High average order value should be an advantage in Performance Max. Higher transaction values give the algorithm more signal. Larger baskets provide room for margin.
In practice, high AOV fashion brands often struggle in PMAX before their lower-priced competitors. The reasons are structural.
The Learning Curve Problem
PMAX requires conversion volume to learn effectively. Google recommends 30+ conversions per month for stable optimisation. High AOV brands often sit below this threshold in individual asset groups.
A brand selling £400 average transactions will typically generate fewer conversions than one selling £80 transactions on equivalent spend. The algorithm has less data to learn from, and learning takes longer.
This creates a frustrating dynamic. The brand with the most margin headroom often has the least algorithmic efficiency.
The Return Rate Amplifier
High AOV often correlates with high return rates in fashion. Premium price points invite more considered purchases—which in practice means ordering multiple options and returning what does not work.
A 40% return rate at £80 AOV is painful. A 40% return rate at £400 AOV is brutal. Each return carries higher handling costs, higher BNPL fees, and higher restocking friction.
PMAX cannot see return rates. It optimises for the conversion it observes, not the net revenue that results. High AOV products with high return rates look attractive to the algorithm but destroy margin.
The Variant Fragmentation Issue
Premium fashion tends toward complexity. More colourways, more size runs, more seasonal variation. A single style might exist as 50 variants.
In PMAX, this fragmentation spreads performance data thin. Each variant competes for the algorithm attention. Learning that would consolidate on a single product instead scatters across dozens of SKUs.
Without strong feed architecture, high AOV brands give PMAX a harder optimisation problem than their simpler competitors.
The Budget Trap
High AOV brands often operate with lower campaign budgets relative to their transaction values. A £5,000 monthly PMAX budget buying £400 transactions generates 12-15 conversions at healthy ROAS. That is below the learning threshold.
The choice becomes uncomfortable: increase budget to improve learning, or accept suboptimal performance at current spend. Neither option is appealing.
What Works
High AOV fashion brands that succeed in PMAX typically:
- Consolidate variant handling to concentrate learning
- Use custom labels to exclude high-return products from PMAX
- Set higher budget thresholds to ensure learning volume
- Run regular audits to catch margin leakage early
- Invest in feed quality to maximise what the algorithm can learn from limited data
PMAX works for high AOV fashion. But it requires more deliberate setup than brands accustomed to standard Shopping often expect.