Harnessing Geo-Targeted Ad Technology for Location-Based Cannabis Marketing

[ {Introduction}

cannabis marketing live I'm your host Jake litkey CEO of MediaJel and today we have Ted montanis um Ted has a long career in the advertising industry which um we'll talk a little bit more about and today we're going to talk about Geo targeting and how you can use geospatial information both from the past and the present to drive effective campaigns and good conversions so start off with a brief introduction here for Ted um Ted has actually recently joined MediaJel um late last year we're super excited to have him been working with Ted for many years now uh he comes from the supply side um he helped us architect the uh original ability that we had to even serve ads uh on mainstream inventory which took a lot of effort and we'll talk a little bit more about that um but Ted maybe you can give us just a quick background on your history and the advertising space and how you ended up here in the regulated cannabis industry yeah um I uh started my career on the television side uh at Fish Network and then Direct TV uh before moving over to you know digital uh spent some time uh with the data company all within advertising in uh New York City um and then I joined a a large independent adte company um and you know our relationship started I guess on a boat you know in can then came to myself about hey you know would you be open to accepting cannabis advertisers uh and at the time you it was a good match for us because you know we were somewhat newer to the US looking for an advantage and we were uh an independent company um you know not us as publicly traded um and thought it would be a little bit easier when we first thought about it but it did take some years a lot of handholding a lot of meetings with the Publishers um and you know it it grew uh quite quickly too because everybody now had a place to advertise online and it wasn't on you know inventory that nobody had ever heard of it was mainstream stuff and you know now almost five years later uh it's come quite a long way yeah the um it it did take a while as you said to get people um get the Publishers on board um you know making sure that they understood that we could handle the compliance correctly we understood the state laws um so let's talk about um and we're talking about mainstream inventory here right and and we still run into people every once in a while that don't think that they can run ads on CNN or whatnot and and we've definitely solved that issue um at this point and so I want to start off with some basic definitions um I think most people that have done some sort of advertising understand the word geofence

{Geo-Framing: Geography plus Timeframe}

basically right okay we're going to we're going to build a border uh around an area and we're going to serve inside ads inside of that um I think where people get a little more confused as when we start talk to talk about what we call geof Framing and we Define geoframe as a combination of geography and a time frame and so when we talk about a geoframe audience we're talking about an audience that is based on device behavior in the past right and that's usually a couple day look back window there's a couple days of you know the data aggregation going through it's various stages but what we do frequently then the most common thing that a Canabis dispensary wants is we have every dispensary geof framed in the US um so that means we have the boundaries of that uh physical location and we're able to capture device IDs for people that have opted in for advertising um put them into an audience and then and then Target that audience at the time that you're serving the ad so we're generally doing two things at the same time we're taking a geospatial audience of where device have been in the past and then we are selectively running ads in a radius around that location the radius varies by the location in a rural area it's going to be larger in a dense area it's going to be smaller um but if you take those two pieces and you put them together you build a pretty powerful data set um and so that's a Geo fence and a geoframe um and then on top of that we can then layer in additional audiences so we can layer in like things like demographic age income gender propensity towards specific activities like concert goers or whatever it may be um it really is a wide open field in terms of geography of what you can choose to do um and people choose to um create audien is kind of based on what their brand Affinity is um so I'll pause and and maybe you can talk about examples of where you've seen this be successful and I'd also like to talk about some of the experiences you've had outside of the canvas space in the way that large advertisers leverage this data yeah because there's a coming into this you know and thinking about you know this as like a topic um you know a lot of the time we see you know advertisers and I've had experience with advertisers where they're you know they pick one thing and they think that that's what it's for and they focus on you know how to utilize it that way like the most common one is um you know a conference I want to get everybody there I'm doing B2B I want to capture everybody um and with you know anything in the Geo you know let's call it realm um there's so many different ways that you can use it on like on a macro level uh and a micro level um if you really design it well so it does work for a lot of Brands especially when you bring in propensity um to it um and also just like with any targeting it's you know one piece of the puzzle it's not going to be the end all because just like with first part party second party you know third contextual Geo um you know they all have uh things that they excel in and then they all have their weaknesses too um but when Gio you know started to first come out mobile advertising was growing um I was actually out a mobile company at the time and um it was you know very interesting a lot of like big advertisers were getting into it um especially and it's kind of put us on the path to retail you know the which is now the hottest topic is retail marketing um but I did work on like a couple interesting campaigns that are you know some of the more you know from the past that are you know proud of or really interesting one particular comes

{Case Study 1: Geofencing Campaign - Purdue Chicken}

to mind which was you know when we first started to get you know Maids mobile IDs and the tracking and um was for uh Purdue chicken um and their you know whole Campaign which one of the you know things about using any of these targeting is that it has to be linked with everything else it's you know like if you just focus on you know the wheels or the seats or the engine like there's still a lot of other components to the car that all have to be working in sync so in this case it was the creative that was done first or the concept of what the adver like The Advertiser wanted to do so Purdue chicken it was a summer campaign uh they had two Targets they wanted the household um decision maker for for food shopping uh as well as converting uh consumers to utilize chicken products uh for outside grilling summer activities um and our offer them was tailgating you know switching from burgers and dogs to chicken you know obviously it's healthier um you know that was part of their pitch so we you know in the early days we you know Geo fenced around all of the uh baseball stadiums and other large sporting events that were going to see the devices coming into there and at ones that have been in there in the past so you know we knew maybe that they went to the games um maybe there was consistent ones we also did uh state parks that had grilling camping grounds um to try to you know Target those people there because a lot of time when you're camping you come then maybe you'll go out and bring your food or get it like we wanted to capture those ahead of time as well so then we linked it with travel sites as you know for the broader Market Market too so it was all kind of going to one storyline um and at the time rich media was you know growing too so the ad unit itself would either swipe through or you could click and it was uh like tailgating chicken recipes and then from there on the location based taking it one step further it would tell you where you could go and buy those products you know so the suit the certain supermarkets too so then we knew that you know to help the first um Target which was the household decision maker on food buying um this is a you know very successful campaign that we ran from uh May to September um you know changing the creative in and out but it was you know one of the first ones i' had worked on with uh you know any sort of Geo targeting um the next we took the similar concept um for I still think one of the most interesting um Advertiser

{Case Study 2: Brand Awareness Campaign Through Geofencing and Geotargeting - Exxon Mobil}

initiatives too because outside of cannabis you know in the more General market like they can do things like this they're not tied to to ad you know they can do more brandings and this company in particular definitely has a lot of money uh so their campaign was awareness based but the focus of the campaign was be an engineer uh and it was for Exon Mobile they proposed you know kind of a Challenger uh creative that said you know we are in threat of not having enough Engineers we're not going to have enough you know people to design and build buildings and bridges and infrastructure or computer engineering like we need that um you know education we need people to become that um obviously and then everything is branded X on mobile um what we did was with the budget we wanted to do a localized thing uh we couldn't Target obviously you know children people under 18 so you know but it did blend with college seniors and people in college so we looked up the top 25 engineering schools in the country um you know we found on their sites when they were doing career fairs and career days so that's when we would do the framing um and we would Target the students there hopefully you know making a decision on their major and what they wanted to do and we focused more uh on at the time the big 10 schools which you know Purdue Wisconsin uh Michigan Ohio State Indi know you know they had some of the better engineering schools uh and that was also you know a great success but we use geofram and geot targeting in different ways to share uh a particular message

{Case Study: Targeting Tourists for a Cannabis Dispensary in Las Vegas}

yep yeah that makes sense um we've done some interest I'll Circle back to some some cannabis examples that we've done I think one of the more interesting ones we did in um in Las Vegas right and so and this is where we used uh time uh and people's place in a in a pretty creative way so we created an audience that was National so was anyone who had been in a dispensary in the last 6 months um and then we created a Geo fence around in and around the Las Vegas airport and uh I don't remember the numbers but there's actually a shockingly large amount of people that land in Vegas and their very first thing they do is go to a dispensary like right off the plane right so yeah it's legal now here in Connecticut but yeah at the time yeah so um so we took a national audience of people that have been in dispensaries but people that did not live in Nevada or Las Vegas specifically right because the particular campaign was just for tourists and so we said okay these are people that we know have been in dispensaries um in the past 6 months and then we served ads basically just in the Las Vegas airport so anyone that is landing they're looking at their phone they're checking the weather they're getting directions whatever it is these ads are showing up on their phone and it was an ad that was specifically not for locals and then it was directing them to a local dispensary right and that's I think a good example of showing how you can use past Behavior for you know uh past Behavior somewhere else um for local activation yeah yeah and one thing for you know in cannabis too I mean it it's still fragmented you know there there's

{Optimizing Cannabis Ad Creative for Different Markets}

nothing that's like truly National um but if you're you know operating in a few different states um you know each each state is different the culture is different the people are different the Topography is different you know the sports of everything is different um but let's say you know you're operating in you know the 3M Michigan Massachusetts maybe Missouri or and you want to add maybe Colorado or something in there like uh each each of those consumers are going to be different their habits are going to be different their interests are going to be different so if you look at it like on a macro level of the types of profiles the types of people that are the majority that live in those States what they like what they do for work then you can start to build you know more of that propensity model um and then you can go up further layer deeper too if you're this brand but you want to make your product successful move off the shelves in those specific States you know the difficult part is that you have to have the creative also align with the messaging for that Community but then you end up learning more about the community and what they're into like we just saw this recently with a case where there was uh in California um it was a dispensary that had you know I think it was seven eight locations six in a concentrated area three particularly very close but looking at the behavioral and demographics of the you know Geo area around each of those locations they were vastly different in their interests their careers um you know their cultural ethnic background was very different even though they were very close so you have to have messaging that speaks to each one of those three but you learn over time in those areas in those communities how to better Market to those specific people because what one area likes another one doesn't it's like if you're a brand and you're advertising in Michigan and Ohio coming online too like your colors of you know blue and gold aren't going to work in Ohio you have to be conscious of that like on a macro level of location and then learn about the different habits in there um and it can also be used too as the Cannabis business grows and um you know more and more dispensaries open up there's more chain dispensaries um and this is something that right when geot targeting came out for prop and propensity was you know somewhat of a new thing in digital um and I think it was you know like Walgreens but I know that uh Lowe's and Home Depot got into this very early where they'd look at it as more of a you know attrition of you know who to take out not who to Target because you know if you ever lived in a city or you know you've you've been a renter so you move quite frequently you're going to go to you know the hardware store that's closest to you you don't have you know a brand loyalty to Home Depot or Lowe's and especially at the time um you know and as a side thing we talk a lot you know recently about GameStop but GameStop was one of the first companies to come out with a very very sophisticated CRM and loyalty program they actually speak on that now that's what made them successful in the early days um and at this time those stores realize that like yeah if your office is here we see your device here and your home is here and we know maybe on uh lat long we can see it based on certain hours the day and you're close to a Lowe's and if I'm Home Depot like I don't want to serve an impression of that person because they're not on my path to purchase physically so it's not you know quite worth it if they move we'd rather focus on the people that are closer to it it's the same with like a CVS or Walgreens like you now you'll have both Loyalty cards but you go to the one that's more convenient so maybe you want to Target somebody for loggings when they're at work they got to go pick up a prescription but on the home hours and things you might not want to do that because they're closer to a CVS yeah yeah we've done some actually

{Personalizing Messaging and Cannabis Campaigns to Target Individual Behaviors}

we did some stuff with uh Home Depot and Lowe's in the past and there are some interesting demographic differences right so and you've got a lot there's a lot of places where Lowe's is right across the street from Home Depot in the suburbs um and Lowe's tends to have a a demographic that use higher income and more female um and Home Depot is the opposite right so but you that's where you start to use that information in terms of your targeting and and personalizing um the the messaging and for your campaign and so if you look at you know alcohol brands are really good with this regionally um so like Guinness right now in California has like Guinness California cans right and if you look at Billboards in San Francisco versus LA you're going to have um like say bardi for example might have a billboard but they they adapt it to be regionally relevant right so they'll use catchphrases and and local terminology um for that region you can do the same thing with a digital campaign and but you're no longer just bisecting people by geography you're looking at their individual behavior and so if you go back to what you were saying around colleges right let's say you've got a big conference game um and you've got two rival schools you can run the same exact campaign to the entire Stadium full of people if you want to do that but if you know what school they're coming from then you serve them the different creative right with the different color scheme or the different catchphrase or whatever it is um and you can do that in in pretty good detail um around any uh propensity that you can come up with whether it is gender political income Sports um and by understanding where your audience came from geographically it allows you to Target them in a way that is uh it really doesn't have anything to do with pii right so you don't need to know who they are you're not you don't need to get their email address you don't need to have an opt-in necessarily because you're not targeting them as individuals but you know that their device home is in this area and then you know all the information about that right so you can start to layer in like with a demograph where we've got you know over a thousand characteristics for each home um if you know that this audience member comes from a home that matches these particular demographics you can use that to not just um decide when and where you want to serve ads but what the creative is that is going to be served to that individual yeah you can also build um you know a a long-term um marketing cycle especially if you're a higher ticket item uh based on propensity and geolocation so if you copy what uh purchase data does with you know PA like analyzing path to purchase and building lookalike models like the longest term one of those would be you know speaking of Home Depot one of their higher ticket items like they don't make as much money and margin of you going and buying you know a Milwaukee or DeWalt drill but you doing either a large project or utilizing them for a large project and so you can look at certain identifiers that you know put somebody on a path to do that and you essentially you could go back all the way to somebody purchasing an engagement ring and then what puts them on that path to then ultimately becoming a homeowner that they're going to probably want to redo the kitchen or something like that and you can collect all this data in very you know compliant ways and that and put those on longer cycles and then build the look likees but you can do something similar with the propensity you can see that you know somebody had been to a concert or a game and the fact um and you know build that out or that they visit these places and come up with a storyline of like they're probably doing this um or they're at this time you know it is somewhat granular but it it does have its specific use cases you know a good a good segue into cannabis is um looking at you know purchases or location um and time spent of you know people that are you know Green thum you know they're at nurseries they're you know like and based on other habits that you pull in and content that they're doing maybe they are growing their own garden and stuff they'd be a perfect person to Market seeds to yeah yeah absolutely that's um the the Home Improvement thing gets back to one of the and this has been used forever um but one of the more valuable uh audience segments that is uh

{“We Just Moved Here.” Targeting New Cannabis Audience Segments}

applicable to a lot of businesses is the new mover audience right when someone moves especially if they move cities they're going to switch a lot of their brand loyalties um that definitely comes into play for a dispensary even moving across town because people do tend to go to local dispensaries that are relatively close to them um and the new mover audience has been around for a long time everyone is experien this when you move you get all kinds of mail right um for for whatever the local businesses are uh and that was and that data still exists and it's based on the US Postal Service when you change your address right that that list gets sold and reused um but we we now have the ability to do this in a digital format which because we can see that this device used to live over here and now it lives in a new location um and that's it's you can get you can get that information and you can be more responsive when you're using digital signals right because there's a time delay when you're dealing well anything with a postal service is going to be slow um but the ability to get in front of someone who's changed their physical location as quickly as possible is extremely valuable right you want to be the first brand that starts to show up to a consumer when they move to new location when they're deciding what their local dispensary is going to be yeah yeah that's a good you know and again it's you know just like one component it's not the end all you know we had you know use cases too for new dispensaries opening up and uh you can

{Using DemoGraph to Understand Cannabis Customer Data}

you know bolster or make that uh concept to work better too by working with somebody that can you know put messaging out uh that either it's opening or would you be interested in you know being a customer at this place you can do some research analysis also in digital um you know to get a feel of what the community is and obviously you can use the you know demograph data to understand you know uh the types of you know political affinities or um you know the neighbor the makeup of uh careers in that in that area too and it all goes into like that story but like using Geo uh targeting definitely enhances everything else you're going to do especially if you're a retail yeah um some of the other things that you can do um with Geographic

{Geographic Behavioral Targeting and Audience Segmentation in Digital Advertising}

Behavior and the and the way to think about it is uh with audiences in general is you want to kind of think think of a a central Hub and then a snowflake of spokes around it with those snowflakes being identifiers um and there's a bunch of them right it could be you have someone's email address you have their home address you have their phone number um those things can be translated into uh Geographic targeting audiences so if you've got um your CRM list of however many thousand email addresses that data can be onboarded in a in a privacy compliant way so the emails get hashed and encrypted but they can then match to the device IDs that we've been talking about for targeting and you can break that up within your CRM list so you can say okay these are my regular Shoppers I just want to do retention marketing uh for these particular uh this particular audience and then you can have these are you know lapsed or or dormant customers and I want to give them some sort of offer you can take that data you can turn it into uh mobile device identifiers and then you can serve them digital campaigns um wherever they may be And to clarify I'm going to try to remember there's we do this so often there's things that I forget that may not be obvious so I'm going to try to remember those things um these ads can be served on anything with this screen

{Versatility and Limitations of Cannabis Ads Across Screens and Platforms}

right so that's your phone your tablet your desktop computer and including CTV or connected TV um we've got many mainstream channels now that will accept cannabis ads on TV which means you can be serving ads on the living room television um we're not serving ads on the the cable networks because those are controlled by the FCC and basically anything with f in front of it isn't going to work very well with cannabis um but most people at this point if you're if you're watching TV on an Amazon fire sck or Roku or a smart TV those things aren't regulated by the the federal government and the channels that you're looking at whether it's Hulu or Netflix that's that's all runs separate from the federal from the FCC essentially so um it's not every provider but there's there's quite a few of them now um mainstream providers that that are you know household names like Discovery MTV uh where we can run cannabis ads and and we can do that in a targeted Way by using information that we know about consumers either the demographic information about households um or other identifiers that we have yeah yeah and we we just like briefly you know talked about in the beginning but when we talk about you know the content and the premium content that people can run on um I think it's you know also important too is like for

{Complexity of Ad Formats, Publishers, and Metrics Across Various Platforms}

you know cannabis Brands and retailers or advertisers to understand that like it's not that you know you're buying CNN or your ESPN or you know this Channel or that that like those specific ad formats in those ad units might be owned by a different company or there might be a a hold code that owns that publisher or that section of you know a multiple uh content Outlets you know I mean the easiest ones that come to mind are you know Gat Hurst uh Pensky K Nast um but then there's even granular ones that have like specific ad units and formats on those pages because you know Publishers which is a whole another you know thing rabbit hole that we can go down to go down in but a lot of those ads on there aren't necessarily you know their specific technology on that page like a good example is with Native like that's usually a third party you have you know out brand or Tula with that so when people ask for a publisher list technically it's you know not accurate it's a uh you want a domains list because you might look at the publisher and say I don't know who that is like I I wanted it on premium stuff then if you look at their apps or you look at their domains that they have then you'll start to recognize those as well because for that content producer that publisher is trying to you know yield the highest return on the eyeballs on their page um which is why the industry as a whole is looking for a new metric outside of uh you know neelon ratings and we're moving more towards an attention-driven um metric for a currency that media runs on um but it's important to know that because it's the formats and the units that you buy on the page and when it comes to mobile devices and in CTV as well too because you have to factor in that it's it could be LG it could be Samsung or it could be Apple TV uh fire stick chomecast or could be Xbox or Playstation you know those are different devices different IDs U but they're still on the same IP address yeah and uh we'll maybe we'll talk about that a little bit um is the

{Layering Targeting Strategies for Maximum Impact}

different types of targeting we've we've thrown out some of these terms um collectively there's a term called Maids which is mobile Advertiser ID you may have heard things like Google advertis ID or idfa those all kind of fall under that which is it's a it's an identifier that lives on the device um it's just a big long string of characters it's not necessarily tied to an individual um but the way that the advertising ecosystem works is it's all happening on an auction kind of like the stock market so when we are buying media on behalf of our advertisers we are looking at looking for specific auctions that we want to participate in right there's I don't know a trillion auctions every day in the US more there's there's a lot of them even in any given Market there's going to be Millions every day right and so when we talk about targeting a specific device based on where it's been in the past because we have that device ID that we saw in a dispensary or at a concert or wherever it is we basically put that in as a filter right so here's all of the Impressions that are available to bid on we are only going to bid on Impressions that match this specific um set of identifiers whether that is a mobile ad ID or a household IP address address um or an IP address that's associated with a specific device that belongs to an individual um and there's a lot of there's a lot of detail to that that we could spend hours on but at a high level um it it does like it is a good point too especially for you know the you know people probably listening to this and you know in cannabis is you know what I've seen in my time in it and you know being having experience in that and now and here is that a lot of it comes with a couple of things I've heard a lot where it's kind of you know gotcha buying you know because companies will share a ton of this data and show they might not necessarily use that you know unless you're sophisticating or looking at you know the bid logs and at like those reports coming to you but you know the the gotta targeting too um is you know detrimental to like what the overall strategy is and got marketing is that like I want it around this radius and these people that do this they have to be this type of person I want to know how in this area based on purchase like and it's while it's as much of like you know I want you to prove that like these are the right people and everything because obviously you have 2 ad like you it has to perform you can't just say like yeah like I just hope everybody is an engineer one day you know and then oh yeah here's our brand like yeah it it it has to be performance- driven and driving sales but you don't want that to get into the way of um making it successful and how everything else you know Works in conjunction with each other so if you're looking at the content and you're like I wanted to run on these sites and this and on this site list but I want it to be this person and that person and that one it does compound the price and whoever you're working with it does severely hinder their ability to make it successful because then the next convers like why I wasn't successful and it's like I mean because of this and you know each different tactic or strategy need to all be somewhat working together so if it's a data driven strategy and you want to go after this type of person and stuff like you'd be very surprised that they might not read the stuff that you think that they read because you don't know everything about them but if you want it to be a contextual strategy um that's you know that needs to be a standalone and you can get creative with who that is which tying that back into Geo dat you can start with contextual people that are reading music websites and maybe they go to a concert you look at the concert venues around the cities you know if it's New York they've been to you know their device has always been at Terminal 5 things they're searching you know this they're reading that type of content and you see those devices across those IDs coming up and maybe they've been to the concert in the past um you know and that they go to Coachella they go to electric force they go to you know Firefly or something like that but you know if that's your audience you're building that model from them and the content that they read and you're s

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