Every figure on these pages is computed from one small made-up dataset, printed on each page and downloadable as monthly.csv, so you can check the arithmetic yourself. The code is run against the same rows before publishing.
Sales and orders
- Average order value (AOV) · Excel, SQL, Python
Average order value = Revenue ÷ Number of orders
- Units per transaction (UPT) · Excel, SQL, Python
Units per transaction = Units sold ÷ Number of orders
- Average selling price (ASP) · Excel, SQL, Python
Average selling price = Revenue ÷ Units sold
- Purchase frequency · Excel, SQL, Python
Purchase frequency = Number of orders ÷ Number of unique customers who ordered
- Revenue per customer · Excel, SQL, Python
Revenue per customer = Revenue ÷ Number of unique customers who ordered
- Average discount per order · Excel, SQL, Python
Average discount per order = Total discounts given ÷ Number of orders
- Discount rate · Excel, SQL, Python
Discount rate = Discounts ÷ (Revenue after discounts + Discounts) × 100
Profit and costs
- Gross margin · Excel, SQL, Python
Gross margin = (Revenue − Cost of goods sold) ÷ Revenue × 100
- Gross profit · Excel, SQL, Python
Gross profit = Revenue − Cost of goods sold
- COGS as a percentage of revenue · Excel, SQL, Python
COGS % = Cost of goods sold ÷ Revenue × 100
- Markup · Excel, SQL, Python
Markup = (Revenue − Cost of goods sold) ÷ Cost of goods sold × 100
- Contribution margin · Excel, SQL, Python
Contribution margin = (Revenue − COGS − Shipping − Marketing) ÷ Revenue × 100
- Shipping cost per order · Excel, SQL, Python
Shipping cost per order = Total shipping cost ÷ Number of orders
- Shipping cost as a percentage of revenue · Excel, SQL, Python
Shipping cost % = Shipping cost ÷ Revenue × 100
- Inventory turnover · Excel, SQL, Python
Inventory turnover = Cost of goods sold ÷ Average inventory value (at cost)
Website conversion
- Conversion rate · Excel, SQL, Python
Conversion rate = Orders ÷ Sessions × 100
- Add-to-cart rate · Excel, SQL, Python
Add-to-cart rate = Sessions with an add-to-cart ÷ Sessions × 100
- Checkout rate · Excel, SQL, Python
Checkout rate = Checkouts started ÷ Sessions × 100
- Checkout conversion rate · Excel, SQL, Python
Checkout conversion rate = Orders ÷ Checkouts started × 100
- Cart abandonment rate · Excel, SQL, Python
Cart abandonment rate = (Carts created − Orders) ÷ Carts created × 100
- Checkout abandonment rate · Excel, SQL, Python
Checkout abandonment rate = (Checkouts started − Orders) ÷ Checkouts started × 100
- Revenue per session · Excel, SQL, Python
Revenue per session = Revenue ÷ Sessions
Marketing and ads
- Customer acquisition cost (CAC) · Excel, SQL, Python
CAC = Marketing spend ÷ New customers acquired
- Return on ad spend (ROAS) · Excel, SQL, Python
ROAS = Revenue attributed to ads ÷ Ad spend
- Marketing efficiency ratio (MER) · Excel, SQL, Python
MER = Total revenue ÷ Total marketing spend
- Break-even ROAS · Excel, SQL, Python
Break-even ROAS = 1 ÷ Gross margin = Revenue ÷ (Revenue − COGS)
- Marketing spend as a percentage of revenue · Excel, SQL, Python
Marketing % of revenue = Marketing spend ÷ Revenue × 100
- Cost per order (CPO) · Excel, SQL, Python
Cost per order = Marketing spend ÷ Orders
- Cost per click (CPC) · Excel, SQL, Python
CPC = Ad spend ÷ Ad clicks
- Cost per thousand impressions (CPM) · Excel, SQL, Python
CPM = Ad spend ÷ Impressions × 1,000
- Click-through rate (CTR) · Excel, SQL, Python
CTR = Clicks ÷ Impressions × 100
Payments and delivery
- Payment success rate · Excel, SQL, Python
Payment success rate = Payments captured ÷ Payment attempts × 100
- Payment failure rate · Excel, SQL, Python
Payment failure rate = (Payment attempts − Payments captured) ÷ Payment attempts × 100
- Cash-on-delivery share (COD %) · Excel, SQL, Python
COD share = Cash-on-delivery orders ÷ All orders × 100
- RTO rate · Excel, SQL, Python
RTO rate = Orders returned to origin ÷ Orders shipped × 100
- Return rate · Excel, SQL, Python
Return rate = Orders returned ÷ Orders delivered × 100
- Refund rate · Excel, SQL, Python
Refund rate = Refunds paid ÷ Revenue × 100
- Net revenue · Excel, SQL, Python
Net revenue = Revenue after discounts − Refunds
Customers and retention
- Repeat customer rate · Excel, SQL, Python
Repeat customer rate = Customers who had ordered before ÷ Customers who ordered × 100
- Customer churn rate · Excel, SQL, Python
Customer churn rate = Customers lost during the period ÷ Customers at the start of the period × 100
- Customer retention rate (CRR) · Excel, SQL, Python
Retention rate = (Customers at end − New customers) ÷ Customers at start × 100
- Customer growth rate · Excel, SQL, Python
Customer growth rate = (Customers at end − Customers at start) ÷ Customers at start × 100
- Customer lifetime value (LTV) · Excel, SQL, Python
LTV = ARPU × Gross margin ÷ Monthly churn rate
- LTV to CAC ratio (LTV:CAC) · Excel, SQL, Python
LTV:CAC = Customer lifetime value ÷ Customer acquisition cost
- CAC payback period · Excel, SQL, Python
CAC payback (months) = CAC ÷ (ARPU × Gross margin)
Subscriptions and MRR
- Average revenue per user (ARPU) · Excel, SQL, Python
ARPU = Monthly recurring revenue ÷ Number of active subscribers
- MRR growth rate · Excel, SQL, Python
MRR growth rate = (MRR at end − MRR at start) ÷ MRR at start × 100
- Net new MRR · Excel, SQL, Python
Net new MRR = New MRR + Expansion MRR − Contraction MRR − Churned MRR
- Gross revenue churn rate · Excel, SQL, Python
Gross revenue churn = (Contraction MRR + Churned MRR) ÷ MRR at start × 100
- Net revenue retention (NRR) · Excel, SQL, Python
NRR = (MRR at start + Expansion − Contraction − Churned) ÷ MRR at start × 100
- Gross revenue retention (GRR) · Excel, SQL, Python
GRR = (MRR at start − Contraction − Churned) ÷ MRR at start × 100
- Expansion MRR rate · Excel, SQL, Python
Expansion MRR rate = Expansion MRR ÷ MRR at start × 100
- SaaS quick ratio · Excel, SQL, Python
Quick ratio = (New MRR + Expansion MRR) ÷ (Contraction MRR + Churned MRR)
Email marketing
- Email open rate · Excel, SQL, Python
Open rate = Unique opens ÷ Emails delivered × 100
- Email click-through rate · Excel, SQL, Python
Email CTR = Unique clicks ÷ Emails delivered × 100
- Click-to-open rate (CTOR) · Excel, SQL, Python
Click-to-open rate = Unique clicks ÷ Unique opens × 100
- Unsubscribe rate · Excel, SQL, Python
Unsubscribe rate = Unsubscribes ÷ Emails delivered × 100
Customer experience
- Net Promoter Score (NPS) · Excel, SQL, Python
NPS = (Promoters − Detractors) ÷ All respondents × 100
- First contact resolution rate (FCR) · Excel, SQL, Python
FCR = Tickets resolved on first contact ÷ All tickets × 100
- Support contact rate · Excel, SQL, Python
Contact rate = Support tickets ÷ Orders × 100
Growth
- Revenue growth rate · Excel, SQL, Python
Revenue growth rate = (Revenue this period − Revenue last period) ÷ Revenue last period × 100
- Compound monthly growth rate (CMGR) · Excel, SQL, Python
CMGR = (Last month ÷ First month)^(1 ÷ Number of months between them) − 1
Looking for the statistics underneath, such as standard deviation, correlation or a t-test? See the statistics methods, or the guides on Excel data analysis and Python and SQL.
Legal entity
Devansh Yadav
Moradabad, Uttar Pradesh
Governing law: Courts at Moradabad, Uttar Pradesh
Phone: +91 8433475698
Legal: legal@analyzeit.in