Understanding Your Market Before You Buy: A Conversation with PeerView AI's Christopher Johnson
Most people buying a small business make one of the biggest financial decisions of their life based on the seller's financials, some demographic research, a few site visits—and a lot of hope. But what if you could see the actual transaction patterns of every competitor in your market? What if you knew whether that business is thriving because of great management, or simply riding a market tailwind?
That's the problem Christopher Johnson set out to solve with PeerView AI. After spending over a decade at Capital One transforming small business underwriting with advanced data analytics, Christopher recognized that the same location intelligence big banks use internally could be invaluable to the entrepreneurs and business buyers who need it most.
In this conversation, we dig into how transaction-level data is changing the game for small business M&A. Christopher shares stories of entrepreneurs who discovered critical market insights—from oversaturated pet care markets in Austin to the gap between franchise promises and actual performance—that fundamentally changed their buy or sell decisions.
We explore questions every business buyer and seller should be asking:
How do you separate a great business from a great location?
When is the right time to sell—and how do you prove your story to skeptical buyers?
What early warning signals suggest a market is about to shift?
How can sellers use objective data to overcome the "lemon problem" and command better valuations?
Whether you're evaluating an acquisition, preparing to sell, or advising clients through the process, this interview offers a fresh perspective on how location intelligence and market-level data can turn uncertainty into confidence.
Introduction
Tell me about your journey to founding PeerView AI. What were you doing before this, and what sparked the idea?
I spent over a decade at Capital One, and one of my biggest accomplishments was transforming how we did small business underwriting from a data perspective. I spent a lot of time building, buying, developing and modeling all kinds of business data to improve our internal models and strategies. But along the way, all of us working had the same thought: this data would actually be useful to the business owners too.
We ran pilots over the years and the customers loved them, but it never really penciled out as a business inside a big bank. After I left, it hit me that what did not make sense at Capital One might work as a focused operation. Especially now, because AI tools can take a lot of the manual, messy work that used to make this impossible and make it scalable.
What problem were you trying to solve when you started PeerView? Was there a specific moment or experience that made you realize small businesses needed better location intelligence?
The moment for me was talking to people who were buying or starting businesses and watching what they had to do to understand their market. It was honestly absurd. People would get multiple haircuts just to see how busy a competitor was, or sit outside a coffee shop during the morning rush with a clipboard counting customers.
And I remember thinking, these are smart, hardworking people making one of the biggest decisions of their life, and the best tools they have are basically detective work. PeerView AI started as a simple idea: what if you could answer the big questions with real transaction patterns instead of folklore.
Why focus specifically on small businesses and franchises rather than larger enterprises? What drew you to this market?
I started with single location entrepreneurs on purpose. I want as many people as possible to be able to take a swing at their dream with their eyes open. Even if the money eventually is bigger in the franchise and corporate world, the mission starts with the person putting their savings on the line.
Big companies already have teams and dashboards. A normal person starting a business has public datasets, grit and hope. I love hope, but hope is not a strategy.
You've been at this for over a year now—what's surprised you most about what you've learned from analyzing all this transaction data?
How quickly narratives get out of date. People make plans based on what the market felt like six months ago, and the market has already moved. The example that still sticks with me is pet related businesses in Austin. Doggie daycare became this perfect “second act” idea, and for a while it really was. The post-Covid spike was real. The problem was it was real for everyone at the same time. When a lot of people are working off the same stale, imperfect signals, they all make the same logical decision, and suddenly a great idea for one operator turns into an oversaturated market for everyone else.
What's been the most rewarding part of helping business owners make better decisions with your platform?
The parting of the uncertainty. My customers tend to be heavy thinkers who are intent on making a wise decision but frustrated with how limited the data is and dislike relying on anecdotes and vibes. My reports are hardly the end of the story, but it immediately helps break the paralysis by laying out the actual market conditions and letting them either move forward with confidence, or accept this is just not the right market and close the book on this chapter.
On Localized Market Intelligence for M&A
Your platform helps businesses make smarter location decisions using transaction data. How often do you see businesses discover through your reports that certain locations are significantly outperforming or underperforming expectations? What surprises you most about what the data reveals?
Businesses, especially small businesses, are snowflakes. No report ever has a set of purely apples to apples competitors, all of them are slightly different (even if it was just a group of Starbucks, some would be drive-thru’s, some in malls, some in the residential vs commercial parts of town). But that level of detail is overwhelming, until my report narrows down the ones that are ‘winning’ and then suddenly it’s possible to start pulling together the threads. It’s not that the results are ‘surprising’ per se, it’s just that there are too many conflicting hypotheses that can’t be resolved without having some actual data to use.
When a franchise or multi-location business is considering a sale, how could localized revenue insights strengthen their positioning with potential buyers? What story does location-level performance data tell that aggregate numbers miss?
Selling expensive nuanced assets like a business is always a combination of cold hard numbers and storytelling. Buyers who don’t believe your story are either going to walk away, or demand a lower price. So when you can tie your business financials to actual external market conditions, you can speak with credibility rather than handwaving every bad quarter away.
On Competitive Intelligence and Market Positioning
You mentioned benchmarking against local competitors—how granular does this get? Can businesses see if they're gaining or losing market share at the location level? How valuable is this intelligence during due diligence?
My data is business location based. I can’t see what you spend at the business, but I can see how many people spend and their average spend at a given location. In the past this would be challenging to use, since a bunch of businesses are still hard to make much sense of. But LLM technology is great at the nitty gritty research required to turn a couple hundred business locations into useful groups for further digging.
What competitive patterns do you see in the data that business owners typically miss? For instance, a new competitor quietly eating into market share in specific geographies?
Business owners usually have a good pulse on their area, where I help sort out the useful signal from all the news they collect. They likely know if their market is shrinking, but what they don’t know is if that is a 5% drop or a 25% drop. Nor do they know if that is still on-going, or whether it’s turned a corner.
On Site Selection and Growth Strategy
Your platform helps with expansion decisions—what revenue patterns in existing locations are the strongest predictors of success in new markets? How does this inform both organic growth and acquisition strategy?
For organic growth, finding markets where all your relevant competitors are crushing it is going to be the best idea. Maybe you could enter a struggling market and turn it around, but why take unnecessary risk.
But acquisitions, that is much more based on your strategy. Maybe you want to buy really good businesses who are undervalued because their market is temporarily weak. Or perhaps the opposite approach, and find killer markets where there is an underperforming business that you can whip into shape and get immediate returns. Regardless of your approach, it does no good if you use the right approach on the wrong business due to flawed analysis.
For franchises specifically, what does transaction data reveal about the health of the franchise system versus individual franchisee performance? How should this inform buy-side or sell-side decisions?
Franchises make a lot of promises about how their system will deliver above industry returns to justify their fees and the various other limitations of operating a franchisee. Some justify it, many don’t. If the choice is between a franchise burger place and a non-franchise burger place, that 10% fee is going to seem like a big barrier unless there is data to back up how much better than the franchise performs.
Before, that kind of analysis was basically impossible for the franchisee to perform. But now, it’s quite viable and I suspect we’ll see compression on those fees over time as underperforming franchises are forced to admit they can’t deliver sufficient value to justify it.
On Data-Driven Decision Making vs. Gut Instinct
You emphasize "not just surveys or reviews" but actual transaction patterns. How often does hard transaction data contradict what owners believe about their business performance? What's the most dramatic disconnect you've encountered?
Transaction data isn’t perfect, when business owners see their own data they quickly go from disbelief at how accurate it is to frustration in places where they notice data gaps (ex: new location opened up and the data isn’t feeding in yet). But generally the biggest disconnect is the recognition of how much their business just tracks the local competitors. They know the hard work they put in everyday and even though they intellectually know their competitors are doing the same, it can be hard to see that all of them are moving in sync.
In the context of selling a business, how can owners use objective, transaction-based data to overcome buyer skepticism or negotiate from a position of strength?
Business transactions are always at a high risk of the lemon problem. The seller knows the business, knows the market and knows where the bodies are buried. Even if the story is good, the buyer might not think it’s credible without 3rd party confirmation. That’s where the objective transaction-based data comes in, so the buyer can see for themselves what the seller already might intuitively know.
On Timing and Market Signals
Can your real-time transaction data help business owners identify the optimal time to sell—when their locations are demonstrating peak performance and market conditions are favorable?
I can certainly identify a great time to sell, I make no promises about the optimal time. But you are going to get a good price if you make sure to wait for a time where everything is going right. Maybe things will get better if you hold on later, but it’s really hard to time the peak of a market and missing the peak is likely going to slash your valuation.
What early warning signals in transaction patterns suggest a market is becoming oversaturated or that competitive dynamics are shifting in ways that could impact valuations?
That’s a more nuanced question than I can cleanly answer here, but generally most industries have about a 6-18 month ramp up period, so it’s possible to see new competitors' potential impact well before it shows up in your business’s financial statements.
On Small Business and Franchise-Specific Insights
Your focus is on small businesses and franchises—what unique challenges do these businesses face in understanding their true market position? How does your platform level the playing field against larger competitors with sophisticated analytics?
The number one thing that a small business needs to know is how much their market is growing/shrinking. For a long time, larger competitors had a lock of getting access to that information. But that isn’t true any longer, the advances in data/technology/AI is making that information available to everyone. Now larger companies still have more analytics that can yield marginal improvements, but the basic stuff will be available to all.
For entrepreneurs considering both growth and exit strategies simultaneously, how can localized revenue intelligence inform that decision-making process?
For the larger scale operator here, the investing advice to buy low and sell high still applies. Find good deals in markets that are in a temporary downswing and then sell your businesses that are benefiting from a market upswing. Once you learn to stop conflating local market conditions with that of an individual business, you’ll be able to make the chaos of the broader market work for you, not against you.
Closing
Well, thank you so much for sharing these insights with us, Christopher. For readers who want to learn more about PeerView AI or get in touch, where should they go?
Thanks for having me, Timothy. The best place to start is our website at peerview.ai. People can also reach out directly through the contact button there, or connect with me on LinkedIn. I'm always happy to talk through specific situations and how location intelligence might help.
About the Interviewee: Christopher Johnson is the founder and CEO of PeerView AI, a platform that provides small businesses and franchises with transaction-based market intelligence. Prior to PeerView, he spent over a decade at Capital One leading small business data strategy and underwriting innovation.