Real Results from Real Brands
See how brands use OuterSignal's customer intelligence to drive results that generic marketing can't.
Mizzen+Main: Highest ROAS Ever with Executive Targeting
20x+
ROAS
Highest-performing campaign in company history
+$50
AOV Lift
Above typical baseline
#1
Meta Ads
Top-performing audience over BFCM
190K+
Researched
Historical customers researched across two rounds
Mizzen+Main, the performance menswear brand, needed a way to reach high-value executive buyers who had purchased in the past but weren't responding to broad-based campaigns. Traditional segmentation fell short — they needed richer signals about who their best customers really were.
Using OuterSignal, they researched over 100,000 historical customer records overnight, uncovering detailed professional and lifestyle attributes. They built a "C-Suite Executive" audience and activated it through PostPilot direct mail and Meta Ads — resulting in their highest-performing campaign in company history, with over 20x ROAS and a $50 AOV lift over baseline.
Round two: alumni winback
Mizzen+Main came back with a harder signal: where a customer went to college. OuterSignal found alumni of specific universities in their historical customer file, and PostPilot mailed them a collegiate postcard timed to football season. Different signal, same playbook, $16.7K in revenue from a segment that didn't exist until OuterSignal built it.
12.38x
ROAS · repeat customers
3.06x
ROAS · first-time customers
“We love OuterSignal. It was our top target audience on Meta over BFCM. The ROAS is insane.”
— Natalie Shaddick, VP of Ecommerce, Mizzen+Main
Gratsi: Persona-Based Email Personalization
Gratsi, the direct-to-consumer Italian wine brand, ran a controlled A/B test to measure the impact of OuterSignal's persona intelligence on email marketing performance. A single campaign was split between a generic control and persona-matched creative variants — same timing, same audience size, different messaging.
+47.2%
Revenue Lift
+54.1%
Click Rate Lift
+56.1%
Subscription Conversion
+41.7%
Order Lift
Key Takeaway
In this campaign, subject lines were kept the same but each persona group received a specific email. Open rates were virtually identical between control and personalized variants (+0.2%), proving that the audiences were consistent. As such, the lift came entirely from higher click-through and conversion, not from deliverability or subject line differences. When your customers see themselves in the message, they act.
Full Results Comparison
| Metric | Control | Personalized | Diff |
|---|---|---|---|
| Revenue | $XX,XXX | $XX,XXX | +47.2% |
| Conversion Rate | 0.12% | 0.17% | +41.7% |
| Click Rate | 0.37% | 0.57% | +54.1% |
| Open Rate | 54.92% | 55.02% | +0.2% |
| New Subscriptions | 0.164% | 0.256% | +56.1% |
Persona Profiles
Click a persona to view the full profile generated by OuterSignal.
Email Creative Variants
The control and four persona-targeted email variants used in the A/B test.
“It's Always a Good Time for Gratsi”
“You've Earned the Right to Slow Down”
“Finally, a Little Time for Yourself”
“Wine This Good Deserves Good Company”
“Zero Sugar. Zero Guilt. Zero Hangovers.”
How Magic Mind earned a $0 Kim Kardashian Instagram story.
After using OuterSignal for one week, Magic Mind discovered Kim Kardashian was already a customer. VIP detection revealed she'd been buying the product organically for nearly two years, with no partnership in place.
With that knowledge, the Magic Mind team sent a thoughtful care package. She then posted the product to her Instagram story, reaching an audience of hundreds of millions. Sponsored posts from Kim Kardashian's account typically cost in the millions of dollars. Magic Mind earned it organically, by recognizing the human behind an order.
Every serious e-commerce brand has VIPs like this hidden in their order data. OuterSignal finds them in week one.
$0
Estimated Media Cost
Cost of the Kim K Insta story (vs. $M+ sponsored-post rate).
~2 yrs
Silent Customer
How long she'd been a silent customer before discovery.
Week 1
Time to Discovery
Time to discovery after installing OuterSignal.
Kim Kardashian's Instagram story featuring Magic Mind.
“Every serious e-commerce brand has VIPs like this hidden in their order data.”
— Noah Friedman, Founder, OuterSignal
Jolie's Top-selling Meta Audience?
Built on OuterSignal.
Jolie, the beauty wellness company, wanted to know if a custom lookalike built from OuterSignal customer intelligence could outperform their existing Meta Ads audiences. Over three months, they tested it head-to-head against their full set of broad, interest, and lookalike audiences. The OuterSignal audience led the set on sales, efficiency, and conversion rate.
+412%
Sales Lift
More sales than the average of Jolie’s other Meta audiences over the 3-month test.
+379%
Spend Lift
Jolie kept pouring more budget in because it kept working.
32%
Lower CPA
Cheaper cost per acquisition than the average peer audience, even as spend scaled.
+90%
Conversion Lift
February conversion rate lift over the rest of the set. The trend kept accelerating into March.
Core Takeaway
Compared to the average of Jolie's other Meta audiences, OuterSignal drove 379% more spend, 412% more sales, and a 32% lower CPA. Scaling campaigns usually means giving up efficiency. Here, Jolie got both.
Monthly Performance Breakdown
Each month, the OuterSignal audience won on a different metric. Per-month deltas vs. the rest of the set — distinct from the 3-month aggregates above.
| Month | Result | What it proves |
|---|---|---|
| Jan | +184% sales | Opened as the top-spending and top-selling audience in the account. |
| Feb | +90% conversion | Scaled hardest and posted the highest conversion rate in the set. |
| Mar | 62% lower CPA | Less spend than Jolie’s other audiences. Still sold more. Still converted better. |
Source: internal January to March 2026 prospecting reporting. Confidential raw values intentionally excluded. All figures shown are relative deltas or position in set.
Princess Polly: 3.3x More Purchases with AI Email Journeys
Princess Polly, one of the biggest names in Gen Z fashion, had already mastered the basics of lifecycle email. The next opportunity was harder: winning back past customers and converting subscribers who had never purchased - without adding blast volume to an inbox their customers love.
Monocle launched six AI journeys on their US store: four winback journeys reaching shoppers from 30 days to a full year after their last purchase, and two activation journeys converting subscribers who had not yet bought. Each day, Monocle's AI selects the shoppers most likely to purchase from each stage of the lifecycle, reaches them through Princess Polly's own Klaviyo account with on-brand creative, and steps aside the moment they buy.
The result: shoppers in Monocle journeys purchased at 3.3x the rate of shoppers who received no journey emails, and generated 3.9x the net revenue per shopper - a 290% increase in revenue from every shopper they reached.
Headline Metrics
3.3x
purchase rate
8.2% of journey shoppers purchased within 30 days vs. 2.5% without
3.9x
revenue per shopper
290% more net revenue per journey entry than shoppers who received no journey emails
Over 400%
revenue uplift
the 180-365 day winback journey drove 5.1x the net revenue per shopper of its holdout
6 of 6
journeys beat control
every journey outperformed its holdout
Real Lift, Not Attribution
These are not attribution numbers. Every Monocle journey automatically holds back a random slice of qualifying shoppers who receive no journey emails - same audience, same time period. The gap between the two groups is revenue that would not exist without Monocle. It is a far stricter standard than standard email attribution, which credits sales that might have happened anyway - and every Princess Polly journey cleared it, with millions of journey entries measured against a 10% control.
Every Journey Beat Its Control
Purchases and net revenue per shopper within 30 days of entering each journey, over a recent 90-day period. "Without Monocle" is each journey's randomized holdout.
| Journey | Purchase rate: Without → With Monocle | Purchase lift | Revenue lift |
|---|---|---|---|
| Purchased 30-60 days ago | 3.4% → 10.3% | 3.0x | 3.7x |
| Purchased 60-90 days ago | 2.7% → 9.5% | 3.5x | 4.1x |
| Purchased 90-180 days ago | 1.8% → 6.0% | 3.3x | 3.9x |
| Purchased 180-365 days ago | 2.8% → 13.7% | 4.9x | 5.1x |
| Activation, 15-90 days since signup | 0.9% → 3.8% | 4.0x | 3.3x |
| Activation, 90-365 days since signup | 0.4% → 2.1% | 5.6x | 4.7x |
| All journeys | 2.5% → 8.2% | 3.3x | 3.9x |
Why It Works
- The right shoppers, not more sends. Monocle's AI selects the shoppers most likely to buy from each lifecycle stage every day, instead of blasting an entire segment. Precision is why a year-lapsed audience came back at 13.7% - 4.9x the rate of shoppers left alone.
- Revenue most flows never touch. The journeys cover the whole lifecycle - from 30 days after a purchase to a full year out, plus subscribers who never bought. And this is winback and activation alone: Princess Polly's Monocle cart and checkout abandonment flows run at full volume on top.
- The brand stays in charge. Every email is sent through Princess Polly's own Klaviyo with on-brand creative. Journeys cap their touches, enforce long cooldowns between entries, and end the moment a shopper purchases - lift without inbox fatigue.
"Monocle has been a game changer for us. Our existing flows were relying on outdated, one-size-fits-all logic, making it difficult to scale the program and deliver truly personalized experiences. Monocle gave us a way to make smarter decisions about what message to send, and when, while platform implemented holdout groups allowed us to measure the incremental impact. It not only demonstrated the value our CRM channels were driving, but also challenged us to think differently about what was possible with personalization."
- Hilary Castleman, Head of CRM, Princess Polly
Source: Monocle measurement warehouse, May 15 - Aug 13, 2026. Lift measured with randomized in-journey holdouts over a 30-day purchase window; all six journeys significant at p < 0.001. Revenue figures are net revenue under Klaviyo attribution.
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