Introduction
The following guidance explains what BFCM Email Marketing Score values represent and how to interpret them. The goal is to provide a clear framework for understanding whether email marketing during BFCM Week was effective, and what actions should be taken as a result.
About Score Values
Each score ranges from 0 to 100.
Scores are calculated using uplift modeling, comparing two cohorts:
Shoppers exposed to a email marketing campaign (e.g., receiving and clicking on email).
Organic traffic.
A score of 50 represents equilibrium—no measurable difference between the two cohorts.
Scores above 50 mean the email marketing produced a positive lift, with the exposed cohort outperforming the control group.
Scores below 50 mean the email marketing underperformed, with exposed shoppers performing worse than those who were not exposed.
Score Interpretation
Score Value
Score values are interpreted in accordance with the table below:
Score Value | Interpretation | Indicator |
0 - 20 | Very Poor | total non-alignment with the brand potential |
21 - 40 | Poor | results below brand potential |
41 - 60 | Fair | in line with brand potential |
61 - 80 | Good | exceeding brand potential |
81 - 100 | Excellent | as good as it gets |
Priorities
In general your BFCM game plan should focus on improving overall and lifecycle stage scores according to the priority schedule below:
Score Value | Priority |
0 - 20 | Very High |
21 - 40 | High |
41 - 60 | Moderate |
61 - 80 | Low |
81 - 100 | None |
Score Spread
Furthermore, even if your scores fall within an acceptable value range, you should also consider the spread of lifecycle scores—the gap between the highest and lowest—as a secondary priority criterion.
Score Spread | Priority | Indicator |
0 - 5 | Low | Consistent performance across all lifecycle stages |
6 - 10 | Moderate | Significant untapped optimization potential |
10+ | High | Urgent need to improve the weakest lifecycle stage |
Act without delay if your overall score, any lifecycle stage score, or the lifecycle score spread is flagged as high priority.
For more actionable insights, evaluate recent email performance data.
Share of All Revenue
The percentage indicates the level of revenue contribution generated by email marketing during BFCM Week.
Share of All Revenue | Contribution |
0 - 10% | Very Low |
11% - 15% | Low |
16%- 20% | Moderate |
21% - 30% | High |
31% - 100% | Very High |
Action
Recommended use of other agents in the BFCM AI Studio:
Weak Lifecycle Stage | Recommended Agent |
Acquisition | |
Engagement | |
Monetization | |
Retention |
Lift Analysis
To evaluate BFCM Week performance, we compare scores across two time intervals:
Pre-BFCM Baseline (30 days before BFCM Week)
BFCM Week (Thanksgiving Day through following Wednesday)
Scores are evaluated both at the overall level and across lifecycle-based sub-components (Acquisition, Engagement, Monetization, Retention).
Lift
Lift is defined as the difference between the BFCM Week score and the Pre-BFCM score.
It shows whether the brand improved or declined in performance during BFCM compared to the pre-BFCM period.
Lift Interpretation
Lift Value
Score values are interpreted in accordance with the table below:
Lift Range | Interpretation |
11+ | Great pickup |
+4 to +10 | Fair pickup |
+1 to +3 | Limited pickup |
0 to -3 | Moderate disconnect |
-4 to -10 | Major disconnect |
-11 or worse | Very big disconnect |
Priorities
Score values are interpreted in accordance with the table below:
Lift Range | Interpretation |
11+ | Low Priority |
+4 to +10 | Priority |
+1 to +3 | High Priority |
-1 to -3 | Very High Priority |
-4 or worse | Top Priority |
Furthermore, even if your scores fall within an acceptable value range, you should also consider the spread of lifecycle scores—the gap between the highest and lowest—as a secondary priority criterion:
Lift Spread | Priority |
0 - 5 | Low |
6 - 10 | Moderate |
10+ | High |
Act without delay if your overall lift, any lifecycle stage lift, or the lifecycle lift spread is flagged as high priority.
For more actionable insights, evaluate recent paid performance data.
Conclusion
By using BFCM Scores and Lift analysis, brands can move beyond surface-level metrics and gain a true picture of how they paid marketing impacts shopper behavior. This approach makes performance both quantifiable and actionable, guiding future improvements in BFCM strategy.