Calculate Your At-Risk Revenue

Sturdy integrates directly with an organization’s primary communication and support platforms, including email systems such as Outlook and Gmail, collaboration platforms like Slack and Zoom, call intelligence from Gong, customer support platforms such as Zendesk and ServiceNow, and customer relationship management systems including Salesforce and HubSpot [1]. By ingesting transcripts, tickets, and digital correspondence, Sturdy creates a consolidated dataset that reflects the voice of the customer in an anonymized, privacy-compliant format [2]. This unification enables the platform to run advanced regression analysis, converting signals such as feature requests, contract renegotiations, or discount demands into churn-probability scores [3]. Because the system applies models across multiple communication types simultaneously, it ensures that customer-facing teams do not miss risk patterns hidden in fragmented data. All models operate automatically without manual training or data tagging, resulting in proactive alerts with minimal administrative burden [4]. As a result, revenue protection teams gain a centralized customer intelligence platform that supports consistent identification of accounts susceptible to attrition.

When churn risks are detected, Sturdy does not stop at surfacing predictive scores; it initiates structured responses designed to stabilize accounts. The system automatically triggers playbooks tailored to specific churn scenarios, such as a customer expressing dissatisfaction with support or signaling disengagement through contractual inquiries [5]. Playbooks contain prescribed steps for customer success managers and account executives, detailing recommended outreach timing, messaging, and escalation pathways. Reps are alerted with actionable guidance, which minimizes uncertainty and ensures uniform responses across the organization. In documented case studies, customers using Sturdy reported retention improvements within short periods, including a 30% month-over-month increase sustained over six weeks [6]. By combining detection with directed intervention, the platform ensures that identified risks translate into measurable churn reduction rather than retrospective analytics. This focus on execution aligns revenue operations with a systematic approach where every flagged risk results in immediate, evidence-based action.

Traditional retention analysis often relies on manually tagged datasets or ongoing model maintenance, but Sturdy avoids those requirements by employing proprietary natural language processing and statistical regression models. The system processes unstructured communication data, extracts recurrent themes such as escalating complaints or declining sentiment, and converts these into quantitative signals of churn probability [7]. Importantly, customers deploying Sturdy are not asked to generate tagged corpora or provide input for iterative training, since the intelligence layer is designed to function autonomously [4]. This makes predictive risk detection rapidly operational at scale, avoiding delays associated with customization or IT intervention. As a result, frontline revenue teams can immediately access account health indicators, driver diagnostics, and intervention recommendations. In live implementations, this design principle allows organizations to protect revenue streams quickly without building new analytical infrastructure, a notable efficiency advantage in enterprise-scale retention management.

Sturdy delivers account-level summaries and executive dashboards that present predictive health ratings, churn drivers, and longitudinal trends across the customer base [8]. These dashboards offer visibility into the percentage of accounts designated as high-risk, as well as patterns of improvement across retention initiatives. For revenue planning, Sturdy includes a Revenue Retention Calculator that quantifies the direct effect of churn on annual recurring revenue, enabling leaders to model business outcomes when applying intervention strategies [7]. Enterprise reporting capabilities extend to risk trend aggregation, showing how proactive measures reduce churn rates month by month. These tools address executive requirements for consistent, auditable intelligence that can be trusted across large organizations. Since the dashboards are embedded within role-based access tiers, business leaders, customer success managers, and revenue operations executives can each extract the insights most relevant to their decision-making responsibilities. This reporting environment supports rigorous monitoring where financial impacts of customer health signals are linked directly to retention campaigns and renewal performance.

Data security and compliance are central to Sturdy’s enterprise deployment model. The company operates a comprehensive Information Security Management program that is fully certified under SOC 2 Type II standards, validating adherence to rigorous security controls [9]. All processing is conducted on Amazon Web Services infrastructure to leverage proven stability and scalability [10]. To protect customer privacy, Sturdy pseudonymizes and anonymizes all personally identifiable information before it enters the machine learning environment, and data is redacted where necessary to ensure compliance with frameworks such as GDPR [2]. Permissions are structured via tiered role-based controls, with the Business and Enterprise plans allowing granular access governance to align with corporate compliance policies [11]. These measures help security-conscious organizations evaluate Sturdy under established SaaS vendor risk assessment frameworks without the need for exception processes. The integration of operationalized compliance procedures ensures that sensitive customer communication data can be leveraged for predictive analytics while adhering to strict corporate and regulatory requirements.