{% extends "layouts/base.html" %} {% block title %} A/B Test Product {% endblock %} {% block stylesheets %}{% endblock stylesheets %} {% block content %}
A/B Test Product
Dimension selection
  • A/B Test Products are bunch of testes which compares effect of Products to the business. There are two metrics which help us to measure comparison values. These are Number of orders counts per product and Average purchase amount per product. Products of Purchasement related to these metrics are tested with Before - After Analysis in order to the answer the question that is "Is there any significant increase/decrease on Order Count / Average Payment Amount per User after the Products is purchased?" While we need to see to the product which increases/decreases the number of orders and the average purchase amount, This Section can help us to find the right product for the business.
  • Hypothesis are;
    H0 : These is no significant Difference with A - B of order count / purchase amount.
    H1 : These is significant Difference with A - B of order count / purchase amount.
  • Bootstrapping Method;
    Each test process are applied with Boostraping Method. So, Iteratively randomly selected A and B sample customers of order count / average purchase amount will be tested acceptable enough time. Each iteration A - B samples are randomly selected. Accepted ratio will show us the confidence of our test results. For instance, There are 100 tests and 75 of them are ended with H0 Accepted. So, test are % 75 our test are H0 Accepted.
  • How are Before - After Product Usage Tests designed?
    It might be very confusion and it might seem very sophisticated A/B Test process, however it basically compares products related to customers of product selection at the basket. Each product selection at the basket is tested separately. Each timestamp of the selected product at the basket are collected for each product per customer individually. Each customers of one week before product selection and one week after the product selection time periods are compared with to metrics which are order count and average purchase amount. We aim to detect significant increase after time period while comparing to before time period about purchase amount or order count.
    related charts are;
    'Before - After Time Periods Customers' Average Purchase Payment Amount Test (Test Accepted!) ',
    'Before - After Time Periods Customers' Average Purchase Payment Amount Test (Test Rejected!)',
    'Before - After Time Periods Customers' Total Purchase Count Test (Test Accepted!)',
    'Before - After Time Periods Customers' Total Purchase Count Test (Test Rejected!)'
Before - After Time Periods Customers' Average Purchase Payment Amount Test (Test Accepted!) {% if data_type['product_usage_before_after_amount_accept'] == False %} sample data {% endif %}
- A : Avg. purc. amount Before the basket with selected product per customer, per product.
- B : Avg. purc. amount After the basket with selected product per customer, per product
- Test Accepted : Significant Increase on Avg. purc. amount after the product is selected in ordered basket.
- Each Test are applied individually (per product).
Before - After Time Periods Customers' Average Purchase Payment Amount Test (Test Rejected!) {% if data_type['product_usage_before_after_amount_reject'] == False %} sample data {% endif %}
- A : Avg. purc. amount Before the basket with selected product per customer, per product.
- B : Avg. purc. amount After the basket with selected product per customer, per product
- Test Rejected : Significant Decrease on Avg. purc. amount after the product is selected in ordered basket.
- Each Test are applied individually (per product).
Before - After Time Periods Customers' Total Purchase Count Test (Test Accepted!) {% if data_type['product_usage_before_after_orders_accept'] == False %} sample data {% endif %}
- A : Total P. Count Before the basket with selected product per customer, per product.
- B : Total P. Count After the basket with selected product per customer, per product
- Test Accepted : Significant Increase on Total P. Counts after the product is selected in ordered basket.
- Each Test are applied individually (per product).
Before - After Time Periods Customers' Total Purchase Count Test (Test Rejected!) {% if data_type['product_usage_before_after_orders_reject'] == False %} sample data {% endif %}
- A : Total P. Count Before the basket with selected product per customer, per product.
- B : Total P. Count After the basket with selected product per customer, per product
- Test Rejected : Significant Decrease on Total P. Counts after the product is selected in ordered basket.
- Each Test are applied individually (per product).
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