Market Basket Abarrotes

Base de datos

bd_limpia <- read.csv("/Users/Karen/Documents/abarrotes_bd_limpia.csv")

Market Basket Analysis

#install.packages("plyr")
library(plyr)
#installed.packages("Matrix")
library(Matrix)
#install.packages ("arules")
library(arules)

## 
## Attaching package: 'arules'

## The following objects are masked from 'package:base':
## 
##     abbreviate, write

#install.packages("arulesViz")
library(arulesViz)
#install.packages("datasets")
library(datasets)

#Ordenar de menor a mayor los tickets
bd_limpia <- bd_limpia[order(bd_limpia$F.Ticket),]
head(bd_limpia)

##   vcClaveTienda  DescGiro      Fecha Hora                      Marca
## 1         MX001 Abarrotes 0020-06-19    8                NUTRI LECHE
## 2         MX001 Abarrotes 0020-06-19    8                     DAN UP
## 3         MX001 Abarrotes 0020-06-19    8                      BIMBO
## 4         MX001 Abarrotes 0020-06-19    8                      PEPSI
## 5         MX001 Abarrotes 0020-06-19    8 BLANCA NIEVES (DETERGENTE)
## 6         MX001 Abarrotes 0020-06-19    8                      FLASH
##                   Fabricante                           Producto Precio
## 1                    MEXILAC                Nutri Leche 1 Litro   16.0
## 2           DANONE DE MEXICO DANUP STRAWBERRY P/BEBER 350GR NAL   14.0
## 3                GRUPO BIMBO                Rebanadas Bimbo 2Pz    5.0
## 4        PEPSI-COLA MEXICANA                   Pepsi N.R. 400Ml    8.0
## 5 FABRICA DE JABON LA CORONA      Detergente Blanca Nieves 500G   19.5
## 6                       ALEN      Flash Xtra Brisa Marina 500Ml    9.5
##   Ult.Costo Unidades F.Ticket NombreDepartamento          NombreFamilia
## 1     12.31        1        1          Abarrotes Lacteos y Refrigerados
## 2     14.00        1        2          Abarrotes Lacteos y Refrigerados
## 3      5.00        1        3          Abarrotes         Pan y Tortilla
## 4      8.00        1        3          Abarrotes                Bebidas
## 5     15.00        1        4          Abarrotes     Limpieza del Hogar
## 6      7.31        1        4          Abarrotes     Limpieza del Hogar
##             NombreCategoria     Estado Mts.2 Tipo.ubicación      Giro
## 1                     Leche Nuevo León    60        Esquina Abarrotes
## 2                    Yogurt Nuevo León    60        Esquina Abarrotes
## 3     Pan Dulce Empaquetado Nuevo León    60        Esquina Abarrotes
## 4 Refrescos Plástico (N.R.) Nuevo León    60        Esquina Abarrotes
## 5                Lavandería Nuevo León    60        Esquina Abarrotes
## 6      Limpiadores Líquidos Nuevo León    60        Esquina Abarrotes
##   Hora.inicio Hora.cierre Dia_de_la_Semana Subtotal Utilidad
## 1        8:00       22:00                6     16.0     3.69
## 2        8:00       22:00                6     14.0     0.00
## 3        8:00       22:00                6      5.0     0.00
## 4        8:00       22:00                6      8.0     0.00
## 5        8:00       22:00                6     19.5     4.50
## 6        8:00       22:00                6      9.5     2.19

tail(bd_limpia)

##        vcClaveTienda   DescGiro      Fecha Hora          Marca
## 107394         MX004 Carnicería 0020-10-15   11         YEMINA
## 167771         MX004 Carnicería 0020-10-15   11     DEL FUERTE
## 149429         MX004 Carnicería 0020-10-15   11 COCA COLA ZERO
## 168750         MX004 Carnicería 0020-10-15   11       DIAMANTE
## 161193         MX004 Carnicería 0020-10-15   12          PEPSI
## 112970         MX004 Carnicería 0020-10-15   12      COCA COLA
##                  Fabricante                       Producto Precio Ult.Costo
## 107394               HERDEZ    PASTA SPAGHETTI YEMINA 200G      7      5.38
## 167771 ALIMENTOS DEL FUERTE PURE DE TOMATE DEL FUERTE 345G     12      9.23
## 149429            COCA COLA           COCA COLA ZERO 600ML     15     11.54
## 168750           EMPACADOS              ARROZ DIAMANTE225G     11      8.46
## 161193  PEPSI-COLA MEXICANA              PEPSI N. R. 500ML     10      7.69
## 112970            COCA COLA     COCA COLA RETORNABLE 500ML     10      7.69
##        Unidades F.Ticket NombreDepartamento        NombreFamilia
## 107394        2   450032          Abarrotes       Sopas y Pastas
## 167771        1   450032          Abarrotes Salsas y Sazonadores
## 149429        2   450034          Abarrotes              Bebidas
## 168750        1   450037          Abarrotes    Granos y Semillas
## 161193        1   450039          Abarrotes              Bebidas
## 112970        8   450040          Abarrotes              Bebidas
##                      NombreCategoria  Estado Mts.2 Tipo.ubicación      Giro
## 107394 Fideos, Spaguetti, Tallarines Sinaloa    53        Esquina Abarrotes
## 167771          Salsa para Spaguetti Sinaloa    53        Esquina Abarrotes
## 149429         Refrescos Retornables Sinaloa    53        Esquina Abarrotes
## 168750                         Arroz Sinaloa    53        Esquina Abarrotes
## 161193     Refrescos Plástico (N.R.) Sinaloa    53        Esquina Abarrotes
## 112970         Refrescos Retornables Sinaloa    53        Esquina Abarrotes
##        Hora.inicio Hora.cierre Dia_de_la_Semana Subtotal Utilidad
## 107394        7:00       23:00                5       14     1.62
## 167771        7:00       23:00                5       12     2.77
## 149429        7:00       23:00                5       30     3.46
## 168750        7:00       23:00                5       11     2.54
## 161193        7:00       23:00                5       10     2.31
## 112970        7:00       23:00                5       80     2.31

Herramienta el generador de valor de datos

Paso 1. Definir el area del negocio que buscamos impactar o mejorar y su KPI

El área de mercadotecnia en el indicador de ventas de abarrotes

Paso 2. Seleccionar plantilla (-s) para crear valor a partir de los datos de los clientes

Visión / Segmentación / Personalización / Contextualización

Paso 3. Generar ideas o conceptos especificos

Elaborar un Market Basket Analysis de la base de datos (ventas de abarrotes)

Paso 4. Reunir los datos requeridos

Importar transacciones y reglas de asociación para agrupar los datos de las ventas de abarrotes

Paso 5. Plan de ejecucion

Mercadotecnia elaborara el plan de ventas para los abarrotes

Sistemas asegurara la captura del markdown en las bases de datos

Generar basket

library(plyr)
basket <- ddply(bd_limpia,c("F.Ticket"),function(bd_limpia)paste(bd_limpia$Marca,collapse ="."))
#View (basket)                      

#Eliminar numero de ticket
basket$F.Ticket <-NULL
      
#Renombramos el nombre de la columna
colnames(basket) <- c("Marca")
      
#Exportar basket
write.csv(basket,"basket.csv",quote = FALSE,row.names = FALSE)

Importar transacciones

tr <- read.transactions("/Users/Karen/Documents/basket.csv",format="basket",sep=".")

## Warning in scan(text = l, what = "character", sep = sep, quote = quote, : EOF
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## Warning in asMethod(object): removing duplicated items in transactions

reglas.asociacion <- apriori(tr, parameter = list(supp=0.001, conf=0.2, maxlen=10))

## Apriori
## 
## Parameter specification:
##  confidence minval smax arem  aval originalSupport maxtime support minlen
##         0.2    0.1    1 none FALSE            TRUE       5   0.001      1
##  maxlen target  ext
##      10  rules TRUE
## 
## Algorithmic control:
##  filter tree heap memopt load sort verbose
##     0.1 TRUE TRUE  FALSE TRUE    2    TRUE
## 
## Absolute minimum support count: 115 
## 
## set item appearances ...[0 item(s)] done [0.00s].
## set transactions ...[605 item(s), 115111 transaction(s)] done [0.03s].
## sorting and recoding items ... [208 item(s)] done [0.00s].
## creating transaction tree ... done [0.07s].
## checking subsets of size 1 2 3 done [0.00s].
## writing ... [11 rule(s)] done [0.00s].
## creating S4 object  ... done [0.01s].

summary (reglas.asociacion)

## set of 11 rules
## 
## rule length distribution (lhs + rhs):sizes
##  2 
## 11 
## 
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##       2       2       2       2       2       2 
## 
## summary of quality measures:
##     support           confidence        coverage             lift       
##  Min.   :0.001016   Min.   :0.2069   Min.   :0.003562   Min.   : 1.325  
##  1st Qu.:0.001103   1st Qu.:0.2356   1st Qu.:0.004504   1st Qu.: 1.787  
##  Median :0.001416   Median :0.2442   Median :0.005803   Median : 3.972  
##  Mean   :0.001519   Mean   :0.2536   Mean   :0.006054   Mean   :17.563  
##  3rd Qu.:0.001651   3rd Qu.:0.2685   3rd Qu.:0.006893   3rd Qu.:21.798  
##  Max.   :0.002745   Max.   :0.3098   Max.   :0.010503   Max.   :65.908  
##      count      
##  Min.   :117.0  
##  1st Qu.:127.0  
##  Median :163.0  
##  Mean   :174.9  
##  3rd Qu.:190.0  
##  Max.   :316.0  
## 
## mining info:
##  data ntransactions support confidence
##    tr        115111   0.001        0.2
##                                                                         call
##  apriori(data = tr, parameter = list(supp = 0.001, conf = 0.2, maxlen = 10))

inspect (reglas.asociacion)

##      lhs                  rhs         support     confidence coverage   
## [1]  {FANTA}           => {COCA COLA} 0.001051159 0.2439516  0.004308884
## [2]  {SALVO}           => {FABULOSO}  0.001103283 0.3097561  0.003561779
## [3]  {FABULOSO}        => {SALVO}     0.001103283 0.2347505  0.004699811
## [4]  {COCA COLA ZERO}  => {COCA COLA} 0.001416025 0.2969035  0.004769310
## [5]  {SPRITE}          => {COCA COLA} 0.001346526 0.2069426  0.006506763
## [6]  {PINOL}           => {CLORALEX}  0.001016410 0.2363636  0.004300197
## [7]  {BLUE HOUSE}      => {BIMBO}     0.001711392 0.2720994  0.006289581
## [8]  {HELLMANN´S}      => {BIMBO}     0.001537646 0.2649701  0.005803094
## [9]  {REYMA}           => {CONVERMEX} 0.002093631 0.2441743  0.008574333
## [10] {FUD}             => {BIMBO}     0.001589770 0.2183771  0.007279930
## [11] {COCA COLA LIGHT} => {COCA COLA} 0.002745176 0.2613730  0.010502906
##      lift      count
## [1]   1.561906 121  
## [2]  65.908196 127  
## [3]  65.908196 127  
## [4]   1.900932 163  
## [5]   1.324955 155  
## [6]  25.030409 117  
## [7]   4.078870 197  
## [8]   3.971997 177  
## [9]  18.564824 241  
## [10]  3.273552 183  
## [11]  1.673447 316

reglas.asociacion <- sort(reglas.asociacion,by='confidence',decreasing=TRUE)
summary(reglas.asociacion)

## set of 11 rules
## 
## rule length distribution (lhs + rhs):sizes
##  2 
## 11 
## 
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##       2       2       2       2       2       2 
## 
## summary of quality measures:
##     support           confidence        coverage             lift       
##  Min.   :0.001016   Min.   :0.2069   Min.   :0.003562   Min.   : 1.325  
##  1st Qu.:0.001103   1st Qu.:0.2356   1st Qu.:0.004504   1st Qu.: 1.787  
##  Median :0.001416   Median :0.2442   Median :0.005803   Median : 3.972  
##  Mean   :0.001519   Mean   :0.2536   Mean   :0.006054   Mean   :17.563  
##  3rd Qu.:0.001651   3rd Qu.:0.2685   3rd Qu.:0.006893   3rd Qu.:21.798  
##  Max.   :0.002745   Max.   :0.3098   Max.   :0.010503   Max.   :65.908  
##      count      
##  Min.   :117.0  
##  1st Qu.:127.0  
##  Median :163.0  
##  Mean   :174.9  
##  3rd Qu.:190.0  
##  Max.   :316.0  
## 
## mining info:
##  data ntransactions support confidence
##    tr        115111   0.001        0.2
##                                                                         call
##  apriori(data = tr, parameter = list(supp = 0.001, conf = 0.2, maxlen = 10))

inspect(reglas.asociacion)

##      lhs                  rhs         support     confidence coverage   
## [1]  {SALVO}           => {FABULOSO}  0.001103283 0.3097561  0.003561779
## [2]  {COCA COLA ZERO}  => {COCA COLA} 0.001416025 0.2969035  0.004769310
## [3]  {BLUE HOUSE}      => {BIMBO}     0.001711392 0.2720994  0.006289581
## [4]  {HELLMANN´S}      => {BIMBO}     0.001537646 0.2649701  0.005803094
## [5]  {COCA COLA LIGHT} => {COCA COLA} 0.002745176 0.2613730  0.010502906
## [6]  {REYMA}           => {CONVERMEX} 0.002093631 0.2441743  0.008574333
## [7]  {FANTA}           => {COCA COLA} 0.001051159 0.2439516  0.004308884
## [8]  {PINOL}           => {CLORALEX}  0.001016410 0.2363636  0.004300197
## [9]  {FABULOSO}        => {SALVO}     0.001103283 0.2347505  0.004699811
## [10] {FUD}             => {BIMBO}     0.001589770 0.2183771  0.007279930
## [11] {SPRITE}          => {COCA COLA} 0.001346526 0.2069426  0.006506763
##      lift      count
## [1]  65.908196 127  
## [2]   1.900932 163  
## [3]   4.078870 197  
## [4]   3.971997 177  
## [5]   1.673447 316  
## [6]  18.564824 241  
## [7]   1.561906 121  
## [8]  25.030409 117  
## [9]  65.908196 127  
## [10]  3.273552 183  
## [11]  1.324955 155

top10reglas<- head(reglas.asociacion,n=10,by="confidence")
plot(top10reglas,method="graph",engine="htmlwidget")

Conclusiones

A través de esta herramienta, logramos crear un diagrama de Market Basket con la base de abarrotes que previamente se limpio en el ejercicio anterior. Se importaron las transacciones y reglas de asociación, las cuales dictaminaron el comportamiento de los datos y visualizaron la agrupación de los mismos, siendo el caso de la compra conjunta de Fabuloso y Salvo, los cuales son productos de limpieza del hogar.

Gracias a R, el Market Basket ayudó a visualizar relaciones co-ocurrentes entre los datos asignados, y de esta forma, la tienda de abarrotes y el área de mercadotecnia, pudiesen empezar a planear el plan de ventas, designando mayores ofertas a los productos encontrados o haciendo paquetes promocionales.