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5 Minutes with a CEO: Dave Carmany of OnlineLabels.com

A CEO explains his favorite analytics techniques and reveals how they played a part in his company's growth.

• By James E. Powell
• January 30, 2017

OnlineLabels.com is one of the Internet's largest suppliers of blank and custom-printed labels. It is privately held and based in Sanford, Florida. CEO and founder Dave Carmany says the driving force behind the company is "our people." He spoke with Upside recently about the importance of using data in his business.

UPSIDE: What trend or idea do you think is the most overhyped today in the corporate world -- be it about analytics or business in general?

Dave Carmany: I think the idea of making mistakes and trying new things in general can be exaggerated. I say that not because I believe that absolute perfection is the answer, but because there's a difference between making mistakes and making an educated decision that has a high probability of success. There's great value in learning from your mistakes, and I encourage business owners and their teams to continue generating cutting edge and out-of-the-box ideas, but data can help tip the odds in your favor while decreasing a lot of the risks.

With the amount of resources we have today, there's no reason that decisions have to be made based on gut feeling alone -- trying things can be very costly. With data, businesses can test their ideas and change course very quickly should they not show any profitability.

What is your favorite tool or technique that has made your job easier (and how is it easier)?

My favorite techniques are A/B switch testing and multivariate testing. They allow us to rely on our opinions less and data more. You can't deny the numbers and they make me much more comfortable; it's easier to make decisions when you have good data that points to a quantifiable impact.

One of my personal favorite tools is SiteSpect, an optimization and testing platform that helps us compare the performance of page variations. It's undoubtedly yielded the most significant outcomes for us. Right now, for instance, we have three tests running -- two are minor improvements to some pages, the other is a more drastic overhaul of a significant process on our site. We get responses and feedback so quickly that within 24 hours we know whether a move is smart or not. That's a huge difference between now and 15 years ago.

Whether it's the latest smartphone or a real-time stock ticker, what's the one thing you can't do your job without?

We use an internal software application that is custom-built to make sense of our data. As a details person, I look at it obsessively. I have data broken down from daily to hourly -- even to the minute. Checking it so frequently allows me to immediately identify when/if something goes wrong, such as the website running unusually slow.

What's the most common roadblock you hit in your work? How do you deal with it?

Sometimes there can be so much data it's hard to infer what's really happening. When you're presented with an overwhelming amount of data, figuring out what the goal was or what we want the main metric to be can be difficult. It can be easy to go down a rabbit hole if you don't know exactly what you're looking for.

Which is going to act as the best and most clear solution? Are we measuring time on page or completed orders? Personally, I've seen far too many examples of companies implementing features and changes that are inherently flawed based on improper A/B testing techniques and standards.

What form of analytics do you use to make sense of all the data (for example, daily reports, real-time dashboards, exception alerts)? Do you have any secrets you'd like to share about extracting the greatest value from your data?

We use daily, weekly, and monthly results from multiple data points and sources to get a well-rounded picture of what's working (and what isn't). We've developed our own real-time dashboards for some things and use built-in dashboards for others.

If you had unlimited resources, what would your data analytics wish list look like? Are there new technologies you want to try? Would you want to hire a data science team?

I'm interested in cloud-based tools right now. We haven't found something that applies to us well yet, but I'd love the ability to dump mass amounts of data into a cloud data service and have it compute answers to our questions.

Where is data analytics/data science headed in the next few years within your organization? Within the world at large?

Data analysis will become increasingly critical; we rely on it to stay competitive. We'll continue to use it to identify trends and mistakes and change course toward profitability.