In using dataminig models for marketing campaigns, a definition of lift could be:

“**lift** is a measure of the performance of a targeting model (association rule) at predicting or classifying cases as having an enhanced response (with respect to the population as a whole), measured against a random choice targeting model.” (wikipedia)

Yeah, wright!

Now in plain language for a normal human being:

*“lift” means: “how much times is my model better than a random selection”.*

**Example**:

Divide you customer database at random in 3 equal parts: part I , part II , and part III

– when you use **no model at all**: take a random selection A of 10,000 persons out of part I. Send them an email in an effort to sell something. Let’s assume that 2% of them bought you product. Result (no model)=2%

– use your first datamining model (**model M1**) to select 10,000 persons out of part II. Send them the same email. Let’s assume that 6% of them bought your product. Result (M1)=6%

– use another datamining model (**model M2**) to select 10,000 persons out of part III. Send them the same email. Let’s assume that 10% of them bought your product. Result (M2)=10%

It is straightforward to see that model M1 does 3 times better than without a model and model M2 does 5 times better.

**Formula**: lift= ( result of Model X) divided by (result of no model at all)

Hence:

Lift of first model (M1) => 6% / 2% = **3**

Lift of second model (M2) => 10% / 2% = **5**

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Associated posts:

Datamining: what is a good lift?

Put a crowbar under your marketing campaign!

Datamining for marketing campaigns: interpretation of lift.

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