Connecty’s Autonomous Semantic Graph dynamically adapts to your business context and goals — mastering 22 types of funnel analysis without manual configuration.
No setup required — start exploring funnel intelligence in minutes.
Connecty’s Decision Intelligence Layer translates complex funnel analysis into daily insights for every function.
See where users drop off in onboarding or activation, measure feature adoption, and prioritize improvements that increase retention.
Link campaigns to conversions, quantify channel ROI, and uncover which audience paths actually drive revenue growth.
Visualize deal progression, detect stalled opportunities, and compare conversion efficiency across reps, products, or regions.
Monitor process throughput, surface workflow bottlenecks, and predict stage delays before they affect business outcomes.
Analyze buyer–seller drop-offs, transaction friction, and liquidity patterns to boost trust and conversion on both sides.
An autonomous semantic graph that understands your business context and dynamically adapts to every type of funnel analysis — from stage progression to behavioral anomalies — without manual configuration.
Connecty's ASG dynamically adapts to your schema, relationships, and goals — automatically selecting the right analysis type and SQL logic patterns as your questions evolve.
Each insight category addresses a different analytics objective — from growth to monetization, retention, and reliability — with its own SQL foundation.
Understand how users move through stages and how conversion rates evolve over time.
LAG() for sequential changeQuantify revenue attribution, customer lifetime value, and incremental lift for monetization analysis.
MIN(timestamp) for cohort anchoringAnalyze timing and efficiency of multi-step conversions for retention and engagement insights.
DATEDIFF() and DATE_ADD() for timingIdentify where and why users exit the funnel, and uncover patterns behind churn or inactivity.
Detect anomalies and ensure process consistency across funnel measurement systems.
Z-Score and CUSUM methodsEach insight category addresses a different analytics objective — from growth to monetization, retention, and reliability — with its own SQL foundation.
Purpose: Understand how users move through stages and how conversion rates evolve over time.
Example Metrics: Stage-to-Stage Conversion Trend, Cumulative / Indexed Growth, Period-over-Period Rate Change, Segmented Stage Trend.
SQL Logic: UNION and GROUP BY for aggregation; window functions with LAG() for sequential change.
Purpose: Quantify revenue attribution, customer lifetime value, and incremental lift for monetization analysis.
Example Metrics: Funnel Value Attribution Efficiency, Cohort-Based Lifetime Value, Incremental Value Lift.
SQL Logic: Reverse JOINs for attribution mapping; MIN(timestamp) for cohort anchoring; date window filters.
Purpose: Analyze timing and efficiency of multi-step conversions for retention and engagement insights.
Example Metrics: Time-Bound Conversion Rate, Time-to-Convert Distribution, Retention / Re-Conversion.
SQL Logic: DATEDIFF() and DATE_ADD() for timing; chained LEFT JOINs across stages.
Purpose: Identify where and why users exit the funnel, and uncover patterns behind churn or inactivity.
Example Metrics: Progressive Drop-Off, Behavioral Drop-Off, Temporal Drop-Off, Recovery / Re-Engagement.
SQL Logic: Time window and multi-stage JOIN filters; segment correlation and hazard rate models.
Purpose: Detect anomalies and ensure process consistency across funnel measurement systems.
Example Metrics: Behavioral Anomalies, Metric Deviation Checks, Segment Reliability Scores.
SQL Logic: Z-Score and CUSUM methods; control limits and KL divergence tests.
Each insight category addresses a different analytics objective — from growth to monetization, retention, and reliability — with its own SQL foundation.
Purpose: Understand how users move through stages and how conversion rates evolve over time.
Example Metrics:
SQL Logic: UNION and GROUP BY for aggregation; window functions with LAG() for sequential change.
Purpose: Quantify revenue attribution, customer lifetime value, and incremental lift for monetization analysis.
Example Metrics:
SQL Logic: Reverse JOINs for attribution mapping; MIN(timestamp) for cohort anchoring; date window filters.
Purpose: Analyze timing and efficiency of multi-step conversions for retention and engagement insights.
Example Metrics:
SQL Logic: DATEDIFF() and DATE_ADD() for timing; chained LEFT JOINs across stages.
Purpose: Identify where and why users exit the funnel, and uncover patterns behind churn or inactivity.
Example Metrics:
SQL Logic: Time window and multi-stage JOIN filters; segment correlation and hazard rate models.
Purpose: Detect anomalies and ensure process consistency across funnel measurement systems.
Example Metrics:
SQL Logic: Z-Score and CUSUM methods; control limits and KL divergence tests.
Start optimizing your funnel today with custom logics - powered by Connecty’s Autonomous Semantic Layer.
