Hey, I’m Maria!

I'm a Head of Customer Success and Growth

Helping Tech Grow Through People-First Strategy

About me

Trajectory

Scenarios

Say Hello

Hey, I’m Maria!

I'm a Head of Customer Success and Growth

Helping Tech Grow Through People-First Strategy

About me

Trajectory

Scenarios

Say Hello

Hey, I’m Maria!


I'm a Head of Customer Success and Growth


Helping Tech Grow Through People-First Strategy


About me

Trajectory

Scenarios

Say Hello

Say Hello

Hey, I’m Maria!


I'm a Head of Customer Success and Growth


Helping Tech Grow Through People-First Strategy


About me

Trajectory

Scenarios

Say Hello

Say Hello

Strategic Focus: AI-Driven Risk Prediction | Multimodal Health Scoring | Scalable Success Intelligence

Simulated Duration: 4 months

Services Provided: Predictive Churn Signal Modeling, NLP-Based Support Sentiment Mining, Behavior Drift Detection, Executive Silence Indexing, AI Composite Scoring Integration


Problem


In 2024, a global AI automation platform serving Fortune 500 clients was quietly bleeding revenue from a blind spot: unanticipated churn in high-value accounts. Their Net Revenue Retention (NRR) was stable at 110%, but leadership couldn’t explain why marquee customers were vanishing—without triggering any red flags in the traditional health scoring system.

The reality? These accounts weren’t failing suddenly. They were disconnecting silently.

CSMs were overloaded with day-to-day operations. Traditional dashboards only reported what was already happening. There was no infrastructure for real-time pattern detection—no intelligent system to sense and forecast disengagement at scale.

What the organization needed was not more check-ins or human intuition—it needed an anticipatory operating layer.

Strategic Focus: AI-Driven Risk Prediction | Multimodal Health Scoring | Scalable Success Intelligence

Simulated Duration: 4 months

Services Provided: Predictive Churn Signal Modeling, NLP-Based Support Sentiment Mining, Behavior Drift Detection, Executive Silence Indexing, AI Composite Scoring Integration


Problem


In 2024, a global AI automation platform serving Fortune 500 clients was quietly bleeding revenue from a blind spot: unanticipated churn in high-value accounts. Their Net Revenue Retention (NRR) was stable at 110%, but leadership couldn’t explain why marquee customers were vanishing—without triggering any red flags in the traditional health scoring system.

The reality? These accounts weren’t failing suddenly. They were disconnecting silently.

CSMs were overloaded with day-to-day operations. Traditional dashboards only reported what was already happening. There was no infrastructure for real-time pattern detection—no intelligent system to sense and forecast disengagement at scale.

What the organization needed was not more check-ins or human intuition—it needed an anticipatory operating layer.

Strategic Focus: AI-Driven Risk Prediction | Multimodal Health Scoring | Scalable Success Intelligence

Simulated Duration: 4 months

Services Provided: Predictive Churn Signal Modeling, NLP-Based Support Sentiment Mining, Behavior Drift Detection, Executive Silence Indexing, AI Composite Scoring Integration


Problem


In 2024, a global AI automation platform serving Fortune 500 clients was quietly bleeding revenue from a blind spot: unanticipated churn in high-value accounts. Their Net Revenue Retention (NRR) was stable at 110%, but leadership couldn’t explain why marquee customers were vanishing—without triggering any red flags in the traditional health scoring system.

The reality? These accounts weren’t failing suddenly. They were disconnecting silently.

CSMs were overloaded with day-to-day operations. Traditional dashboards only reported what was already happening. There was no infrastructure for real-time pattern detection—no intelligent system to sense and forecast disengagement at scale.

What the organization needed was not more check-ins or human intuition—it needed an anticipatory operating layer.

Solution


I designed and deployed the Neural Success Anticipation System™: a predictive intelligence engine for Success teams, built to forecast risk using AI-driven behavioral, linguistic, and relational signals.

This was not a dashboard—it was a nervous system.

The solution architecture was based on four interlocking AI models:

  1. NLP-Based Sentiment Drift Monitor. We deployed a natural language model to parse every inbound support ticket, classifying tone, urgency, and frictional emotion over time. Micro-shifts in polarity across segments flagged emotional disengagement up to 6 weeks before product usage declined.

  2. Behavioral Drift Detection Engine. Instead of relying on flat usage metrics, we mapped historical engagement curves and built anomaly detectors per feature set. A 17% week-over-week usage drift (vs. cohort baseline) triggered internal alerts—long before full churn behaviors emerged.

  3. Executive Silence Index (ESI). Using metadata from calendar integrations, QBR attendance, and CRM engagement logs, we tracked C-level touchpoint decay. When a key executive went silent for >28 days post-renewal, the ESI dropped—signaling decreased strategic alignment and future risk.

  4. Composite AI Health Score Layer. I designed a weighted scoring architecture to synthesize sentiment, behavior, and stakeholder signals into a single, dynamic risk indicator—auto-synced with Salesforce. Accounts were re-prioritized weekly via predictive likelihood of churn or renewal success.


Simulated Business Impact


  • 3.2x increase in risk anticipation lead time across all strategic accounts.

  • 87% correlation between AI health score and renewal outcomes.

  • 41% reduction in unexpected churn across Q2 and Q3.

  • Full adoption of AI-Augmented Success Ops, triggering action before attrition even begins.