Data analysis page which contains the basic analysis of #of orders, total throughput time, total net profit, and customer satisfaction.
Graphics of order page containing the graphics of orders made in certain districts and types of pizzas made.
Process overview page that summarizes events through out the day.
Weekly glance of profit and customer satisfaction throughout the week.
The Celonis challenge provided data from a real-world takeaway pizza place. At first glance, the business seems to be going well and they have sustainable traffic that keeps the pizzeria running. However, after just slight investigation, one can note that they have a very low rating for their authentic recipe and they are often loosing money with each pizza made. We wanted to investigate the data and see why a friendly local restaurant is not receiving the recognition it deserves.
The tools of excavation
For this challenge, we used the suite of tools built within the Celonis suite. Mainly, we made a workspace with analyses that contains a large variety of components that made it easy for us to view the subtle differences the change of variables can make.
Problems buried in the data
- Confusing website
- Orders that are made through the website are on average 18 minutes slower from order placed to pizza received.
- All credit card purchases are done through the website and they are on average 9 minutes slower
- Orders made through the website are not descriptive enough and require chefs to call the customer in order to clarify the order. Orders that require calling the customer drastically reduces the customer satisfaction.
- Remaking pizzas
- pizzas that needed to be remade have much worse customer satisfaction
- all pizzas that need to be remade are ordered through the phone
- Munich District One and Munich District Three make up 63% of the orders that need to be remade
- Getting lost in deliveries
- if the pizza departs before the route for the delivery is planned, the average time for the order to be completed increases by 19 minutes
What could be improved?
Website The website should be designed so that it is easier for credit card payment. The goal of the design should be that a credit card check out is quick and few buttons to click. A website remodel would allow credit card users to not waste so much time on the website.
The menu on the website also should be completely revamped. Very frequently, the chefs making the pizza need to call the customers to clarify the order. If the menu on the website accurately represents the pizzas available at the restaurant, then there should not be such a high occurrence of customer calls.
Pizza Remakes The phone orders from Munich Districts One and Three have significantly higher rate that the chefs remake the pizzas. We speculate that this might due to language/cultural barriers that exist between the cashiers picking up the call and the customers from those selected districts, leading to misinformation. If the store hires employees from those districts who would understand the different language or accent, then there would be a lot less need to remake pizzas while improving the effectiveness of phone orders.
Delivery The restaurant should have some positive reinforcements for employees who regularly plan their routes or is familiar with the routes to eliminate a time-waster like simply getting lost.
Challenges we ran into
One of the biggest challenges we ran into was trying to find relevant relationships between the different data and variables. We also had problems with finding the right way to express the data values as well as how to navigate the Celonis web software.
Accomplishments that we're proud of
We are very proud of our organized data information that we have compiled together, and the neatness of all our combined charts and graphs. We are also proud of finding multiple different problems and solutions to those problems that caused customers to not come back to the pizzeria.
What we learned
Through this experience, we have learned:
- how to navigate through the Celonis web-software
- how to create different types of charts and graphs within Celonis
- how to isolate and compare data across various formats and variables
- how to create visual data analysis and use them to gain higher level understanding
- how to recognize trends between variables
- how to conduct trend impact analysis
What's next for Lost Fishies Buys Pizza
We would listen to the feedback that the restaurant provides in accordance to whether to implement the suggested changes. Afterwards, we customize the plan to tailor to the realistic changes that the restaurant could make. When the proposal becomes implementation, we could observe and analyze data again after some periods of time.