Random Targeting Strategy

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.

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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.

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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.

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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.

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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.

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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.