Account Intelligence: Configuration Guide
Who this is for: Customers who have been provisioned for Account Intelligence and are ready to configure their workspace.
Goal: Complete your Account Intelligence setup — including integrations, scoring, personas, research agents, and user roles — so your team can start using it right away.
Before You Begin
Before diving into configuration, make sure the following are in place:
Open Atlas from the sidebar. If you don't see it, contact your HockeyStack admin to grant you workspace admin access.
In Atlas, check the Integrations section and complete any that are still marked incomplete.
Confirm that at least one Funnel with stages exists under Funnel Stages. If not, create one before continuing.
If you have Salesforce CRM, confirm that your Salesforce iFrame is connected and enabled.
Once all of the above are in place, you're ready to configure Account Intelligence.
Overview
Note: your instance may look different from what's shown in the video, depending on your access level and plan
Step 1: Add Users and Roles
Before configuring any account intelligence features, set up your team so the right people have access.
Users and roles are managed in Workspace Settings → Team. Only Admin users can access this section.
1a. Invite Team Members
Navigate to Workspace Settings → Team.
Click Invite Member.
Enter the user's email address.
Select an access level:
Admin: Full access to platform, configuration, and workspace settings. Recommended for ops admins.
Member: Full access to platform and configuration. Recommended for Team-Leads and Managers
Viewer: Full access to platform, no access to configuration. Recommended for IC’s.
Under Products, grant access to Sales.
Click Invite. The user will receive an email and appear in the team list as "Invited" until they accept.
1b. Create User Roles
User roles are separate from access levels. They define categories within your sales team (e.g., AE, SDR) and are required for users to be assigned to workflows.
In Workspace Settings → Team, open the User Roles section.
Click Add Role.
Enter a name that reflects the function (e.g., "AE", "SDR", "Sales Manager").
Click Save.
Repeat for each role your team uses.
1c. Assign Roles to Team Members
In the team member list, click Edit on a member's row.
Set their User Role from the dropdown (e.g., AE or SDR).
Optionally, set the Reports To field to establish a reporting hierarchy, this allows managers to view their team's activity.
Click Save.
Tip: Every rep who will use Account Intelligence should have a role assigned. Users without a role assigned will have limited functionality.
Step 2: Create Target Account Views
Account views define which accounts are scored and researched by Account Intelligence. Everything downstream; scoring, stakeholder discovery, AI research - is scoped to the views you configure here.
In the sidebar, navigate to Companies → All Companies.
Open the view selector dropdown in the toolbar and click Create new view.
Give the view a clear, descriptive name (e.g., Tier 1 Target Accounts - Enterprise).
Apply filters to define which companies appear in this view.
Useful filter types include:
Company properties: Industry, employee count, annual revenue, country
Toggle on Advanced to see more granular level of filters.
Click Save for everyone to make the view available to your full team.
Important: Views are shared workspace-wide. When you save a view, all team members will see it. Use descriptive names so it's clear what each view represents.
Tip: Start with one view that captures your core target accounts for the whole team. You can create additional views later to segment by tier, region, or team ownership.
Step 3: Configure Account Scoring
Account Scoring determines how HockeyStack evaluates and prioritizes your accounts. Scores are computed automatically and appear on each company page to help your team focus on the accounts most likely to convert.
Using legacy Intent Scoring? This step covers the new Account Scoring model (Behavior and Fit Scores). If your workspace still uses the older Intent Score (the Cold / Warm / Hot / On Fire tiers), follow the Intent Scoring guide instead and skip to Step 4.
3a. Set Up Account Scoring
Account Scoring uses a machine learning model that learns from your historical data to predict how likely each account is to reach a goal — such as closing a deal or booking a demo. Instead of manually weighting signals, the model studies the patterns shared by accounts that have already converted and scores every account from 0 to 100 based on how closely it matches those patterns. Scores update automatically and come with scoring factors that explain why each account scored the way it did.
Scores are configured from Atlas > Activate > Scoring. You can create multiple scores, each tied to a different conversion goal (e.g., one for "Closed Won" and another for "Demo Booked").
To create a score, click Create Score and complete the guided wizard:
Name your score — Use a descriptive name your team will recognize (e.g., "Deal Won Score", "Demo Readiness Score").
Choose the score type
Select Behavior Score to score on engagement signals like page visits, content downloads, email opens, and event attendance.
Select Fit Score to score based on firmographic data to know how the prospect matches your ICP.
Choose the entity level — Select Company Score to aggregate all activity across an organization into a single score. (Person Score is coming soon.)
Set a conversion goal — Pick the outcome the model should learn to predict (e.g., "Deal Won", "Demo Booked", "MQL Achieved"). The model studies accounts that have already reached this goal to learn what they have in common.
Select your account list(s) — Choose one or more Sales Views (with less than 10,000 accounts in total) that define which accounts to score (use the view(s) created in Step 2). For best results, pick a broader view such as All companies with domain so the model has enough training data.
Scoring details
Behavior Score — Optionally add up to 3 Touchpoint Properties (engagement signals like Channel or Asset Type) and up to 3 Score Breakdowns (dimensions like industry or company size that train a separate model per segment for more precise scoring).
Fit Score — Define what properties should contribute to evaluating ICP fit for this score.
Field name for CRM sync — Provide the custom field/property name to use when syncing this score to Salesforce or HubSpot. Confirm field visibility settings with your CRM admins.
Use in Blueprints — Toggle on to power the Marketing Blueprints feature with this score's data.
Click Save. The model begins training and shows a "Running" status; once it changes to "Completed" (usually a few minutes, depending on data volume), scores are available across the platform — on the Companies list, on individual company pages, and synced to CRM.
To edit or delete a score, use the Scoring Configuration screen. Each score in the list shows its name, type (Behavior or Fit), entity level (Company or Person), conversion goal, and current processing status.
Tip: Start with your primary conversion goal (e.g., "Closed Won") and validate it against recently closed deals — would the model have scored them highly? — before rolling out additional scores. The properties most teams find useful as Touchpoint Properties are Channel and Asset Type.
Note: Accounts with no verified domain are not scored. Scores recalculate automatically with a recency decay that gradually lowers the score of accounts that go quiet, so stale engagement doesn't keep an account artificially high. Do not manually trigger a rescore — reach out to your HockeyStack contact if you believe one is needed.
Note: While we recommend picking a broad view to train the scoring model, the scoring can be performed on a max of 10,000 accounts at once.
More details available here.
Step 4: Define Personas
Personas define the buyer profiles your team targets within prospect organizations. They drive stakeholder discovery, contact identification, and how the AI researches and summarizes accounts. Without well-defined personas, the system has no targeting criteria.
Open Atlas → Categorize →Personas.
Click Add Persona.
Fill in the required fields:
Name: A unique, descriptive label (e.g., "VP of Engineering", "Security Decision Maker").
Department: The organizational function this persona belongs to (e.g., Engineering and Technical, Sales, Finance).
Job Levels: Select one or more seniority tiers that apply (e.g., C-Level, Director, Manager). At least one is required.
Description: A natural-language explanation of who this persona represents. Maximum 1,000 characters. See guidance below.
Optionally fill in:
Subdepartments: Narrow the department further (e.g., under Engineering: "Cybersecurity", "DevOps", "Cloud / Mobility").
Job Title Keywords (Include): Keywords that must appear in a contact's title to match (e.g., "security", "infrastructure", "platform"). Comma-separated. Use this when department + level alone is too broad.
Job Title Keywords (Exclude): Keywords that disqualify a contact even if other criteria match (e.g., "intern", "assistant", "coordinator"). Use this to filter out junior or adjacent roles.
Click Add Persona to save.
Repeat for each buyer persona relevant to your sales process.
Writing Strong Persona Descriptions
The description is the most impactful input for AI quality. A vague description produces vague outputs. Include:
What they're responsible for: "Owns the cloud infrastructure budget and vendor relationships"
What they evaluate: "Evaluates observability platforms, APM tools, and cost optimization solutions"
Who they report to: "Reports to CTO or VP of Engineering"
Key pain points: "Concerned with mean time to recovery, deployment frequency, and cloud spend"
Why they matter: "Often the technical champion who drives internal evaluation"
Example of a strong persona: "DevOps Decision Maker":
Department: Engineering and Technical
Subdepartments: DevOps, Cloud / Mobility
Job Levels: Director, Head, Manager
Include Keywords: devops, infrastructure, platform, SRE, reliability
Exclude Keywords: intern, junior, associate
Description: "Technical leaders responsible for infrastructure and deployment strategy. They evaluate CI/CD tools, cloud platforms, and developer productivity solutions. Often report to CTO or VP of Engineering. Key pain points include deployment velocity, incident response, and infrastructure costs."
Example of a poor persona: "Engineering":
Department: Engineering and Technical
Job Levels: Director, Head, Manager, Senior, Specialist
Description: "People in engineering."
This fails because it's too broad and gives the AI nothing to work with.
Rule of thumb: If your persona would match tens of people at a mid-sized company or hundreds at a large one, it's too broad. Narrow it down.
Tip: For Job Title inclusions, try not to add entire job titles (VP of engineering) try to include individual keywords (devops) - additionally, it is best practice to start with only adding Job Title exclusions first, then adding inclusions if the people sourced are still too broad.
How Many Personas to Create
Start with 2–3 well-defined personas representing your primary buyer and technical champion. A few precise personas will outperform many vague ones. Add personas for economic buyers, influencers, or blockers as your process matures.
Testing Your Personas
Before enabling personas across all your accounts, validate them on a single company:
Open a persona for editing.
In the right panel, find the Test Stakeholder Discovery section.
Enter a Company Domain (e.g.,
acme.com).Click Run and review the discovered contacts.
Verify that the people found match your expectations, both that the right people are included and that irrelevant people are not.
If results look off, adjust the department, job levels, or keywords and test again.
Step 5: Enable Stakeholder Mapping
Stakeholder Mapping automatically identifies and maps key contacts at your target accounts based on your personas. Discovered contacts appear in each company's People tab with email addresses and LinkedIn profiles.
Navigate to Atlas → Categorize → Personas and switch to the Stakeholder Mapping tab.
Click Add View to select which account views to run discovery across (use the view(s) created in Step 2).
Review the mapping table, which shows:
Companies: Total accounts in the view
With Stakeholders: Accounts with at least one discovered contact
Click Run Now on the view row to trigger discovery immediately.
Note: Discovery will run automatically every 90 days going forward, and overnight after any persona changes.
Going forward, clicking Run Now will skip accounts that have already been refreshed within the last 90 days and will not re-process their data. To refresh a specific company before the 90-day window expires, use Re-generate from the People tab.
Note: Views with more than 15,000 accounts are not supported for stakeholder mapping. If your view exceeds this limit, create a more targeted filtered view before enabling mapping.
Step 6: Set Up AI Properties
AI Properties are custom research signals the AI evaluates for each company in your target views. You define the question, the system researches every account to produce a structured, evidence-backed answer.
These signals appear on company pages, help prioritize accounts, and inform how the AI reasons about each account.
Create an AI Property
Navigate to Atlas → AI Properties
Click New Property.
Fill in:
Name: A clear label for what you're researching (e.g., "Recent Funding Round", "Hiring for Data Roles").
Instructions: Tell the AI exactly what to look for and how to evaluate the answer. Be specific (see guidance below).
Output type: Choose the format that fits your signal:
Yes / No: Binary answer (e.g., "Does this company use Kubernetes?")
Dropdown: One value from a list you define (e.g., industry vertical)
Score: A 0–10 numeric rating (e.g., "How strong is their product-market fit signal?")
Freeform: Open-ended text (e.g., "What is their primary competitive differentiator?")
Refresh Every (days): How often the answer should be re-evaluated (e.g., 7 for fast-changing signals, 30 for stable ones).
Click Save.
Writing Effective Instructions
Vague instructions produce vague answers. Include:
Instructions: “Your goal is to identify whether this company has had a recent funding round in the last 90 days”
What Sources to use: "Check the company's careers page, recent press releases, and LinkedIn job postings for roles mentioning Kubernetes, Docker, or container orchestration."
How to evaluate: "Answer Yes only if there is direct evidence of production usage, not just job postings mentioning the technology."
Edge cases: "If the company is a consultancy that implements the technology for clients but does not use it internally, answer No."
💡 Tip: If you’re having a hard time creating good research prompts, then you can leverage Claude or ChatGPT for prompt building assistance.
Enable Auto Research
Once a property is created, enable auto research so it runs on a recurring schedule across your target views:
Open the property in the AI Agent Builder.
In the Auto Research section, toggle Enable Auto Research.
Confirm the Refresh Every (days) interval (Minimum 30 days).
Select the account view(s) to target.
Click Save.
💡 Tip: Start with 3–5 high-value signals rather than trying to track everything at once. Good signals are things your team would research manually if they had unlimited time; recent funding, specific hiring activity, technology usage, or expansion into new markets.
Note: AI Properties runs on views with max 5,000 accounts or contacts. AI Properties refresh every 30 days minimum.
Step 7: Add Products
Products give the AI context about what your team is selling. This context shapes how accounts are researched and summarized.
Navigate to Atlas → Categorize → Products.
Click Add Product.
Fill in the following fields:
Name (required): The product name as your team refers to it (e.g., "Revenue Intelligence Platform").
Description (recommended): A detailed explanation of what the product does, who it is for, and its key value propositions. See guidance below.
Click Save.
Repeat for each distinct product or solution your team sells.
Writing Effective Product Descriptions
The description is read by the AI when generating account insights. Include:
What it does: e.g., "An AI-powered platform that surfaces buying signals and maps stakeholders at target accounts."
Who it's for: e.g., "Built for B2B sales teams with 10+ reps selling into enterprise accounts."
Key differentiators: e.g., "Unlike generic tools, insights are grounded in the customer's own CRM and engagement data."
Tip: If your company sells multiple products but one is almost always the entry point, focus your primary product description on that product. Describe the others as expansion or add-on offerings, and note how your team typically sequences them.
Step 8: Add Business Context
Help Account Intelligence understand your business so it can deliver more relevant insights across all accounts.
Navigate to Atlas → Categorize → Business Context section.
Upload any relevant context documents. For example, your ICP definition, competitive positioning, or company overview.
Add a high-level description of your company and what you do.
This context is used by the AI when generating account summaries, scoring reasoning, and research outputs.
Step 9: QA Your Account Details
Before rolling out to your full team, review a sample of accounts to verify everything is configured correctly.
Open your Target Account View and click into several accounts.
For each account, review the following tabs:
Overview: Does the account summary look accurate and relevant?
Journey: Are funnel stages and touchpoints appearing correctly?
People: Are the right contacts being surfaced based on your personas?
Stakeholders: Are personas mapping to the right individuals?
Signals: Are your AI Properties returning relevant, useful results?
Review at least 3 accounts, and do one complete, thorough pass on a single account.
If anything looks off, return to the relevant configuration step and adjust.
Step 10: Set Up Workflows (Optional)
Workflows allow you to automate actions based on Account Intelligence data — for example, pushing signals or scores into Salesforce fields, or triggering alerts when an account's status changes.
This step is optional and depends on your team's specific use case. If you'd like to configure workflows, please read through the documentation here and reach out to your HockeyStack contact for guidance on the best setup for your situation.
You're Ready 🎉
Once you've completed the steps above, your team is set up to use Account Intelligence. Here's a quick summary of what you've configured:
✅ Team members invited and roles assigned
✅ Target account views created
✅ Account scoring configured
✅ Personas defined and tested
✅ Stakeholder mapping enabled
✅ Products defined with clear descriptions
✅ Business context added
✅ Account details reviewed and validated
Need Help?
If you run into any issues during setup, reach out to your HockeyStack onboarding contact.
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