Kevin: Welcome to the Location Insights podcast.
Kevin: I'm your host, Kevin, Global Marketing Manager at unerry Inc.
Kevin: unerry is a location analytics platform and data insights company based in Tokyo, Japan.
Kevin: Here with me today is Pete Mickartz.
Kevin: He's the Director of Business Development at Veraset.
Kevin: Thanks for taking the time, Pete.
Kevin: Welcome to the show.
Pete: Yeah, absolutely.
Pete: Thanks for having me.
Kevin: Good to have you.
Kevin: So how did you get into the location marketing industry?
Kevin: What is your background like?
Kevin: Where did you previously work?
Pete: I kind of have a bit of an eclectic background, I guess you could say.
Pete: Started off my career early twenties.
Pete: working for Enterprise Fleet and really got my kind of cut my teeth in sales almost 20 years ago now, which is kind of scary to think about.
Pete: And got the sales bug and was there for a few years and then ultimately moved into the startup world.
Pete: And sort of transportation, transpormation tech specifically.
Pete: So using location data at that point was something we did not all too often, but we did a little bit.
Pete: So had a little bit of a taste there, but wasn't on the radar by any means.
Pete: And went through four or five different startups over the last twelve years and landed at Veraset through a mutual connection over three years from now.
Pete: So it's been a fun ride at Veraset for the last three years.
Kevin: That's a good background there.
Kevin: And we've been working together for pretty much that whole time.
Kevin: And you're a good partner for us at unerry with Trusted Valued Data.
Kevin: So that kind of leads us into what the business of Veraset is.
Kevin: And I've been hearing some kind of buzz going around about this new web app called Orchestrator.
Pete: Yeah, yeah, it's a brand new release from us, quite frankly, just released last week at Ramp Up, Live Ramps Camp Conference in San Francisco.
Pete: We got a little kiosk
Pete: Of course, giving live demos and everything.
Pete: But ultimately, Orchestrator is a self-serve intelligence platform.
Pete: So, you know, Veraset being a location data company and
Pete: Licensing location data, these big heavy data sets to the Fortune 500, to all these companies that have engineering and data teams to make use of the data.
Pete: we were really searching for a platform or a way, a method to derive insights and kind of, for lack of a less cliche term, democratize data, right?
Pete: and extracting value from this data without engineering, without several data scientists on staff.
Pete: How do we go about doing that?
Pete: And Orchestrator is there to change that and to provide that value quickly without the technical chops needed and being able to put this data in the hands of everybody, really.
Kevin: Wow.
Kevin: So this just launched like last week or this week, literally.
Pete: Yeah, it's been in the works for several months, if not on my work on the year now, but officially launched
Pete: last week as of March 1, and we're excited to get it out in the wild.
Pete: The feedback thus far has been fantastic.
Pete: And we're really excited to continue building it out.
Pete: and adding features, adding more data in there to make it as valuable as possible for everyone across various verticals, which we created to, of course.
Pete: But yeah.
Kevin: And so for this new web app or web tool, what kind of problems does it solve?
Kevin: Like what does it change for your partners and clients?
Pete: Yeah, yeah, it definitely shifts.
Pete: It broadens kind of our TAM, right, our total adjustable market.
Pete: Historically, if you were going to leverage Ferriset location data, you had a team of engineers.
Pete: You had a data scientist, if not several, or a team of them.
Pete: and working with big, heavy, noisy data sets that are producing tens of billions of pings a day, and you start to tack on months and months, if not years, of data, the data can get really unwieldy.
Pete: and having the infrastructure in place to be able to make use of this data is difficult, slow, and can be expensive.
Pete: Orchestrator was built to change that.
Pete: And it's a self-served platform.
Pete: I can get in there and use it.
Pete: I'm not exactly the most technical person in the world by any means.
Pete: And I can get in there and easily create value from data ad hoc just on the fly.
Pete: So you can define locations, analyze behavior, build audiences, automate workflows.
Pete: There's a lot of things you can do with Orchestrator quite literally on the fly.
Pete: You can just ad hoc and get extract value in minutes.
Pete: It's pretty cool.
Kevin: Awesome.
Kevin: Yeah, that's kind of exciting to hear about this.
Kevin: So like previously before Orchestrator
Kevin: From my understanding, clients would receive large data sets, countrywide talking
Kevin: It's bulk data.
Kevin: They need data scientists, data analysts to sort through and analyze and make sense of it.
Kevin: So what changes with Orchestrator?
Kevin: From what I've heard, and correct me if I'm wrong, it's more points of interest driven or brand driven or POI driven?
Pete: Yeah, yeah.
Pete: So historically, if any entity were to license location data from Veraset, they're going to receive this daily file, right, daily data.
Pete: Whether that was via S3, servers or server transfer, whatever storage facility you use, or pulling it down via API.
Pete: And the API, of course, is a little technical.
Pete: I mean, various endpoints, you have to stitch together different products, crosswalk over IDs potentially.
Pete: It was a lot of
Pete: Technical chops you had to have to orchestrate your final output and your kind of custom output.
Pete: Whereas with Orchestrator,
Pete: All the data is in there.
Pete: It's POI-based, yes, in that you can go and define your own POIs.
Pete: You can leverage Veraset POIs.
Pete: You can draw your own, upload your own.
Pete: And then run several products through those POIs, right?
Pete: Or create cohorts from those devices seen in specific POIs at specific times.
Pete: It is really cool.
Pete: So it really orchestrates.
Pete: The API endpoints on the back end to create and stitch together multiple data sorts in one.
Pete: So rather than having to have individual API calls
Pete: Or heaven forbid, just to make sense of a massive data in an S3 bucket, having a UI that you can easily manipulate data on the fly is the goal.
Kevin: And what is the business model?
Kevin: You're describing it as self serve SaaS model.
Kevin: So can
Kevin: a company that's interested in this just like go and sign up?
Kevin: Or is it right now early days and you're hand walking through and bringing people on?
Pete: Yeah, I guess the answer to both those questions is yes.
Pete: You can absolutely come to us and we can have a couple discussions around what your use case is to really understand how Orchestrator can fit so we can set it up correctly for you.
Pete: At the same time, our current customers are gaining access to this if they so choose.
Pete: Maybe it's an insight through marketing team.
Pete: Really, we're starting to see a much wider net as far as the ability to use Orchestrator within organizations compared to the prototypical or historical location data.
Pete: So
Pete: That is out of for digital media and out of home measurements, municipalities.
Pete: We've had some fixed early on, consultants, real estate, site selection, multi-location operators of
Pete: Individual restaurants, regional restaurant chains, a lot of these historically were just kind of off-limit for us.
Pete: There was no way that they could leverage Veraset location data.
Pete: So to be able to
Pete: see and feel and kind of touch with the web app, right?
Pete: It's pretty cool.
Pete: Create insights on the fly and then actionable insights, even to push to a DSP and activate those to advertise to those customers.
Pete: So it's pretty select, it really is.
Kevin: So it sounds like it's an easier experience than previously, where you would need to have data analysts or data scientists to sort through the data and analyze the data and then create some outputs.
Kevin: This creates more structured data, more POI targeted data than before.
Pete: Yeah, exactly.
Pete: It's you can kind of ask yourself a question of any POI
Pete: how location data based on various schemas could be helpful and get that exact cut of data that you're looking for, quite literally minutes as a software platform.
Kevin: What are some of the examples of use cases that you see different clients looking to use it for?
Pete: Yeah.
Pete: So use cases.
Pete: One early on that we're seeing a lot of is with ad tech and creating audiences
Pete: Creating location-based audiences, live location-based audiences, right?
Pete: And dynamic, I should say, location-based audiences, because you can run a
Pete: Workflow for devices that have visited specific locations, let's say, brands.
Pete: even the custom brands that you upload.
Pete: And you can create that and run that in the background or even have that refreshed on a daily basis.
Pete: It's really slick to create some of these physical location based audiences
Pete: Measurements also a big one with the measurement of outdoor advertising.
Pete: That's pretty critical, obviously, to get the ROI and calculations there.
Pete: Commercial real estate and municipalities, I kind of bucket into one because the use case is much the same, right?
Pete: They're looking at how foot traffic is impacted by different events, by different buildings.
Pete: site selection or building site selection are all kind of lumped into municipality commercial real estate workflow and use case.
Pete: So we're seeing a lot of traction there.
Pete: And the last one, which is new for us, is this multilocation kind of small and medium sized business.
Pete: It's like Danny's fried chicken that has six retail restaurants.
Pete: you know, in North Carolina or somewhere, and that is looking for an edge under their local competition and being able to put location data in
Pete: a customer's hands like that, that it was not even possible a couple of years ago by any means.
Pete: It's pretty cool to see to show them the power of this data and how they can use it.
Kevin: So you're able to reach new clients, small to medium size, and they're able to utilize the orchestrator tool to
Kevin: Target their own stores and maybe even competitor stores and get insights.
Pete: Yeah, exactly.
Pete: It's all self-served.
Pete: It's pretty cool.
Pete: Anytime I've shown these mid-market kind of companies, small and medium companies,
Pete: Like, hey, I can show you the foot traffic of your own stores in under five minutes.
Pete: Right.
Pete: So, without a doubt, it's pretty cool to see their faces light up and to understand.
Pete: who their customers are, who their power customers are, and who where are they coming from?
Pete: Are they coming from home?
Pete: Are they coming from work?
Pete: How do they even get to those locations to really understand their customer better and hopefully grow their customer base?
Kevin: Are there other tools in the market?
Kevin: And how does Orchestrator differ in terms of its features and functionality from other services?
Pete: Yeah.
Pete: So there are other tools in the market, of course, right?
Pete: There's the big location data platforms that are out there.
Pete: But really, the main differences between some of those platforms and Veraset is
Pete: A couple of major things.
Pete: The first one is the data granularity, right?
Pete: With 200 plus million devices daily in the US, close to 700, 800 million daily globally.
Pete: And that those devices are producing anywhere between 10 to 15, 16 billion rows of data a day.
Pete: So you actually get the hands on the data itself to create insights there and be able to aggregate cohorts and advertise those cohorts
Pete: Whereas most platforms historically are much more aggregated in fashion.
Pete: You have a total visit count, or you understand maybe a zip code of where folks are coming from.
Pete: We had a really cool use case with a commuter company that actually hooks up commuters, matches commuters going in and out of New York City.
Pete: And understanding origin points, destination points, we're able to
Pete: create some more business for that company seeing these devices go in and out of the city every day and hook those people up together so they can commute and save some money and hopefully make some friends.
Pete: So it's pretty neat the power of this data when used for good and the self-serve kind of intelligence platform.
Pete: Yeah, it's a bit daunting, but at the same time, again, if I can get in there and create insights from it, I genuinely believe anyone can.
Pete: It's pretty neat.
Kevin: Sounds awesome.
Kevin: So some listeners at home may be thinking like data, this is really big data.
Kevin: How does Veraset collect data in a privacy centric model?
Pete: Yeah, yeah.
Pete: So Veraset, we license data from individual apps, SDKs, and of course other data companies.
Pete: There's a lot that goes into the privacy policy and ensuring that, one, we are mostly legally compliant with CCPA, GDPR, but every last device is consented to within our panel.
Pete: We collect various consent workflows from all of our suppliers, do annual audits, all those kind of things, and ensure that the data is being used for responsible purposes as well.
Pete: Each individual use case is monitored.
Pete: And by company, by use case, it gets pretty in depth.
Pete: There's several people that actually touch each individual contract that goes out with Veraset to ensure that the data is not getting in the wrong hands.
Pete: There are so many checks and balances throughout the entire process from licensing the data from first-party suppliers all the way through getting data in the hands of individuals behind a screen that
Pete: We have learned that this data can be used for bad purposes and for nefarious purposes.
Pete: That's less than we want.
Pete: So, ensuring that we go above and beyond, whether it's our sensitive location filtering, whether it's our opt-out flow, that we run weekly.
Pete: Whether it's even blacking out the latest states, Maryland and Oregon, that have passed laws, taken a really conscious and privacy-centric approach to blackout the entire state.
Pete: There's a lot of things that we do at Barisset to ensure privacy.
Kevin: That's great to hear.
Kevin: And it's a very important point, I think, for your clients, partners, that the service, the data, the service that you provide is
Kevin: trustworthy, it's opt in, it's privacy centric, and it has that level of validation and trust built into it.
Kevin: Yeah.
Kevin: So what do you see coming down the pipe?
Kevin: I know Orchestra just launched, it's early days, but what are some of the things that
Kevin: And you're hearing internally from your team features or plans that are coming down the line in terms of stuff getting added in the future that clients can look forward to.
Pete: Yeah, I feel for our product team right now is they're getting requests after requests and that it's starting to pile up and they got to sift through and prioritize a lot of these things.
Pete: So officially launched Orchestrator, really at the end of the day, it was kind of last week in San Francisco.
Pete: And the initial feedback's been great.
Pete: And we've gotten orchestrator in the hands of a few select customers for the last few months and kind of iterating on the process and fine-tuning things.
Pete: the biggest thing I think is kind of getting back to our ad tech roots a little bit.
Pete: And we have some ad tech folks in the product team that are wizards in that realm.
Pete: And they have a much broader vision for Orchestrator to become a true orchestration and activation
Pete: Layer across the digital media ecosystem.
Pete: And it's a lot of words to say you can come to Orchestrator to really build dynamic
Pete: interesting niche audiences that integrate with your DSPs, that integrate with Google Ad Manager, social media platforms, marketplaces, obviously DSPs, FSPs.
Pete: and handle the entire programmatic kind of media landscape.
Pete: So it's that's a big ask, right?
Pete: So much to do.
Pete: At the same time, if we can really bit by bit block or build this thing with the foundational layer of location data being as powerful as it is, we're pretty excited about it.
Kevin: So there's a lot you're getting a lot of feature requests from your early clients and early testers, and that always happens with a new product.
Kevin: And then
Kevin: Comes down to prioritization, like, okay, what's really going to drive this to the next level?
Kevin: That's also part of fun.
Pete: Yeah, exactly.
Pete: It is.
Pete: And it's really cool to see the varying requests from folks that are getting demos or even customers that are using it on a daily basis now.
Pete: It's really interesting kind of the wide spectrum of requests that we're getting.
Pete: At the same time, we weren't getting requests and folks aren't using the platform.
Pete: So it's a good thing.
Pete: The more requests, the better, as I view it, at least in the short term here.
Pete: And then hopefully, the orchestrator can be this full funnel opportunity kind of audience strategy across the EdTech
Pete: universe as well as a really cool tool for agencies and mid market companies, even local kind of regional companies to be able to use and create value from location data that was never even a possibility before.
Kevin: Yes, definitely.
Kevin: Touching on unerry's services.
Kevin: So as we provide location analytic services for a number of retailers across Japan and other Southeast Asian and expanding into North America in the future,
Kevin: We always appreciate feedback from early testers, early clients.
Kevin: It helps us to prioritize and focus on, okay, which areas
Kevin: are your trouble points?
Kevin: Which areas do you need help in that our service can provide that value in, right?
Kevin: So that's always great to hear that you're getting a lot of good feedback from early testers and clients.
Kevin: I hope our listeners have enjoyed this discussion, learning about Orchestrator from Veraset.
Kevin: If they want to hear more and learn more, where's the best place that they can find information?
Pete: Yeah, yeah, of course.
Pete: Number one place is always our website, Veraset.com
Pete: You can receive regular updates if you follow Veraset on LinkedIn.
Pete: Obviously, you can contact us on the webpage there and learn more about it.
Pete: And we can give you a quick demo and see how we may be disposed to you.
Kevin: Awesome.
Kevin: Thanks a lot for taking the time today, Pete.
Kevin: For those listening at home, please do subscribe to the Location Insights podcast feed so you get episodes automatically in your podcast app.
Kevin: I've been Kevin, Global Marketing Manager at unerry, and thanks for listening.