Customer Retention
Cohort Retention Diagnostics
Diagnoses retention behavior by signup cohort, tenure band, product adoption profile, and contract model so teams can identify where persistence is deteriorating and why. Instead of high-level aggregate rates, this app isolates structural breakpoints in customer lifecycle progression and quantifies the economic impact of each breakpoint on future recurring revenue.
The main analysis compares expected versus observed survival by month-since-start for each cohort and segment. Supporting decomposition attributes retention loss to deterministic factors such as low feature adoption, unresolved support backlog, delayed onboarding completion, and weak executive sponsorship. Users can separate new-customer stabilization problems from late-tenure value-decay patterns in one controlled workspace.
Outputs include a ranked cohort leakage matrix, month-band hazard profile, and quantified upside from restoring selected cohorts to baseline survival assumptions. The app is intentionally diagnostic and reproducible, providing the same intervention ranking when identical filters and thresholds are applied.
Customer Retention Hub
Provides a unified retention command view that consolidates logo retention, gross revenue retention, net revenue retention, renewal attainment, and active risk concentration across segments and geographies. The app is designed for executive operating cadence where leaders need an immediately interpretable snapshot of whether retention performance is on-track, where risk is concentrated, and which levers are likely to protect or improve next-period outcomes.
The primary visual layer combines KPI cards, retention trend trajectories, and segment contribution decomposition. A supporting diagnostics area shows where renewal pressure is accelerating, where downgrade risk is emerging, and where expansion offsets are compensating for contraction. This ensures users can distinguish temporary volatility from structural retention degradation without changing the underlying deterministic data model.
The app supports quarter planning, monthly business reviews, and weekly success forums by producing reproducible outputs: retention status classification, target gap sizing, and prioritized focus areas by segment and account tier. All outputs are deterministic so two users applying the same filters receive identical decisions and narrative context.
Expansion Opportunity Tracker
Tracks customer expansion opportunities with a deterministic managed table seeded from starter rows. The app summarizes visible opportunities by total target value, weighted value, and average win probability, then lets users search and filter by segment and stage to focus the pipeline. A pair of charts shows weighted value by stage and target value by segment so managers can see where expansion upside is concentrated.
Users can add new opportunities with account, segment, owner, ARR, target value, probability, stage, close date, next step, and notes, then update the stage or mark an opportunity won or lost directly from the table. The app hydrates the Expansion Opportunities table through bf.ready, bf.getRows, and bf.onTableSync while keeping the deterministic seed rows visible until real workbook data arrives.
Renewal Variance Monitor
Monitors renewal outcomes against committed operating plan assumptions and flags where variance is accumulating by segment, contract type, and renewal month. The app is built for deterministic variance governance where teams need to understand whether deviations are timing noise, execution slippage, or structural risk requiring immediate intervention.
A renewal variance bridge quantifies contribution from logo churn, downsell, late-cycle discounting, and delayed closes. A secondary timeline panel tracks rolling forecast drift, highlighting whether renewal gaps are improving, stable, or worsening as quarter end approaches. This enables revenue, customer success, and finance teams to align assumptions with a common evidence base.
Outputs include signed renewal variance by component, cumulative ARR gap to plan, and threshold-based escalation states. The app prioritizes repeatability and operational trust by ensuring all displayed values derive from deterministic input tables and fixed scenario controls.
Retention Action Queue
Converts retention risk signals into a deterministic account-level execution queue that assigns each record a priority score, owner, due date, and recommended intervention type. The app is designed for daily and weekly operating workflows where managers need to see which accounts require immediate action and how much save value remains exposed if those actions are delayed.
The primary view combines queue-level metrics, an owner load breakdown, a top-priority shortlist, and a row-level register with one-click completion and snooze controls. Users can filter by status, owner, action type, and urgency window to focus the queue on the subset they want to manage in a given working session.
Outputs include open action counts, urgent and overdue risk flags, completed-action tracking, and a deterministic save summary for the active queue. The app favors transparent queue governance so the same filters and starter data always produce the same ordering and intervention context.
Risk Segment Explorer
Explores how churn and downgrade risk concentrates across customer segments, product tiers, geography, tenure bands, and engagement profiles. The purpose is to reveal where risk-adjusted ARR exposure is disproportionately high and where targeted programs can produce the largest retention lift.
The app includes a multi-dimensional segment matrix, risk distribution curves, and exposure-weighted contribution charts. Users can pivot from broad segment comparisons to focused slices such as high-ARR, low-adoption accounts in specific regions. A deterministic scoring frame ensures segment rankings remain stable for the same filter combination and threshold values.
Outputs include segment risk leaderboard, concentration index, and intervention opportunity map. These outputs support strategic planning decisions, including where to deploy specialist teams, where to standardize playbooks, and where to redesign onboarding or adoption motions.
Save Offer Simulator
Simulates the impact of save-offer strategies on renewal probability, gross margin, and net retained ARR so teams can choose interventions that maximize long-term value rather than short-term logo saves alone. The app supports deterministic what-if analysis across discount level, term extension, feature bundle, success-plan commitment, and margin floor settings.
The core model compares baseline no-offer outcomes with configured save scenarios and quantifies expected uplift and economic trade-offs at both account and portfolio levels. It presents a ranked offer list, a selected-account economics panel, and a chart that compares baseline retained ARR with scenario retained ARR for each seeded account. A margin guardrail layer makes it easy to see which offers are policy compliant and which ones should be softened or rejected.
Outputs include expected save rate, retained ARR, concession cost, net economic value, gross margin, and policy-compliant ROI. Teams use this to standardize commercial decisions and reduce inconsistent, ad-hoc negotiation behavior across managers and regions.