Overview

Campaign Analytics in Feedback Automator provides visibility into how campaigns perform after they are activated. It helps sellers understand email delivery status, customer response, and the impact of campaigns on product ratings and seller feedback.

This module exists to measure effectiveness, identify trends, and support data-driven optimization of feedback and rating outreach.

Explanation

Analytics are tied to:

1. The campaign configuration

2. The selected email template

3. The date range and comparison period chosen

Different templates populate different metrics, depending on whether the campaign requests product ratings, seller feedback, or both.

Campaign Analytics Scope

Analytics include:

1. Email delivery performance

2. Ratings and feedback received

3. Conversion effectiveness

4. Trends over time

Basic Campaign Details

1. Campaign Name

2. Campaign Status

3. Start Date

4. End Date

This context helps interpret performance accurately within the campaign’s active period.

Email Performance Metrics

Tracked email metrics include:

1. Sent Emails – Emails successfully delivered

2. Scheduled Emails – Emails queued and pending delivery

3. Failed Emails – Emails that could not be delivered

4. Email Start Date – First date emails were sent

These metrics help sellers confirm whether campaigns are executing as expected.

Campaign Impact Metrics

Date Range and Comparison
Analytics support:

1. A primary date range selection

2. A comparison date range

Comparison applies specifically to ratings and feedback metrics to show improvement or decline over time.

Ratings and Feedback Metrics

1. Ratings Received

   1. Total number of product ratings received

2. Feedback Received

   1. Total seller feedback received

   2. Breakdown of positive and negative feedback

   3. Percentage change versus the comparison period

3. Average Rating

   1. Average product rating during the selected period

4. Average Feedback per Day

   1. Average number of seller feedback entries per day

Only metrics relevant to the campaign’s email template are populated.

Conversion Metrics

Tracked conversion metrics include:

1. Order to Ratings

   1. Percentage of ratings received out of total orders

   2. Formula: (Total Ratings ÷ Total Orders) × 100

2. Order to Feedback

   1. Percentage of feedback received out of total orders

   2. Formula: (Total Feedback ÷ Total Orders) × 100

3. Request to Ratings

   1. Percentage of ratings received out of total rating requests sent

   2. Formula: (Total Ratings ÷ Total Requests) × 100

4. Request to Feedback

   1. Percentage of feedback received out of total feedback requests sent

   2. Formula: (Total Feedback ÷ Total Requests) × 100

These metrics help evaluate campaign effectiveness beyond email volume.

Trends Analysis

Tracked trend metrics include:

1. Emails sent

2. Positive and negative ratings

3. Positive and negative feedback

Grouping behavior:

1. Data can be grouped by day

2. Trends help identify performance patterns and anomalies

Important Rules and Behavior

1. Analytics data updates automatically as emails and responses are processed

2. Metrics are campaign-specific and do not aggregate across campaigns

3. Comparison periods apply only to ratings and feedback metrics

4. Template changes do not retroactively affect historical analytics
   

Optimizing Campaigns Using Analytics (Iterative Testing)

If a campaign is not delivering the expected results, sellers can use Campaign Analytics to identify gaps and refine campaign configuration.

Optimization approach:

1. Review email delivery metrics to confirm emails are being sent as expected

2. Analyze conversion metrics to understand buyer response

3. Identify underperforming rules such as buyer targeting, product scope, or order filters

4. Update campaign rules, scheduling, or template selection as needed

5. Monitor performance after changes using the same analytics metrics

This iterative process is similar to A/B testing. Sellers adjust campaign rules over time and use analytics comparisons to determine which configurations produce better results.

Changes apply going forward and do not affect historical data, allowing clear before-and-after performance evaluation.

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Summary