grid, you will find the ➕ (Add Column) button.

This is the entry point for all enrichment actions.

Enrichment in graph8 is column-driven — you create a column first, then define how its values should be generated.

1️⃣ AI Columns

AI Columns allow you to generate intelligent data using natural language prompts.

Typical use cases:

Classification (e.g., decision maker detection)
Research & summarization
Data extraction & normalization
Scoring & validation logic

How it works

Describe what you want to generate
Reference existing fields using {{column_name}}
AI Columns operate per record, meaning each row is processed independently using the available context.

AI Columns are ideal when logic requires reasoning rather than simple lookups.

How AI Columns Work

When creating an AI Column, you define:

1️⃣ Prompt Logic

You describe what the AI should generate.

Example tasks:

Determine if a contact is a decision maker
Extract company positioning
Classify lead quality
Summarize company activity
Normalize messy text fields

AI Columns accept natural language instructions rather than rigid syntax.

2️⃣ Column References (Critical Concept)

AI Columns can use existing list data as structured input.

You reference fields using: {{COLUMN_NAME}}

xamples:

{{CONTACT_JOB_TITLE}}
{{COMPANY_DOMAIN}}
{{COMPANY_EMPLOYEE_COUNT}}

During execution, graph8 replaces placeholders with actual row values.

This allows AI logic to adapt dynamically per record.

3️⃣ Output Behavior

The AI generates one value per row and writes it into the column.

Outputs can be:

Text
Labels / categories
Scores
Structured responses
Binary decisions (Yes / No / Maybe)

The column behaves like a native field after enrichment.

When to Use AI Columns

AI Columns are best suited for:

✅ Classification problems

✅ Ambiguous decision logic

✅ Text-heavy analysis

✅ Data interpretation tasks

✅ Multi-field reasoning

They are not ideal for simple lookups like email discovery — use Waterfall for that.

Credits & Models

AI Columns consume credits based on:

Selected model
Records processed
Complexity of generation

Different models may have different credit costs per record.

Before execution, graph8 validates credit availability.

2️⃣ Waterfall Enrichment

Waterfall enrichment retrieves data from external providers using a defined fallback sequence.

Instead of relying on a single source, providers are tried in order until one succeeds.

Typical use cases:

Email discovery
Phone enrichment
Company data retrieval
Verification workflows

How Waterfall Columns Work

When configuring a Waterfall column, you define:

1️⃣ Column Definition

Column name
Data type (Text, Number, etc.)

This defines where results will be stored.

2️⃣ Provider Pipeline

You add one or more providers.

Each provider represents a specific enrichment action, such as:

Email discovery
Phone enrichment
Company attributes
Verification
Data validation

Providers differ in:

Coverage
Accuracy
Credit cost
Returned fields

3️⃣ Provider Order (Important)

Provider order controls fallback behavior.

Typical strategies:

Cheapest → Expensive
Fastest → Slowest
Highest coverage → Specialized

Ordering directly affects credit consumption efficiency.

4️⃣ Enrichment Execution

For each row:

Provider sequence runs
Stops once data is found
Writes result into column

When to Use Waterfall

Waterfall enrichment is ideal for:

✅ Email / phone discovery

✅ Provider-backed data

✅ Verification workflows

✅ Deterministic factual retrieval

✅ Multi-source fallback logic

3️⃣ Formula Columns

Formula Columns generate values using deterministic logic instead of AI or providers.

Typical use cases:

Text transformations
Conditional logic
Derived values
Field normalization

How it works

1️⃣ Define a formula : enter a formula or select one of the available templates

2️⃣ Add Column Name and select output type

3️⃣ Deterministic Logic

Formulas typically include:
- Conditions
- Text checks
- Transformations
- Comparisons
- Calculations
  Example:
  if ({{CONTACT_WORK_EMAIL}}.includes('gmail.com'))
  return 'Personal';
  else
  return 'Professional';

3️⃣Test results before saving

4️⃣ Immediate Evaluation

Formula outputs are computed directly from available data.

No enrichment runs required.

No provider calls.

No AI inference.

When to Use Formula Columns

Formula Columns are best for:

✅ Text formatting

✅ Normalization rules

✅ Derived values

✅ Conditional labels

✅ Lightweight scoring

They should be used whenever logic can be defined explicitly.

Credits Consumption

Formula Columns do not consume credits.

They are computational, not enrichment-based.

Running Enrichment

After creating an enrichment column, you can execute enrichment directly from the column controls.

Available run options typically include:

First N rows — Quick testing / validation
Selected rows — Targeted enrichment
All rows — Full dataset processing

This allows controlled execution and credit management.

Credits Consumption

Enrichment operations consume credits.

Credit usage depends on:

Enrichment type (AI / provider / formula)
Selected model or provider
Number of processed records

Before execution, graph8 displays a credit validation dialog showing:

Estimated records
Credits required
Available credits
Credits per record

Enrichment runs only when sufficient credits are available.

Enrichment Results

Once enrichment completes:

Column values populate automatically
Data persists at the record level
Columns behave like native list fields
Results can be filtered, exported, or reused
Enriched columns can be used inside Sequences

Dynamic Columns created via enrichment remain attached to the list.

Auto-Enrich

graph8 supports automatic enrichment triggers.

Auto-Enrich allows selected columns to run whenever new records are added to the list.

Typical scenarios:

Auto-classify incoming leads
Auto-validate emails
Auto-enrich contact attributes
Continuous data enhancement

How it works

Click Auto-Enrich
Select eligible columns
Enable triggers for:
- Insert — Run when new records appear
- Update — Run when records change

Once enabled, enrichment runs automatically without manual execution.