Interview with Zac Cherin, Founder of a $1M SaaS Built for Agencies

Most people chasing an AI-powered launch system for creators or the next big SaaS idea keep looking in the loudest places. That is usually a mistake. The better opportunities are often buried inside boring workflows, agency back offices, and the kind of repetitive tasks nobody posts about because they are too busy doing them. Zac Cherin built Hero Analytics into a business that crossed $1 million in ARR in about 19 months by doing exactly that.

Instead of selling to individual brands one by one, Hero sells to agencies that already manage dozens of brands. That small shift changes distribution, pricing, retention, and product design. It is one of those business models that feels obvious right after someone explains it, which is annoying, but useful.

🧠 Why This Model Actually Matters

What did Zac build, and why did it grow so quickly?

Zac and his partner Brendan built Hero Analytics, an analytics and reporting platform for email and SMS marketing agencies. More specifically, it helps agencies that run platforms like Klaviyo and similar tools for their clients. The product pulls campaign and performance data into one place, automates reporting, and gives agencies a way to manage multiple client accounts without drowning in manual work.

The reason it grew fast was not just that it made nice charts. Plenty of products can do that. The real value was that it removed a painful recurring task from agency operations. Agencies need to report results constantly. They need to track pacing, performance, and account health across many clients at once. Doing that manually across 10, 20, or 30 brands gets old fast.

So Hero did not win by being flashy. It won by being useful in a category where useful beats impressive almost every time.

🛠️ The Setup

What was Hero’s business model?

Hero is sold as a subscription, with a free trial up front. Pricing is tied mainly to the number of clients an agency manages, and then it expands with additional user seats. A smaller agency might pay a few hundred dollars per month. Larger agencies with lots of accounts can pay much more.

That pricing structure matters because it maps directly to agency economics. The more clients an agency serves, the more reporting pain it has, and the more value Hero can create. This is cleaner than forcing every customer into the same flat plan and pretending a 5 client shop is the same as a 200 client operation.

It also creates natural expansion revenue. Once an agency is in, additional strategists, account managers, and even client facing seats can increase account value without requiring a whole new sales cycle.

What tools did Zac use to get the product live?

Early on, the stack was practical, not glamorous. Zac and Brendan had some oddly useful background in ETL and data work, so they knew how to structure data pipelines and tables even if they were not seasoned app infrastructure people.

In the beginning, they leaned on:

• AWS for cloud infrastructure
• Snowflake for warehousing data
• Retool for building the early internal app experience

That let them get something working without spending months building every layer from scratch. Later, once the product had traction, they moved toward a more fully coded setup.

That part is important for solo builders and anyone piecing together a solo builder tech stack. The first version does not need to be architected like it is going public next Thursday. It needs to solve the problem, survive contact with customers, and not light your cloud bill on fire too aggressively.

📈 The Magic Behind Selling to Agencies

Why target agencies instead of brands directly?

This is the whole game.

If Hero sold directly to brands, every new account would require convincing one company at a time. One brand, one subscription. Slow, expensive, and kind of miserable if the pricing is low.

Agencies collapse that problem.

One agency might manage 10, 20, or 30 brands. That means one sale can effectively unlock access to dozens of end accounts. On top of that, agencies often have internal teams using the software, plus clients who may need access too.

So instead of fighting for one $10 subscription from one brand, Hero can land an agency and immediately become embedded across a whole portfolio. Better contract value, more users, and stronger stickiness.

Zac’s framing here is brutally simple: agencies are leverage. They are a multiplier on distribution.

How does one agency create more revenue than one brand?

Because the agency is not a single user. It is a cluster.

An agency might have:

• 10 strategists who each need a seat
• 20 client accounts they manage
• Some number of client users who also want visibility

Now the account is not just software access. It is workflow infrastructure. That changes how pricing works and how difficult it is to rip the product out later.

And this is where a lot of “AI funnel builder” type thinking falls apart. People obsess over clever front end acquisition tricks but ignore customer structure. Zac found a market where the customer already sits in the middle of many downstream relationships. That is smarter than building another shiny thing and hoping paid ads save it.

Why are agencies easier to sell to than people assume?

Because they are easy to find.

This is one of the least sexy but most useful parts of the whole model. Platforms like Shopify and Klaviyo maintain partner or agency directories. These directories often rank agencies by specialization, scale, or influence. So instead of guessing who might need the product, the market basically hands over a filtered prospect list.

That means customer discovery is easier. Outreach is easier. And segmentation is easier.

Even better, agencies built on the same platform usually share similar operational pain. If they all use the same core systems, they often all run into the same friction. That lets product development become more efficient too. A feature that matters for one likely matters for many.

🔍 Replicating the Strategy

If someone wanted to build this kind of SaaS, where should they start?

Zac’s answer is refreshingly unromantic. Start with an industry already understood. Not a random market picked because a chatbot said it looked hot. An industry where there is actual context, some inside knowledge, and at least a rough understanding of what matters.

The pattern looks like this:

1. Pick an industry with clear software platforms and service providers.
2. Look for the agency or operator layer that sits between the platform and end customer.
3. Find repetitive reporting, coordination, or workflow pain.
4. Build the product around saving time, not around looking clever.

He gave the example of restaurants. Restaurants often work with agencies that manage delivery app ads, promotions, or marketplace presence. Those agencies still need to report performance back to restaurant owners. Same pattern, different vertical.

That is the bigger takeaway. Every industry has middle layers. Agencies, consultants, operators, managed service firms, outsourced specialists. If a group sits between tools and customers, there is a good chance they are doing repetitive work badly in spreadsheets.

What kind of problems work best in this model?

The best problems have two traits:

• They are common across many agencies
• They save time or reduce headcount pressure

Zac put it clearly. Agency owners are always trying to take on more clients without hiring more people. That is the core tension. If software helps them increase capacity without expanding payroll, it gets attention.

So the pitch is not “look at these elegant graphs.” The pitch is “this removes repetitive work from your team.”

That distinction matters. The visible output might be a dashboard. The actual product is labor reduction.

What is the shortcut for finding ideas fast?

One practical move mentioned in the conversation was using AI to map agency layers across industries. That part actually fits the broader idea of an AI-powered launch system for creators if it is used properly. Not as a substitute for judgment, but as a way to generate structured research faster.

A simple prompt could be:

List industries where software platforms have agency, consultant, or partner ecosystems.
For each one, identify:
1. the main platform
2. the middle layer provider
3. recurring reporting or ops tasks
4. where time is wasted manually
5. what a small SaaS could automate

That will not build the company for anyone. It will, however, give a faster starting map than staring into the void and pretending inspiration is a process.

⚠️ The Risk Question

What about platform risk? Could a big player just build the same thing?

Reasonable concern. If Hero integrates with tools like Klaviyo, why would those companies not just copy the product?

Zac’s answer had two parts.

First, a small standalone SaaS at around $1 million ARR can be a great business for a tiny team, but it may not matter much to a large public company. Big companies do not always chase every niche revenue pocket, especially if it does not move the overall business.

Second, Hero is not tied to only one platform. Agencies often manage clients across several providers, not just one. So even if one platform built native analytics for its own ecosystem, agencies would still need a cross platform view. That interoperability is part of the moat.

This is another useful builder lesson. If the market depends on one source, the product is fragile. If the product helps customers operate across multiple systems, it becomes harder to replace.

🧱 The Real Build Behind the Overnight Success Story

Was Hero really built in 19 months?

Technically, yes. Realistically, no.

The product launched and hit the numbers in roughly 19 months. But the capability to build it took years. Zac and Brendan had worked in data visualization before. That company was acquired by Amazon. They spent time inside Amazon. They also tried things that did not work, including a newsletter business. That led into learning email marketing, which led into building an email agency. And that agency experience created the exact problem Hero eventually solved.

That is the annoying truth behind most “fast” success. The visible sprint is often sitting on top of a decade of weird, disconnected, occasionally painful setup.

Which is actually good news. Skills stack. Failed projects are not always dead weight. Sometimes they are just ugly compost for the useful thing that comes later.

What would Zac tell someone earlier in the journey?

Do not treat the path as linear, and do not dismiss odd experiences that seem unrelated. The business that works might depend on things learned from jobs, failed products, service work, and side experiments that did not look important at the time.

That is especially relevant for overworked builders trying to launch with AI tools or piece together no code systems on weekends. The pressure to look focused all the time is overrated. What matters is whether the experiences are giving sharper judgment about a real problem.

🧪 The Real Talk

What should builders actually take away from this?

A few things.

• Unsexy markets are often better. Less noise, clearer pain, fewer tourists.
• Distribution matters more than novelty. Selling to the middle layer can beat selling to the masses.
• Time savings is a stronger promise than prettier dashboards.
• Directories are underrated. If a market publishes its own customer list, that is not a subtle hint. That is a gift.
• Early tools are allowed to be messy. Get the product live before trying to sound important about architecture.

Would this exact model work in every market? Obviously not. Some agency layers are too thin. Some problems are too custom. Some niches are crowded with incumbents who already own the workflow.

But the pattern is strong. Find the operator between the platform and the end customer. Find what they do manually every week. Remove it. Charge based on the scale they manage. Keep it simple.

That is a much better use of energy than building another pretend productivity tool for people who already have eleven tabs open and no intention of changing software again this quarter.

For anyone building an AI-powered launch system for creators, the useful lesson here is not “add AI everywhere.” It is to use AI as a research and execution assistant while aiming the product at a market with leverage, repeat pain, and obvious distribution paths.

❓FAQ

What is Hero Analytics?

Hero Analytics is an analytics and reporting platform designed for email and SMS marketing agencies. It helps agencies pull client data into one place, automate reporting, and manage performance across multiple accounts.

Why is selling SaaS to agencies so effective?

Because one agency can represent many end customers at once. Instead of selling one subscription per brand, a SaaS can land one agency and reach dozens of brands plus internal team members through that single account.

How did Zac find customers?

A big advantage was using partner and agency directories from existing platforms like Shopify and Klaviyo. These directories make it easier to find agencies that likely share similar operational problems.

What makes an agency-focused SaaS sticky?

The product becomes part of the agency’s internal workflow. If it saves time across reporting, coordination, and client management, it is harder to replace because removing it would create more manual work for the team.

What should someone build first if they want to copy this model?

They should start in an industry they already understand, identify the agency or service layer inside that market, and look for repetitive tasks that can be standardized and automated.

Does this count as an AI funnel builder or no-code startup tools strategy?

Not directly. The bigger idea is market strategy, not just tooling. AI and no-code startup tools can help with research, prototyping, and launching, but the main advantage comes from choosing the right customer and solving a repeat problem with clear economic value.