cd {/TigerInsight} python run.py
Try 127.0.0.1:5000 in your browser
The Web App is based on Flask framework, so try to correctly install all dependencies. The machine learning algorithm is built on Panda.
- Overview: all functions provided in this app
- Analytics: upload input data (customer visiting history) or view results of analysis of such data
- Potential Customer: browse potential cusomer list generated by analyzing input data
- Recommendation: browse customized restaurants and music styles recommendations for those potential revisiting customer
- Food & Drink Styles: visualize the styles of restaurants, cafes, bars, and so on in the San Padro Square
- Music Styles: visualize the music styles we pool for recommendation
- Customer Profile: browse customer background information
Username: admin@email.com Password: admin
- Login
- Go to Analytics section
- Upload TigerInsight/data/[different data size]login.csv, and wait for results coming up
- Browse other sections
Achieved functions as put in Instruction of Dashboard
It is obvious that retail stores and business owners are willing to absorb more customers. This goal is partially followed through marketing campaigns and promotions. However, their budget for marketing is limited as well. Hence, it is important to first identify those targets that are more valuable to be marketed and then find the appropriate type of promotional offer for targeted marketing.
As a potential problem to be addressed, there is lack of a novel method or intelligent system to identify various types of (potential) customers based on their likelihood of revisiting and hence purchasing from stores repeatedly. Such identification of visitors/customers provides business owners with more effective as well as affordable targeted marketing.
- Categorizes visitors/customers of the mall based on the visiting history
- Makes predictions on visiting likelihood of visitors
- Makes decision for target selection in targeted marketing
- Captures shopping interests of visitors
- Makes recommendations for efficient targeted marketing
Analyze WiFi access history data of each customer and get a conclusion whether this person would visit the mall again. Send offer via e-mail to the potentially non-returning customers in order to attract them back.