Yes, so just kicking things off today. So, Charles, so you've begun looking at the sales
items. And so what's this you're saying about novel values?
When I merge it with the valid lab results, they all turn out, no, but I think there's
a typo somewhere because one of the columns is not getting renamed properly. And I need
to figure out. I think that's causing the problem.
Okay, we can talk about that today. So essentially mapping these things out. So it seems that
the sales items just have the global inventory ID. And so then we have to then hunt down
the inventory item. And then I think the inventory item has the lab result ID that we need.
Well, actually, the lab results have the inventory ID.
Exactly. So basically, we have to match them via the inventory item is the middle man,
so to speak.
Although I kind of remember there was something like that inventory ID doesn't match the batch
ID. And if you have to go, maybe I had to go to the batch file and get the inventory
ID from that in order to make it work.
You're saying that file BAT?
BATCH. It's a batch file. Pardon.
Oh, they're batches. It's batches of, of Canada's.
Oh, gotcha. Thought it was dot bat. I was like, okay, thanks.
So we'll need to make a mental map of it. So to welcome Paul. So we're just talking
about how we're planning on wrangling the sales data for the future. However, however,
for today, Charles and Heather and Paul, if you're still on board, Paul's put together
some groundbreaking research on cannabis dispensaries. So I was thinking that today would be a good
platform for Paul to present. So maybe at the end, we can talk a bit more about wrangling
these sales items. But what do you think, Paul, you want to present your market basket
analysis?
Sure. Yeah. What I'll do, sorry with you Keegan, is I'll go through some just highlights
of the paper that I did. So there's some visuals that people, that people, Heather and Charles
and you can look at. So I was going to start off just by conceptually telling you what
market basket analysis is, and then go from there if that sounds okay.
Definitely. So usually I'm up on my podium. So this week, let's, let's listen to your
research. So I'm all ears and I've got a couple of questions and then, and anyone feel
free to chime in if you've got questions at any point. So the floor is yours, Paul.
All right. So I'm going to, just going to share out some information here. This is from
my graduate project on market basket analysis used to identify interesting product combinations
and dispensaries. So can everybody see my screen?
Yes.
Okay. All right. So market basket analysis has been around for a long time as a data
mining technique. It started in the early 1990s when point of sale systems came on board
where at the checkout counter, you could scan in the UPC code and it helped out the checkout
person to speed up the process of tying up what you bought and also helped inventory
control with everything that was in a, in a grocery store or retail store. So these
systems really came into full swing in the, in the early nineties. That combined with
the low cost of data storage really kind of set the stage for a perfect storm in the retail
market for deeper analytics because all this information was being collected. So research
was done by a couple of kind of groundbreaking scientists at the time in this area and they
came up with this approach and it's called different terms, but probably the most popular
term is market basket analysis. The idea behind market basket analysis is that if customers
buy products in certain combinations, it can tell you a little bit of a story. So if you're,
I got some examples here in the middle of the page here. So if you buy peanut butter
at the grocery store, you may be likely to buy jelly. And something that's maybe even
more interesting would be if in the third example, if you bought hot dogs and buns,
you may be likely to buy ketchup. So you kind of get the idea that these interesting useful
combinations start to emerge in the data. So I thought it'd be interesting to apply
this kind of proven technique to a new market, which is cannabis retail sales and dispensaries
to see if we could start seeing, you know, it's more of an exploratory analysis to see
what kind of product groupings are starting to pop up. So before I could really do that,
I reached out to Keegan and he was kind enough to, and Charles, both of you were kind enough
to help me out and get me started with a data set. And that was the Michigan State seed
to sale, Michigan State, I'm sorry, Washington State seed to sale data set. So I got those
raw files and imported those up into the cloud. I had to do some data conversion and things
to make them usable. And the files were very, very large, larger than what I'm typically
used to working on, you know, on personal projects at work, I work on some large data
sets, but this was this is pretty good size.
Can I ask you how large were the files once they were unzipped? So these sales items,
is that the data you were working with?
Yeah, yeah. So some of the larger ones were about 15 gig. And of course, that was too
way too large to work on my local laptop. They would just keep it just keep crashing.
So that that information is out actually on Google Cloud. And I think I've got about a
week's worth of free time left to to use those those those tables that I might export it
up to the cloud. So if you guys are interested to try and take a look at those before the
the free the free cloud, I guess, they give you like a 60 or 90 days worth of free time.
Well, if you guys are interested in trying to get access that we can figure that out
in case you want to poke around.
Well, the task that I had kind of set Charles down this rabbit hole, and I cannot talk to
you about it as well is there's this one data point that I really need, essentially, trying
to answer the long question, does THC matter? And so that is the total sales per lab result.
And so the algorithm that I sketched out, let me open it up so I can essentially read
it verbatim. Because I basically wrote like an algorithm that Okay, so how can we go about
getting these?
Okay, so you and Charles are working with, with complete data sets, are you or are you?
Well, essentially, we were just reading in the sales items chunk by chunk. And so you've
got them in the cloud. But regardless, the logic is still similar. Given the sales items,
each sale item has a global inventory ID. If you match the global inventory ID to the
inventories, you can get the global lab result ID. And essentially, we would like to just
iterate over all the sales items, and then add the price total for each sale item to
running total for each global lab result ID.
So let's go through those IDs one more time. Is this for the Washington State data? Is
this for a different state? Yes, this is the there should be the same data set that you've
been. Yeah, okay. So you have you have a sales item ID to a global inventory ID. And then
the global inventory ID goes to just the what was the next one? It goes to the inventories.
So there's a data set called inventories. And it has a global ID. Right. So that should
be your connection there. And then the inventories data set has the global lab result ID. Okay,
global lab result. And that would have to price that final lab result. Exactly. And
so then if you're thinking about just like an Excel spreadsheet, essentially, I just
would love one column, that's just global lab result ID. And then the other column is
total sales. Okay, so lab result ID, plus the total sales column.
And so essentially, what I'm trying to get after there is a couple things. One, that's,
if you take the average of that, that's the that's the expected cost of failure. So that's
an interesting metric. And then two, I want to run a regression of total sales on on two
things one on cannabinoid concentration, to find out if there's a statistically significant
relationship between say THC and sales, or even CBD and sales. And then also, well, I
was actually talking to a purchasing manager recently, so someone who does purchasing for
a retailer, a large retailer. And so they said in the early days, so they've been in
the industry, maybe 12 years or so. And so they said in the early days, you know, your
buddy would come over, and they would just, they would, they would manually just, they
would just try a sample out. And that's how product was sold. These days, you know, the
large retailer will, they'll typically get a, they'll get an Excel spreadsheet, and maybe,
you know, 30 certificates of analysis. And then they need to select their purchases,
based off of those certificate of analyses. And so, so it's really interesting, because,
you know, they're not actually seeing the product hands on, they just have to go straight
off the certificates. And so I asked this purchasing manager, okay, what are the variables
that you look at on a certificate? And surprisingly, they said, you know, you look at your cannabinoids,
and surprisingly, they said they look at the moisture content, because their producing
manager was taking a moisture corrected adjustment of the cannabinoid concentrations. So you
would divide by one plus the moisture content to try to be able to compare products with
different moisture contents. So trying to, they're trying to normalize, normalize it
so they can compare. Exactly. And so I'm interested to look at this question, because just anecdotally,
in my personal belief is that maybe the lower quality cannabis may tend to be drier. And
then the it's a tricky balance. I don't think you want like overly moist cannabis, but I
don't think you want overly dry cannabis either. Yeah, it's not unlike the idea behind a humidor
and cigars, right? Exactly. And so I'm curious, I'm just curious, what does the THC concentration
even look like once you take the moisture correction? And two, if that introduces another
interesting question is, okay, let's say, you know, we're asking the question, does
THC predict sales? Well, we can also ask, which predicts sales better, just your THC
content or your moisture corrected THC content? Right, or any other factor you might consider.
Exactly. Yeah. And so at least starting with those two, you know, we could just do a regression
and essentially, whichever regression has the high higher R squared, that that variable
would do a better job of explaining total sales. So then you could tell purchasing managers,
hey, you know, from the data, it's better to make your purchases based off of either
the THC concentration or the moisture corrected THC concentration. Yeah, that's a valuable
decision point, right? So I'll take a look at Keegan as far as pulling that data together.
So just let me go through that one more time to make sure I've got the tables and fields
knitted together correctly. So you got the sales item ID. And of course, the sale item
has a global inventory ID is right? Is that right? Exactly. And you're all pumping in
the chat and also send send it to you afterwards. Okay, and then I'll see what I can find for
you and Charles as far as pulling that data out. And if it's, you know, depending on the
size of the file that it generates, we can get that to you. Exactly. And it shouldn't
the file size shouldn't be it may be a few gigabytes. So you may need to share via Google
Drive or something. But it shouldn't be overly large, because it's just an observation for
each lab result ID. Yeah. Okay. Yeah, I'd be glad to do that. Sorry to take up your
time and send you on a wild goose chase. But that's the direction that my research is heading.
So and then what possible we can look at it next week. Sure. Yeah, I what's great is because
you're talking to so many people in the industry, you're finding out what's important to them.
And that's what matters. So it's
got a new guest joining here in one second. So
welcome, Brad. Welcome to the cannabis data science meetup group.
We're discussing cannabis sales at dispensaries. And Paul is presenting what's called a market
basket analysis, which essentially looks at the breakdown of products that people buy
at the same time. Okay. Welcome to have you. Thanks. Sorry, I'm late.
So I was just going through some very generic examples of what market basket analysis, they're
called associate association looks like in a grocery store setting, before going into
what doing the same thing in a in a dispensary setting. So so there's a few examples here,
I just was mentioning that if you went to a store, and lots of customers, you know,
bought hot dogs and hot hot dog buns, then you could potentially infer that they would
buy ketchup. And that's kind of what more market basket analysis is, right? It just
has these different common sense associations. Sometimes they're not so common sense, you
can find some interesting things, but these little association rules pop up in in retail
sales. So on the project that I did, I looked at, I wanted to look at some good candidate
dispensaries to run this analysis on. So part of my process was picking out three, what
I what I decided was going to be three dispensaries to run this analysis on. And I did that. Let's
see if I can come here. So the criteria for picking dispensaries to run my analysis on,
there's three or four factors I was looking at. I was looking at dispensaries, the total
number of sales transactions that a dispensary would have. So the higher the number of transactions,
the better, because it would be potentially be a richer data set to look at. And along
the same lines, total revenue, so those dispensaries that are just selling a lot. Also here in panel
C, there's dispensaries with the distinct distinct product sold. So they've got a lot
of variety that they're selling to their customers. And then probably one of the more interesting
parts is I wanted to differentiate dispensaries by the the area that you can find them in
based on the median income in their zip code area. So we have dispensaries that are in
lower income areas and some in higher income areas. And I wanted to pick three dispensaries
that represented that that span of median income. And if you look at this distribution
here, we've got the count of dispensaries on our y axis, how many fell into these different
levels of median income across the bottom here. And you kind of get this bimodal distribution
here where you've got this big hump here in the lower median income bracket, and then
more towards the the medium income bracket, you've got this other hump and then actually,
I guess it's trimodal. So and then finally, you've got a higher income median income bracket
where dispensaries are located. So I took that signal and broke it into these three
different sections with these dotted lines, and decided that I was going to pick a dispensary
out of each one of these three areas that I was going to run my market basket analysis
on. So if you come to these, these three plots here, we've got every dot here represents
a dispensary. And this is in the low median income bracket, less than 80k per year, median
income. And then on the left hand side, we have the count of products that the dispensary
selling across the x axis is the number of transactions. And then the size of the bubble
is the amount of sales they're making. So we've got in this particular left hand plot
here, we've got four variables, counter products, number of transactions, the median income
bracket that's in and then the size of the bubble is how much money they're making. So
looking at these three distributions by income level, I picked these three different dispensaries.
And so DL means dispensary low DM is dispensary medium and DH is dispensary high. And I forgot
where I put it in my paper, but they these dispensaries map to specific dispensaries
in Washington State and I forget I think I think DL here is Zips dispensary and I forgot
what the other two are called off the top of my head. So those were the three dispensaries
that I picked to run my market basket analysis on. Here we go. Here they are. So the low
income dispensary is Zips, the median is place called PRC and the high median income is a
place called Green Theory Factoria. And I think Green Theory Factoria is in, is it called
Bellevue? Kagan, I think they use Bellevue?
Yes Bellevue. So that's where Microsoft is headquartered.
So right, right. So kind of more of an up market area, I guess. So those are the three
dispensaries. Now, go ahead.
Not to throw you off track, I just sort of had a question while we're on the previous
chart. So Kanekan, I heard, let's see, it was the president of Love Buds to Brett Harris
and he was talking about, so he runs a retail chain and he was talking about, okay, so what
does he do? He's constantly stocking new products and stocking a lot of products. So it looks
like in looking from your charts, there may be a positive correlation between just the
number of products you have in your transactions. Is that something that you saw or?
So the number of the counter products and the number of transactions, like a positive
correlation, it does seem to be that way, if that's what you're saying.
Yeah. And I guess the bigger question would be essentially, do stores with the more variety,
do they have higher sales on average?
I see what you're saying. I guess not necessarily, when you say overall sales, not necessarily.
So if you look at the size of the bubbles in these three charts, DL and DH are, I think,
about the same size, so they have the same amount of income. And then you got the counter
product. So that DH has less products, less number of transactions, but they are making,
they must be charging a premium for what they're selling, I guess. Just my hypothesis.
So you're saying DH?
So DH in the high median income area, they have lower number of transactions, lower account
of products, but the size of their bubble is not much different than DL. So in other
words, they're making relatively the same amount of money, not too much difference.
Okay, I see the size of the bubbles, their revenue. So one could argue that they're not
in the same markets. So one could argue that DH is only competing against, well, it may
not be competing against all the other ones. I guess these may not necessarily be in the
same geographic region.
No, I don't believe they are. And I can get you more information on that. But I think
the DH is more probably a boutique, I would think more of a boutique type business. And
they have more margin, they can charge higher prices just because that's what the market
will bear.
Can I just ask, are the number of transactions, the number of total transactions of people
buying more, that would be per transaction that could make a higher revenue?
Oh, I see you're saying so for every transaction, are they buying more products per transaction?
Yeah, people buying $400 worth instead of $100 worth.
Yeah, I guess that that could be possible. Yeah, that could be possible. And I actually
didn't look into segmenting out the transactions by like products purchased in a specific transaction.
I was looking just at dollar amounts of transaction, but it could be.
So long story short, I'll let you continue Paul, I just wanted to bring up the fact that
I think people that run retail establishments, they may want to stress variety. But at least
that's what that manager was saying.
Yeah, no, and thank you for the questions. Because whenever I'm presenting something,
and people start digging and asking more questions, that means that we're kind of onto something,
right? I mean, it seems to make sense. And if it generates more questions, then it probably
has there's more value there to be had. So thanks. Thanks for that.
So yeah, this is my kind of simple way of trying to find some good candidate dispensaries
to run my market basket analysis on. So something to know about market basket analysis is there's
a couple of different metrics that you use to determine how important your association
rules are. So, you know, peanut butter jelly is likely to yield a bread purchase, that
particular combination has a value has several different kinds of values attributed to it.
In my analysis, I'm using three different metrics to determine how valuable those combinations
are. The most basic one is called support. And that's on the x axis here. And support
is considered kind of a popular popularity measure, right? So out of all the different
rule combinations I could produce, some are going to rise to the top. All right. And it's
a very simple, proportional metric. It's called support. There's another metric called confidence.
And when you look at a rule, as we've seen before, you got peanut butter and jelly is
likely to yield bread. You've got two parts of the rule. There's the peanut butter and
jelly. And there's the bread. So you got the antecedent, which comes first and the consequent
that comes second. So you've got two pieces. And the likelihood or the strength of association
with the first part of the rule, going to the second part of the rule, and I hope this
is making sense. It's kind of the tightness of association going in one direction. It's
called the confidence. And that's what we've got here on the left-hand side. So you got
popularity. You got strength of association in one direction. And then there's a final
measurement called lift. And lift takes into account the strength of association in both
directions. So if I have peanut butter, what's my strength of association with bread? And
then if I have bread, what's my strength of association with peanut butter and jelly?
I think I left jelly out the first part there. And that's a much stronger metric to use than
confidence. So when we look at this chart here, we're really looking for those items
that have the highest support and the highest lift. And lift is based on the darkness of
the color and support is how far over to the right it is. We only see a few plot points
here because there's literally thousands upon thousands of combinations. And we can set
thresholds that screen out kind of the very numerous low interest combinations that have
kind of low value for us. And we can set those thresholds for support and confidence. We
can set those thresholds and only tease out those things that are kind of at the top of
the pyramid, so to speak. And so that's what these scatter plots are here, just various
association rule combinations that have kind of bubbled to the top. And I'm going to show
you what those associations look like for the three dispensaries, the actual rules.
So this is a bit of an eye chart. Let me see if I can zoom in a little bit here. So here
is our top 10 association rules for the DL dispensary low median income. And we can see
right out the gate here, you've got a wax called Chernobyl and another wax called pink.
And those two together were fought in association with a wax called Starfighter. And you notice
that all these rules down here, and these are all ranked by lift, that strength of association
between the two rules back and forth. And a lift value starts at one and it can go up
to, you know, I've seen them up into the 170s before. But something like around 75 is a pretty
very reasonably strong association between these two halves of the rule. And so but we
notice right off the gate, right off the gate here that all we're dealing with here are
wax products, which I thought was, to me was not know much about the industry, was pretty
interesting because I would have expected to see more kind of like a cross association
with other types of products. But I was talking about some of these results with Keegan and
it seems like just a first blush that the cannabis industry might be like the alcohol
business right where you might be a beer drinker or a wine drinker or a spirits drinker. And
you kind of stay in your swim line, you know, you stick with the things that you like. And
so could the next table? Yeah, go ahead.
Chime in for a second. Yeah. So I was just thinking, so I don't know. So if we harken
back if a few months ago, we talked about inflation and we looked at, okay, in Oregon,
we hypothesized, okay, what if we essentially said, okay, what if people buy a basket of
goods and they buy 60% flour, 40% wax? So that was just an assumption we made that,
okay, you know, people are, you know, spending 60% of their money on flour, 40% on concentrates.
Well, that may just be the breakdown of the market, you know, flour, people may buy 100%
flour, concentrate, people may buy 100% concentrate. So there really may not be people buying these
baskets is essentially is that that's what I'm starting to take away. But yeah. But anyways,
just gonna jump in real quick. I'll let you get.
Yeah, no, that's, that's a good observation. And, you know, got to keep in mind, we're
only looking at three stores here. And it'd be interesting to look at a lot more to try
and get a sense of, of that. But it's you're right, it seems like that's kind of the direction
this is pointing in. So the second table here is for the median income dispensary. And here
we do see a little bit of a mix of different products. But when I say a mix, there's two
different categories, there's blunts, and there's waxes. But again, they're not being
bought in, in tandem together, they're blunts are with blunts and waxes are with waxes.
And so we can see that these rules here seem to be consistent in your people staying in
their own consumer swim lane. And then finally, with the high median income dispensary, there's
a little more going on here. And the category changes over to consumables. Am I saying that
right? Keegan consumables or so these are like, oh, actually, sorry, there's pre rolls
in here as well. Sorry about that. So I think they mean, I mean, so I think we're seeing
is it a few edibles. So there's like the panda panda fruit drops look like look like edibles
same for the blueberry belt, apple rings. Yeah, there was a those sound a lot actually,
and given that they say 100 milligrams, typically, that's a giveaway that you're working with
an edible. Yes. Okay. Okay, that's good to know. So edibles and then pre rolls are in
here as well. But again, if you look at the top rule, we got pre rolls or pre rolls, go
down a few and you've got edibles with edibles. So it seems like people are staying still
staying consistent in in purchasing the things that they like to get, you know, the same
category things together. But those are higher end more expensive items. Okay, okay. And
this is this is what I found too. I just found that edibles they're they're priced high for
you know. And so it's Yeah, it's real interesting that you're finding that those are some of
the top pairings at this is the dispensary and Bellevue. Yeah, yeah. And so this is what
I mean, this is interesting in that, you know, I don't know if edibles cost more to produce.
Therefore, they're just a higher selling product. But what's really interesting is the is the
demographics because maybe there's more disposable income here, but also smoking in and of itself,
and I'm going out on a limb here, but it you know, as time has gone by, especially over
the last, you know, 2030 years, smoking has been kind of looked down upon as being obviously
a less healthy lifestyle. But here you can still consume cannabis and not have to worry
about the smoking aspect of it. But I mean, pre rolls are here, we see that as so popular,
but can I just throw in an economic concept real quick, I'm not sure how it plays plays
in. But essentially, the amount that you can raise prices depends on essentially the price
elasticity that consumers have for a given good. So I'm thinking that edibles may have
a fairly inelastic. They may be fairly inelastic in regards to price. So people who aren't
going to buy edibles, they're just not going to buy them even if you mark them way down.
And then the people who are going to buy them are still going to buy them even if you mark
them way up. So that may be what we're seeing with edibles where there's just there's low
elasticity of demand. So they're just raising the price. Yeah. Yeah, there's a lot of interesting
about going through this, this work is just there's a lot of once you start seeing some
of the results, it just your mind immediately wants to try and start filling in the the
gaps in some of these things, which I think is really compelling, it'd be fun to do more
work in this area. Or also, you know, work with with a dispensary with maybe their point
of sale data set that they actually have from the store. And if they were willing to share
that just kind of dive deep into their particular their particular market backs, basket analysis
and do some more work in that area. But that is, go ahead. I just think it's so interesting
seeing the the pre rules being at one of the top selling pairs at the Bellevue one. So
maybe that's those are just people just going and getting a couple pre rolls. But that was
that's unexpected to me. So yeah, anyways, I'll let you know.
Sure. I'm almost done here. I just wanted to, you know, kind of fair warning with any
kind of research product project. There are limitations here. And one of the limitations
I ran into with the market basket analysis is that I'm doing a market basket analysis
on these very specific product names. And typically do market basket analysis on categories
of purchased items. So not necessarily skippy peanut butter, but just peanut butter, right.
And what that does is if you have kind of intermediate naming convention for your different
products, you tend to get a richer data set at the end of it. And for instance, the support
values of these these combinations are very, very low, which means they're they're low
low popularity compared to all the rope, the rules that are available. But that's because
there's just so many rules, because there's so many different name products, and it just
gets very, very granular, very noisy. So follow on work for this, I think there would need
to be some naming convention or a taxonomy developed, where each of these different types
of products like, you know, edibles and pre rolls actually fit into something that's not
really just like an edible category, because that's too general, but something in between.
So I think we could squeeze more value out of this by developing that taxonomy. And then,
you know, approaching a customer and running through the analysis for them. So I just wanted
to share that.
I think. So in that vein, it may be one of the variables that you have to sort of chain
in there. However, there should be essentially what's called an intermediate type for these
these products.
There there is that intermediate type, there's a I think there's like 24, some around 24
different types that you can assign these products to. But we could definitely do an
analysis to that level. But I still think that is that would be too high, or too broad
or vague.
Oh, okay. So I see what you're saying. So you're looking for something more granular
than the intermediate types, but not quite so granular as these just the absolutely straight
things. Yeah, and I think that's a value proposition for anybody that's going to be doing cannabis
sales analytics, especially in kind of customer segmentation. And just this this type of technique
is we'd have to develop some sort of, you know, nomenclature that we could use these
products to fit into.
And this is where, like you said, the retail establishment by retail establishment analysis
may work well because different retailers may have different naming conventions. So for
example, this is maybe to not granular enough, but most retail places, they typically mark
flower as either hybrid, indica or sativa, just to kind of put them in three broad categories
of flower. So that would be helpful. So for your market basket analysis, like I think
it would be interesting. You could apply it just to flower. So like what I would expect
is, you know, so it'd be interesting to see, okay, do people buy hybrids with sativas?
Do they buy a mix of indica and sativa?
Yeah, that's a great idea, Keegan. You know, some more work would have to be done, but
it seems like people buy within their particular swim lane of product. So we could just pluck
out a swim lane, recategorize the products that are in that particular swim lane and
run an analysis within that. Yeah. So for flower, for edibles, for, you know, whatever.
I'm not certain if they, I don't know as much about the concentrates. They may still do
the hybrid sativa, indica dichotomy, or I guess not a dichotomy, but they may do that
distinction with oils. I'm not certain they do though.
They do. Yes.
Oh, they do. So, okay, so Heather's chiming in. So, so yeah, so there's even that distinction
within concentrates as well.
Yeah.
Yes, oils, concentrates, so RSOs and concentrates. Absolutely.
So I wonder if it'd be worthwhile to pull out from the Washington state data, pull out
the, those 24, 25 or whatever it is, different kinds of categories, send it out to the group
and see if they have any recommendations for under each one of those categories, how they
could be broken down into kind of a more granular description.
Definitely. So that way we can get some better categories for you.
Yeah. Yeah. So I'll do that. I'll send it out to the group and if anybody wants to chime
in with their opinion, I'd be glad to see any kind of input that there is.
But that's essentially it for the research I did here. Are there any other questions
before I hand it back over to you Keegan?
I had a quick question. Okay, so the global variables that you're referring to, that's
global to your state where I can't pick up that ID and use that like a VIN anywhere in
the US to be able to locate that. Maybe is that your point or is that, so you're saying
global to your state?
Well, so this is, this is interesting. So this is specific to Washington state, at least
the IDs are. And so this is where each state is its own little island in the cannabis space.
However, one could start to potentially draw some inferences. So you could see, oh, in
Washington state, at this dispensary in Bellevue, they're primarily selling edibles. So maybe
you could try to find like, like a sister city, so to speak of Bellevue in your given
state. So okay, in Maryland, you know, where there are some of the, you know, higher income
localities, and then they, those may be selling mostly edibles, or they may, so you can start
to draw inferences, but it is still, they are still islands at this time.
Thank you.
At least that's my take. Is that essentially how you would explain things Paul?
That seems reasonable to me.
Okay.
All right.
My main question is kind of broad is just, do we just know like the percent, like the
percentage breakdowns of these, like, well, I guess that's more just the intermediate
types. So like, essentially, like, like how much concentrates being sold, how much flowers
being sold?
Yeah, I didn't, I didn't really look at, look at the percentages by those categories. But
that's, that's a good, I mean, to your point, if you mentioned this several times when dealing
with these data sets is getting basic summary statistics is the starting point really, isn't
it? I mean, that should inform some of the questions and levels of kind of research that
you want to head in. But for this particular project, I didn't actually do any of those
summaries.
It's okay. It's just sort of, you know, the state where we're at, you know, there's a
lot of other industries, these summary statistics, people just sort of know offhand, oh, you
know, like, you know, like, for example, at the grocery stores, they have a much better
idea of, okay, what's the breakdown of people buying milk and things like that. So yeah,
that's, yeah, they've had that stuff for years and years and inside now.
So that's why the summary statistics are just so meaningful. And I've just found that if
you just keep taking like conditional, conditional averages, you can get really far with that.
So yeah, yeah, absolutely.
So I think this is incredibly interesting. So unless anyone else wants to chime in, basically,
my main takeaway is, is, and it kind of confirmed the sneaking suspicion I had was there's almost
just different types of cannabis consumers. Yeah. And they don't cross. So, and so it
may like, so for example, like the cross selling, it may be like futile. So, like, you know,
if like you're like a flower person, it really may just be tough for anyone to convince you
to spend money on a pre-roll or spend money on an edible or oils. Yeah. Don't waste your
time trying to convert somebody that's not going to get converted. Yeah. So that was
my main like takeaway that you can like act on today is upsell versus cross sell. If you're
right. Yeah, that's, that's a great, that's a great way of putting it Keegan. Yeah. I
didn't really think of it in those terms, but yeah, upsell as opposed to cross sell.
Yeah. Yeah. If you're into edibles, you can, every, every form of the flower or the product
may work for you. So if you consume edibles, the flower, the concentrate, RSOs, those will
work for you. So edibles are sold sort of as an alternative to smoking. And so, and
I believe that's why they sort of, you know, they, they get a premium for them. They also,
I think are more expensive to produce. I'm sure they're more expensive to produce. Yes.
Pre-rolls, I mean, somebody has to do the, well, I'm sure it's done by machine, you know,
they're pre-rolled, but it is an extra step in the process. And it's probably geared more
towards people who don't have like smoking paraphernalia or don't have the skills to roll
their own and can afford to have someone else do it for them. Right. Or they just don't
want to, they, they're not too thrilled about the smell, right? There's always that aspect
to it as well. So. It's interesting. So I think the pre-rolls are worth looking at more.
So that was one that always sort of flew under my radar. So I just never paid too much attention
to those, but there was a talk at this latest CannaCon about pre-rolls and they're maybe
more popular than I give them credit for. So that's another takeaway. Also, it's good
to see you. Tafique. I was going to say, Hey, and introduce you to the group. I didn't want
to interrupt Paul. So, so now. Yeah, I wasn't sure what a good point to do that was. But
it's good to have you. So in YouTube, Brad. So I guess before we get out of here, you
wouldn't mind maybe introducing yourselves just so we can get to know each other. Yeah,
no problem. So currently I'm a student in a data science bootcamp right now going through
with thankful I'm coming up on the last bit. So pretty much like final capstones, technical
interviews like learning how to proceed through those. And then on the side, which is why
I found this particular media very interesting is I work part time at the dispensary. So
this was very, very interesting enough. Yeah. So I'm listening in a little bit more intensely
to like how you guys are collecting data, how you guys are analyzing it. Because at
least for me, like if I use cannabis, like I don't use pre rolls at all because they
are more convenient. But I mean, the smell is a factor. Living situation is a factor.
And then I think tolerance is also like a very nebulous concept as well to where if
you are used to edibles or concentrate for instance, then maybe pre rolls don't hit as
consistently. So, but yeah. That's cool Tawfiq that you're enrolled in a program and you
work at a dispensary, you're kind of at a really cool nexus there in between those two
overlaps. But if you have any like further questions about the stuff that I did here,
because I just, this is part of my graduate program and this is part of my capstone. Well,
this was my capstone. Oh, okay. So yeah, so if you have you reach out if you have any
other questions or whatever they are. It'll be interesting. I'll probably hang around
afterwards. Okay. You'll have to just share some anecdotal evidence if you can with us
and just kind of, or just keep an eye out yourself and just see like, okay, like are
people mixing products and then if so, like, what are they mixing? Yes. Even like when
they're purchasing or usage. So basically, like, so when the consumer comes in, if they
buy more than one product, what are those products? So are they buying a wax and an
edible? Are they buying a wax and an RSO? Are they buying like two things of flower?
So that's sort of the idea is what like combination of products, if any, are people generally
buying this sort of we're trying to figure out. I have to think about that. There's like
a lot of I don't know if this is very universal across dispensaries in Michigan or even in
other states like Washington or California. But I know that the dispensary that I work
at runs promotion specifically to either double up on certain products or like bundle deals
or like specials like buy two of any brand product and then you get one free of a equal
or lesser value. So typically, sometimes they'll buy maybe flower or edibles and then maybe
some paraphernalia or maybe an edible on top of that. But it's I haven't seen enough to
kind of like make a kind of consistent rule, I would say, and I don't have access to this
data, although I've been trying to ask for it.
Well, and that's why you kind of mix the anecdotes with the data. So you look at the data to
actually get the look and then you just do your real world observations just to make
sure things are just for like a sanity check, essentially. So what you mentioned with the
discounts is those look to me like essentially quantity discounts.
Right, the bundling.
So it seems like just people have just sort of stumbled or maybe they've done the research
at the dispensary, but it seems that people have just kind of come to the realization
that, OK, we can sell more by giving people quantity discounts, maybe not necessarily
cross promotional.
Yeah, this is great that you're able to join today, Todd, because they're based on this
conversation that kind of aligns with what we're seeing in the results that people stay
within their own products. And so much so that it seems like dispensaries either just
going to try and upsell or try and increase quantity, but they're not going to really
bother with trying to cross sell. So it seems like it's heading in that direction anyway.
It could even lead for like future, you know, some future research, because if we've got
these different consumer groups, then it may be worth trying to figure out, OK, what puts
a consumer in a particular group. And so I think this is where there is some sort of
research being done. So this is where I was saying a lot of people are interested in,
OK, what are the demographics of cannabis consumers, what demographics by certain products.
So now I think we're finally sort of joining the current state of cannabis research, which
is basically what do different people buy? Right. So we're finally connecting all the
dots here.
Yeah, it'd be nice to know what some other people have done in consumer analytics in
this space. I know Brad hasn't had much chance to talk, but I was just curious what Brad's
background is and if he's got any overlap in this space or any of the other spaces within
the industry.
No, not really. I've been a contract software developer most of my life and I'm making a
break into data science. I had some work at Leo Bernetta Marketing Company in this space.
I'm taking a boot camp now, so I'm kind of new also.
Very good.
It's really interesting to see the work you're doing.
Basically the only things I've seen people concretely do are just age and gender. So,
OK, you know, I just vaguely remember that I think older people tend to buy more edibles.
That was the main breakaway. And then, of course, there's I think there's more male
consumers. You're just that's just the way the cannabis industry is. It's kind of dominated
by young males. But that's the whole thing is not it may be for specific products. Right.
So if you look at edibles, young males may not necessarily be the people buying edibles
per se. So that's that's kind of what people are breaking into. But there's just. I was
just saying there's just so much more that can be done there.
I wonder if part of it, because I know that when we scan IDs, at least in the state of
Michigan, the only demographic data that is collected is I mean, I do think that the gender
is collected and there is a non-binary option, but I don't know if that's included in any
of the database that we have internally. So I don't know whether we even have any in-house
like data analytics that's being done, because usually for like rewards or anything like
that, we go through a third party called Alpine IQ. They run some analytics software, but
there isn't really anything done on our end specifically.
This is the final tidbit I've got to offer. So the other thing that I heard was, OK, people
tend to only buy things like within their immediate zip code or potentially on the way
to work. So the way we could potentially look at this in Washington state is just say, OK,
people are probably buying things locally. So you could try to maybe do some correlation
between like you were saying, like essentially your income and then like the average sales
in that zip code. So maybe your income affects the products you buy and then you can add
whatever variables you can potentially get at the zip code level.
So yeah, yeah. Some summary stats at the zip code level based on median income could probably
confirm a lot of that. Maybe profession. I don't know if you can uncover that, like what
profession you work at may determine. I don't know. I'm just spitballing at this point.
So but yeah, so the median income by zip code I got from the US Census Bureau from their
website. So they may have other interesting geographic. You could do average age. So yeah,
yeah, there could be some quite a few other things that we could tie into that just by
the zip code of dispensary. So that brings us here to the end of the hour. And so I think
Paul, you've done a fantastic job. So I think it's groundbreaking research. I think it answers
an important question. Do people buy different types of goods? And I think it provides evidence
that there's distinct consumers and everybody can take their own takeaways from this. But
I think that that in itself is valuable to retailers because now you just know that if
you're trying to cross sell, you may be barking up the wrong tree.
Yeah. Yeah. And thanks to you and Charles for kind of taking me into the group here
a few months back and getting me started. And I really appreciate the help you've given
me. It was a great presentation. Thank you. Very, very interesting. So it's given us a
lot of avenues. And so for next week, we'll try to wrangle the sales per lab result and
then see if THC matters. And then if so, how much so just before you wrap up one quick
thing, I don't know if I is it Taufeek? I'm sorry if I'm pronouncing you wrong incorrectly.
That's close enough.
Okay. Well, I just to let you know, I do have to drop. I know you wanted to talk, but I'm
on my lunch break. So I have to drop, but I'll reach out to you through the meetup. So if
you have any follow on questions, we can talk.
Okay, perfect. That sounds good.
Okay. All right.
All right, everyone. Well, thank you for coming to the Canvas Data Science Meetup Group and
enjoy your week and stay productive. So.
Bye guys. Thanks.
Have a good one.
Bye now.
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