🎯 Goal
Engage target accounts with content that feels relevant from the first touch, even when you don’t yet know the individual buyers. This playbook shows how to pre-personalize content at scale so ABM teams can move faster, stay consistent, and generate stronger account-level signals without creating bespoke assets one by one.
Before you begin
This playbook is designed to help you plan and execute scaleable ABM campaigns with confidence. It walks through what to expect at each stage, what’s supported today, and where value builds over time so you can make informed decisions before launching.
You don’t need to follow every step end to end. Use the parts that align with your goals, and combine them with other playbooks to fit your ABM motion, tooling, and team setup.
This diagram presents a high-level overview of the playbook, illustrating each step and the integrations supported along the way. The article then explores each step in more detail.
Step 1. Create Content in Turtl
Start with a single, high-quality asset designed to scale across your target accounts. This base content contains the full narrative: the problem you solve, the value you deliver, and the proof points that matter most to your ICP.
From the outset, identify which elements will be adapted per account - such as sector, persona, or geography - while the rest of the content remains consistent.
Hatch AI supports this creation stage by helping teams draft faster, refine messaging, translate and maintain consistency across copy, visuals, and formats. This ensures the core narrative is solid before personalization begins.
With a strong, reusable foundation in place, teams can scale personalization without rewriting content for every account.
Additional Reading
See more on content creation
Step 2. Set Up Personalization Form
Before generating anything at scale, reconfirm what should change within the base asset and why. This step is about deciding which elements need to feel tailored so your content lands as intentional, not templated.
Once you’ve decided where relevance matters most, configure personalization so the base asset can automatically produce multiple tailored versions in the next step. This is done by marking variable text or visuals with personalization tokens and setting page rules that show or hide sections based on account attributes - so each reader only sees what applies to them.
Behind the scenes, a personalization form acts as the control layer for the base asset. This form defines which data replaces each token and which page rules are applied.
You can set up a personalization form in two ways:
Manual form field creation: Create form fields and map them to tokens and page rules.
Automated form field creation via integrations: If planning to leverage data from 6sense or Demandbase to generate personalized content, you can create form fields that are automatically set up with a token to match the data points in those platforms. This speeds up the mapping process when you come to generate your batch.
Once this is done, you’ve locked the logic. From here on, personalization scales cleanly without rework.
💡Top tip
If you’re using integrations, create the personalization form first, then add the corresponding tokens into your content. This avoids rework later.
Additional Reading
How to set up content for personalization
Step 3. Generate personalized content at scale
With your base asset and personalization logic in place, this step is about execution. You’re generating multiple account-specific versions from the same source asset, turning a single piece of content into many tailored outputs ready for campaigns or sales outreach.
The objective here is efficiency and consistency. Every account receives content that feels intentionally built for them, while all versions remain aligned to the same approved narrative and structure - without manual rework or risk.
At this stage, you can:
Generate a batch of personalized content
Create multiple account-specific content at once by uploading a CSV or selecting an account segment directly from your ABM platform where supported: currently 6sense only. Each row or account creates a new version from the same base asset.
Apply the same approved structure to every account
Every version inherits the same layout, narrative flow, and core messaging from the master asset. Only the predefined tokens and sections change per account, ensuring consistency across the entire campaign.
Produce assets that are immediately usable
Each personalized asset is created with its own unique URL and shareable visuals, making it easy to activate across sales and marketing channels.
Once this step is complete, your ABM content is no longer theoretical. You have a set of personalized assets that can be distributed confidently from which we can track and measure engagement.
Additional Reading
How to create a batch of personalized content via file upload
Step 4. Share content with target accounts
This step is about putting personalized content in front of the right accounts at the right moment. The goal is not broad reach. It’s controlled exposure that supports your ABM motion and creates meaningful engagement signals.
How you activate content will depend on how your team works and where accounts are in the buying journey. What matters is that distribution is deliberate and measurable.
At this stage, teams typically:
Enable sales and account teams
Share personalized links with account managers so they can use them in outreach, follow-ups, or deal conversations, backed by content that already reflects the account context.
Support targeted paid campaigns
Use personalized content as a destination for ads aimed at specific accounts, increasing relevance once prospects click through.
Coordinate timing across channels
Align content sharing with sales plays, campaigns, or key account moments so engagement happens when it’s most likely to matter.
This step turns personalization into action. You’re no longer preparing content for accounts. You’re actively using it to open conversations, generate intent signals and drive pipeline.
Additional Resources
Learn best practices for sharing Turtl content
Step 5. Learn about your audience
As target accounts engage with your content, Turtl begins to surface engagement signals in your dashboards.
At this stage, your data will likely be primarily made up of unknown readers - engagement from people at your target accounts who haven’t identified themselves yet. Even if individuals are unknown, you can still see how they interact with each personalized asset via the analytics dashboards, helping you understand which accounts are showing signs of interest:
Which target accounts are engaging at all?
Which personalized messages are getting traction?
Which content is working and what can we refine/improve?
Where should sales or marketing focus next?
Think of this as early signal detection. You’re building context and confidence before pushing for identification or conversion.
Account-level insights (coming soon) will provide an aggregated view of engagement at an account level, automatically identified using IP data. This makes it easier to see account coverage, depth of engagement, and which accounts are warming up, even without form fills.
Additional Resources
Reviewing analytics of personalized content
Step 6. Monitor content performance and audience intent in Dashboards
With engagement data coming in, this step is about turning signals into priorities. Instead of reporting activity, you’re looking for patterns that tell you which messages, formats, and accounts deserve more attention. Hatch AI plays a key role here, with an analytics chatbot enabling you to query and interrogate data without a heavy lift.
Some key data points to consider:
% Accounts Engaged: Breadth of engagement across account list
Average Read Time: Depth of engagement across campaign as a whole
Avg. % of Doc Read: Depth of engagement / effectiveness of each chapter
Bounce Rate: Relevance, initial impact and distribution success
Top Accounts: Personalizations with highest Avg. Read Time
Section Performance: What topics are capturing attention
By reviewing these signals regularly, you can spot patterns early, focus effort where it matters most, and make informed decisions about what to repeat, refine, or retire.
Additional Resources
How to improve key Turtl Analytics metrics
Step 7: Turn anonymous engagement into identified interest over time
Supported integrations
HubSpot
Marketo
Pardot
In ABM, buyers rarely identify themselves on the first touch. As accounts continue to interact with your brand and later convert through gated content or other channels, Turtl connects the dots so earlier engagement isn’t lost.
At this stage, teams benefit from:
Automatic resolution of returning readers
When someone who previously engaged anonymously is later identified in your CRM or MAP, their past Turtl engagement is linked back to them when they return.
Preserving early buying signals
Engagement that happened before form fills or sales conversations is fully captured, giving you a complete picture of engagement over time.
Better context for sales and marketing
Instead of starting from zero at identification, teams can see how long an account or contact has been engaging and what they’ve spent time on.
This step rewards patience. You’re not forcing identification upfront. You’re allowing buyers to engage on their terms, while still capturing the value of that engagement when it matters.
Additional Reading
Learn more about intent signal matching
Step 8: Sync Intent Data to External Platforms
Supported Integrations
HubSpot
Marketo
Pardot
Demandbase and 6sense (with Turtl Support)
At this stage, your ABM content is generating real engagement signals. This step is about making those signals useful beyond Turtl, so other systems and teams can act quickly with better context.
The goal isn’t to flood your tools with more data. It’s to enrich what you already have so prioritisation, outreach, and campaigns are better informed.
Turtl supports syncing intent data into platforms such as HubSpot, Marketo, Pardot, Demandbase, and 6sense. Exactly what can be synced depends on the specific platform.
Teams typically use this step to:
Enrich known contacts with content engagement
When readers become identified (Known Readers) engagement signals such as page views, time spent and topics viewed can be synced back to your CRM or MAP. This adds context to existing leads and contacts.
Support account prioritization (where supported)
By adding an ABM platform tracking script to Turtl content, you can send aggregated engagement signals such as page views and link clicks into your ABM platform. This gives visibility into account-level interest, even when individual readers aren’t identified.
Align marketing and sales around the same signals
By surfacing content engagement alongside existing data, teams can coordinate follow-up, messaging, and timing with a shared view of what accounts have engaged with.
This step is about continuity. Engagement in Turtl shouldn’t live in isolation. It should strengthen the decisions you’re already making across your ABM stack.
Additional Reading
Syncing Turtl data to external platforms
Step 9: Understand how content contributes to revenue over time
Supported Integrations
HubSpot
Salesforce (with Turtl Support)
Dynamics (with Turtl Support)
This is where long-term ABM efforts start to show commercial impact. Once you have Known Readers in Turtl, those readers are matched to CRM contacts, and those contacts are linked to opportunities, Turtl’s Revenue Dashboard can show how content engagement aligns with pipeline movement and closed revenue.
At this stage, teams use Turtl to:
See which content shows up in active deals
Understand what content is being consumed by contacts associated with opportunities, and how deeply they engage during the sales process.
Identify patterns across won and lost deals
Spot recurring themes in the content that appears in successful opportunities versus those that stall or drop.
Support clearer revenue conversations
Use engagement data as supporting evidence when discussing pipeline influence with sales and leadership, without over-claiming causation.
Looking ahead, account-level revenue analytics (coming soon) will extend this view, reducing the reliance on Known Readers in Turtl to connect content to revenue. This will make it possible to understand how content engagement influences deals at the account level, even when individual buyers aren’t explicitly identified.
This step is about credibility and confidence. You’re not trying to prove that content alone closed a deal. You’re showing, with evidence, how content supports momentum, shapes buying journeys, and contributes to revenue over time.
Additional Reading
Learn more about the Revenue Dashboard