Introduction
Planning for the next BFCM season starts with a clear understanding of what worked—and what didn’t—the year before. Historical data provides the foundation for assessing the effectiveness of past BFCM strategies, but that data is only valuable if it is interpreted with the right frame of reference.
Many brands rely on market benchmarks to gauge success. The problem with benchmarks is that they are based on averages.
Each brand operates in a unique market environment, with its own products, customers, and competitive pressures. Comparing results to a market average can easily mislead—creating false positives that suggest success where there wasn’t, or false negatives that undervalue strong performance.
Why We Developed Performance Scores
To overcome the limitations of benchmarks, we developed credit-like scoring of BFCM results. This method evaluates performance maraketing results not against external averages, but relative to the long-term market potential produced by brand marketing.
Multi-metric approach – Instead of focusing on a single indicator, the score is derived from multiple key performance metrics.
Brand-specific context – Each score reflects how well performance marketing performed against long-term brand marketing metrics.
Comprehensive assessment – The score blends conversion and revenue performance metrics across lifecycle stages to deliver a complete picture.
Actionable insights – The underlying metrics show not just the overall grade, but also where performance was strong and where strategy underperformed.
Lifecycle Metrics Feeding the Score
The BFCM Performance Score is built from lifecycle metrics defined in Google Analytics, ensuring both familiarity and precision. For each lifecycle stage, we apply a conversion metric and a revenue metric:
Acquisition
Engagement Rate (conversion metric)
Revenue Per Session (revenue metric)
Engagement
Item View Rate (conversion metric)
Revenue Per View (revenue metric)
Monetization
Item View to Order Rate (conversion metric)
Average Order Value (revenue metric)
Retention
Shopper Lifetime Days (conversion metric)
Revenue Per Lifetime Day (revenue metric)
Uplift Modeling
To calculate the BFCM Performance Score, we apply uplift modeling, a method designed to measure the true incremental effect of performance marketing.
The process compares two cohorts of shoppers:
Performance Marketing Cohort
Shoppers who were directly exposed to performance marketing campaigns, such as receiving an email or being shown a paid advertisement.Brand Marketing Cohort
Shoppers who reached the site organically, without being subject to the marketing intervention.
By comparing these two groups, uplift modeling isolates the impact of the performance marketing itself. Instead of simply tracking raw performance (which could be influenced by seasonality, promotions, or other external factors), uplift modeling shows how much incremental lift the performance marketing activity generated.
This approach ensures that the BFCM Performance Score reflects real cause-and-effect, not just correlation. It allows brands to understand whether marketing efforts created measurable improvements—or if results would have been the same without the intervention.
Conclusion
BFCM Performance Scores offer a precise, reliable, and actionable way to measure how last year’s BFCM truly performed. This ensures planning for the upcoming season is guided by insights that are both accurate and relevant—turning historical data into a powerful tool for decision-making.