Why the other 48% didn't buy

The contrarian half of the data — and where the gaps actually are.

Part of the Infused Beverage Study series — findings from Cohort 1, a real-world, longitudinal study of 2,580 consumers across 20 products.

After every product trial in our Infused Beverage Study, we asked consumers whether they'd buy. About half said yes. We went through every open-text response from the other half to find out why.

The headline number

Of 1,975 Cohort 1 participants who completed the final survey after trying a product:

• 51.7% said they would buy it (already purchased or will definitely purchase)

• 35.4% were undecided

• 12.9% said they would not purchase

Nearly half the sample is in play. Undecided is not the same as lost — it's a signal that the product almost worked. The question is what's blocking the "yes."

What the "no" and "maybe" responses said

We pulled every open-text comment these 954 participants left across the exit survey — purchase intent follow-up, alcohol replacement reasoning, negative effects, and product reviews — and themed them. Four patterns emerged, in order of how actionable they are for an operator:

1. Dose / effect — the #1 actionable complaint. The specific issue is "didn't feel anything at 5mg." Participants describe drinking 2–4 cans trying to reach an effect.

2. Taste — the most frequently raised topic overall, and mixed. Some detractor taste comments were decisive ("just a horrible taste"), others were qualified ("wanted to like it"). Taste is the entry gate — it determines whether a participant even gets to evaluate the effect.

3. Format — sugar load, carbonation, and serving-size-vs-dose mismatch. Participants repeatedly flagged that the volume they had to drink to reach an effect wasn't a ratio that matched how they actually wanted to consume the product.

4. Price — rarely raised spontaneously, but decisive when it was. Participants who mentioned price cited the cost-per-effect math against alcohol directly.

Representative quotes

Dose / effect:

> "It's not really strong enough to replace alcohol. In order to feel effects I'd have to drink 3–4 cans and that is too much."

> "I would be more likely to substitute this product for alcohol if the dosage were higher. The 5mg I tried had minimal effects unless I consumed 3–4 servings."

Taste:

> "The flavor was poorly crafted and lacked balance… a slight cherry cough medicine taste, while the aftertaste was too strong of an off-note."

Format:

> "Absolutely packed with sugar (but doesn't really taste like it)… Only effect I noticed was being tired even after 3 cans."

Price:

> "It costs much more to consume enough infused drinks to feel the effects compared to that of alcohol."

What this suggests

1. The category's biggest loss isn't the 13% who hated it — it's the 35% who are on the fence. That undecided middle is the swing group that determines whether infused beverages graduate from curiosity to repeat purchase. And the reasons they gave back are product problems, not category problems.

2. Dose-to-effect is the structural drag on the category. When consumers have to drink 3–4 servings to feel anything, you've reversed the value proposition against alcohol — more volume, more sugar, more cost, more time, for an uncertain result. Brands that can deliver a reliable effect per serving without running afoul of dose limits will pull ahead of the rest of the category on repeat purchase alone.

3. The regulatory structure makes this harder on purpose. State-level dose caps per serving mean brands can't simply up the dose. That pushes the problem into formulation — emulsification, onset, ratios, the supporting cast of minor compounds. The brand that solves the effect-per-legal-serving equation is holding the pen on category positioning.

4. Price is a lagging indicator. Nobody's complaining about price in the abstract. They're complaining about the cost-per-effect math, which is a dose-and-format problem wearing a price costume. Solve the first two and the third clears up on its own.

Method note: Self-reported, real-world observational dataset (Cohort 1; N=2,580; 20 beverages).