1) Meaning

A Random Targeting Strategy means selecting customers (or units) at random for an intervention (e.g., a marketing campaign, medical treatment, policy program) instead of using a predictive or uplift model.

In uplift modeling, random targeting is the baseline against which we compare a model’s performance.

  • If a model cannot beat random targeting, it’s not useful.

2) How It Works

  • Population = all potential customers.
  • Choose a random proportion (say 20%, 50%, or 100%) to treat.
  • Since the targeting is random, the treatment effect spreads evenly across the population.

In evaluation (Qini or uplift curve):

  • X-axis: proportion of population targeted.
  • Y-axis: cumulative incremental gain.
  • The random strategy produces a straight diagonal line (linear growth), because uplift is distributed evenly.

3) Example

Suppose:

  • Total customers = 10,000
  • Treatment increases conversion probability by +5% on average.
  • If you randomly target 20% (2,000 customers):
    • Incremental conversions ≈ 2,000 × 0.05 = 100
  • If you target 40% (4,000 customers):
    • Incremental conversions ≈ 4,000 × 0.05 = 200
  • At 100% population targeted:
    • Incremental conversions ≈ 10,000 × 0.05 = 500 (Total Incremental Benefit)

The line from (0,0) to (100%, 500) is the random targeting baseline.

4) Role in Uplift Modeling

  • The random line serves as a benchmark in uplift/Qini curves.
  • A good uplift model’s curve should:
    • Start steep (finding Persuadables first).
    • Stay above the random baseline throughout.
  • If the model curve ≈ random line → model adds no value.

5) Advantages & Disadvantages

Advantages

  • Simple, requires no data or modeling.
  • Fair in experimental settings (used in A/B testing).

Disadvantages

  • Wastes resources on Sure Things, Lost Causes, and Do-Not-Disturbs.
  • Much lower ROI compared to targeted strategies.
  • Cannot adapt to customer heterogeneity.

Bottom line:
The Random Targeting Strategy is the “do-nothing-smart” baseline for uplift evaluation. It applies treatment uniformly, producing a linear cumulative uplift curve. Uplift models are considered valuable only if they outperform this baseline.