🔗  Random Targeting Strategy

Random Targeting Strategy#

A random-selection baseline used to benchmark uplift models.

Important

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What it is#

A random targeting strategy selects customers (or units) at random for an intervention — a campaign, treatment or policy — instead of using a predictive or uplift model. In uplift modelling it is the baseline against which a model is judged: if a model cannot beat random targeting, it is not useful.

The diagonal baseline#

Treat a random proportion of the whole population — 20%, 50%, 100% — and, because the choice is random, the treatment effect spreads evenly. On a Qini or uplift curve (proportion targeted on the X-axis, cumulative incremental gain on the Y-axis) this traces a straight diagonal line: uplift accumulates linearly with the fraction treated.

A worked example#

With 10,000 customers and an average treatment effect of +5%, random targeting of 20% (2,000) yields about 100 incremental conversions; 40% yields about 200; and 100% yields about 500 — the campaign’s total incremental benefit. The line from (0, 0) to (100%, 500) is the random baseline.

Role, pros and cons#

That line is the benchmark: a good model’s curve starts steep — finding persuadables first — and stays above it throughout; a curve that hugs the diagonal adds no value. Random targeting is simple and fair (hence its use in A/B testing), but it wastes budget on sure things, lost causes and do-not-disturbs, delivers lower ROI, and cannot adapt to customer heterogeneity.


Theme: Causal Inference & Uplift  ·  All terminology



See also

Source article Adapted (context, re-expressed) in our own words from: Random Targeting Strategy (insightful-data-lab.com).

Tags: purpose: reference topic: terminology level: advanced