Opinions expressed by Entrepreneur contributors are their own.
Key Takeaways
- Customer acquisition isn’t a volume problem; it’s a targeting problem, and most businesses are solving the wrong one.
- Your first-time buyer, repeat buyer and best customer are three different people, so stop marketing to them as one.
- Study your best customers, not your most recent ones. That pattern is the start of a real targeting model.
When I started VeraData in 2007, I thought I understood customer acquisition. I had studied the models, understood the data, and believed that if you built something genuinely useful, the right people would find it.
I was wrong about how they’d find it.
Our first client was a nonprofit organization trying to grow its donor base. They were doing what almost every organization does when they want more customers, donors, members, whatever the right word is for their business: they were talking to everyone. Broad outreach, general messaging, high volume. They figured if they cast a wide enough net, they’d catch enough of the right people.
They were catching plenty of people. Almost none of them stayed.
What I learned from that first engagement, and from the roughly 900 organizations we’ve worked with since, is that the problem was never the product. The problem was that they had no real picture of who their best customer actually was. Not a vague sense of a demographic. A precise, data-backed understanding of the specific individual most likely to engage, come back and bring others with them.
That gap costs businesses far more than they realize.
Acquisition is a targeting problem
Most entrepreneurs I talk to think of customer acquisition as a volume problem. Get in front of more people, close a higher percentage and the math works out. The data show that acquisition is a targeting problem. The difference between a good prospecting model and a broad one is not a few percentage points of conversion rate. It is, in many cases, the difference between a business that compounds and one that churns.
Here’s what I mean. When we model a client’s customer file, we are not asking who bought once. We are asking which specific individuals, given everything we know about their behavior, their demographics and their history, are statistically most likely to buy, buy again and eventually become high-value long-term customers. Those three outcomes require three different models because the signals that predict each one are different.
Your first-time buyer is not the same person as your repeat buyer. Your repeat buyer is not the same as your highest-value customer. Treating them as a single target audience is one of the most expensive mistakes a growing company can make, and it is nearly universal.
What changed things for our clients was treating data not as a reporting tool but as a prospecting tool. Instead of analyzing who had already bought and working backwards, we started building models that identified who was most likely to buy before they ever raised their hand. Predictive, not reactive.
Start with your best customers, not your most recent ones
The practical version of this for most entrepreneurs is simpler than it sounds. Start by looking hard at your best existing customers. Not your most recent ones. Your best ones: the people who came back, spent more time and sent others your way. What do they have in common? Not just demographics. Behavior. Timing. How they first found you. What they engaged with before they bought. That pattern, even at a small scale, is the beginning of a real targeting model.
Most businesses have more signal than they think. They just have not organized it in a way that lets them act on it.
The other mistake I see consistently is what I’d call premature broad reach. There is pressure, especially early on, to grow fast and reach as many potential customers as possible. The problem is that undifferentiated outreach burns acquisition budget on people who were never going to stay. You get the vanity metric of volume and you miss the compounding effect of retention.
The organizations that outperform over time are not the ones with the biggest outreach. They are the ones who have gotten very good at knowing exactly who they are looking for before they spend a dollar trying to find them.
This is not a problem that only large companies with data science teams can solve. I have worked with organizations of every size, and the ones that grow consistently share one trait: they have decided to treat customer knowledge as a competitive asset rather than a byproduct of sales activity. They use what they already know to sharpen where they look next.
That compounding effect is real. Every campaign, every transaction, and every customer interaction adds a signal that makes the next decision sharper. The businesses that start building that engine early look very different three years from now than the ones that are still casting wide nets and hoping.
It took me a long time to understand that finding the right customers is not a creative problem. It is a math problem. And like most math problems, once you have the right equation, the answer starts to repeat.
The good news is that the data to build that equation is usually closer than you think. You just have to decide to use it.
Key Takeaways
- Customer acquisition isn’t a volume problem; it’s a targeting problem, and most businesses are solving the wrong one.
- Your first-time buyer, repeat buyer and best customer are three different people, so stop marketing to them as one.
- Study your best customers, not your most recent ones. That pattern is the start of a real targeting model.
When I started VeraData in 2007, I thought I understood customer acquisition. I had studied the models, understood the data, and believed that if you built something genuinely useful, the right people would find it.
I was wrong about how they’d find it.
Our first client was a nonprofit organization trying to grow its donor base. They were doing what almost every organization does when they want more customers, donors, members, whatever the right word is for their business: they were talking to everyone. Broad outreach, general messaging, high volume. They figured if they cast a wide enough net, they’d catch enough of the right people.
