Yes. The key is to test the economics of the increase, not just whether conversion falls.
A 15% price increase can be a win even if conversion drops, because you care about contribution margin / profit per visitor.
1. Run a clean price A/B test
For example:
- Control: $100
- Variant: $115
- Randomize users consistently so returning visitors don't bounce between prices.
- Keep everything else identical: ads, merchandising, shipping, creative, promotions.
- Run until you have enough purchases to measure revenue and contribution profit per visitor, not merely conversion rate.
The important metrics:
Revenue/visitor = conversion rate × average order value
Better still:
Contribution profit/visitor = conversion × (AOV − variable costs)
At +15%, you can tolerate a conversion decline of roughly 13% and still have the same revenue per visitor, assuming AOV otherwise stays constant.
So if conversion goes from 4.0% → 3.5%, that's a 12.5% decline and could actually be economically positive.
2. Don't necessarily raise every SKU 15%
I'd test several architectures:
| Strategy | Example | Why test it |
|---|
| Straight increase | $100 → $115 | Cleanest measurement |
| Psychological price | $115 → $114.99 | Can reduce perceived friction |
| Good/better/best | $99 / $129 / $159 | Creates a reference point |
| Premium anchor | $99 / $149 / $199 | Makes middle option feel attractive |
| Bundle | $115 single / $210 two-pack | Raises AOV while preserving value perception |
| Feature packaging | $115 standard / $145 premium | Lets customers self-select |
I'd particularly test packaging before discounting. A customer may accept $115 if the product feels like a meaningfully better proposition, while simply changing "$100" to "$115" can make the increase feel like a penalty.
3. Use price psychology carefully
A few tactics that tend to be worth testing:
Charm pricing: $114.99 instead of $115.
Useful particularly for lower-priced, transactional products. But don't assume .99 always wins—premium products can benefit from clean numbers like $115 or $120.
Anchoring: Give customers a legitimate higher-priced comparison.
Standard $115
Premium $149
The $149 option can make $115 feel more reasonable even if relatively few people buy it.
Decoy / three-tier packaging:
Instead of one product:
- Basic — $99
- Most Popular — $129
- Premium — $169
Now customers aren't asking "Is $129 expensive?" so much as "Which version makes the most sense?"
Bundle economics: Rather than saying "prices increased 15%," make the higher price buy more:
- 1 × $115
- 2 × $210
- 3 × $295
That shifts the conversation from price to value per unit and can increase AOV.
4. Segment the experiment
A 15% increase might be disastrous for one segment and excellent for another.
I'd look separately at:
- New vs. returning customers
- Organic vs. paid traffic
- High-intent vs. low-intent landing pages
- Mobile vs. desktop
- Existing customers vs. first purchase
- High- vs. low-priced SKUs
- Different acquisition channels
You may discover something like: new customers tolerate $115, while returning customers are much more price-sensitive.
That opens up interesting strategies such as grandfathering existing customers, loyalty pricing, or raising prices primarily on acquisition traffic.
5. Don't judge the test too quickly
Price tests can have delayed effects. Someone who sees $115 today might buy three days later after comparing alternatives.
I'd establish the primary metric before launching:
Contribution profit per visitor
Then use conversion, AOV, refund rate, repeat purchase rate, and customer acquisition economics as secondary metrics.
Also watch refunds, cancellations, customer-support complaints, and repeat purchase behavior. A price increase that boosts immediate profit but destroys repeat purchasing isn't necessarily a good trade.
One tactic I'd test first
If you're currently at $100, I'd run:
A: $100 single
B: $115 single
C: $115 single + $210 two-pack
D: $115 standard + $149 premium
That gives you four different hypotheses: price sensitivity, higher AOV, bundling, and anchoring/upsell.
If you tell me your current price, gross margin, AOV, conversion rate, and approximate monthly traffic/orders, I can calculate exactly how much conversion you can afford to lose and lay out a test matrix.
