AY1516 T2 Team13 Natasha Studio Project Overview Methodology
| BACKGROUND | DATA | METHODOLOGY |
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Contents
SCOPE OF WORK
METHODOLOGY
Relational Database
Currently, Natasha Studio uses a flat file database, though a simple excel spreadsheet to keep all their records. Day-to-day operations would require the counter staff to enter information into the excel spreadsheet whenever there is a new sale. This method of storing data encourages inconsistency and could potentially result in many problems. In addition, Natasha Studio’s move over to hardcopy data through logs book further increased the data inconsistency. Missing data was also much more apparent as seen in our data cleaning and exploratory data analysis. For example, in the log book, there are many occurrences in which the type of genre for open classes packages is not recorded. As such, this is more pertinent analytical problem and we would advise our client to use a relational database management system (RDMS) instead for its many advantages explained below.
Evaluation of database tools
Based on our client’s requirements, our team finds that SQLite would be a better choice for Natasha Studio. Although MySQL seems like the most popular solution that is used by most of the big players, it requires a server to connect to the database. Our client has mentioned to us that different counter staff uses different computers and there might not be Wi-Fi readily available at the studio. Thus, the server-less SQLite would be a more appropriate choice. Even though PostgreSQL is highly customizable, it is also not suitable for our client due to its overly complex nature. Its steep learning curve with regards to daily usage is likely to pose as a barrier for our client too.
Association Rule Mining
Currently, our client does not have a formal sales monitoring system. Customers’ purchases of dance packages are simply recorded either onto an Excel spreadsheet or a log book without much further analysis. Our preliminary data exploration highlighted the transactional nature of the purchases data. In the purchases data, packages are bought by a particular member at a particular date. Thus, given the availability of transaction data, we apply Association Rule Mining (ARM) to this dataset, hoping to identify purchasing patterns which would be useful to our client in marketing and promoting dance packages to their future members. The results will enable us to implement suitable sales promotions, cross-selling promotions and recommendations in order to capture more sales per customer. Furthermore, sequence discovery will be used to analyze the purchasing patterns of customers. This allows us to understand the trends and decisions made by the customers after the expiration of every package. Following which, suitable sales and marketing efforts can be implemented in order to drive repeat sales.
