KlientBoost is a world-class performance marketing agency led by CEO Jonathan Dane.
They focus on quantifiable business outcomes such as lowering CPA, improving ROAS, and increasing conversion rates. Their ideal clients are SaaS and eCommerce companies with significant digital ad spend.
Their value proposition is built around measurable results, which makes their go to market motion highly dependent on engaging the right accounts with the right timing and context.
At the time, KlientBoost had scaled to roughly $20M in annual recurring revenue
Which came with:
• High website traffic across service pages
• A large database of closed-lost and past opportunities
• Consistent LinkedIn engagement
• Strong brand recognition in their space
KlientBoost needed a system that could systematically capture high intent signals across the web, re engage past relationships with precision, and turn intent into sales ready opportunities through a value led experience.
The goal was to build an evergreen engine that converts prospects into inbound style opportunities using automation, signal capture, and integrated RevOps workflows.
Despite strong traffic and brand presence:
• Website visits weren’t being monetized systematically
• Closed-lost and dormant pipeline were underutilized
• LinkedIn engagement wasn’t connected to outbound
• Outbound lacked signal-based prioritization
They weren’t missing activity.
They were missing coordination.
Without a unified system, traffic, database history, social engagement, and signals were operating in silos instead of driving one cohesive demand motion.
We designed and deployed a custom signal based ABM engine built around KlientBoost’s Marketing Plan 2.0 offer.
The objective was to convert their existing assets into a structured and scalable demand system.
This included:
• High website traffic
• A large historical pipeline
• Strong LinkedIn visibility
• Ongoing market activity across their ICP
The full system was architected and implemented within three months.
It was organized into five strategic play groups, each aligned with KlientBoost’s growth stage and market position.
KlientBoost was generating consistent traffic across high value service pages.
We built structured plays that:
• Identified company level visitors
• Mapped accounts to decision makers
• Segmented outreach by service level interest
• Triggered tailored messaging aligned with specific engagement
This ensured that outreach reflected real interest and real timing.
Website engagement became an active pipeline driver.
KlientBoost had years of historical deal and lead data.
We structured a reactivation framework that:
• Segmented previous opportunities by stage and service
• Monitored renewed engagement signals
• Triggered contextual re outreach
• Maintained prior conversation history in messaging
This transformed past pipeline into a monitored opportunity layer.
We implemented outbound plays triggered by observable market activity.
Signals included:
• Hiring patterns
• Company expansion indicators
• Paid media behavior shifts
• Technology adoption changes
Outbound was initiated based on movement in the market, not static lists.
This created a higher quality entry point into conversations.
KlientBoost had an established LinkedIn presence with consistent engagement.
We structured this channel into a measurable pipeline contributor by:
• Tracking profile viewers from target accounts
• Capturing ICP post engagement
• Identifying high value interactions
• Initiating structured follow up sequences
LinkedIn activity was integrated directly into the ABM system.
At the center of the system was Marketing Plan 2.0.
When a prospect engaged:
• A tailored marketing plan was delivered
• Engagement behavior was tracked
• Follow up adjusted based on interaction
• Outreach continued across channels with context
• Sales stepped in when engagement reached defined thresholds
This created a controlled conversion path from interest to qualified conversation.
Within 3 months, KlientBoost had:
Book a strategy call to build an outbound engine like this
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