Welcome everybody. So we're going to be talking canvas data today. So I guess just to, I guess
we've got Cecilia here today. So I guess I'll introduce myself then I guess we could go around
and just do a quick introduction to each other. So my name is Keegan and I've been in the cannabis
space for a little while. My background is in economics and so it's fun to apply economics,
data science to problems in the cannabis industry. And so I've coincidentally started a company,
Canelytics, that does exactly that. And so we help people in the cannabis industry make sense of their
data, wrangle data, and get value from their data. But enough of me, I'll let Charles and Paul and
then you as well Cecilia introduce yourselves to each other. I'm Charles and I have 27 years of
software development experience and I've been working in data science for the last year and a
half and I guess Cecilia is gone so we can skip that. No, do go on Charles. I didn't know you
had 27 years experience, that's quite a bit. Yeah, it's been a long, long time of programming.
And if you can share, how goes your forays into machine learning?
It's going pretty good. I've been making some progress in the Canel competition. I'm at 97%
and the leaders are at 98, 99% so I just got to eek that little extra percentage out there.
Good for you, that's good. Thanks. And I found out, actually somebody told me about this a couple
months ago, and I tried it last night, there's this library called Pandas Profiling and you can
give it a data frame and it will tell you about each one of the variables or each one of the
columns like how many missing values there are, what percentage of, you know, what the most common
values are in it. It will give you a correlation matrix. Wow. It just does all these things and
it'll export it to an interactive HTML file. So you can bring it up in a browser and just
click on different buttons and tabs and stuff and look at it. It's amazing.
It sounds pretty powerful. It is.
Welcome, Sean. We're just sort of introducing each other at the moment, or introducing ourselves.
So my name is Keegan, this is Charles, Paul. We meet up regularly to talk about cannabis data.
So we're happy to have you. Thank you. Can you hear me, guys?
All right. Barely.
How about that? Much better.
Perfect. Well, nice to meet you all. Yeah, I guess I'll introduce myself. So my name is Sean.
I've worked in the cannabis industry here in Denver and Northern Colorado
for about seven years. And I just recently finished a data science program
to kind of upskill myself. And so I'm just looking for any opportunity to apply, you know,
my cannabis experience with this newfound skill of data science that I have.
Well, you found the right group.
Perfect. Is everyone in Colorado?
We're scattered actually in various states. So I found a company, CanLinic,
that's based out of Olympia, Washington. Charles is in Oregon, and Paul is in Michigan.
So we sort of represent cannabis markets all across the country.
We'll be in Colorado Springs over the July 4th week to visit some friends out there. So we'll
be too far away from you, Sean. Yeah, nice. That's a good time to visit.
Yeah.
Can you, I guess, do you have like any topics of interest that are like, so what like are people
talking about in Colorado? If I can ask. Yeah, Colorado, last year passed legislation for
cannabis clubs, basically a bar you can go to and smoke up. And that's been the buzz lately.
So I don't know how that plays in the analytics. I actually have some data from a dispenser I worked
at that I wanted to see if I could get some advice from you guys on how to crunch these numbers,
or if you know any algorithms to do some. My idea was to do like a sort of practice with some time
series prediction and use it's about five months worth of sales data and see if I could use that
five months. I know it's not perfect because not a full year is worth the sales data, but
at least try and play with some algorithms to get some time series predictions.
Yeah. Keegan, do you mind if I step in? Yeah, so Sean, I'm just actually wrapping up a master's
program in data science from University of Wisconsin and I'm working on my capstone project
right now. Thanks to Charles and Keegan, I was able to get hold of some Washington state sales data.
What I'm doing right now is going to be using some market basket analysis on some sales
information to see if I could make some sales recommendations from dispensaries to customers.
What I really wanted to do was do a project on sequential pattern mining, which kind of is the
same thing as market basket analysis where it makes recommendations, but it makes recommendations
over time. I just found out yesterday, last night, unfortunately, I was going through the data and
the sequential patterns I was looking to uncover was between the suppliers or the producers,
the product producers and the dispensaries. So the product producers could say, okay,
you bought these products from us. Maybe you could buy product X and Y as well.
What I found out though is those transactions that are recorded in the Washington state data
are what the producers are doing is they're recording their transactions in a big batch.
So instead of every time there's a transaction, they record it, they just pile up all the
transactions to enter them in one day. So I can't actually use that data. So the data is pretty bad
in that respect. But I did notice that the sales transactions are definitely spread out over time
and I can use a market basket analysis approach. So it sounds like you're trying to do something
like with retail sales as well, but you want to do more of what? Trending, sales trending or
something like that? Yeah, just kind of a way to see if I can use the data I have to
kind of predict the next week's sales or the next month's sales. So some time series,
like a halt winters or some seasonal type forecasting. Yeah, exactly. And unfortunately
it's only like five months worth of data. So I can't get that full year spectrum in there,
but it does include 420. So that kind of adds an interesting outlier in there.
Okay, so I don't know what that means. What does 420 mean? Oh, 420, the weed holiday that everyone
goes and buys up the most weed. Okay, I didn't even know about that. Yeah, he told him I'm green
to the space, pun intended. Everyone has their big sale. Okay. Yeah, it's like the Black Friday of
the weed industry. Gotcha. Okay. Oh, that's interesting. Cool. Yeah, I can pull that up if
you guys give me a moment and then maybe you can give me your two cents on the data. Unfortunately,
it doesn't specify product. It just has the dollar amounts and then the time and then
I've redacted it because it had all the employee names. So I just changed the employee names to
employee one, two, three, et cetera, et cetera. So is this from their point of sale system at the...
Okay. Yeah. It's using metric. That's the standard here in Colorado for POS systems.
But yeah, just give me a moment. I'll pull that up for us to check out.
Just thoughts right off the bat. If you've got daily data, you could start uncovering...
Some of my favorite things to look at are day of the week effects. So what day are you having the
most sales? I have done some analysis like that and pinpointed which hours of the day you're
going to do the hottest and which days of the month are hottest. So I have been able to do some
analysis there, but at the end of the day, it just seems more like an HR tool to motivate employees
to sell more or something. That's kind of the gist I get from it. It's not really like predictive.
It's more like management tool or planning tool. Exactly. For essentially optimal staffing.
Yeah, exactly. Yeah. The way I would go about it is you'd partition your man hours. So it's essentially
if you have 12 employees, then they're each working 30, 40 hours a week. You would just have your
total hours and then you'd almost want to match that with... If you're doing hourly, then you'd
almost want to weight your staffing in accordance to your predicted sales spikes. If you're doing
that correctly, then you'd have your total hours and then you'd almost want to match that with
your predicted sales spikes. To the best you can. But if you can share, what were some of the...
I'm trying to remember where I put that in the analysis. This is sort of what Paul and I talk
about is there's so many opportunities here and I'm sure people have done them. So you're doing
them yourselves, but there's not that much out there in the public. So there's not that much
public research about, or at least that I've found. Point me in the direction if I'm wrong,
but it's research about say the busiest times of the day. So I don't know. There's just lots
of opportunities for you to get your research out there and people to get some eyeballs on it.
What would you say is a good platform for getting that out there?
Well, I may be biased, but at the moment,
Candlitics is essentially trying to provide data to people in the cannabis industry. So
it's still a seedling company. I'm always just a big fan of self-publishing. So if you have
your own website, put it on your own website. GitHub. Yeah, I've done Medium and GitHub.
And that's about it so far, but I'm always... Or LinkedIn. So I'm just always looking for
new platform really. So that's essentially what Candlitics is. So if you have something
in particular you want to get out there, we can send me a message and we can
try to get your data and your analysis out there. Great. Yeah. And just to be fully transparent,
my main motivation is to find a job in data science. Same here. Specifically in the cannabis
industry or just any... In general, but I think my domain knowledge would be best supplied in
the cannabis industry, but I know data science applies to anything really. Yeah. So for Charles
and Sean, if you guys are both looking for work, I work for General Motors here in Southeast
Michigan and we have this big push going on right now as far as data science analytics,
because it's really driving all parts of the company. So I can probably send you guys a link
to our job posting board. I can't guarantee anything. The good thing is now that we're
doing a lot of our positions, especially positions like data science remotely. So take a look at the
job postings. I can't guarantee anything, but I can always reach out to the hiring manager that
you'd be applying to and put in a word for you. But yeah, I'll send that link to both of you if
you're interested. Yeah. That would be awesome. Great. And I got those charts. You guys still
want to take a look at them? You're welcome to share your screen if you're able. Okay. Yeah,
I'm just not familiar with this one. The screen share is present now.
Share your screen.
Does it require permission or something?
Okay.
Does it seem like I need to require permissions?
Yes. Yeah, on the share button, it's all blacked out. So it seems... Oh no, sorry. That's my fault.
Okay. Is that working now? Yeah, we can see you, Sean.
Okay. So how do I drive this?
So is it moving? Yes. Okay. So this came from... I'll show you the data real quick. So this is kind
of the raw format of the data. So we have our price, the tax, and then total price and total paid are
the same numbers. So I eliminated one of those columns. Register, this column was kind of
unnecessary data. So I eliminated that as well. And this is where I redacted the employee names
from their actual name to E5 or E1, et cetera, et cetera. And this was the time of date. This was
taken back in 2019 as of the first of the year, all the way to June 2nd. So roughly five months.
And then this was the total gross revenue for the dispensary within that five-month time frame.
Quick idea. I don't know how much of interest it is to you, but you could potentially
go to the dispensary or a similar dispensary with this idea is you could basically try to...
I think... Sorry, I was just thinking for a second. We'll have to maybe pinpoint the specific
regression, but you could essentially run a regression and try to pinpoint essentially
if any of the... Like, if you're going to run a regression, you could run a regression and
you could essentially run a regression and try to pinpoint essentially if any of the salespeople
have above average sales. And I did something like that. So I'll go into some of that right now.
So this was my first initial analysis. This is in a Google Collab notebook running on Python.
So the original data set, I'm changing some of the column names here. And then this is
the pie chart indicating which employees had the most sales. Now, keep in mind that not all employees
are strictly doing sales work like this E10 and E9. They were mostly front desk checking IDs and
such. But as you can see, E7, E1, E2 had the lion's share of the sales. And then in another
little analysis. So this is where I was able to pinpoint
which hour of the day and which day of the month had the highest sales.
So based on... And forgive me, it's been a while since I looked at this. But this would have...
I believe this is where I pinpointed. So the Y column is... Oh, these numbers don't make sense now.
Sorry about that, guys. Yeah, it's been a while since I looked at these. But what I was trying to
do is I'm going to look at the numbers. So I'm going to look at the numbers. So I'm going to
do it as where it's blackest, it's the highest average sale relative to all the other days.
Here's the way that you could potentially simplify this to make it meaningful.
Or more meaningful. Or meaningful in a different way. So I was thinking,
you could almost lump the times of day. So the simplest would just be a dichotomous morning
or afternoon. And that way you could compare the two. So you could see, okay, what's the average
sales before 12 PM? And what's the average sales after 12 PM? I'm not sure.
I mean, they may be the exact same, but it would be interesting to see if, I don't know, maybe
average receipt goes up or down. And if so, by how much?
Yeah, I had something like that where I compared each employee to the average ticket price,
average sale price, or average total receipt. Just kind of comparing that way. So I can
cruise around and see if I can find it. But yeah, that's kind of the extent of what I had.
Just curious, Sean, do you know that you friends with the owner of the dispensary?
So the, no, well, yes, but they sold. So I caught wind of that and I tried to
get some financing together to kind of buy them out. And I actually got three million
lined up to make an offer, made an offer, and they laughed in my face.
Three million? Oh my gosh. I'm really lowballing you, I guess. I don't know.
But yeah, they rejected my offer and I'm pretty sure what they got was closer to 10 million.
Wow. So maybe you guys have a better perspective on this than I do, but I'm sure you do,
actually. But when it comes to dispensaries, they're all cash businesses, right?
Yeah, they're progressively, you know, debit cards and credit cards are working their way in
there, but it's through loopholes. It's not really anything official.
Dealing with that much cash on hand must be a real pain for the businesses to deal with, huh?
Yeah, it had its own challenges. We had two gun safes on site, and that's where our petty cash
and our product was stored. Even then, it got robbed twice while I was working there.
I went there for over a year, but both those robberies were kind of like almost joke instances.
Like one, they went into the grow room and then just literally dragged the plants out by their
stems with a trail of dirt following them. And then they were like, oh, my gosh,
they're down the block. And another one, they did a smash and grab after hours, broke through the
front door, went into the blood tending room, and then just took all the concentrates and then tried
to resell them. And then they were caught reselling them because they had all the initial RFID tags
and all that stuff. So, it's a bunch of jokesters, really. But still, I mean, that kind of goes to
show you that it's still a threat of safety. And I was thinking that would increase costs
across the board, whether you get robbed or not, because now everybody has to spend a small fortune
on security cameras, security doors. I think they use the armored cars for the transportation.
So, there's all sorts of extra expenses involved. For context, this owner, she was really
not privy to spending money on the business. She just wanted to collect as much,
I guess, margin from the revenue as possible. So, anytime where she could skip out. I mean,
we had security cameras all over the place. But when it came to reinforcing doors or anything
like that, she didn't care. She just ate that minor cost compared to whatever costs that
reinforced the doors. So, she's just selling out and leaving the industry or she thinks she can
reinvent? Yeah. That's my impression. She got started when it was just the people who were
growing in the basement that had the willingness to jump into this industry. And that crowd is
kind of getting phased out now. And then the market's consolidating in Colorado to more
chain type operations. But she is kind of like the mom and pop type of business owner and didn't
really have ambitions to kind of grow it. Just kind of walk away and cash out and walk away.
And she cashed out pretty well. I mean, you're not.
Yeah. So, you talked about consolidation. I think we've talked about that on this call a
couple of times, but that seems like that's inevitable, right? I mean, every industry
goes through that phase. And the intersection of data and consolidation, I wonder what kind of
opportunities I'm just kind of throwing this out as a brainstorming idea, but
market consolidation and data, right? So, what could be some opportunities there? I mean,
obviously every business, if you're going to get bought up by somebody else, you got to have your
numbers, right? You got to have your typical finances in line, your POS and everything else.
But can you guys think of anything that might be useful to large kind of, not corporate,
but maybe corporate, yeah, entities that are going to consolidate and pull the industry together?
Well, one data source that I wanted to play with was, at least in Colorado, all license owners are
listed publicly. So, to take that list of license owners and see whether it has shrunk or whether
there's more licenses, but fewer owners, just kind of get some preliminary analysis from that,
just to see, kind of measure that consolidation that you're mentioning.
Yeah. So, kind of have an idea of what's on the horizon or where it's trending. Yeah.
I'm sure it's just the same in the other states. All license owners are listed on a state list
somewhere. Yeah. It's hard to, I know in Michigan, it's not like Washington, right? Where Keegan was
able to supply some of the state data for Washington, but it's, Keegan, you're saying
that Washington state has like a very liberal Freedom of Information Act, and that's how
the data came about. Yes. However, Colorado does do a good job about publishing data. And in fact,
Yeah, we do. When, I have done a little analysis of Colorado data, it may be a little tricky, but
so one thing you can do with their data, so they publish, I think the licensee lists monthly.
So you can actually, you can track exits and entries. So you can say, oh, this person's
entered. And then at a certain point, they've exited the market. So it's an interesting thing
to track because say your licensees, say they increased by 50 in the month. Well, there may
have been 80 entrants and 30 exits. And so in the Colorado data, you can actually track, you know,
who's entering and who's leaving, you know, when they do it. It's not the easiest analysis in the
world, but it is possible. Yeah, that would offer a lot of good insight to see what the average time
or lifespan is for a license holder. At least the license holders that are seeing turnover versus
the ones that are sticking around. Exactly. So I'll have to, maybe next week, I can share this
analysis with you. So I'll share what I've done with Colorado. So I was just doing
entries and exits for each month. And then it would be a little more detailed, but I think
like you said, you could do essentially, I think in economics, they would call that survival analysis.
So, you know, how long are these companies surviving on average?
I think initially that would be, that would have been a, because in Colorado is kind of
an opportunity for a slow growth type of business model.
So I bet initially that was kind of a longer sort of timeframe. Whereas now
you don't have, you know, two million at least in capital to start something up, then
you kind of have to have to work. What about Keegan, with all your background in the lab
testing environment, just curious to start up a lab, that must be a huge investment, right?
Yes. So from what I've heard, it seems you're not going to really start up a lab for less than
about, you know, a million dollars in investment. And the more I learn about labs, it seems that
maybe be low. So maybe one to two million, just to get all your standard instruments
and get them calibrated, get your team of scientists do testing. However, that may be,
you know, peanuts compared to what some of these cultivators are spending on their companies.
So that's one thing, like you'll see at CannaCon, there were like some, you know, people who were
setting up some high dollar processing facilities, as well as cultivations. So I think the people who
are seriously going after large scale production, I think they're at much higher costs, but it's
definitely not trivial to set up a lab. And I would actually think just because of the some of the,
you know, the hurdles you have to jump, you know, getting your scientific team, so you've got to get
people with masters, preferably PhDs in chemistry or microbiology. So those are tough requirements
to meet, even if they're not nominally cost. Yeah, yeah, that was a big thing in Oregon. We had
legalization, and then they had all these really stringent testing requirements. But there were no
labs. And then people thought, oh, I'll make a bunch of money. So they, you know, they bought a
bunch of lab equipment. But yeah, then they couldn't find people to run the lab equipment.
There was this huge backlog, and they had to sort of relax the requirements until the labs caught up
to, you know, to be able to do the actual testing that they wanted.
I mean, that's interesting, because as we talk through these different kind of pain points,
there's an opportunity right there. So my brother-in-law, he does medical hiring, right,
for nurses. And so it's a whole industry on this type of thing. Well, there's an industry,
what you just stated there, Charles, there's a whole industry potentially there, right? How do you
find the right people to man these labs? That's just amazing that all through this chain, this
kind of value chain, there's all kinds of different things you could approach and, you know, try
something with.
Well, it honestly seems like every lab is looking for a good laboratory director, and or quality
assurance manager, because it's almost one of the things where if you have a good enough quality
assurance manager on the market, you may as well get two, if you can find them, because they're
that valuable and hard to find. Same for scientific director slash laboratory directory. Like if
there's two that you can get, like you'd almost want two. But like, you were all hitting on,
it's tough to find them and connect the dots, because
apparently if you've got a PhD in chemistry or, you know, there's a high demand for your skills.
Yeah.
And then the other thing with the cannabis industry is it's tough to find people that are,
I guess, on board to work in the cannabis industry.
So you find some people that are enthusiastic to work there. However, just to be frank,
there's still a little shunned in the scientific community. So just, you know, from word of mouth,
I have heard that, you know, it can be tricky to, to maybe move into other industries and other
lines of work if your background is at a lab that test. I imagine as time changes, probably in the
near future, right, especially as more and more money comes into this industry. I think this can
probably be less and less of an issue. Yes, I speak to that. I have two resumes, one with my
cannabis experience and one without.
Well, if you're looking for something at GM, send them on without because there's a bunch of
old timers in this, in this company. I swear these engineers, I get emails all the time that say,
you know, somebody's heading out the door of retirement, you know, with 42 years of service,
and I'm just blown away. I've been with the company six years, but these people have been
here since forever. These old engineers, it's amazing. And I'm just, I'm just, I'm just,
and then, yeah, so I, yeah, kind of got a feel for what I was
trying to show you guys what was better. So I'll just clear that up real quick if you don't mind.
Yeah, so for the Internship Luolun is all in control of CAMP, the
CAP, and we used there, I guess what Will and El rode there for course,
So this is the x-axis is a minute of the hour.
And so the y is day of the month.
So on let's say the 27th minute in the 27th day,
which sounds weird, but the average ticket price was $86.
So that'd be, I guess, a way to pinpoint, you know, target those times.
So guessing. Oh, so never mind.
That's the average of every minute within each hour.
OK. Why don't you you can bin it like by the hour.
Yeah, I think I was trying to do that in my function here.
So the column is based on the aggregating function was the average price.
I thought it was a little cleaner than that.
I know sort of taking big averages does lose your granular data,
but I just like to start with just a simple snapshot. So I love the hourly minutes.
I think just looking at the date, the day of the week's would be useful
and then then getting more granular as you go, because I think people have expectations that,
oh, yes, probably Friday, Saturday are busy days. But, you know, to what extent?
And like our Sunday's busy. Right.
And one thing I wish one data data point that I wish we took was
location of drivers license we looked at.
Because from looking at all the IDs at every customer that walked in,
I could, you know, I was working in dispensary in northern Colorado.
So we were pretty close to Wyoming and, you know, just ballparking from the IDs that I saw.
I would say, you know, 20 percent of our customers came, drove down from Wyoming,
which is which cannabis is still illegal there, but they didn't care.
And the cops weren't really pulling people over, driving back from Colorado into Wyoming.
And so I think one interesting data point would be, you know, where are people coming from?
Because we would get people that would say, oh, I come from North Dakota once a month
just to stock up on my stuff and or Texans.
I always love selling to Texans because they always had the highest receipts.
They would spend easily over three hundred dollars for each tourist from Texas.
So, yeah, we've hit on these good uses of these
or just these ways that these data points can be used in unexpected ways.
So, like you said, I think Colorado.
Correct me if I'm wrong, but they may even break down revenue by resident or non-resident.
You know, I think I guess this is probably a factor of the dispenser I worked at
because they wanted to do the bare minimum to get by.
But other dispensaries track your your ID and that's in a database somewhere that they keep, I'm sure.
But the dispenser I worked at was more of the.
All right, we're not in jail. OK, let's keep doing what we're doing.
Yeah, I definitely get this sense. I get the sense after going through the Washington State data that
the quality is really low and it's probably going to be that while for it's going to be like that for a while, I would think,
until there's kind of more stringent enforcement of, you know, for the dispensaries.
But I think Keegan, you mentioned this before, they're trying just to get these businesses.
They're trying to get their tax money, right?
And their tax money, the states are. Yeah, the states are, of course.
So the more more of these companies are operating, the more tax revenue they're going to get.
So I'm sure they don't want to push too hard to push some of these folks out of business, but to make it more time.
Oregon used to track people's driver's license.
And then Jeff Sessions became attorney general and they realized that like he could come in at any time and take all that information.
So then they that the governor signed an order to purge all that information.
They can't keep that information for more than a day or two, or maybe they don't even keep it anymore.
So you're tracking it and then they stop tracking it. So, yeah, it's.
You've hit on something. I don't know if it necessarily applies here, but there's a principle I heard is almost be careful what you measure,
because that dictates a lot of your analysis.
But in this case, it depends on who wants to be careful. But like in Colorado, like, for example.
Exactly. So if you measured the amount of sales going to people in Wyoming,
that may lead you to suspect that it's on this crossing.
We had this conversation a couple of months ago.
There was an article in the Oregonian about those towns along the Idaho border, and they had disproportionately high cannabis sales
because all these people were coming over from Idaho and buying cannabis.
And the parking lots of all the dispensaries were full of people with Idaho license plates.
And yeah, there was this thing, New Mexico, when the legislators of New Mexico said, yeah, we want to we want to legalize it before Texas does so we can get all that revenue from Texas.
So there's a great analysis there you could do like a heat map along the county, you know, along the borders.
Yeah, you know where it's legal where it's not legal.
So I tried to make that pitch to a town closer to the border of Wyoming.
And I pitched it to them. I said, hey, about 20% of our customers are coming from Wyoming.
You could capture that whole market because they were in between the town I was working at and Wyoming border.
So they would have they would have saved all these people driving from Wyoming about 30 minutes, which is a huge convenience for them.
And inevitably, the town shot it down because they were skeptical about weed in the first place and they didn't want it on their streets, which it was it already was there because they were driving down to my town.
And then, I don't know, smoking it there. But, you know, it is what it is.
You can't fight culture sometimes.
I think you mentioned this earlier where you were in if you didn't then I can mention it now.
At Cana Con, a regulator from Oklahoma spoke and she just generally said that they're not really there just to bust people for breaking the violations.
They want to help businesses operate by the rules.
And so I think that just sort of, you know, gets at the idea where there's all the if they just blanket ban it, then there's sort of shady things that happen.
And so, you know, maybe stuff was coming in before Oklahoma legalized it from Colorado to Oklahoma.
And so they're like, OK, you know, but just set some regulations and then let people operate by these rules and help them operate by their rules.
And so that's just the trend I've sort of observed.
It may kind of help people like, you know, not have to like take things across the borders because that was another thing that was brought up is that's actually, you know, that's pretty it is a federal fence.
So it's definitely not something to take lightly.
My one rebuttal to that is people were throwing up resistance to like, oh, these people are going over state lines and that's the federal offense, like you mentioned.
But my rebuttal to that is like every year right before the Fourth of July, people go to Wyoming from Colorado to buy fireworks.
Fireworks are illegal in Colorado, but they go up to Wyoming anyway and take them down to Colorado and shoot them off. And, you know, we have fireworks injuries left and right from kids just playing with them in their backyards.
So that was my one rebuttal. But yeah, no, it is a good point because it is.
I think it's a federal crime to bring drugs over state line versus fireworks over state line.
Well, it was just something that was brought up once again at CannaCon. Somebody spoke about just the legality.
So he basically said, OK, you know, if you're operating in the cannabis industry, you've sort of got a mark on your back.
You know, you have to operate professionally and within the scope of the law. And the last thing you want to do is take a bunch of cannabis from one state where it's legal to another state where it's not.
That's like a whole can of worms.
But anywho, I don't know. It just makes me think that, like, you know, I think Oklahoma does touch Colorado. So the panhandle supported me things that they were just just tired of busting people all day long.
And they decided, OK, we just need to get some regulations in place.
I don't know why they actually did it. There's a town in the panhandle called Texahoma, I think.
And they that's right where Colorado touches Oklahoma.
And I think they just just like position some extra police force there just for that reason.
But yeah, because I have a friend that started a business down Oklahoma, right, when it opened up because he was doing everything janky up in Colorado and decided to go legit when Oklahoma legalized.
We kind of touched on this last week is when you when they do do that, you know, it brings this whole, you know, company, all this revenue out from the black market into like the open into the legitimate business now.
So now they're you know, they're employing their employing attorneys are getting a bank. They're they're hiring marketers to employees.
So on and so forth.
So just.
I just think it's bringing things into the sunlight so that way that people can actually operate legitimately by the rules.
It's more transparent.
In the long run, it creates a safer product.
And it provides what we care about is better data. So that's a maybe we have a.
Or in a sense, thank you.
Well, it's been a good conversation today. So.
I have prepared just.
Just like I guess just with two minutes, Sean, maybe I can show you in Paul and Chris as well.
Charles just I just did a quick forecast of Oklahoma sales. So this could be just.
So this could show you how you could go about forecasting with your data because you're going to have a lot more data points than I do.
So basically.
Just real quick, and I'll publish this to to GitHub, but.
You can actually get so Oklahoma is publishing just their monthly tax revenue.
And so you can download that.
And pretty good monthly numbers.
Yes, so they're getting about five million dollars a month in tax revenue.
And so you can actually back out the total sales rate. So if the tax is seven percent, then we can back out the total revenue.
So you're all just good and just run this whole thing. So essentially, we'll just back out the revenue from the tax from the amount of tax.
And then essentially forecast that forward.
And I'll share the script with you. So we're essentially just using or bringing the models for forecasting.
And I've got some links at the top for you to sort of read up on a reading of forecasting.
But it's basically just using historic values to the forecast forward.
So just to show you so far, they started publishing data in July of 2020 and going through.
May of 2021.
And so cannabis revenues on average about 78 to about 80 million in sales per month.
And just to show you the forecasting model, we only have 11 observations.
And we're basically just forecasting within a are wine.
So that's just using the past month's observations essentially to forecast forward.
So our forecasts aren't the best. And so if you plot this out, you know, I don't know how realistic those forecasts are.
But, you know, it at least gives us, you know, a dollar amount.
So just to wrap this up real quick.
You know, essentially, I'm forecasting, you know, Oklahoma to have, you know, one billion in cannabis sales in 2021.
And that would be a, you know, I think like a conservative estimate.
So I would, you know, I would say more than that. But, you know, there could be errors, you know, maybe, maybe sales dip way back down in the fall.
So we'll see. But that's sort of a principle I like to share of forecasting is an iterative process.
So basically, we're going to save these forecasts.
And in the coming months, you know, we can see what the actuals were.
And at the end of the year, how far we were off.
Yeah. One billion sounds reasonable. That's I don't know for the first year of sales, Colorado saw one billion, I think, on the fourth year.
I don't know, correct me if I'm wrong there.
But I think that's what I remember.
And that's, that's why it's so interesting to look at these numbers is I think you're right. I think it took Colorado quite a while to get to one billion in sales.
So there was still a lot of resistance and hesitancy initially, whereas Oklahoma is coming in at it with all these other states kind of paving the path already.
I wonder if the kind of the late bloomers will actually move super fast because they'll have probably better access to investors will see less risk.
Yeah. Yeah, because all the banks still don't want to touch it.
They have the technology. So we've got to so, you know, in the production function in economics, you've got technology.
And so what's happened is so like a can of corn is all the existing technology that's been produced in Colorado and Oregon, California, Michigan, or where have you.
It's all the actually there is some people who are doing some amazing stuff with hydrocarbons in Illinois.
They were there. So you have people bring their technologies to Oklahoma, and then they're able to just start up quickly, but it just starting up at a staggering staggering rate.
That's that's something I noticed as well as people were getting were kind of cutting their teeth on the Colorado market. And then after so many years, you know, their friend back in their home state was saying, Hey, let's start a business.
This state just legalize. Just take everything you know and bring it over here. And that's kind of what my friends did in Oklahoma they got really good at extraction, and then took all their experience and then use that to kind of create their
business out of it.
Exactly. So, just for example, there are like some like, like, trimming machines that were built in Colorado and maybe not Colorado I'm not sure where they were built but in one of those states, and then, yes, the processing.
And this is where a lot of players are missing out because, you know, I'm sure there are like manufacturers out there who do build processing equipment and stuff like that, but they just didn't want to touch the cannabis space.
They just left an opening for a startup to come in and basically say okay we're doing, you know, cannabis processing equipment.
And now they've got this little successful business there so just interesting.
It's been, I mean, I'm a little biased but I personally think it's been a fascinating and just see the industry to watch grow and kind of take its form, just because it does have such unique circumstances in which it has grown and formed, you know,
dual state legalized federally illegal, kind of everything is kind of create a push and pull dynamic on how it's going to progress and move forward.
So, for next week, I'm going to dust off my old Colorado analysis and see how I get that up to up to speed. And then, yeah, I'd love to hear more about your analysis.
And then we can continue to bounce ideas off each other.
And I think, I think Paul could be a good resource for you Sean because he is quite the retail analysis expert.
I wouldn't say that I'm learning as I go.
I was gonna say I'm more than happy to share the data that I have with you guys for you to play with on your own time. I have no attachment to it or no need to keep it secret.
So, I'm more than happy to share that with you guys.
It could always be interesting like, like I said, the data points I'm interested in are basically the day of the week effects. So, what days of the week have the biggest sales.
And I'm kind of curious about morning versus afternoon.
But I'm sure there's tons of good questions but those are just the two that come to mind.
If you have any questions, you know, reach out to us.
And then you know check out Keegan's GitHub, my GitHub, cannabis industry data analysis there.
Yeah, so Sean I joined this group of probably what four weeks ago or so five weeks ago maybe.
And I think Keegan have been super helpful with my master's program answering kinds of questions about data and stuff. So, yeah, thanks guys. I really appreciate that you give me.
Sure.
It's always fun. I have a lot of fun every week. So, we'll be in touch. Until then, have fun crunching numbers and we'll be here next week.
Quick I don't know if you guys saw I put a link in the chat box chat window rather to GM's job posting board so if you see anything you're interested in just give me a shout out at my email account and we'll see what we can do.
Yeah, I'm copying pasting that right now.
All right.
Great to meet you all.
I'm having a fun engaging conversation. This is, I don't usually get to nerd out on numbers like this so this is kind of a nice fun group here.
Definitely. That's, that's the that's the whole point you know, join a group of like minded individuals because it's fun so we'll, we'll crunch some numbers next week and like I said I'll dust off my own Colorado analysis because I think you'll
enjoy that and I'll share it with you and then maybe you can pick up from what I have.
Sweet. Yeah, I'm excited for that.
Awesome. Well, until then, have a productive week.
Take care guys.
Good to see you. Good to meet you.
Bye.