Proof

We predict. Reality confirms.

Anyone can claim their AI understands consumers. We put our predictions in front of live A/B tests and let the revenue numbers decide.

Case study

Multi-national charityThis client would like to remain private

The question

A multi-national charity had two donation appeal creatives and one budget. Which one raises more money?

The prediction

We ran both appeals against a simulated audience matched to their donor demographic, and named the winner before any media spend was committed.

The test

They then ran a live A/B test: similar spend, same audience, both variants in market. The kind of properly powered test most teams never get budget for.

The result

The variant we picked generated 13.5% more donation revenue. They are now structuring a second test with us.

+13.5%
donation revenue
confirmed by a live A/B test
−58%
cost per purchase
in-platform, vs the campaign average

Case study · consumer devices

Allay Lamps

The question

Allay had 14 finished creatives and one Black Friday budget — historically their most profitable quarter. Which assets deserved the spend?

The prediction

We identified two of the top ads in the campaign from the creative asset alone, before launch.

The test

Their agency ran live A/B tests worth a third of the campaign budget against our picks, with ROAS controlled for.

The result

The two creatives we identified accounted for 50% of in-platform attributed conversions while consuming 31% of the budget.

“Moving forward, we will definitely be concentrating our ad spend on the creative that [streetlamp] identifies as best, and will use the insights that the platform provides to inform our ad creative briefs.”
Benoit, Owner — marketing agency for Allay Lamps

Case study · fast-moving CPG

Dove Men+Care, Unilever

Dove Men+Care’s Whole Body Deo, a one-year-old line inside a $100bn portfolio, was launching one of its first celebrity campaigns, with Marshawn Lynch, on a timeline measured in days. Unilever normally will not run creative without a full audience study first. There was no time for one. We simulated tens of thousands of personas and returned predicted effectiveness, emotional response and attention heatmaps in hours.

Unilever limited what we can publish from this engagement, so unlike the results above, these outcomes are qualitative. There is more we can walk through on a call.

The forecast aligned with in-market performance

Predicted effectiveness was checked against real-world sales data once the campaign was in market, and the two lined up.

Humor beat the product pitch

Our personas and social listening data consistently agreed: the comedic Marshawn Lynch creative significantly outperformed more direct, informational messaging.

Emotion predicted performance

Subjective emotional measures — particularly humor and relatability — held up as reliable front-end predictors of overall campaign effectiveness.

“It’s actually really amazing that they are [launching] right now. [Unilever has] told us, usually they won’t run any creative unless they have a full on audience study before they dive into a market.”
Eddy — Outfront
18%vs40%
synthetic audience vs focus group

The flattery gap

What a focus group won't tell you

A global CPG brand asked us to evaluate a logo their own focus group had rated 40% five-star. Our synthetic audience said 18%.

Real people don't tell a moderator the logo is mediocre. Paid participants flatter the room, and the affluent customers premium brands target rarely show up for a gift card in the first place. Synthetic respondents have nothing to be polite about.

Keeping us honest

Every engagement is checkable

A prediction you can't verify is just another opinion. Ours come with the test built in.

Metric agreed first

Campaign revenue or conversion rate against a held-out control, defined before the test starts. No moving the goalposts after.

A/B designed in

Pilots ship with a live validation test as part of the engagement, not as an afterthought you have to fight for.

Reality decides

If the prediction is wrong, you will see it in the numbers. That risk is ours to carry, and it is why the proof above exists.

Put it to the test

Run the study before you run the campaign.

A campaign is a study you pay for in media. We run it first, against a simulated audience, then structure a pilot with live A/B validation built in and a success metric agreed up front. We win when the prediction holds.