Purpose of the Report:
The purpose of this report is to analyze the sales data of three coffee shops -
Southamtop, Portsmouth, and Blackpool - and identify the best shop to invest in
expanding its floor area. The report will present an overall project plan for delivering
the project and explain how a data analytics framework can be used to address the
core business questions assigned to us. Additionally, the report will present COTS’s
coffee shops Key Performance Indicators (KPIs) and how improved analytics enables
improvements against these KPIs. Finally, the report will list and explain generic data
problems and how to identify them, and list all the data problems identified with the
COTS dataset and propose solutions to address them.
Report Structure and Contents:
The report will be structured as follows:
1. Introduction 2. Project Plan 3. Data Analytics Framework 4. KPIs and Analytics 5.
Generic Data Problems and Solutions 6. Data Problems with COTS Dataset and
Solutions 7. Conclusion
Overall Project Plan:
The overall project plan for delivering the project includes the following steps:
1. Collect and analyze sales data from the three coffee shops 2. Identify the best
performing shop based on sales value and volume analysis 3. Evaluate the menu of
each shop and identify products that are not performing well 4. Recommend the best
shop for expanding its floor area 5. Develop a plan for expanding the shop's floor
area and increasing sales 6. Monitor the performance of the shop after expansion
and make necessary adjustments
Data Analytics Framework:
We will use the CRISP-DM (Cross-Industry Standard Process for Data Mining)
framework to address the core business questions assigned to us. The framework
consists of six stages: Business Understanding, Data Understanding, Data
Preparation, Modeling, Evaluation, and Deployment. We will use this framework to
understand the business problem, explore the data, prepare the data for analysis,
build models, evaluate the models, and deploy the solution.
COTS’s Coffee Shops Key Performance Indicators (KPIs) and Analytics:
The KPIs for COTS’s coffee shops include sales revenue, sales volume, customer
satisfaction, and employee productivity. Improved analytics can enable
improvements against these KPIs by providing insights into customer behavior,
product performance, and employee performance. For example, analyzing customer
data can help identify trends and preferences, which can be used to improve product
offerings and increase sales. Analyzing employee performance data can help identify
areas for improvement and training needs, which can increase productivity and
customer satisfaction.
Generic Data Problems and Solutions:
Generic data problems include missing data, inconsistent data, inaccurate data, and
duplicate data. These problems can be identified by performing data profiling, which
involves analyzing the data to identify patterns and inconsistencies. Solutions to
these problems include data cleansing, data transformation, and data integration.
Data Problems with COTS Dataset and Solutions:
The data problems identified with the COTS dataset include missing data,
inconsistent data, and inaccurate data. Missing data can be addressed by imputing
the missing values using statistical techniques. Inconsistent data can be addressed by
standardizing the data and resolving conflicts. Inaccurate data can be addressed by
validating the data against external sources and correcting errors.
Conclusion:
In conclusion, data-driven decision making is crucial for businesses to remain
competitive and profitable. By using a data analytics framework and addressing data
problems, businesses can gain insights into customer behavior, product performance,
and employee productivity, which can lead to increased sales and customer
satisfaction. In the case of COTS’s coffee shops, we recommend investing in
expanding the floor area of the best performing shop and removing sandwiches
from the menu.
