Statistical Model

Uplift Modeling (Causal ML)

Who should we market to—and who we shouldn’t.

Pairs perfectly with Discount Addiction Index to save margin.

The Problem This Model Solves

Traditional targeting finds people likely to buy. Uplift models find people likely to buy ONLY IF treated. It avoids wasting money on "Sure Things" (would buy anyway) and "Lost Causes" (won't buy regardless).

Questions This Model Answers

"Who are the persuadables?"

"Are we wasting coupons on loyalists?"

"Who are we actually annoying with ads (Do Not Disturb)?"

How the Model Thinks

It uses two models (Treatment vs Control) or one unified model to predict the Difference in probability to buy caused by the marketing action.

Core Business Use Cases

1Discount Control

The Problem

Eroding margin with unnecessary 20% offs.

What It Reveals

Users who need the nudge vs users who don't.

Decision Enabled

Suppress offers for the 'Sure Things'.

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How We Use This Model

The brain behind the Discount Addiction Index. It segments your customer base by incremental response probability.

Example Output

A Qini Curve showing how much incremental revenue you get by targeting the top X% of uplift-scored users.

Works Best When

  • CRM marketing
  • Email segmentation
  • Retention offers

Be Cautious When

  • Broad awareness campaigns (cannot target)

Stop Guessing. Start Knowing.

See how Uplift Modeling (Causal ML) changes your budget decisions with a live incrementality audit.

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