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: Predictive Retention | Lifecycle Optimization | Scalable CS Systems

Simulated Duration: 4 months

Services Provided: Behavioral Analytics Mapping, Lifecycle Framework Design, Churn Elasticity Modeling, Workflow Automation


Problem


In 2024, a high-velocity B2B SaaS company specializing in remote team collaboration software reached a critical inflection point: growth metrics were rising, but retention was silently collapsing. Within the first 60 days post-onboarding, churn had surged to 28%, triggering alarms across MRR forecasts and undermining investor confidence.

The core challenge wasn’t rooted in acquisition or product-market fit—it stemmed from what I define as the Expectation–Activation Gap: the psychological and functional dissonance between what users believed they were buying and the exact moments that activated enduring product value.

Customer Success operated reactively. Onboarding touchpoints were template-based. NPS fell below 40. Meanwhile, essential behavioral signals were fragmented across HubSpot, Intercom, SQL dashboards, and legacy systems—with no unified visibility or predictive insight into disengagement triggers.

The organization didn’t need more onboarding. It needed a predictive retention engine, a system that functioned like an autonomous nervous system, continuously sensing friction, forecasting churn, and self-correcting in real-time.

Strategic Focus: Predictive Retention | Lifecycle Optimization | Scalable CS Systems

Simulated Duration: 4 months

Services Provided: Behavioral Analytics Mapping, Lifecycle Framework Design, Churn Elasticity Modeling, Workflow Automation


Problem


In 2024, a high-velocity B2B SaaS company specializing in remote team collaboration software reached a critical inflection point: growth metrics were rising, but retention was silently collapsing. Within the first 60 days post-onboarding, churn had surged to 28%, triggering alarms across MRR forecasts and undermining investor confidence.

The core challenge wasn’t rooted in acquisition or product-market fit—it stemmed from what I define as the Expectation–Activation Gap: the psychological and functional dissonance between what users believed they were buying and the exact moments that activated enduring product value.

Customer Success operated reactively. Onboarding touchpoints were template-based. NPS fell below 40. Meanwhile, essential behavioral signals were fragmented across HubSpot, Intercom, SQL dashboards, and legacy systems—with no unified visibility or predictive insight into disengagement triggers.

The organization didn’t need more onboarding. It needed a predictive retention engine, a system that functioned like an autonomous nervous system, continuously sensing friction, forecasting churn, and self-correcting in real-time.

Strategic Focus: Predictive Retention | Lifecycle Optimization | Scalable CS Systems

Simulated Duration: 4 months

Services Provided: Behavioral Analytics Mapping, Lifecycle Framework Design, Churn Elasticity Modeling, Workflow Automation


Problem


In 2024, a high-velocity B2B SaaS company specializing in remote team collaboration software reached a critical inflection point: growth metrics were rising, but retention was silently collapsing. Within the first 60 days post-onboarding, churn had surged to 28%, triggering alarms across MRR forecasts and undermining investor confidence.

The core challenge wasn’t rooted in acquisition or product-market fit—it stemmed from what I define as the Expectation–Activation Gap: the psychological and functional dissonance between what users believed they were buying and the exact moments that activated enduring product value.

Customer Success operated reactively. Onboarding touchpoints were template-based. NPS fell below 40. Meanwhile, essential behavioral signals were fragmented across HubSpot, Intercom, SQL dashboards, and legacy systems—with no unified visibility or predictive insight into disengagement triggers.

The organization didn’t need more onboarding. It needed a predictive retention engine, a system that functioned like an autonomous nervous system, continuously sensing friction, forecasting churn, and self-correcting in real-time.

Solution


I designed and implemented a predictive, lifecycle-aware retention infrastructure based on four core pillars:

1. Elastic Churn Threshold Modeling. Using segmented historical cohort data (by activation time, usage frequency, and role), I created adaptive churn thresholds that adjusted dynamically with behavioral context—transforming static red flags into fluid risk profiles.

2. NPS-Based Lifecycle Checkpoints. Instead of waiting until the end of onboarding, I deployed micro-NPS probes at behavioral friction points (e.g., failed integration, inactive workspace at Day 14). These converted passive sentiment into predictive signals for churn—or advocacy.

3. Friction-Point Automation Layer. Threshold breaches activated tiered interventions: Intercom nudges, HubSpot workflows, success milestone resets, and bandwidth-aware CS outreach. This approach scaled intelligently with team capacity while protecting UX fluidity.

4. Behavioral Intelligence Dashboard. I built a centralized behavioral intelligence system that consolidated drop-off momentum, activation velocity, and recovery windows—empowering CS managers to operate as strategic retention analysts, not ticket processors.

This wasn’t about throwing more effort at churn. It was about replacing guesswork with systemized precision, and transforming CS into a predictive growth engine.


Simulated Business Impact


  • +37% uplift in MRR retention over 2 quarters.

  • –42% reduction in reactive ticket volume.

  • +19% acceleration in ARR cycle due to better expansion eligibility and re-engagement timing.