SayPro A/B Testing Template

SayPro Monthly January SCMR-5 SayPro Monthly Classified Traffic Monitoring: Track site traffic and user behaviour using analytics tools by SayPro Classified Office under SayPro Marketing Royalty SCMR

Experiment Overview:

This section provides a summary of the A/B test, outlining the purpose of the test, the specific feature or page being tested, and the duration of the experiment.

Experiment Name:

• Example: “SayPro Classified Traffic Monitoring: January Traffic Optimization Test”

Test Duration:

• Example: “January 1st – January 31st”

Objective of the Test:

• Example: “To test the impact of two different layout designs on user engagement on the classified ads homepage.”

---

Hypothesis:

This section outlines the hypothesis or assumption you are testing through the A/B experiment.

Hypothesis Statement:

• Example: “We believe that the new layout design with highlighted categories will increase the time users spend on the classified ads page by at least 15%.”

---

Test Variables:

This section identifies the specific elements being tested in the A/B experiment, and provides details about each variant.

Control (A) – Current Version:

• Example: “Current classified ads page design with standard layout and category display.”

Variant (B) – Test Version:

• Example: “Revised classified ads page with prominent category highlights and dynamic filtering options.”

---

Metrics to Measure Success:

This section defines the key performance indicators (KPIs) to track the success of the A/B test.

Primary Metrics:

• Example:
  1. Page Engagement (Time on Page) – Measure how much time users spend on the classified ads page.
  2. Click-through Rate (CTR) – Track how many users click on classified ads within the page.
  3. Bounce Rate – Track the rate at which users leave the page without engaging.

Secondary Metrics:

• Example:
  1. Conversion Rate – Track how many users submit ads after visiting the page.
  2. User Feedback (via surveys or polls) – Gather qualitative data on the user experience.

---

Target Audience:

Define the demographic or segment of the user base that will participate in the A/B test.

Target Audience:

• Example: “Users who visit the SayPro Classified homepage and have been active in the past 30 days.”

---

Test Implementation:

Details on how the A/B test will be set up and run.

Implementation Plan:

• Example:
  1. Tool Used for A/B Testing: Google Optimize (or similar).
  2. Traffic Allocation: 50% of traffic will see Variant A, and 50% will see Variant B.
  3. Traffic Source: All traffic from desktop visitors will be included.
  4. Additional Considerations: Ensure the same traffic sample group does not see both variants in one session.

---

Data Collection:

Outline the process and tools used to collect data from the test.

Analytics Tools Used:

• Example: Google Analytics, Hotjar, or any other tool used for tracking site traffic and user behavior.

Metrics Tracking:

• Example: Use Google Analytics to track time spent on page and CTR. Use Hotjar to collect user interaction heatmaps.

---

Results:

Provide a summary of the results after the test is completed. The key findings should include both quantitative and qualitative data.

Test Results Summary:

• Example:
  1. Variant A (Control):
     • Average Time on Page: 3 minutes
     • CTR: 6%
     • Bounce Rate: 45%
  2. Variant B (Test):
     • Average Time on Page: 3.45 minutes (+15%)
     • CTR: 7.2% (+1.2%)
     • Bounce Rate: 42% (-3%)

Conclusion:

• Example: “Variant B, with the revised layout, showed a 15% increase in time spent on the page and a 1.2% increase in CTR. Therefore, Variant B is the winner, and it will be rolled out for all users starting February 1st.”

---

Lessons Learned & Recommendations:

This section highlights what you learned from the test and how the results will impact future experiments or decisions.

Key Insights:

• Example: “The addition of highlighted categories and dynamic filtering improved user engagement significantly.”

Next Steps:

• Example: “Test additional variations such as different types of filters or category layouts to refine the design further.”

---

A/B Test Experiment Documentation:

• Date Completed:
• Test Owner:
• Team Involved:
• Documentation Link (optional):
• Related Experiments:

---

This template ensures that the A/B testing process is clear, data-driven, and aligned with the goals of the SayPro Marketing Royalty SCMR and the classified traffic monitoring goals.