Difference between revisions of "IS480 Team wiki: 2018T1 analyteaka projectscope"
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{| class="wikitable;style="background-color:white; color:white padding: 5px 0 0 0;" width="100%" height=50px cellspacing="0" cellpadding="0" valign="top" border="0" | {| class="wikitable;style="background-color:white; color:white padding: 5px 0 0 0;" width="100%" height=50px cellspacing="0" cellpadding="0" valign="top" border="0" | ||
− | | style="vertical-align:top;width: | + | | style="vertical-align:top;width:16%;" | <div style="padding: 0px; font-weight: bold font-family:Century Gothic; text-align:center; line-height: wrap_content; font-size:85%; border-bottom:3px solid #7D5B53; ">[[IS480 Team wiki: 2018T1 analyteaka ProjectOverview Description | <font color="#35332E"><b>PROJECT DESCRIPTION</b></font>]] |
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Revision as of 16:48, 13 August 2018
This module provide weekly and monthly report based on data provided by the store and customer profiling module and provide recommendations based on the data. It will make use of data visualization module to generate reports for end users based on their roles (sales executives, senior management, etc.).
Data Analytics is the process of examining data sets in order to draw conclusions about the information they contain, increasingly with the aid of specialized systems and software.
Typical mechanisms: Database (only Data)
Typical timeframe: Offline
The outcome of analytics is informed business decisions to verify or disprove scientific models, theories and hypotheses. The typical goals is to improve efficiency, optimize processes, increase revenues etc.
The hardest part of analytics project is asking the question. As Robert Half once mentioned, "Asking the right questions takes as much skill as giving the right answers." - Robert Half
Descriptive analytics | Insight into the past:
Data operations:
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Predictive analytics | Understanding the future:
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Prescriptive analytics | Advise on possible outcomes:
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Based on the above details our modules are split into the respective section
Descriptive:
- Customer Profile module
- Store Profile
- Staff profile
Predictive:
- Machine Learning
Prescriptive:
- Data visualisation module
- Analytics and reporting module
Customer categorization | Based on the historical sales data provided by Scanteak, we are going to work out the customers’ race (from name), gender (from name), age (from NRIC), income level (based on their housing district), and if they are return customers (based on the past transaction records). |
Customer profiling | Moving forward from customer categorization, which isolates various identifiable traits (age, race, gender etc.), we are going to generate several profiles/personas based on a combination of identifiable traits.
Examples of descriptive analytics would include:
Examples of business questions that will be answered:
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Part 1 | This module is responsible for providing descriptive analytics for different products and their respective categories. It will provide the foundation for predictive analytics (e.g. recommended product and quantity allocation for each store).
Examples of descriptive analytics would include:
Examples of business questions that will be answered:
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Part 2 | This module is responsible for providing predictive analytics for different stores and their respective locations based on part 1.
Examples of descriptive analytics would include:
Examples of business questions that will be answered:
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A new sales system, meant to replace Scanteak’s legacy system, is currently being developed by Scanteak’s in-house developers. As the new sales system is still in the midst of completion, the bootstrap module will allow the user to upload the customer data exported from the new sales system. Once the new sales system has been completed, the manual bootstrapping of CSV files will be phased out and the bootstrap module will be modified to interact with the new sales system directly through API calls.
Furthermore, the new system will allow for higher level of data quality, providing better predictive analytics result.
Steps for bootstrapping
1. Data cleaning by removing the duplicate row (double entry, invalid rows)
2. Infer columns required
- Gender and ethnicity based on first name and last name
- Residential district/housing type/ house value based on postal code
- Age and citizenship based on NRIC.
3. Convert into datastore request objects
4. Uploading data to datastore
This module will make use of Bootstrap, flask and Dash by Plothy to generate charts based on data output generated by customer and store profiling module.
This module is responsible for providing descriptive analytics for individual sales executives at respective outlets.
Examples of descriptive analytics would include:
- Revenue generated by each sales executive
- Number of items from each product type sold by each sales executive
- Number of deals closed from each type of customer (based on traits/personas) by each sales executive
Examples of business questions that will be answered:
- Who is the best performing sales executive?
- What is the best-selling product type for each sales executive?
- Is the sales executive’s performance consistent? What’s the possible reason?
- What is the customer composition (based on traits/personas) for each sales executive?
This module will contain the machine learning system. As we are using Python as our main programming language, we will be utilizing libraries such as – SciPy, NumPy, matplotlib, pandas, Scikit-learn to help us complete this module. Using the training dataset (6 months’ worth of offline retail data) we have prepared, we will train the system to provide predictive analytics for both customers and stores.
The entire process can be automated, whereby the system will retrieve raw sales data from the in-house sales system, process the raw sales data in the machine learning module before handing it over to the analytics & reporting module.
Examples of predictive analytics:
- Recommended products for different customer profiles
- Recommended price range for different customer profiles
- Recommended products to be displayed for different stores
- Best selling type of item and item category for different stores
Examples of business questions that will be answered:
- What kind of furniture (based on price range) should you recommend to a certain customer profile (e.g. 30-year-old Chinese male at the Suntec Branch)
- What kind of customers (e.g. Chinese) are you expecting at a certain branch (e.g. Suntec) and what kind of furniture (e.g. Oriental-style) should you display?