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PIZZA PLACE SALES

Year 2015 worth of sales from a fictitious pizza place, including the date and time of each order,etc.

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Problem & background

“A year's worth of sales from a fictitious pizza place” in which various data are given.Here I need to find out no. of customer a pizza shop can expect per day, how many pizzas a customer would order, and is there any peak hours. Furthermore I, need to find out the bestseller pizza of the shop,total revenue generated and finally determining pizza’s which are not popular among the people.

Solution

A year's worth of sales data from customer insights are given from a fictitious pizza place. The problem can be solved by taking use of excel and also use of Pivot table . Which helps us to more understandable the data. From this data we also see the seasonal growth of the sales. and also see the total revenue of the sales. And also very useful to know that which pizza product needs more promotion.

Methodology & Project scope

Final data should be calculated and easy to understand is the main focus behind the work of this project

  • Firstly, Arrange the data in single Workbook, without understanding the data

  • For execution of calculations part we need to arrange the data , which collects the data for customer orders details, pizzas types, date, time, size and price of pizzas and then, calculations are carried out on excel.

  • After that  we used the  pivot table from it and use excel functions to solved the calculation part and then create a understanding pivot charts for the recommended analysis part of this project to provide solution.

  • At last a dashboard is prepared in which all the recommended analysis part is calculated.

Goals & KPIs

The success of my project is measured and carried out in following goals which includes:

  • Goal 1:  Data understandable.

  • Goal 2: Calculate the peak hours, bestsellers and seasonality of pizzas from the data.

  • Goal 3: Calculate the revenue of any pizza place from its sales data.

Concepts Used

  • Concept 1: COUNT, AVERAGE

  • Concept 2: SUMPRODUCT

  • Concept 3: NESTED SORTING, FILTERS

  • Concept 4: PIVOT TABLE, GROUPING

  • Concept 5: MAX, MIN

Conclusion

From this data it helps us to understand that which pizza is popular and which pizza is not popular . Also this data set is very useful for the business for generating the more profit . and aslo used this data to identified which pizza product should  promote.