Key metrics LONG-ENG 1-10
1. New size recommendation algorithm
In the experiment testing a new size recommendation algorithm on the product page to reduce returns due to incorrect sizing, the definition of key metrics aims to capture both long-term business impact and short-term algorithm effectiveness.
The North Star Metrics include Size-related Return Rate and Net Revenue per Order. Size-related Return Rate reflects the core long-term objective of the Size & Fit domain, which is to reduce operational and customer experience costs caused by poor fit. Net Revenue per Order captures the overall business impact of fewer returned items and a higher proportion of kept orders.
The Primary Success Metrics are Size-related Return Rate in the experiment and Correct Size Selection Rate. These metrics directly measure whether the new algorithm improves users’ ability to choose a size that they eventually keep, and therefore determine the success or failure of the experiment.
The Guardrail Metrics include Conversion Rate and Time on Product Page, which ensure that improvements in fit quality do not come at the expense of purchase completion or create confusion during the decision process.
Finally, the Secondary Metrics such as Recommendation Click Rate, Size Change Rate before Purchase, and Fit Feedback Score provide deeper diagnostic insight into how users interact with the recommendation and where further optimization may be needed.
2. Personalized size suggestion
In the experiment evaluating a personalized size suggestion feature on the product page, the goal is to improve fit accuracy by leveraging user-specific information while preserving overall purchase performance.
The North Star Metrics are Size-related Return Rate and Repeat Purchase Rate. Size-related Return Rate measures whether personalization reduces fit-related returns over time, while Repeat Purchase Rate captures longer-term customer satisfaction and loyalty effects driven by better fit experiences.
The Primary Success Metrics include Size-related Return Rate in the experiment and Correct Size Selection Rate, which directly quantify whether personalization improves the quality of size choices at the individual user level.
The Guardrail Metrics, Conversion Rate and Drop-off Rate, are used to ensure that the additional personalization logic does not introduce friction or increase abandonment during the purchase flow.
The Secondary Metrics, such as Fit Feedback Score and Recommendation Usage Rate, help explain adoption and perceived quality of the personalized recommendation.
3. Size guide redesign
In the study redesigning the size guide to improve users’ understanding of sizing information, the primary objective is to support better self-guided size decisions without increasing cognitive load.
The North Star Metrics include Conversion Rate and Size-related Return Rate. Conversion Rate reflects the overall commercial success of the product page, while Size-related Return Rate captures whether better guidance leads to fewer fit-related returns.
The Primary Success Metrics are Size Guide Usage Rate and Conversion Rate on the Product Page, which directly measure whether users engage with the new guide and whether this engagement improves purchasing outcomes.
The Guardrail Metrics, Time on Product Page and Exit Rate, ensure that the redesigned guide does not slow down decision making or cause users to leave the page prematurely.
The Secondary Metrics, such as Scroll Depth and Help Link Click Rate, provide insight into how users consume the guide content and where comprehension difficulties may remain.
4. Adding body measurement input
In the experiment introducing a body measurement input flow to improve size prediction quality, the objective is to collect higher-quality user signals while minimizing friction in the purchase journey.
The North Star Metrics are Size-related Return Rate and Net Revenue per Order, which capture the long-term reduction of returns and the financial benefit of improved fit accuracy.
The Primary Success Metrics, Measurement Completion Rate and Correct Size Selection Rate, measure whether users successfully complete the input flow and whether the collected data leads to better size choices.
The Guardrail Metrics, Drop-off Rate and Form Error Rate, control the risk that the additional input step increases abandonment or creates usability issues.
The Secondary Metrics, including Time to Complete and Field Correction Rate, help diagnose complexity and interaction problems within the measurement flow.
5. Default size pre-selection
In the experiment introducing default size pre-selection to simplify the size choice process, the objective is to reduce decision friction while maintaining fit quality.
The North Star Metrics include Conversion Rate and Size-related Return Rate, which capture both immediate commercial impact and long-term fit performance.
The Primary Success Metrics, Conversion Rate in the experiment and Add-to-Cart Conversion, directly measure whether default pre-selection improves progression through the purchase funnel.
The Guardrail Metrics, Size-related Return Rate and Complaint Rate, ensure that simplifying the choice does not increase incorrect purchases or customer dissatisfaction.
The Secondary Metrics, such as Size Change Rate and Manual Override Rate, explain how often users deviate from the default and whether the default choice aligns with user intent.
6. “Fits true to size” badge
In the experiment adding a “fits true to size” badge to support user decision making, the objective is to increase user confidence in size selection while maintaining overall purchase performance.
The North Star Metrics include Conversion Rate and Size-related Return Rate. Conversion Rate reflects the immediate commercial impact of the badge, while Size-related Return Rate captures whether the badge leads to fewer incorrect size purchases over time.
The Primary Success Metrics are Conversion Rate on the Product Page and Add-to-Cart Conversion, which directly measure whether the badge improves progression through the purchase funnel.
The Guardrail Metrics, Return Rate and Complaint Rate, ensure that the badge does not create misleading expectations or increase customer dissatisfaction.
The Secondary Metrics, such as Badge Click Rate and Trust Interaction Rate, provide insight into how users perceive and interact with the badge.
7. Brand-specific sizing normalization
In the experiment normalizing sizing across brands to reduce brand-level fit inconsistencies, the objective is to improve cross-brand size comparability and reduce systematic return patterns.
The North Star Metrics include Return Rate by Brand and Net Revenue per Order. Return Rate by Brand reflects long-term improvements in brand-level fit quality, while Net Revenue per Order captures the financial impact of reduced returns.
The Primary Success Metrics are Size-related Return Rate and Correct Size Selection Rate, which directly measure whether normalization improves size choice accuracy across brands.
The Guardrail Metrics, Conversion Rate and Complaint Rate, ensure that the normalization logic does not reduce purchase completion or create customer trust issues.
The Secondary Metrics, such as Fit Feedback Score and Brand-level Variance, help diagnose remaining inconsistencies between brands.
8. Size recommendation in add-to-cart modal
In the experiment adding size recommendation inside the add-to-cart modal to influence last-mile size decisions, the objective is to correct size choices at the final decision point before purchase.
The North Star Metrics include Conversion Rate and Net Revenue per Order. Conversion Rate captures the immediate impact on purchase completion, while Net Revenue per Order reflects the financial benefit of fewer returns.
The Primary Success Metrics are Add-to-Cart Conversion and Checkout Conversion, which directly measure whether the modal intervention improves funnel progression at the critical step.
The Guardrail Metrics, Drop-off Rate and Time to Checkout, ensure that the additional modal does not slow down or disrupt the checkout flow.
The Secondary Metrics, such as Modal Interaction Rate and Close Rate, explain how users engage with or dismiss the recommendation.
9. Mobile vs desktop size UX
In the comparison of size selection experience between mobile and desktop devices, the objective is to identify device-specific usability issues that affect fit and conversion performance.
The North Star Metrics include Conversion Rate and Size-related Return Rate. Conversion Rate captures overall commercial performance across devices, while Size-related Return Rate reflects fit quality differences between channels.
The Primary Success Metrics are Conversion Rate by Device and Add-to-Cart Conversion by Device, which directly measure performance gaps between mobile and desktop.
The Guardrail Metrics, Error Rate and Crash Rate, ensure that UX differences are not driven by technical instability or interaction failures.
The Secondary Metrics, such as Time on Product Page and Tap Error Rate, provide insight into device-specific friction points.
10. Cold-start size recommendation
In the experiment evaluating size recommendation performance for first-time users, the objective is to provide accurate size guidance despite the lack of historical user data.
The North Star Metrics include First Purchase Success Rate and Size-related Return Rate. First Purchase Success Rate reflects onboarding quality and early satisfaction, while Size-related Return Rate captures long-term fit performance for new users.
The Primary Success Metrics are Return Rate for New Users and Correct Size Selection Rate, which directly measure whether the cold-start logic leads to correct initial size choices.
The Guardrail Metrics, Conversion Rate and Onboarding Drop-off Rate, ensure that the recommendation logic does not harm early-stage funnel performance.
The Secondary Metrics, such as Time to First Purchase and First-session Abandonment Rate, provide insight into early user behavior patterns.
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