Ecommerce Analytics & Performance Metrics Flashcards
6 cards from real CES practice questions. Tap to flip, then mark Knew It or Still Learning — missed cards come back until you master them.
Read the first 6 Ecommerce Analytics & Performance Metrics flashcards as text
What is the Net Promoter Score (NPS) used to measure in ecommerce?
Answer: Customer loyalty and the likelihood of customers recommending the brand to others
NPS is derived from asking customers how likely they are to recommend the store (0–10 scale), with detractors subtracted from promoters to yield the overall score.
What does 'bounce rate' indicate on an ecommerce website?
Answer: The percentage of visitors who leave after viewing only one page without interacting further
A high bounce rate means visitors are landing on a page and leaving immediately without exploring further, which can signal poor landing page relevance or user experience.
What is the Customer Acquisition Cost (CAC) formula?
Answer: Total marketing and sales spend divided by the number of new customers acquired in the period
CAC = Total Sales & Marketing Spend ÷ Number of New Customers Acquired, indicating how much it costs to acquire each new customer.
What does a high 'refund rate' typically indicate for an ecommerce business?
Answer: Problems with product quality, inaccurate descriptions, or poor customer expectations management
A high refund rate signals underlying issues such as misleading product descriptions, quality defects, or mismatched customer expectations that need to be addressed.
What is a 'heat map' used for in ecommerce website analytics?
Answer: To visually represent where users click, scroll, and spend time on a webpage
Heat maps use color gradients to show aggregated user behavior data — clicks, scrolls, and mouse movements — helping identify which page elements attract or repel visitors.
What is the purpose of A/B testing in ecommerce?
Answer: To compare two versions of a page or element to determine which performs better with real users
A/B testing (split testing) shows two variants to separate user groups simultaneously, using statistical significance to determine which version drives better conversion outcomes.