Scale-or-Kill Decision Diagnostic — A Healthcare Framework

The 5 factors

Scale decision

• 4-5 scale signals: Scale 20-30% this week. Re-evaluate after 7 days.
• 3 scale signals + 2 neutral: Hold steady, monitor.
• 2 scale signals + 3 mixed: Stay neutral. Don't scale.

Kill decision

• 3+ kill signals: Kill within 48 hours. Document outcome.
• 2 kill signals: 1-week observation period. Re-evaluate.
• 1 kill signal: Watch but don't act.

Why 20-30% scale increments (not 50%+)

Meta's algorithm requires re-learning when budget jumps significantly. 50%+ scale typically resets the learning phase, producing 7-14 days of elevated CPL during re-learning. 20-30% increments stay within the learning tolerance, preserving performance.

Across 100+ healthcare ad set scale events in the Meta Catalyst IQ dataset:

• 20-30% increments: CPL preserved in 78% of scale events
• 40-50% increments: CPL preserved in 51% of scale events
• 60%+ increments: CPL preserved in 28% of scale events

The lesson: incremental scaling produces consistent results; aggressive scaling produces volatility.

The "early kill" mistake

Most teams kill too early. New ad sets need 14-21 days minimum to:

• Exit the learning phase (Meta requires ~50 events typically)
• Establish a representative CPL baseline (random variance smooths out)
• Reveal audience response patterns

Killing on day 5-7 because "CPL looks bad" — the diagnostic shows kill signal = 1 (CPL high). Other factors (Hold Rate, Frequency, Saturation, Trend) need 14-21 days of data to assess. Premature kills throw away ad sets that would have stabilized.

The "late kill" mistake

Equally common: keeping ad sets alive long after they should be killed. Reasons:

• "It used to work, maybe it'll come back" — anchoring bias
• "We spent so much building this audience, can't kill it now" — sunk cost
• "Manager said don't kill without 30-day data" — bureaucracy

3+ kill signals sustained over 2 weeks = unambiguous kill. Keeping it alive wastes budget.

Healthcare-specific calibration

The 5-factor framework adjusts for specialty:

• IVF: longer evaluation window (28+ days) due to high consideration journey
• Dermatology (cosmetic): faster evaluation (14 days) due to lower consideration time
• Hair transplant: medium evaluation (21 days)
• Dental aesthetic: medium (21 days)
• Eye (LASIK): longer (28 days)

The compound learning effect

Teams using consistent scale-or-kill framework over 6-12 months produce:

• Consistent CPL improvement (8-15% YoY)
• Reduced wasted spend (Money Wastage trend down)
• Compounding audience signal quality
• Faster identification of Core Performers (good ad sets identified weeks earlier)