INTRODUCTION

• 47 stores
• 40,365 unique customers
• 791 menu items
• Categorical Variables: order_id, customer_id, item_no, location_no, postalcode, store_id, gender
• Numerical Variables: order_total, disctotal
• Reproducibility: Github repo
• Blog

Domain Problem Charactarization

• Maximize profit
• Increase sales
• Minimize inventory waste
• Retain existing/repititive customers
• attract new customers

Goal

Primary Question: For each store, how sales can be increased?

In this analysis we will focus more on store level data. We will explore characteristics for each store, reveal patterns and trends.

• Question 1: Which day of the week, most sales occur?
• Question 2: How the order amount varied over time?
• Question 3: What are the most popular menu items for each store?
• Question 4: Who are the regular customers and time of visit?
• Question 5: What are the peak hours for each day?

Which day of the week, most sales occur?

How the order amount varied over time?

## # A tibble: 182 x 3
## # Groups:   date_modified [182]
##    date_modified store_id total
##    <date>        <chr>    <dbl>
##  1 2018-01-01    1151     2002.
##  2 2018-01-02    1151     3642.
##  3 2018-01-03    1151     2331.
##  4 2018-01-04    1151     2323.
##  5 2018-01-05    1151     3857.
##  6 2018-01-06    1151     5830.
##  7 2018-01-07    1151     1511.
##  8 2018-01-08    1151     2506.
##  9 2018-01-09    1151     3168.
## 10 2018-01-10    1151     2865.
## # ... with 172 more rows

What are the most popular menu items for each store?

Who are the regular customers?

What are the peak hours for each day?

tree map of menu items

Geographic representation of stores

Store Level Analysis

For store level analysis, visit https://kabita-paul.shinyapps.io/StoreAnalysisApp/

Key streategies to implement

• customized discounts (via email/ message) for regular customers
• happy hours( special discounts) on days (and hours) with least orders
• optimize inventory storage ( increase stocks of raw materials for most ordered items)
• utilization of workforce- more stuffs on duty in peak hours.

Future Work

• customers’ income level and socio-economic situation of the location can be analysed.
• menu item customization and pairing of items given the detailed menu data are provided.
• analysis of customized combo meals for items which are ordered together (better pricing strategy)
• Improve Performance: The shiny app takes about 3-5 seconds to get loaded. Profiling has to be done to improve performance.

LINKS

• Please see the shiny app link:

https://kabita-paul.shinyapps.io/StoreAnalysisApp/

• Please see the blog link:

http://rpubs.com/kabitapaul11/fastfoodchain_analysis/

• Please see the Github link:

https://github.com/kabitapaul11/FastFoodChain/