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Alex Song
CEO at Innovation Department
Alex Song is a prominent entrepreneur and the Co-Founder & Chairman of Innovation Department, a venture studio based in New York that focuses on building and investing in consumer brands. He has been in this role since 2012, where his company aims to create, acquire, and scale innovative consumer brands through a tech-enabled platform that leverages a comprehensive suite of tools, including a customer intelligence database and proprietary acquisition software.12
Background and Career
Before venturing into entrepreneurship, Alex Song spent nearly a decade in finance, working as both a private equity and public investor at prestigious firms such as Goldman Sachs and Pershing Square Capital Management. His experiences in these roles provided him with valuable insights into the importance of data and analytics for business growth. He graduated with highest honors from the University of California, Berkeley, earning a BA in Business Administration.23
In addition to his work with Innovation Department, Alex is also the Founder & CEO of Proxima, a data intelligence company he established in 2022. Proxima focuses on helping digital businesses enhance their marketing performance across major platforms like Facebook, Instagram, and TikTok by utilizing AI-driven data solutions.34
Key Achievements
- Innovation Department: Founded in 2012, it has built a portfolio of consumer brands that aim to positively impact people's lives.
- Proxima: Launched in 2022, it has rapidly grown, achieving significant milestones such as a 400% increase in revenue and expanding its network of consumer connections.3
- Investment Background: His previous roles in finance have equipped him with the skills necessary to navigate the challenges of entrepreneurship effectively.23
Through his ventures, Alex Song continues to influence the landscape of consumer brands and digital marketing by emphasizing the critical role of data intelligence in driving business success.
Highlights
Founder communities are incredible for learning. But there’s a hidden trap that’s causing expensive failed experiments.
ICYDK: Tight-knit founder groups are coming together to share strategies, tools, learnings.
And don't get me wrong, they can be powerful and incredibly helpful.
But this blind following leads marketing teams to either jump on winning experiments or avoid certain approaches at all costs. And when they do attempt to replicate others' experiments, they often fail for 2 critical reasons:
- They don't define success correctly
When brands rush to mimic others without understanding WHY it worked for them, they often skip defining what success means for their own business. They're following tactics without strategy.
You're dead on arrival when you can't articulate what winning looks like for YOUR specific situation, audience, and business model.
For example, a fitness supplement brand might copy a skincare brand's influencer strategy because they saw "great engagement," but they never defined whether they were going for awareness, conversions, or retention. Same tactic, completely different success metrics, predictable failure.
- They under-resource experiments
The pressure to quickly check the box on the latest trend means they don't commit adequate capital, time, or effort - leading to half-baked experiments.
A brand might hear "SMS drove 25% of our revenue" and launch with a limited budget, generic templates, and a poorly optimized funnel, then wonder why they're seeing high unsubscribe rates instead of revenue.
When you half-commit to a test, you get false negatives. You write off potentially strong opportunities before they have a real chance to succeed.
-- So instead of succumbing to the fast follower experimentation mentality of rushing to jump on trends without discipline...set clear metrics, commit real resources, and give experiments time to work.
That's how you go from burning money trying to keep up with everyone else to turning experimentation into a real growth driver.
There's a misconception that data-enriched lookalikes are just "better targeting." That it's about finding more of the same audience that broad finds, just with better accuracy and a tighter net. Wrong.
Data-enriched lookalikes are whatever you need them to be.
Want to pack your business with high-LTV customers? Build lookalikes from your top spenders. Running a clearance sale next week? Build lookalikes from your discount buyers who convert on promotions.
With broad targeting, you're throwing spaghetti at the wall. It forces you to use the same targeting for a flash sale that you'd use for a luxury launch because you get whoever Meta decides to give you—discount seekers mixed with big spenders, one-time buyers mixed with loyalists.
It's pure chaos, and minimal helpful indicators of improving LTV, CAC, etc.
One of the biggest advantages data-enriched lookalikes give you is fluid audience targeting. You don't have to only target the same audience. You can target who you want, when you need to.
And with @ProximaAI having access to billions of commerce datapoints—demographics, purchase patterns, line item details, brand preferences—we can dial in exactly who you need. Men, women, specific ages, chemical-free consumers, discount lovers, subscription loyalists, whatever drives your business forward right now.
Playing the spray-and-pray game was left in 2020. The brands crushing it right now are building specific audiences for specific goals - and watching every metric improve as a result.
DM me if you're dealing with this same audience targeting mess. Happy to brainstorm what would work better.
