Allocate incentives like capital, not campaigns.
Every customer responds differently to incentives. Emli learns those response curves and allocates promotional spend where it earns the highest return.
For teams spending real money on discounts, retention offers, and promotional credit.
Companies spend millions deciding who gets an offer. Almost nothing goes into deciding how much.
Discounts, retention offers, and promotional credit still get assigned with blanket rules or coarse segments, even though every customer responds differently.
One offer for everyone
- Everyone in a segment gets the same 50% off.
- Rules are set once and rarely revisited.
- Segments are too coarse to reflect real behavior.
The right offer for each customer
- Some customers return with a 10% discount.
- Others genuinely need 60% to convert.
- Most companies never learn the difference, so budget gets wasted either way.
Every customer has a different response curve
Drag the slider to change the discount. Watch how two customers respond differently to the exact same offer.
Same discount, different customers
Discount: 35%The optimal incentive isn't the same for everyone.
The same win-back campaign, allocated two different ways
An illustrative example of a churn win-back program for 10,000 customers.
for every churned customer
spent to win back 10,000 customers
of them would have returned with far less
each customer gets exactly the incentive they need
spent to win back the same 10,000 customers
for the same retention outcome
Illustrative example, not results from a live deployment.
A closed loop that keeps learning
No equations required. A system that learns what works, for whom, and keeps getting better.
Connect customer data
Emli reads from your warehouse or CRM: purchase history, engagement, and past offers.
Learn each customer's response curve
For every customer, Emli estimates how likely they are to convert at different incentive levels.
Choose the optimal incentive
Emli allocates spend to the offer size that maximizes expected return for that customer.
Measure outcomes
Every offer is tracked against redemption, revenue, and retention, not clicks.
Continuously improve
New outcomes feed back into the model, so allocation gets sharper over time.
Marketing automation vs. Emli
Traditional tools optimize campaigns. Emli optimizes each customer relationship, with spend as the constraint.
- Optimize message, timing, and channel
- Fixed rules and broad segments
- The same discount for a whole group
- Set once, run for a quarter
- Optimize the economic outcome
- Individual, per-customer allocation
- The right incentive for each person
- Always-on, adjusts as customers respond
Marketing has spent twenty years optimizing who gets an offer. Emli optimizes exactly how much each one should receive.
Built to measure cause, not correlation.
The gold-standard method for isolating what actually caused a customer to convert.
Explores safely in production without hurting performance.
Response curves adapt to each customer's context, not a fixed rule.
Reads and writes directly to Snowflake, BigQuery, or Redshift.
The conditions have changed
Causal ML is finally practical
Modern machine learning can estimate individualized treatment effects at production scale.
Companies already spend billions
Promotional spend is one of the largest controllable costs in consumer businesses.
Better allocation beats bigger budgets
The biggest gains come from spending smarter, not spending more.
Every company teaches Emli something.
Every offer Emli allocates teaches it a little more about how people respond to incentives. Over time, new customers start to benefit from everything the system has already learned, often from day one.
The system companies rely on to decide how their incentive spend gets allocated.
See what Emli would allocate for your customers.
Book a 30-minute conversation. We'll walk through how Emli works and whether it's a fit for your incentive spend.
We're working closely with a small number of forward-thinking teams before general availability.