Root Cause Analysis
Monday Morning. Revenue Is Down 12%. Leadership Wants Answers.
Your team spends 2 days opening dashboards, running queries, and debating theories. By the time you have an answer, the window to fix it has closed. Fig delivers the same diagnosis in 30 seconds — with evidence, not guesses.
The Problem
Two Days of Investigation. One Theory. Too Late.
Your revenue dropped 12% this month. Here's what happens next without Fig:
4 dashboards opened
30 minutes
6 data queries written
2 hours
3 meetings to discuss findings
3 hours
2 more days of investigation
2 days
Someone has a theory
Maybe.
Fig does this in 30 seconds. Structured root cause analysis with evidence, confidence levels, and recommended next steps.
How It Works
Three Steps. Thirty Seconds. An Answer You Can Act On.
Fig catches the signal, follows the causal chain, and backs every finding with real data from your warehouse — so that your team skips the investigation and goes straight to the decision.
Catch the Movement
Fig spots the anomaly automatically — or you ask 'why did X change?' and the investigation begins immediately. Either way, the work starts before anyone has to notice.
Fig distinguishes real problems from normal fluctuation. A 5% drop in revenue might be noise; a 5% drop in a metric that almost never moves is a genuine signal. So that you only investigate what actually deserves your attention.
Follow the Chain
Fig walks backward through your causal map, identifying every metric and segment that could have contributed to the change — automatically, the way a skilled analyst would but in seconds.
If revenue dropped, Fig checks volume, price, and mix. Then checks what drives volume: pipeline, conversion, deal size. Then checks what drives each of those. The causal map tells Fig exactly where to look — so that nothing gets missed.
Back Every Finding with Data
For every suspected driver, Fig pulls the actual numbers from your data warehouse and rates the finding as low, medium, or high confidence — so that the report you receive is ready to act on, not a starting point for more investigation.
No speculation. Every finding comes with a specific, checkable result: 'Discount rate increased from 12% to 18% in Enterprise segment — high confidence — explains roughly 40% of the revenue decline.'
Output Format
What an RCA Report Looks Like
Every root cause analysis produces a structured report — not a wall of text. Here's the actual output format.
Headline
Revenue declined 15.2% MoM driven primarily by Enterprise discount rate increase and elevated return rates in APAC.
Summary
October revenue of $3.4M represents a $612K decline vs. September. Analysis of 14 upstream metrics identified two primary drivers accounting for ~78% of the variance. Enterprise discount rates increased 6.2pp following Q4 promotional approval. APAC return rates spiked to 14.3% (vs. 8.1% baseline) correlated with a shipping carrier change on Oct 3.
Suspected Drivers
Enterprise discount rate: 12.1% -> 18.3%
HighExplains ~$340K (55%) of decline. 23 deals closed with >20% discount in Oct vs. 4 in Sep.
APAC return rate: 8.1% -> 14.3%
HighExplains ~$142K (23%) of decline. Spike begins Oct 3, correlates with carrier switch.
SMB pipeline volume: -18% MoM
MediumExplains ~$85K (14%) of decline. Lead volume dropped but conversion held steady.
Recommended Next Queries
"Show me all Enterprise deals closed in October with discount >20%"
"Compare APAC return rates by shipping carrier for the last 90 days"
"What is the SMB lead-to-close funnel for October vs. September?"
Assumptions & Limitations
Analysis based on data through Oct 31. Discount rate sourced from deals table; some manual overrides may not be captured. APAC return rate excludes RMA items processed after Nov 5 cutoff.
Real Scenarios
Root Cause Analysis in Action
Every business metric has upstream drivers. Fig traces the causal chain, runs the queries, and tells you exactly what changed and why.
Revenue Declined 15%
The Problem
Monthly revenue dropped from $4.0M to $3.4M. Leadership wants answers by the board meeting.
Fig Traces
Fig traces through the knowledge graph: Revenue -> Volume x Price -> Segments -> Discount Rates + Return Rates. Identifies Enterprise discount rate increase and APAC return rate spike as primary drivers.
Outcome: so that the CFO walks into the board meeting with a specific, evidence-backed explanation and a remediation plan — not a theory.
Customer Churn Spiked
The Problem
Monthly churn rate jumped from 2.1% to 3.8%. CS team suspects product issues but has no proof.
Fig Traces
Fig traces: Churn Rate -> Cancellation Reasons -> Support Ticket Volume -> Resolution Time -> Product Areas. Finds that average ticket resolution time increased from 4.2 hours to 11.7 hours after a support team restructure.
Outcome: so that the VP of Customer Success can pinpoint the operational change that caused churn and reverse it — instead of launching a broad, unfocused retention campaign.
Gross Margin Eroding
The Problem
Gross margin declined 3.2pp over two quarters. The trend is subtle but accelerating.
Fig Traces
Fig traces: Gross Margin -> COGS Components -> Raw Material Costs + Labor + Product Mix. Identifies that raw material costs for two SKUs increased 22% while product mix shifted toward those SKUs by 8pp.
Outcome: so that the COO can renegotiate supplier contracts on the specific materials driving the cost increase — before margin erosion compounds further.
Explore the Platform
Metric Relationship Tree
Causal map of what drives what in your business
Learn moreMetric Definitions
Versioned, approved metrics for every agent and team
Learn moreBusiness Policies & Rules
Your business logic enforced on every query
Learn moreBusiness Entities
Accounts, customers, products resolved across sources
Learn moreFlows
Signal → Decide → Act automated end-to-end
Learn moreMonitoring
24/7 anomaly detection with automatic root cause
Learn moreDecision Algorithms
12 built-in algorithms for governed analysis
Learn moreIn-Memory Cache
Reduce warehouse costs, sub-second answers
Learn moreReady to Trace Root Causes Automatically?
Free forever — no credit card required. Connect your data warehouse and see your first root cause analysis in under 5 minutes.