Use Cases
Real answers to real business questions.
CausoAI is built for marketing, sales, and customer analytics teams who need to move from correlation to causation — and from insight to action.
Marketing Teams
Marketing Attribution
"You're spending $2M/month across 8 channels. Which ones actually cause conversions — and which are just riding on organic demand?"
Traditional multi-touch attribution models — last-touch, first-touch, linear — all measure correlation, not causation. They tell you which channels appeared before a conversion, not which ones caused it. CausoAI discovers the true causal structure of your marketing data and estimates the actual lift from each channel.
See marketing demo →What CausoAI delivers
30–40%
Reduction in wasted spend
Reallocate budget from correlated to causally effective channels
True causal ROI
Per channel, per campaign
Not last-touch — actual causal lift with confidence intervals
Optimal mix
Budget allocation recommendations
Counterfactual simulation: what if we shifted 20% from channel A to B?
What CausoAI delivers
15–25%
Margin protection
Stop offering discounts to deals that would close anyway
Causal threshold
Optimal discount level
Dose-response curve: at what discount % does close probability actually increase?
Segment insights
Heterogeneous effects by deal type
Enterprise vs SMB, inbound vs outbound — different causal effects, different strategy
Sales Teams
Sales Discount Strategy
"Are your discounts actually closing deals — or are you just giving away margin on deals that would have closed regardless?"
High-performing reps often offer discounts strategically. But without causal analysis, it's impossible to separate the discount effect from deal quality. CausoAI identifies the causal effect of discounting on close rate, controlling for deal size, rep, stage, and customer segment.
See sales demo →Customer Success Teams
Customer Churn Prevention
"You can predict which customers will churn — but which interventions actually prevent it, and for which customers?"
Churn prediction models tell you who is at risk. Causal models tell you what will actually work to retain them. CausoAI identifies which onboarding actions, feature adoptions, and success touchpoints causally reduce 90-day churn — and which are merely correlated with users who were going to stay anyway.
See customer success demo →What CausoAI delivers
Causal drivers
Not correlates of retention
Which onboarding steps actually cause long-term retention?
Right cohort
Targeted intervention
Identify which at-risk segments will actually respond to each intervention
Measurable lift
Retention impact with CI
Validate interventions with confidence intervals before scaling