An Interview with Ideal Spot CEO Marc Smookler
Marc Smookler has started five companies – three of which are market leaders in their respective spaces: a commercial real estate data platform, an online retailer and a unified communications provider.
Mr. Smookler’s largest success to date is Fonality – a cloud- and open-source-based PBX competitor to Avaya and Cisco. He also founded sakesocial.com, the largest online retailer and highest-ranking online destination for all things Japanese sake.
Prior to creating companies and products, Marc was an investment banker for U.S. Bancorp in Minneapolis and JPMorgan in New York City. Now based in Austin, Texas, he is a partner and mentor for Austin’s tech incubator/funds Capital Factory and TechStars, and serves as a board member and advisor for a number of companies in the B2SMB/B2C spaces.
Marc’s most recent project, IdealSpot, helps everyone in the commercial real estate and retail ecosystems better understand local markets and communities through the use of better data. In fact, the Coin Laundry Association has collaborated with IdealSpot to deliver more robust demographic reports for today’s vended laundry owners and potential investors.
PlanetLaundry Editor Bob Nieman recently chatted with Mr. Smookler about his new venture and how it can help laundromat operators thrive in today’s ever-changing business environment.
Tell me about Ideal Spot and the services it offers.
Ideal Spot pulls in a lot of hyper-local data, as it relates to specific addresses or locations across the United States. We pull in a mixture of traditional data sets, which are the typical things you might have gotten for the past 20 to 30 years, such as demographic data, psychographic data, even traffic data and spending data, and so on – a lot of the typical data sets that people expect when they want to pull data in a market.
However, what we’re known for is mixing in a lot of non-traditional data sets. When I refer to that, I’m talking about our search data, where people are signaling that they want or need something, as well as social data, where people are signaling that they want to do something on Instagram, Facebook or whatever the case might be.
For instance, if someone mentions on Facebook that he needs to go to a laundromat this weekend to wash his clothes, that’s a signal that this person is looking specifically for a laundry in a specific market. We capture that demand, and we map it to get a more accurate sense of where the demand is for certain products and services, who is going there, and so on.
The final major pillar of data is the movement of people in an out of local markets and how they’re getting there – whether it’s by a vehicle, public transportation, a bicycle, walking, you name it. We’re tracking vehicles. We’re tracking mobile devices. We’re doing a lot of tracking. Let me be clear – we’re acquiring that data and then we’re mixing, matching and bottling it, and presenting it for people to consume.
Please explain the program you’re currently offering CLA members.
We have a pre-built template that we’ve worked on with the help of the association, featuring the most interesting and important data points and categories specifically beneficial for those who own laundromats. We then boil down a lot of the data sets that we have into much more specific templates. Between those two things, you can quickly find highly relevant data to help you with an existing location, as well as with vetting a new location.
Why is having accurate demographic information so important for today’s small-business owners, such as laundromat operators?
Accuracy matters. I think the days of “build it and they will come” are long gone. There are a lot more options for people to spend their money today than ever before. There are a lot more opportunities for people to get what they need, including laundry pickup and delivery services.
To put a location where it’s most in demand is of the utmost importance. The example I like to use is from a few years ago, when I still had very young children in diapers – and I remember driving to a Target late at night to buy diapers. Of course, today, I can go to Amazon Prime Now and get those same diapers delivered to my door in an hour at about the same price as it would cost me if I went to Target, not including the cost of gas, personal time and having to fight my way down the toy aisle, etc.
If you take that and expand it out to other products and services we need, it’s very much about this economy of convenience. Perhaps I want vegan food. OK, there’s no vegan restaurant near me; I’ll call up and order it.
Demographics are a key piece to the puzzle. You need to get deeper into the funnel, closer to the purchaser in order to get a sense of what someone’s signaling they want or need.
For instance, I’m a 42-year-old, white male, making a certain income, with a certain amount of education, etc. That is what I am. But you still don’t know who I am. You don’t even know whether or not I have a washer and dryer in my house. However, if I could flat-out tell you in any market across the U.S. within a five-minute drive time of any address how many people are going into their mobile devices and typing in “Laundromat Near Me,” does it really matter what they look like, their gender, how old they are, their ethnicity or the language they speak? No.
Our theory is that it doesn’t really matter what their identity is. What really matters is how many people are signally that they want or need a laundromat. I’m not trying to say that identity is unimportant. But, for a long time, business owners have been missing a key piece of the puzzle, and now that piece is becoming more and more important.
In the past, store operators had to make assumptions based on identities and on who they thought would be more likely to use a laundromat. For instance, single males, large families, college students, etc. You’re “building that story.” By contrast, I’m saying let’s go directly to the wants and needs, which is more accurate.
What are some of the hot trends or latest news with regard to demographic data collection?
Mobile data is very trendy right now. I see it as another piece of the puzzle, another piece of interesting data – mapping where people are going. From that perspective, I still see it as a bit of a “halo product.” I think it’s interesting to see how many people are going from Point A to Point B by tracking their mobile devices, but you still don’t know intent. You still don’t know if the people who are walking by a location want a laundromat or are going to stop at a laundromat. I think that, as part of a bigger story, it’s important and interesting. But you still have to aggregate it together. To get more accuracy, you still need the full story.
There are a lot of really cool data sets right now. There is a company that – when people post photos on Instagram – can geo-locate those images based on the surrounding environment. I don’t know how that helps restaurant owners and retailers. I guess what I’m saying is there are a lot of “noisy” data sets out there. And you have to understand that, from a data science perspective, you have to be careful because as you add more and more data sets that aren’t relevant, in my perspective it just waters down the important things.
It’s our responsibility at Ideal Spot to bring in the data sets that are helpful and relevant – those that help laundry owners make better business decisions. As for the data sets that aren’t adding value, let’s keep them out.
How has demographic data advanced and improved in recent years, versus what the demographics industry was like in the past?
In our three or four years that we’ve been aggregating data, we’ve seen a lot more focus on the accuracy of the data and where the data is coming from. We’re also seeing a lot more providers of data and aggregators of data, so to a degree it’s driving down the costs of acquiring data.
Clearly, you need to be careful with regard to who has quality data and who doesn’t. But as we see more providers coming in, it has helped the industry become more data-driven when it comes to choosing locations or just being more cognizant of pulling data, rather than just going with their gut and making assumptions.
What tells you that a certain site would be a good store location? What makes you think that when you build it, people will come? Where are they going to come from? Who are these people? How far would they be driving to come to your laundromat? Where is the nearest competing laundromat? You can start to go down this deeper path of data.
Let’s talk about new technology with regard to demographic information.
Ideal Spot certainly could be considered new technology. Overall, there are just a lot more options in terms of mapping data. There are more options in terms of how to acquire data.
If you’re a business owner and perhaps you’re looking at and weighing the advantages and disadvantages of multiple locations, it seems crazy to me that you wouldn’t take advantage of all this new data that’s now available. In fact, if you’re going to spend hundreds of thousands of dollars and commit to something, I think solid data would be a requirement. There is a lot of risk involved, and any opportunity a laundry owner has to decrease that risk with strong data is crucial and should be taken advantage of.
Again, there are a lot of options to be able to do that now and to do it at a reasonable cost. Technology is enabling that. It’s democratizing and it’s bringing more easy-to-use data to today’s small-business owners.
Can you discuss the importance of search engine and social media data for laundry owners, as well as how Ideal Spot can assist?
Basically, search data is actual, active intent: “I want something now,” or “I’m looking to do something in the near future.” Social data is passive intent. I would consider social data to be the new psychographics, and I think psychographic data from surveys is very noisy. It’s from a very small sample, and you don’t know where those samples are coming from.
Case in point: I’ve never filled out a survey. I don’t know where they’re getting data on me. And, if you walked into any neighborhood coffee shop and simply asked the people in there the last time they filled out a demographic survey form, you’re going to get a lot of shrugs.
On the other hand, my dad, who is 78 years old, faithfully fills out his survey. So, if they’re, for instance, modeling millennial tastes off of my dad’s responses, that’s going to be a pretty bad data set.
With that said, I can tell you that, as an example, Facebook has a market share of, let’s say, 80 percent in Austin, Texas. If I’m able to tap into that, I can see, in any given market, who likes to play golf or who watches NFL football or whatever the case may be. I think that’s more accurate and more telling of who people are beyond just your traditional psychographic data.
Search and social are both very important. They’ve been critical pieces of who we are as a company, and I think any resource that only provides one or the other is missing an importance piece of the puzzle. You need to have both. And we’ve bet our future on being exceptional at understanding search and social data in local markets, and building derivative data sets out of it.
What can laundry owners learn from those in other industries and how they utilize demographic information for their businesses?
I see a lot of real cool trends, and we work with a number of retailers that try to get hyper-local with their businesses. We see owners moving away from the typical brick-and-mortar location – doing grocery stores on wheels, pop-up trailers in big shopping malls with much smaller footprints and so on.
It’s being open to this concept of fulfilling the consumers’ wants and needs. You have to think differently. You have to think smaller. You have to be where the population centers are, where the huge market voids are – and perhaps being open to not necessarily looking at your typical strip malls.
There are a lot of interesting developments. We’re seeing shopping malls being converted into housing and medical centers. We’re seeing mixed-use commercial real estate – where you might have retail space on the bottom floor, residential units in the middle and offices at the top.
All in all, laundry owners should be open to looking at different types of footprints and business models.
What are the most common mistakes you see small-business owners make with regard to the use of demographics?
Obviously, choosing bad locations is a pretty clear mistake. However, in general, the biggest mistake may be not adapting to the market.
There will always be change in the marketplace. I think we’re currently witnessing big changes that are causing some rather major movements in restaurant, retail and real estate. If you’re doing the same thing you were doing 10 years ago, you’re significantly decreasing your chance of success. You need to be aware of how people’s purchasing patterns have changed. You need to keep your eyes open, do some research and see what’s happening in other segments of the market and with market leaders.
What is Target doing differently? What about Amazon and Walmart? How are they reacting? What does it mean when Target goes into stores with much small footprints in urban locations? What does it mean when Walmart is spending a lot of money on online businesses and services? What does it mean when grocery stores are acquiring technology companies for their delivery services? What is that trend? Take that trend out 10 years. What happens when Amazon now knows what’s in your refrigerator and will automatically drop off groceries based on what you want to cook that night?
You have to go through this thought process and think about where the economy is heading. How are purchasing patterns going to change?
What’s the one piece of advice you’d like laundry owners to take away from this interview?
Simply put, laundry owners – and all small-business operators – need to be more data-driven in their processes. That’s why we exist. The data is now available. It’s cheap. It’s accurate. And it’s very easy to access. There is no longer an excuse not to do it.