Pet Retail Market and Valuation Outlook
Appendix 22
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22: Foot Traffic Analysis
To understand the competitive dynamics of the industry in the best possible way, we created the Foot Traffic analysis to understand two main points: (I) If the
megastore model is indeed the big winner in this industry; (II) If there is any crucial difference in customer acquisition between Petz and Cobasi; and (III) How is the
traffic of a new store.
Our analysis was performed using a proprietary algorithm developed by Team 7, using Google Cloud and R. In this analysis, we start from specific features used by
large geolocation companies such as Waze and Uber. This way, the study selects all pet shops in a given radius and thus identifies the average traffic of each of the
stores and compares them. That said, we decided to perform our analysis in several competitive scenarios, taking into account the company's expansion plan.
How did we do our analysis?
Foot Traffic Analysis
Compare and Filter with R
Extract Data from Google Cloud
#install.packages ("remotes")
#install.packages("tidyverse")
#remotes::install github ("JosiahParty/populartimes")
Sys.setenv("GOOGLE_KEY" = "AIzaSyAVUZbrxuqeunvvwfq0zorpHu8A1ROTRES")
library (populartimes)
library (tidyverse)
# Site para as coordenadas http://boxfandex.com/#0.000000 0.000000 0.000000 0.000000
SW <- c(-23.548329,-46.738988)
ne <- c(-23.521404,-46.709620)
# Site para type https://developers.google.com/maps/documentation/places/web-service/supported_types
manch_bars <- search_pop_times (sw, ne, radius = 1500, type = "pet_store")
manch_bars <- manch_bare > filter (popular_times != "list ()")
manch_teste <- manch_bars > unnest (popular_times)
+ Elora todas as pontes em grático para dar um Sanity Check
manch_bars > filter (name == "Petz") %>unnest (popular_times) > ggplot (aes (hour, popularity)) + geom point (colour = "#FCD702")
manch bars filter (name == "Cobasi Osasco") > unnest (popular_times) > ggplot (aes (hour, popularity)) + geom point (colour = "#003E62")
manch bars > filter (name != "Petz") %> filter (name = "Cobasi Pet Center") > unnest (popular times] > ggplot (aes (hour, popularity)) + g
# Regressão usando os pontos anteriores
ggplot (manch_bars *>* unnest (popular times), aes (hour, popularity)) +
geom smooth (data = manch bars > filter (name != "Petz") %> filter (name != "Cobasi Pet Center") >unnest (popular times), aes (colour = "#
geom smooth (data = manch bars > filter (name == "Petz") > unnest (popular times), aes (colour = "#FCD702"), ymax =100, method="gam", fo
geom smooth (data = manch bars > filter (name = "Cobasi Pet Center") %> unnest (popular times), aea (colour = "003E62"), ymax = 100, metho
geom_smooth (data - manch_bars > filter (name -- "Petland") > unnest (popular_times), acs (colour "#D9D9D9"), ymax 100, method "gam",
scale_color_identity (name "Avg. Traffic",
breaks = c("#FCD702", "#003562", "#003562", "D9D9D9"),
labels = c("Petz", "Cobasi", "Petshops Locais", "Petland"),
guide = "legend") +theme (panel.grid.major element_blank (), panel.grid.minor = element_blank (),
panel.background element blank (), axis.line element line (colour = "black"))
Source: R; Google Cloud; Team 7View entire presentation