Final Project: Flower Sales over the Fall Semester

Final

Author

Michaela Bowen

Published

December 22, 2022

Code

library(tidyverse)
library(dplyr)
library(readr)
library(readxl)
library(lubridate)
library(viridis)

knitr::opts_chunk$set(echo = TRUE)

Data Summary

Throughout the fall of 2022, the cannabis industry has been going through drastic market changes. With the rise of inflation, and an over saturated market, the cost of a pound of flower(bud) has decreased drastically. This data comes from a mid level adult recreational dispensary that is in a community of nearly 12 other dispensaries with similar product and clientele. There are three months worth of transaction data from the months of September, October, November and the beginning of December. Promotional items, sales, and deals have been a theme of many dispensaries in the Pioneer Valley and this dispensary has not been excluded from that.

The transaction data contains cumulatively about 36,000 transactions over the course of 3 months. Each transaction contains information for analyzing sales, and for the purposes of this document, the flower promotional sales. These transaction are inventory based, meaning that each product sold is one observation.

Research Questions

Were fall flower promotions effective?:
- Was the most drastic promotion effective?
- Are the strains still popular outside of the promotional window?

Data Wrangling and Manipulation of Main Data

Each of the 3 months of transactions were read in similarly with unused columns deleted, and the rest renamed with more functional naming conventions. This data was of separate inventory transactions, there needed to be a full join to include all observations (transactions). There are several muated columns that were necessary in the overall analysis of the data.

Code

#September transactions read in
transactions_sept_orig <- read_excel("_data/Inventory Transactions Sept_2022.xlsx",
    skip = 5,
    col_names = c("pos_id","product","delete","patient_name","transaction_date","qty_sold","daily_allottment_oz","weight_grams","cost","price","owner_name","owner_location","vendor","sold_by","receipt_no","delete","delete","delete","delete","delete"))%>%
  filter(sold_by != "Michaela Bowen")%>%
  select(!contains("delete"))



#October transactions read in
transactions_oct_orig <- read_excel("_data/Inventory Transactions Oct_2022.xlsx",
    skip = 5,
    col_names = c("pos_id","product","delete","patient_name","transaction_date","qty_sold","daily_allottment_oz","weight_grams","cost","price","owner_name","owner_location","vendor","sold_by","receipt_no","delete","delete","delete","delete","delete"))%>%
  filter(sold_by != "Michaela Bowen")%>%
  select(!contains("delete"))


#November transactions read in
transactions_nov_orig <- read_excel("_data/Inventory Transactions Nov_Current_2022.xlsx",
    skip = 5,
    col_names = c("pos_id","product","delete","patient_name","transaction_date","qty_sold","daily_allottment_oz","weight_grams","cost","price","owner_name","owner_location","vendor","sold_by","receipt_no","delete","delete","delete","delete","delete"))%>%
  filter(sold_by != "Michaela Bowen")%>%
  select(!contains("delete"))



#joint all three dataframes of transactions, also uniting common columns
transactions_fall <- full_join(transactions_sept_orig,transactions_oct_orig, by = 'transaction_date')%>%
  full_join(.,transactions_nov_orig)%>%
  unite("product", c(product, product.x, product.y), na.rm = TRUE)%>%
  unite("pos_id", c(pos_id, pos_id.x, pos_id.y), na.rm = TRUE)%>%
  unite("patient_name", c(patient_name, patient_name.x, patient_name.y), na.rm = TRUE)%>%
  unite("qty_sold", c(qty_sold, qty_sold.x, qty_sold.y), na.rm = TRUE)%>%
  unite("daily_allottment_oz", c(daily_allottment_oz, daily_allottment_oz.x, daily_allottment_oz.y), na.rm = TRUE)%>%
  unite("weight_grams", c(weight_grams, weight_grams.x, weight_grams.y), na.rm = TRUE)%>%
  unite("cost", c(cost, cost.x, cost.y), na.rm = TRUE)%>%
  unite("price", c(price, price.x, price.y), na.rm = TRUE)%>%
  unite("owner_name", c(owner_name, owner_name.x, owner_name.y), na.rm = TRUE)%>%
  unite("owner_location", c(owner_location, owner_location.x, owner_location.y), na.rm = TRUE)%>%
  unite("vendor", c(vendor, vendor.x, vendor.y), na.rm = TRUE)%>%
  unite("sold_by", c(sold_by, sold_by.x, sold_by.y), na.rm = TRUE)%>%
  unite("receipt_no", c(receipt_no, receipt_no.x, receipt_no.y), na.rm = TRUE)
  

  
#mutating necessary columns
transactions_fall <- transactions_fall%>%
#creating date, day, hour, minute, second, columns 
  mutate(
    date = as.Date(transaction_date),
    day = day(transaction_date),
    hour = hour(transaction_date),
    minute = minute(transaction_date),
    second = second(transaction_date))%>%
  mutate(
    format_date = format(date, "%m/%d/%Y"),
    format_hour = paste(hour, minute, second, sep = ":")
  )%>%
#pulling the category abbreviation to determine category and create a category column 
  mutate(
    category = substr(product,1,3),
  )%>%
#changing the abbreviations into full category names
  mutate(
    category_names = case_when(
      category == "FLO" | category == "Flo" ~ "Flower",
      category == "PRJ" & weight_grams == 0.5 ~ "Joint 0.5g",
      category == "PRJ" & weight_grams == 1 ~ "Joint 1g",
      category == "PRJ" & weight_grams == 2.5 ~ "Joint 0.5g 5pk",
      category == "PRJ" ~ "Other Joint",
      category == "EDI" ~ "Edible",
      category == "CON" | category == "Con" | category == "MIP" ~ "Concentrate",
      category == "VAP" | category == "Vap" ~ "Vaporizer",
      category == "ACC" | category == "Pax" | category == "PAX" | category == "Hig" | category == "Bov" ~ "Accessories",
      category == "CLO" | category == "Res" ~ "Clothing",
      category == "HTC" ~ "ignore",
      category == "SAM" ~ "ignore",
      category == "TOP" ~ "Topical",
      category == "REW" ~ "ignore")
  )%>%
#created a logical variable to determine if flower was in house, or 3rd party
  mutate(
    house_product = case_when(
      vendor == "Resinate, Inc." ~ TRUE,
      vendor != "Resinate, Inc." ~ FALSE
    )
)

Data Wrangling and Manipulation of Flower Data

Given that the price of the pound of flower is down, lowering the price of the 8th, as well as promoting different strains is necessary to compete within the market. In order to do that, Flower transactions need to be isolated. There are several mutated variables that are necessary in the flower analysis included, total product sold by date, products sold by date and strain, and percent of products sold by date and strain.

Code

#create flower transactions only dataframe 

transactions_flower <- transactions_fall%>%
  filter(category == "FLO")%>%

  #create strain variable  
  
  mutate(
    weight_grams = as.double(weight_grams),
    strain = case_when(
      grepl("Flower", product) & (weight_grams == 1 | weight_grams == 14) ~ (str_extract(product,"(?<=Flower ).+(?= 1)" )),
      grepl("Flower", product) & (weight_grams == 3.5)~ (str_extract(product,"(?<=Flower ).+(?= 3.)" )),
      grepl("Material", product) & (weight_grams == 7)~ (str_extract(product,"(?<=Material ).+(?= 7)" )),
      grepl("Shake", product) & (weight_grams == 7) ~ (str_extract(product,"(?<=Shake ).+(?= 7)" )),
      grepl("Shake", product) & (weight_grams == 1 | weight_grams == 14) ~ (str_extract(product,"(?<=Shake ).+(?= 1)" )),
      grepl("Popcorn", product) ~ (str_extract(product,"(?<=Popcorn ).+(?= 3.)" )),
      grepl("Collective", product) ~ (str_extract(product,"(?<=Collective ).+(?= 3.)" ))
      )
    )%>%
  
  #create total  product sold by strain and date
  
    group_by(strain,date)%>%
    mutate(sold_by_strain = sum(as.double(qty_sold)),
  )%>%
  
  #create total product sold by date

  group_by(date)%>%
    mutate(sold_by_date = sum(as.double(qty_sold)),
  )%>%
  
  mutate(percent_sold = 100*(sold_by_strain/sold_by_date),
        )

All Resinate Strains

Code

percent_strain_day <- transactions_flower%>%
  filter(house_product == TRUE)%>%
  arrange(date, -percent_sold)%>%
  distinct(strain, .keep_all = TRUE)%>%
  ggplot(aes(x = date, y = percent_sold, group = strain, fill = strain)) +
  geom_area() +
  scale_fill_viridis(discrete = TRUE) +
  labs(
    title =  "% Sold by Strain",
    caption = "Data provided by Resinate, Inc."
    )+
  xlab(" ") +
  ylab("% of flower sales") +
  theme_bw() +
  theme(
      legend.position="none",
      panel.spacing = unit(0.2, "lines"),
      panel.spacing.x = unit(.5, "lines"),
      strip.text.x = element_text(size = 8),
      plot.title = element_text(size=14)
    ) +
  scale_x_date(
    date_breaks = "1 month",
    date_labels = "%b"
  ) +
  facet_wrap(~strain)

percent_strain_day

Best Promoted Strains

When referring to the Biweekly promotions it is clear that the promotions were effective as each strain has peaks related to the weeks of their promotions. The steepest promotions of the fall were for the month of November from 11/2 - 11/17, where four strains (Truthband, Lemon 18, Cappachino and Jigglers), were promoted for the price of $18.

Code

best_promo <- transactions_flower%>%
  filter(strain == c("Lemon 18", "Truthband", "Cappachino", "Jigglers"))%>%
  ggplot(aes(x = date, y = percent_sold, group = strain, color = strain)) +
  geom_point(size = 1.5) +
  geom_line() +
  scale_x_date(
    date_labels  = "%d-%b",
    date_breaks = "2 weeks",
    date_minor_breaks = "days"
  ) +
  scale_color_manual(values = c("#440154FF", "#31688EFF", "#35B779FF", "#FDE725FF")) +
  labs(
        title =  "Percentage of Strain Sales by Date",
        subtitle = "$18 Eighths",
       caption = "Data provided by Resinate, Inc.")+
  xlab(" ") +
  ylab("% of flower sales") +
  ylim(0,100) +
  theme_bw() +
  theme(
    legend.position = "none",
    axis.text.x = element_text(angle = 60, hjust = 1)
    ) +
  facet_wrap(~strain)

best_promo

We can see that on 11/3 there is a sharp increase in Truthband sales, when the weeks before sales had been relatively low. We can see for the two week period in which Truthband was $18, that strain accounted for 40%+ of total flower sales for that day, and when the price increased to $25, sales dropped off.
- Lemon 18 was also price dropped to $18 on the same date as the Truthband, it is interesting to note that there was not a drastic increase in sales for this strain
During the second wave of $18 eighths, Cappachino and Jigglers were featured, and it is clear that each of those strains accounted for a larger percentage of the total flower sales for the day.

Common Strain

Code

sundae_sunset_transactions <- transactions_flower%>%
  filter(strain == "Sundae Sunset")%>%
  ggplot(aes(x = date, y = percent_sold, group = strain, color = strain)) +
  geom_point(size = 1.5) +
  geom_line() +
  scale_x_date(
    date_labels  = "%d-%b",
    date_breaks = "2 weeks",
    date_minor_breaks = "days"
  ) +
  scale_color_manual(values = "#440154FF") +
  labs(
        title =  "Percentage of Sundae Sunset Sales by Date",
       caption = "Data provided by Resinate, Inc.")+
  xlab(" ") +
  ylab("% of flower sales") +
  ylim(0,100) +
  theme_bw() +
  theme(
    legend.position = "none",
    axis.text.x = element_text(angle = 60, hjust = 1)
    ) +
  facet_wrap(~strain)

sundae_sunset_transactions

Sundae Sunset is a strain that is almost always in stock, it is a staple well liked strain. During the first month of fall from 9/8 - 10/5, it was on sale for $25 and then $20. During that timeframe the overall percentage of Sundae Sunset sales was significantly higher, and drops off drastically on 10/5, when the item fell off promotion for the rest of the season.

Promotions

Within a two week promotional period, each category has one or two deals or promotional items. Typically, the flower category will always contain one strain priced between $18-$35, which tend to be the most popular for the time they are on sale.

9/8 - 9/21
- Lemon 18 & Sundae Sunset $25
9/22 - 10/5
- Phone Home, Jigglers & Sundae Sunset $20
- Cappachino $35
10/6 - 10/19
- Phone Home, Lava Cake $20
- Cappachino $35
10/20 - 11/02
- Phone Home, Lava Cake $20
- Cappachino $30
11/03 - 11/17
- Lemon 18, Truthband $18
- Cappachino $25
11/18 - 11/30
- Jigglers, Cappachino $18
- Truthband $25
12/1 - 12/14
- El Chapo $20
- Cappachino $25

--- title: "Final Project: Flower Sales over the Fall Semester" author: "Michaela Bowen" desription: "Tracked Cannabis Flower Sales over Sept, Oct, Nov" date: "12/22/2022" format: html: toc: true code-fold: true code-copy: true code-tools: true categories: - Final --- ```{r} #| label: setup #| warning: false library(tidyverse) library(dplyr) library(readr) library(readxl) library(lubridate) library(viridis) knitr::opts_chunk$set(echo = TRUE) ``` ::: panel-tabset ## Introduction ### Data Summary Throughout the fall of 2022, the cannabis industry has been going through drastic market changes. With the rise of inflation, and an over saturated market, the cost of a pound of flower(bud) has decreased drastically. This data comes from a mid level adult recreational dispensary that is in a community of nearly 12 other dispensaries with similar product and clientele. There are three months worth of transaction data from the months of September, October, November and the beginning of December. Promotional items, sales, and deals have been a theme of many dispensaries in the Pioneer Valley and this dispensary has not been excluded from that. The transaction data contains cumulatively about 36,000 transactions over the course of 3 months. Each transaction contains information for analyzing sales, and for the purposes of this document, the flower promotional sales. These transaction are inventory based, meaning that each product sold is one observation. ### Research Questions - **Were fall flower promotions effective?:** - Was the most drastic promotion effective? - Are the strains still popular outside of the promotional window? ## Read In ### Data Wrangling and Manipulation of Main Data Each of the 3 months of transactions were read in similarly with unused columns deleted, and the rest renamed with more functional naming conventions. This data was of separate inventory transactions, there needed to be a full join to include all observations (transactions). There are several muated columns that were necessary in the overall analysis of the data. ```{r, warning = FALSE, message=FALSE} #| label: read in #| warning: false #September transactions read in transactions_sept_orig <- read_excel("_data/Inventory Transactions Sept_2022.xlsx", skip = 5, col_names = c("pos_id","product","delete","patient_name","transaction_date","qty_sold","daily_allottment_oz","weight_grams","cost","price","owner_name","owner_location","vendor","sold_by","receipt_no","delete","delete","delete","delete","delete"))%>% filter(sold_by != "Michaela Bowen")%>% select(!contains("delete")) #October transactions read in transactions_oct_orig <- read_excel("_data/Inventory Transactions Oct_2022.xlsx", skip = 5, col_names = c("pos_id","product","delete","patient_name","transaction_date","qty_sold","daily_allottment_oz","weight_grams","cost","price","owner_name","owner_location","vendor","sold_by","receipt_no","delete","delete","delete","delete","delete"))%>% filter(sold_by != "Michaela Bowen")%>% select(!contains("delete")) #November transactions read in transactions_nov_orig <- read_excel("_data/Inventory Transactions Nov_Current_2022.xlsx", skip = 5, col_names = c("pos_id","product","delete","patient_name","transaction_date","qty_sold","daily_allottment_oz","weight_grams","cost","price","owner_name","owner_location","vendor","sold_by","receipt_no","delete","delete","delete","delete","delete"))%>% filter(sold_by != "Michaela Bowen")%>% select(!contains("delete")) #joint all three dataframes of transactions, also uniting common columns transactions_fall <- full_join(transactions_sept_orig,transactions_oct_orig, by = 'transaction_date')%>% full_join(.,transactions_nov_orig)%>% unite("product", c(product, product.x, product.y), na.rm = TRUE)%>% unite("pos_id", c(pos_id, pos_id.x, pos_id.y), na.rm = TRUE)%>% unite("patient_name", c(patient_name, patient_name.x, patient_name.y), na.rm = TRUE)%>% unite("qty_sold", c(qty_sold, qty_sold.x, qty_sold.y), na.rm = TRUE)%>% unite("daily_allottment_oz", c(daily_allottment_oz, daily_allottment_oz.x, daily_allottment_oz.y), na.rm = TRUE)%>% unite("weight_grams", c(weight_grams, weight_grams.x, weight_grams.y), na.rm = TRUE)%>% unite("cost", c(cost, cost.x, cost.y), na.rm = TRUE)%>% unite("price", c(price, price.x, price.y), na.rm = TRUE)%>% unite("owner_name", c(owner_name, owner_name.x, owner_name.y), na.rm = TRUE)%>% unite("owner_location", c(owner_location, owner_location.x, owner_location.y), na.rm = TRUE)%>% unite("vendor", c(vendor, vendor.x, vendor.y), na.rm = TRUE)%>% unite("sold_by", c(sold_by, sold_by.x, sold_by.y), na.rm = TRUE)%>% unite("receipt_no", c(receipt_no, receipt_no.x, receipt_no.y), na.rm = TRUE) #mutating necessary columns transactions_fall <- transactions_fall%>% #creating date, day, hour, minute, second, columns mutate( date = as.Date(transaction_date), day = day(transaction_date), hour = hour(transaction_date), minute = minute(transaction_date), second = second(transaction_date))%>% mutate( format_date = format(date, "%m/%d/%Y"), format_hour = paste(hour, minute, second, sep = ":") )%>% #pulling the category abbreviation to determine category and create a category column mutate( category = substr(product,1,3), )%>% #changing the abbreviations into full category names mutate( category_names = case_when( category == "FLO" | category == "Flo" ~ "Flower", category == "PRJ" & weight_grams == 0.5 ~ "Joint 0.5g", category == "PRJ" & weight_grams == 1 ~ "Joint 1g", category == "PRJ" & weight_grams == 2.5 ~ "Joint 0.5g 5pk", category == "PRJ" ~ "Other Joint", category == "EDI" ~ "Edible", category == "CON" | category == "Con" | category == "MIP" ~ "Concentrate", category == "VAP" | category == "Vap" ~ "Vaporizer", category == "ACC" | category == "Pax" | category == "PAX" | category == "Hig" | category == "Bov" ~ "Accessories", category == "CLO" | category == "Res" ~ "Clothing", category == "HTC" ~ "ignore", category == "SAM" ~ "ignore", category == "TOP" ~ "Topical", category == "REW" ~ "ignore") )%>% #created a logical variable to determine if flower was in house, or 3rd party mutate( house_product = case_when( vendor == "Resinate, Inc." ~ TRUE, vendor != "Resinate, Inc." ~ FALSE ) ) ``` ### Data Wrangling and Manipulation of Flower Data Given that the price of the pound of flower is down, lowering the price of the 8th, as well as promoting different strains is necessary to compete within the market. In order to do that, Flower transactions need to be isolated. There are several mutated variables that are necessary in the flower analysis included, total product sold by date, products sold by date and strain, and percent of products sold by date and strain. ```{r} #| label: flower_transactions #| warning: false #create flower transactions only dataframe transactions_flower <- transactions_fall%>% filter(category == "FLO")%>% #create strain variable mutate( weight_grams = as.double(weight_grams), strain = case_when( grepl("Flower", product) & (weight_grams == 1 | weight_grams == 14) ~ (str_extract(product,"(?<=Flower ).+(?= 1)" )), grepl("Flower", product) & (weight_grams == 3.5)~ (str_extract(product,"(?<=Flower ).+(?= 3.)" )), grepl("Material", product) & (weight_grams == 7)~ (str_extract(product,"(?<=Material ).+(?= 7)" )), grepl("Shake", product) & (weight_grams == 7) ~ (str_extract(product,"(?<=Shake ).+(?= 7)" )), grepl("Shake", product) & (weight_grams == 1 | weight_grams == 14) ~ (str_extract(product,"(?<=Shake ).+(?= 1)" )), grepl("Popcorn", product) ~ (str_extract(product,"(?<=Popcorn ).+(?= 3.)" )), grepl("Collective", product) ~ (str_extract(product,"(?<=Collective ).+(?= 3.)" )) ) )%>% #create total product sold by strain and date group_by(strain,date)%>% mutate(sold_by_strain = sum(as.double(qty_sold)), )%>% #create total product sold by date group_by(date)%>% mutate(sold_by_date = sum(as.double(qty_sold)), )%>% mutate(percent_sold = 100*(sold_by_strain/sold_by_date), ) ``` ## Flower Transactions over Time by Strain ### All Resinate Strains ```{r} #| label: main visualization #| warning: false percent_strain_day <- transactions_flower%>% filter(house_product == TRUE)%>% arrange(date, -percent_sold)%>% distinct(strain, .keep_all = TRUE)%>% ggplot(aes(x = date, y = percent_sold, group = strain, fill = strain)) + geom_area() + scale_fill_viridis(discrete = TRUE) + labs( title = "% Sold by Strain", caption = "Data provided by Resinate, Inc." )+ xlab(" ") + ylab("% of flower sales") + theme_bw() + theme( legend.position="none", panel.spacing = unit(0.2, "lines"), panel.spacing.x = unit(.5, "lines"), strip.text.x = element_text(size = 8), plot.title = element_text(size=14) ) + scale_x_date( date_breaks = "1 month", date_labels = "%b" ) + facet_wrap(~strain) percent_strain_day ``` ### Best Promoted Strains When referring to the Biweekly promotions it is clear that the promotions were effective as each strain has peaks related to the weeks of their promotions. The steepest promotions of the fall were for the month of November from 11/2 - 11/17, where four strains (Truthband, Lemon 18, Cappachino and Jigglers), were promoted for the price of \$18. ```{r} #| label: individual strain graphs #| warning: false best_promo <- transactions_flower%>% filter(strain == c("Lemon 18", "Truthband", "Cappachino", "Jigglers"))%>% ggplot(aes(x = date, y = percent_sold, group = strain, color = strain)) + geom_point(size = 1.5) + geom_line() + scale_x_date( date_labels = "%d-%b", date_breaks = "2 weeks", date_minor_breaks = "days" ) + scale_color_manual(values = c("#440154FF", "#31688EFF", "#35B779FF", "#FDE725FF")) + labs( title = "Percentage of Strain Sales by Date", subtitle = "$18 Eighths", caption = "Data provided by Resinate, Inc.")+ xlab(" ") + ylab("% of flower sales") + ylim(0,100) + theme_bw() + theme( legend.position = "none", axis.text.x = element_text(angle = 60, hjust = 1) ) + facet_wrap(~strain) best_promo ``` - We can see that on 11/3 there is a sharp increase in Truthband sales, when the weeks before sales had been relatively low. We can see for the two week period in which Truthband was \$18, that strain accounted for 40%+ of total flower sales for that day, and when the price increased to \$25, sales dropped off. - Lemon 18 was also price dropped to \$18 on the same date as the Truthband, it is interesting to note that there was not a drastic increase in sales for this strain - During the second wave of \$18 eighths, Cappachino and Jigglers were featured, and it is clear that each of those strains accounted for a larger percentage of the total flower sales for the day. ### Common Strain ```{r} sundae_sunset_transactions <- transactions_flower%>% filter(strain == "Sundae Sunset")%>% ggplot(aes(x = date, y = percent_sold, group = strain, color = strain)) + geom_point(size = 1.5) + geom_line() + scale_x_date( date_labels = "%d-%b", date_breaks = "2 weeks", date_minor_breaks = "days" ) + scale_color_manual(values = "#440154FF") + labs( title = "Percentage of Sundae Sunset Sales by Date", caption = "Data provided by Resinate, Inc.")+ xlab(" ") + ylab("% of flower sales") + ylim(0,100) + theme_bw() + theme( legend.position = "none", axis.text.x = element_text(angle = 60, hjust = 1) ) + facet_wrap(~strain) sundae_sunset_transactions ``` - Sundae Sunset is a strain that is almost always in stock, it is a staple well liked strain. During the first month of fall from 9/8 - 10/5, it was on sale for \$25 and then \$20. During that timeframe the overall percentage of Sundae Sunset sales was significantly higher, and drops off drastically on 10/5, when the item fell off promotion for the rest of the season. ### Promotions Within a two week promotional period, each category has one or two deals or promotional items. Typically, the flower category will always contain one strain priced between \$18-\$35, which tend to be the most popular for the time they are on sale. - 9/8 - 9/21 - Lemon 18 & Sundae Sunset \$25 - 9/22 - 10/5 - Phone Home, Jigglers & Sundae Sunset \$20 - Cappachino \$35 - 10/6 - 10/19 - Phone Home, Lava Cake \$20 - Cappachino \$35 - 10/20 - 11/02 - Phone Home, Lava Cake \$20 - Cappachino \$30 - 11/03 - 11/17 - Lemon 18, Truthband \$18 - Cappachino \$25 - 11/18 - 11/30 - Jigglers, Cappachino \$18 - Truthband \$25 - 12/1 - 12/14 - El Chapo \$20 - Cappachino \$25 :::