Metrics
Know your numbers. Grow your business.
Welcome to Daasity’s Metrics Glossary—a single source of truth for the KPIs that power your retail and e-commerce decisions. Every entry includes a plain-English definition, the exact calculation, and links to where you’ll see it in Daasity.
What’s inside each metric page: Object Type, Team, Domain, Used In, Description, Calculation (with formulas/SQL where relevant), plus Related Metrics.
Built for: merchants and operators who want consistent, trustworthy numbers across channels.
$ per Point of Distribution
Object Type: Metric Team: Retail Domain: Velocity Used In: Retail velocity analysis in syndicated reports (comparing sales normalized by distribution) Description: Average dollar sales per 1% of ACV distribution. This velocity metric standardizes a product’s sales by its distribution level. It shows how much revenue is generated for each point of ACV distribution. A higher $ per point of distribution means the product sells more strongly in the stores where it’s available (i.e., it has high velocity). This allows comparison of products with different distribution levels on an even footing. Calculation: $ per ACV Point = Total Dollar Sales / % ACV. (For instance, if a product has $500k in sales at 50% ACV, then $ per ACV point = $500k / 50 ≈ $10k per point.)
$ per Store per Week
Object Type: Metric Team: Retail Domain: Velocity Used In: Retail velocity analysis in syndicated reports (comparing sales normalized by distribution) Description: Average dollar sales per store selling, per week. This metric shows the weekly sales velocity of a product in each store, on average. By standardizing to a per-store-per-week basis, it allows comparison across different products or time periods irrespective of how many stores stock the product. A higher $ per store/week means each store moves more dollars of the product weekly (high store-level velocity). Calculation: $/Store/Week = Total Dollar Sales / (Number of Stores Selling × Number of Weeks in period).
% Dollar Sales on Promotion
Object Type: Metric Team: Retail Domain: Promotion Used In: Retail promotional performance reports (evaluating lift and promo contribution in retail data) Description: The percentage of the product’s dollar sales that occurred during promotional periods. This metric shows how reliant the product’s revenue is on promotions. For example, 30% means nearly a third of all sales dollars came from weeks when the item was on deal. Calculation: (Dollar Sales on Promotion / Total Dollar Sales) × 100%.
% Unit Sales on Promotion
Object Type: Metric Team: Retail Domain: Promotion Used In: Retail promotional performance reports (evaluating lift and promo contribution in retail data) Description: The percentage of the product’s unit sales volume that was sold on promotion. This indicates what portion of all units sold were under a promotional discount. A high percentage suggests a large share of volume is driven by promotions. Calculation: (Units Sold on Promotion / Total Units Sold) × 100%.
ACV Gap
Object Type: Metric Team: Retail Domain: Advanced Analysis Used In: Retail distribution opportunity analysis (identifying gaps in market coverage) Description: ACV Gap represents the distribution opportunity – the gap between the product’s current % ACV and a target or maximum % ACV (often 100%, or sometimes compared to a key competitor’s % ACV). Essentially, it’s how much ACV is not covered by the product’s distribution. For example, if a product has 75% ACV, the gap to full distribution is 25 percentage points. This highlights how much market coverage is missing and can help prioritize efforts to gain distribution in new stores. Calculation: ACV Gap = Target % ACV (e.g. 100% for full distribution) – Current % ACV.
ACV Gap: Dollar Opportunity
Object Type: Metric Team: Retail Domain: Advanced Analysis Used In: Retail distribution opportunity analysis (identifying gaps in market coverage) Description: Dollar Opportunity is the estimated incremental sales that could be gained by closing the distribution gap. It translates the ACV Gap into a revenue opportunity. Typically, it’s calculated by multiplying the ACV Gap (in percentage points) by the product’s velocity (sales per point of ACV). This metric provides a dollar value for unrealized sales – essentially, how much more revenue the product might generate if it achieved full distribution or a specific distribution target. Calculation: Example: Dollar Opportunity ≈ ACV Gap (in percentage points) × Dollar Sales per point of ACV. (For instance, if ACV Gap is 20 points and the product sells $50K per ACV point, the Dollar Opportunity is about $1M in additional sales.)
Average Order Value (AOV)
Object Type: Metric Team: E-commerce Domain: Sales & Profitability Used In: E-commerce sales performance dashboards (e.g., Shopify/Amazon sales reports) Description: The average dollar amount spent per order on the website. AOV is an important metric for e-commerce health, showing how much customers tend to spend in one transaction. Increasing AOV (through strategies like upselling or cross-selling) can drive higher revenue without needing to acquire more orders. This is the same concept as overall AOV, but focused on the e-commerce channel. Calculation: AOV = Total Online Net Sales / Number of Online Orders.
Average Price
Object Type: Metric Team: Omnichannel Domain: Sales Used In: Total Company (Omnichannel) sales dashboards and explores Description: The average price paid by consumers per unit of the product. (Referred to as “ARP” – Average Retail Price – in retail data, and “ASP” – Average Selling Price – in e-commerce.) This metric provides the average price point of a product in the market. Calculation: Average Price (ARP) = Total Dollar Sales / Total Units Sold.
Average Retail Price (Base)
Object Type: Metric Team: Retail Domain: Pricing Used In: Retail pricing analytics (comparing promo vs. base pricing in retail data) Description: The average price per unit for units sold not on promotion (at regular price). This is essentially the same as the non-promo ARP described above – reflecting the everyday base price. (Sometimes “Base” price is used interchangeably with non-promo price.) Calculation: Base ARP = Dollar Sales (base/non-promoted sales) / Units Sold (base). (If “Base” is defined identically to non-promoted sales, this formula mirrors the Non-Promo ARP.)
Average Retail Price (Non-Promo)
Object Type: Metric Team: Retail Domain: Pricing Used In: Retail pricing analytics (comparing promo vs. base pricing in retail data) Description: The average price per unit for units sold not on promotion (i.e., sold at regular price). This metric shows the typical selling price when the product is not discounted. It’s calculated using only the sales where no promotional discount was applied. Generally, this reflects the everyday or base price of the product in store. Calculation: Non-Promo ARP = Dollar Sales (of non-promoted units) / Units Sold (non-promoted).
Average Retail Price (Promo)
Object Type: Metric Team: Retail Domain: Pricing Used In: Retail pricing analytics (comparing promo vs. base pricing in retail data) Description: The average price per unit for units sold on promotion. This captures the effective unit price consumers paid during promotional periods (when discounts or deals were in effect). It’s usually lower than the non-promo ARP, reflecting the discount offered. Calculation: Promo ARP = Dollar Sales (promoted units) / Units Sold (promoted).
Base Dollars
Object Type: Metric Team: Retail Domain: Sales Volume Used In: Retail sales volume analyses (baseline vs. lift in syndicated retail reports) Description: Base Dollars represent the baseline sales (in dollars) that would have been expected without any promotional influence. During a promotion period, base dollars are the portion of sales that would likely have occurred anyway (modeled or estimated from historical/non-promotional sales). This serves as a benchmark of normal sales level. Calculation: Base Dollars = Promo Dollars – Incremental Dollars. (In a promotional period, base dollars are the estimated sales if no promo ran. They can be derived by subtracting the incremental lift from actual promotional sales.)
Baseline Sales (Pre-Promotion Expected Sales)
Object Type: Metric Team: Retail Domain: Trade Promotion Used In: Description: Baseline sales represent the normal expected sales of a product without promotional influence. It serves as the standard benchmark to measure incremental promotional lift. Calculation: Typically calculated using historical sales trends or modeling techniques from non-promotional periods.
Bounce Rate
Object Type: Metric Team: E-commerce Domain: Site Performance Used In: Website analytics dashboards (site engagement reports, e.g., Google Analytics) Description: The percentage of visits where the user leaves the website after viewing only a single page (i.e., they bounced without further interaction). A high bounce rate indicates that many visitors did not engage beyond the landing page, which may suggest they didn’t find what they were looking for or were not compelled to explore further. Bounce Rate is a gauge of initial engagement; for example, a high bounce rate on a landing page may signal issues with content relevance or user experience on that page. Calculation: Bounce Rate = (Single-page sessions / Total sessions) × 100%. (This is typically provided by web analytics tools as well.)
Carrying Cost %
Object Type: Metric Team: Omnichannel Domain: Inventory Used In: Inventory management dashboards and stock level reports Description: Carrying Cost Percentage (Inventory Holding Cost %) is the annual cost of holding and storing inventory, expressed as a percentage of the inventory’s value. It includes all the expenses associated with keeping inventory, such as warehouse storage costs, insurance, taxes, depreciation, and the cost of capital tied up in inventory. For instance, a carrying cost of 25% means that holding $100 of inventory for a year costs about $25 in storage-related expenses. Monitoring this helps businesses balance inventory levels against holding costs. Calculation: Carrying Cost % = (Annual Inventory Holding Costs / Average Inventory Value) × 100%.
Cart Abandonment Rate
Object Type: Metric Team: E-commerce Domain: Site Performance Used In: Website analytics dashboards (traffic and conversion reports, e.g., Google Analytics integration) Description: The percentage of online shopping carts that are created but not ultimately converted into orders. In other words, it’s the share of shoppers who add items to their cart but leave without completing the purchase. A high cart abandonment rate can point to friction in the checkout process or hesitation (such as unexpected costs like shipping, or requiring account creation). Reducing this rate (through optimizations like simplified checkout, free shipping, etc.) is key to recovering otherwise lost sales. Calculation: Cart Abandonment Rate = (Number of Abandoned Carts / Number of Initiated Carts) × 100%. (For example, if 200 carts were started and 50 resulted in no purchase, that’s a 25% abandonment rate. An “abandoned cart” is typically defined as a cart with items added but no order placed.)
Channel Sales Mix
Object Type: Metric Team: Omnichannel Domain: Sales Used In: Total Company (Omnichannel) sales dashboards and explores Description: %(Channel) of Total Sales – a calculated metric showing what proportion of total omnichannel sales each channel represents. Business use: Helps in evaluating channel strategy and dependency. For instance, if 80% of sales are DTC vs 20% retail, strategies can be adjusted accordingly. Calculation: (Channel Sales / Total Sales) × 100%.
Click-Through Rate (CTR)
Object Type: Metric Team: Marketing Domain: Advertising Reach & Engagement Used In: Digital advertising channel reports (impression and click metrics in ad platforms like Google/Facebook) Description: CTR is the percentage of ad impressions that resulted in a click. It’s an indicator of how effective an ad is at capturing interest. A higher CTR means a larger proportion of people who see the ad are clicking on it. This can reflect the ad’s relevance, creative effectiveness, or targeting accuracy. Calculation: CTR = (Clicks / Impressions) × 100%. (For instance, 50 clicks on 1,000 impressions would be a 5% CTR.)
Clicks
Object Type: Metric Team: Marketing Domain: Advertising Reach & Engagement Used In: Digital advertising channel reports (impression and click metrics in ad platforms like Google/Facebook) Description: The total number of clicks an advertisement or link receives. This metric indicates how many times users engaged with an ad by clicking on it, typically leading to your website or landing page. Clicks are a primary measure of ad engagement and the traffic driven from ads. Calculation: Tracked by advertising platforms as the count of clicks on your ads or links. (Simply the sum of all ad clicks recorded.)
Contribution Margin
Object Type: Metric Team: E-commerce Domain: Sales & Profitability Used In: E-commerce profitability analysis (product margins, financial reports) Description: Contribution Margin is the profit per unit or order after all variable costs are subtracted from Net Sales. It goes a step beyond gross margin by also subtracting variable non-COGS expenses (for example, shipping costs, transaction fees, or other costs directly tied to the sale). This metric helps in understanding profitability on a per-unit or per-order basis and how much each sale contributes toward covering fixed costs and profit. Calculation: Contribution Margin = Net Sales – (COGS + other variable costs). (Often evaluated per unit or per order. For instance, if Net Sales for a product is $100, COGS is $50, and additional variable costs like shipping are $10, then Contribution Margin = $40.)
Cost per Click (CPC)
Object Type: Metric Team: Marketing Domain: Advertising Cost Used In: Digital advertising spend reports (cost efficiency metrics in ad platform dashboards) Description: The average cost paid for each click in a pay-per-click advertising campaign. CPC tells you how much you spend every time someone clicks your ad. It’s a key metric for managing ad spend efficiency – lower CPC means you are getting traffic (clicks) at a lower cost, which is generally desirable if the traffic is of good quality. Calculation: CPC = Total Ad Spend / Total Clicks. (For example, $200 spent for 500 clicks gives a $0.40 CPC on average.)
Cost per Incremental Unit/Dollar
Object Type: Metric Team: Retail Domain: Trade Promotion Used In: Description: Represents promotional cost efficiency by calculating spend required per incremental sale unit or dollar. Calculation: Cost per Incremental Unit = Total Promo Spend / Incremental Units Sold.
Cost per Mille (CPM)
Object Type: Metric Team: Marketing Domain: Advertising Cost Used In: Digital advertising spend reports (cost efficiency metrics in ad platform dashboards) Description: CPM stands for Cost per Mille, meaning cost per 1,000 impressions. It’s the cost to have your ad shown one thousand times. CPM is commonly used in display and social advertising to price and measure the efficiency of ad reach. It reflects how expensive it is to reach a large audience. Calculation: CPM = (Total Ad Spend / Total Impressions) × 1,000. (E.g., $500 spent for 100,000 impressions is a $5.00 CPM.)
Customer Acquisition Cost (CAC)
Object Type: Metric Team: Marketing Domain: Advertising Cost Used In: Marketing acquisition dashboards (cost per acquisition tracking) Description: Customer Acquisition Cost is the average cost to acquire one new customer. CAC accounts for marketing and sales expenses used to attract customers. It’s calculated by dividing all costs spent on acquiring customers (over a period) by the number of new customers acquired in that period. This metric is critical for understanding the efficiency of customer acquisition efforts and is often compared to the value of those customers (LTV). Calculation: CAC = Total Marketing & Sales Spend for Acquisition / Number of New Customers Acquired. (For example, if $10,000 is spent on campaigns in a month and 200 new customers were acquired, CAC = $50 per customer.)
Customer Churn Rate
Object Type: Metric Team: E-commerce Domain: Customers Used In: Customer analytics dashboards (cohort retention analysis) Description: The percentage of customers that stop purchasing (are lost) in a given period. It’s the inverse of retention. For example, if out of those 100 customers at the start of the year, 40 did not purchase again that year, the churn rate is 40%. Churn Rate is critical in subscription businesses, but for e-commerce it can be applied to repeat purchasing behavior. Lower churn means more customers are staying engaged with the brand. Calculation: Churn Rate = 100% – Customer Retention Rate. (E.g., if retention is 60%, then churn is 40%.)
Customer Lifetime Value (LTV)
Object Type: Metric Team: E-commerce Domain: Customers Used In: Customer analytics dashboards (lifetime value and ROI analysis) Description: Customer Lifetime Value is the estimated total value (revenue or profit) that a customer will contribute over their entire relationship with the brand. Often, LTV is calculated in terms of gross profit per customer across all their purchases. A basic approach is: Average Order Value × Purchase Frequency × Expected Customer Lifespan (in number of orders or time), sometimes focusing on profit rather than revenue. LTV is crucial for understanding how much a customer is worth to the business and informs how much can be spent to acquire customers (CAC) while remaining profitable. Calculation: There are multiple methods. One simple method: LTV = average gross margin per customer × average number of purchases over the customer’s lifetime. (More complex models use retention rates and discounting future cash flows for precision.)
Dollar Sales
Object Type: Metric Team: Omnichannel Domain: Sales Used In: Total Company (Omnichannel) sales dashboards and explores Description: Total dollar value of product sold in a given market and time frame. (For e-commerce sales, the custom-defined Total Revenue metric will be used – by default this is Net Sales.) Calculation: Sum of all dollar sales.
SUM(dollars)Forward-Buy Indicator
Object Type: Metric Team: Retail Domain: Trade Promotion Used In: Description: Identifies potential forward-buying by comparing wholesale shipments to actual retail sales during promotions. Calculation: Forward Buy % = (Wholesale Units Shipped - Retail Units Sold) / Wholesale Units Shipped.
Gross Margin (Gross Profit)
Object Type: Metric Team: E-commerce Domain: Sales & Profitability Used In: E-commerce profitability analysis (product margins, financial reports) Description: Gross Margin (often expressed as a dollar amount, a.k.a. Gross Profit) is the profit remaining after subtracting the direct cost of goods sold (COGS) from Net Sales. Gross Margin shows how much money is left from sales after covering the product costs. This is essentially the gross profit from e-commerce sales and indicates how efficiently products are produced and sold relative to their cost. Calculation: Gross Margin = Net Sales – Cost of Goods Sold (COGS). (Typically calculated for the period or per order/product.)
Gross Margin %
Object Type: Metric Team: E-commerce Domain: Sales & Profitability Used In: E-commerce profitability analysis (product margins, financial reports) Description: Gross Margin % is Gross Margin expressed as a percentage of Net Sales. It indicates the proportion of revenue retained as profit after product costs. For example, a 40% gross margin means $0.40 of every $1 in sales is profit after accounting for COGS. This percentage is a key indicator of product profitability and cost management. Calculation: Gross Margin % = (Gross Margin / Net Sales) × 100%.
Gross Sales
Object Type: Metric Team: E-commerce Domain: Sales & Profitability Used In: E-commerce sales performance dashboards (e.g., Shopify/Amazon sales reports) Description: In e-commerce, Gross Sales represents the total merchandise value of orders before any discounts, promotions, or returns. It is the initial revenue booked from all transactions in the period. Gross Sales for online channels is often also called Gross Merchandise Value (GMV). This metric is useful for understanding total demand and order volume before any adjustments. Calculation: Sum of order values at checkout (prior to applying discount codes, refunds, or returns).
Impressions
Object Type: Metric Team: Marketing Domain: Advertising Reach & Engagement Used In: Digital advertising channel reports (impression and click metrics in ad platforms like Google/Facebook) Description: The number of times an advertisement is displayed or seen. Each impression represents one view of your ad by a user (regardless of whether they click it or not). Impressions measure the reach of your advertising campaign in terms of total ad exposures. It’s often used to gauge brand visibility and the scale of an ad campaign. Calculation: Tracked by advertising platforms as the count of ad displays. (This is usually provided directly by the ad platform; no manual formula beyond summing up impressions.)
Incremental Dollar Sales (Promotional Lift)
Object Type: Metric Team: Retail Domain: Promotion Used In: Retail promotional performance reports (evaluating lift and promo contribution in retail data) Description: The additional dollar revenue generated due to promotions, beyond what would have been sold at the baseline. Incremental Dollar Sales (often called Dollar Lift) represents the extra sales dollars attributable to the promotional incentive. It is computed by estimating the baseline sales (had there been no promotion) and subtracting that from the actual sales during the promotion. The remainder is the increment (lift) due to the promo. Calculation: Incremental $ = Actual Dollar Sales during promotion – Baseline Dollar Sales (expected without promo).
Incremental Dollars
Object Type: Metric Team: Retail Domain: Sales Volume Used In: Retail sales volume analyses (baseline vs. lift in syndicated retail reports) Description: Incremental Dollars (Promotional Lift in dollar terms) are the additional sales revenue generated due to promotions, beyond the baseline. It represents the extra dollar sales attributable to the promotional activity – essentially the lift above what would have been sold without the promotion. Calculation: Incremental Dollar Sales = Actual Dollar Sales during promotion – Baseline Dollar Sales (expected without promo). (This is the “lift” in dollars from the promo. See also Promotional Incrementality % for efficiency of this lift.)
Incremental Sales (Lift Volume or Value)
Object Type: Metric Team: Retail Domain: Trade Promotion Used In: Description: Incremental sales quantify additional sales generated specifically by the promotion, beyond baseline expectations. Calculation: Incremental Sales = Actual Promotional Sales - Baseline Sales.
Incremental Unit Sales (Unit Lift)
Object Type: Metric Team: Retail Domain: Promotion Used In: Retail promotional performance reports (evaluating lift and promo contribution in retail data) Description: The additional units sold because of the promotion, above the baseline level. Unit Lift represents the extra volume that the promotion drove, in terms of units. It highlights the volume impact of promotional activity on top of normal sales. Calculation: Incremental Units = Actual Units Sold during promotion – Baseline Units (expected without promo).
Inventory On Hand (Units)
Object Type: Metric Team: Omnichannel Domain: Inventory Used In: Inventory management dashboards and stock level reports Description: The total quantity of units currently in stock (available inventory) across all warehouses or selling locations. It represents the physical inventory on hand that is available for sale as of the current date. Calculation: Not a derived formula – this is a direct count of all units in stock.
Inventory On Order (Units)
Object Type: Metric Team: Omnichannel Domain: Inventory Used In: Inventory management dashboards and stock level reports Description: The total number of units that have been ordered from suppliers but not yet received into stock. These are incoming units (open purchase orders) that will be added to inventory on hand once they arrive. Calculation: Not a derived formula – it is the sum of units on outstanding purchase orders.
Inventory Turnover
Object Type: Metric Team: Omnichannel Domain: Inventory Used In: Inventory management dashboards and stock level reports Description: Inventory Turnover indicates how many times the inventory is sold (or “turned”) over a given period, usually a year. It’s a measure of how quickly inventory is moving through the business. A higher turnover means inventory is selling quickly and being replenished more often, whereas a low turnover can signal overstocking or slow sales. This metric helps assess inventory efficiency and product demand. Calculation: Inventory Turnover = Cost of Goods Sold (for the period) / Average Inventory Value (during the period).
Inventory Value
Object Type: Metric Team: Omnichannel Domain: Inventory Used In: Inventory management dashboards and stock level reports Description: The total monetary value of the inventory on hand. This is typically calculated as the number of units in stock multiplied by the cost per unit (at cost basis). It represents the amount of capital currently tied up in inventory. Calculation: Inventory Value = Units on Hand × Unit Cost. (Retail value can also be used, but cost basis is the common approach for this metric.)
LTV:CAC Ratio
Object Type: Metric Team: E-commerce Domain: Customers Used In: Customer analytics dashboards (LTV vs CAC efficiency analysis) Description: The ratio of Customer Lifetime Value to Customer Acquisition Cost. It compares the long-term value of a customer to the cost of acquiring that customer. For example, an LTV:CAC of 3:1 means the average customer’s lifetime value is 3 times the cost to acquire them. This ratio is used to evaluate the efficiency and sustainability of marketing and customer acquisition efforts. Generally, a higher LTV:CAC (well above 1:1) is desired — it means customers bring in significantly more value than they cost to acquire. Calculation: LTV:CAC = Customer Lifetime Value / Customer Acquisition Cost. (For instance, if LTV is $300 and CAC is $100, the LTV:CAC = 3.0, often expressed as “3:1”.)
Market Share Change
Object Type: Metric Team: Retail Domain: Trade Promotion Used In: Description: Tracks shifts in product market share due to promotional activities. Calculation: Market Share Change = (Promotional Period Market Share - Pre-Promotion Market Share).
Marketing Efficiency Ratio (MER)
Object Type: Metric Team: Marketing Domain: Marketing ROI Used In: Overall marketing performance dashboards (revenue vs spend efficiency) Description: Marketing Efficiency Ratio (MER) is the ratio of total revenue to total marketing spend. Also known as “Blended ROAS,” it looks at the return on all marketing dollars holistically (not just attributable ad spend). MER answers the question: for every $1 spent on marketing (across all channels), how many dollars of revenue is the business generating overall? It’s a high-level indicator of marketing’s contribution to revenue. Calculation: MER = Total Revenue / Total Marketing Spend. (For example, MER = 5.0× means $5 in revenue for every $1 in total marketing spend. This is equivalent to a 500% return on overall marketing investment.)
Max ACV (%)
Object Type: Metric Team: Retail Domain: Distribution Used In: Retail distribution metrics in syndicated data explores (e.g., Circana/Nielsen reports) Description: Max % ACV is the weighted percentage of total store sales in which a product is available. All Commodity Volume (ACV) represents the total annual sales volume of all products in a store (a measure of store size). The % ACV for a product is the percentage of the total market ACV that comes from stores carrying that product. This metric indicates the breadth of distribution weighted by store importance (sales volume). A higher % ACV means the product is available in stores that account for a larger portion of total market sales. Calculation: % ACV = (Total ACV of stores selling the product / Total ACV of all stores in the market) × 100%. (Note: ACV for a given store is typically that store’s total sales of all products over a year.)
Net Sales
Object Type: Metric Team: E-commerce Domain: Sales & Profitability Used In: E-commerce sales performance dashboards (e.g., Shopify/Amazon sales reports) Description: Net Sales (for e-commerce) is the actual realized revenue from online orders after subtracting discounts, promotions, refunds, and returns. This is the net revenue the business retains from online sales, and it reflects what was actually earned. Net Sales is often used for financial reporting as the true sales number after all adjustments. Calculation: Gross eCommerce Sales – Discounts – Refunds/Returns = Net eCommerce Sales. (In other words, Gross Sales minus all promotions and returns.)
New Customer Count
Object Type: Metric Team: E-commerce Domain: Customers Used In: Customer analytics dashboards (acquisition reports, new vs returning customer tracking) Description: The number of first-time customers acquired in a given period. These are customers who made their first-ever purchase from your brand during that time window. This metric indicates how well you are attracting new buyers. Growing the new customer count is essential for expanding the customer base. Calculation: Count of unique customers who placed their first order with the company in the period.
Non-Promo Dollars
Object Type: Metric Team: Retail Domain: Sales Volume Used In: Retail sales volume analyses (promotion vs. non-promotion breakdowns) Description: Non-Promo Dollars are the total dollar sales that occurred when no promotion was running. This measures the sales in regular, non-promotional periods. It represents the “baseline” actual sales outside of promos. (In combination with Promo Dollars, this accounts for all sales.) Calculation: Non-Promo Dollars = Sum of Dollar Sales in periods with no promotions. (Essentially, total Dollar Sales minus the Promo Dollars.)
Orders
Object Type: Metric Team: E-commerce Domain: Sales & Profitability Used In: E-commerce sales performance dashboards (e.g., Shopify/Amazon sales reports) Description: The total number of orders placed on the e-commerce website in the given period. Each order is a completed transaction through the online store (regardless of how many items were in it). This metric indicates sales volume in terms of transactions and is fundamental for gauging online sales activity. Calculation: Count of all orders placed on the website in the period.
Post-Promotion Baseline Lift
Object Type: Metric Team: Retail Domain: Trade Promotion Used In: Description: Measures sustained sales lift post-promotion, evaluating lasting impact. Calculation: Post-Promotion Baseline Lift (%) = ((Post-Promo Baseline Sales - Pre-Promo Baseline Sales) / Pre-Promo Baseline Sales) × 100.
Promo Dollars
Object Type: Metric Team: Retail Domain: Sales Volume Used In: Retail sales volume analyses (promotion vs. non-promotion breakdowns) Description: Promo Dollars are the total dollar sales that occurred during promotional periods. This includes both the baseline sales and the incremental sales during those promo weeks. It indicates how many dollars of product were sold when a promotion was active. (Promo Dollars plus Non-Promo Dollars would sum up to total Dollar Sales for the product.) Calculation: Promo Dollars = Sum of Dollar Sales in periods when a promotion was running. (This can be calculated by filtering sales data to promotional weeks.)
Promotional Incrementality (%)
Object Type: Metric Team: Retail Domain: Promotion Used In: Retail promotional performance reports (evaluating lift and promo contribution in retail data) Description: The percentage of promotional-period sales that were truly incremental (i.e., would not have occurred without the promotion). This metric shows how effective a promotion was in generating new sales versus merely shifting the timing of sales that would have happened anyway. A higher incrementality percentage means most of the promo-period sales were additional, not just cannibalizing regular sales. Calculation: Promotional Incrementality % = (Incremental Sales during promo / Total Sales during promo) × 100%. (This can be computed in dollars or in units.)
Promotional Lift Percentage (% Lift)
Object Type: Metric Team: Retail Domain: Trade Promotion Used In: Description: Measures promotional effectiveness as the percentage increase over baseline sales. Calculation: Promotional Lift (%) = (Incremental Sales / Baseline Sales) × 100.
Promotion ROI (Return on Investment)
Object Type: Metric Team: Retail Domain: Trade Promotion Used In: Description: Evaluates financial performance of a promotion by measuring incremental gross margin relative to promotional spend, expressed as a percentage. Calculation: ROI (%) = ((Incremental Gross Margin - Trade Spend) / Trade Spend) × 100.
Purchase Frequency
Object Type: Metric Team: E-commerce Domain: Customers Used In: Customer analytics dashboards (purchase behavior analysis) Description: The average number of orders each customer makes within a given period. It reflects how frequently customers purchase on average. For example, a purchase frequency of 2.0 per year means the average customer places two orders per year. This metric, combined with average order value, helps determine overall customer value. Strategies that increase purchase frequency (like subscriptions or loyalty programs) can boost revenue. Calculation: Purchase Frequency = Total Number of Orders in the period / Total Number of Unique Customers who ordered in the period. (This yields the average orders per customer for that timeframe.)
Repeat Purchase Rate
Object Type: Metric Team: E-commerce Domain: Customers Used In: Customer analytics dashboards (repeat vs one-time customer analysis) Description: Also known as Repeat Customer Rate or Repurchase Rate, this is the percentage of customers who have made more than one purchase. It’s a measure of customer loyalty within a timeframe. For example, if out of 100 customers who bought in Q1, 25 made at least one additional order later, the repeat purchase rate is 25%. A higher repeat rate means more of your customers are returning for additional orders, indicating good retention. Calculation: Repeat Purchase Rate = (Number of customers with more than one purchase / Total unique customers in the period) × 100%.
Return on Ad Spend (ROAS)
Object Type: Metric Team: Marketing Domain: Marketing ROI Used In: Overall marketing performance dashboards (revenue vs spend efficiency) Description: ROAS measures the revenue return for each dollar spent on advertising. It is expressed as a ratio or multiplier (e.g., 4× means $4 revenue for every $1 spent). ROAS helps evaluate the effectiveness of advertising campaigns in driving sales. A higher ROAS means more revenue generated per dollar of ad spend, indicating more effective advertising. Calculation: ROAS = Revenue directly generated from ads / Advertising Spend. (Sometimes expressed as a ratio like “4:1” or as a percentage by multiplying by 100%. For instance, $50,000 revenue on $10,000 ad spend = 5× ROAS or 500%.)
Return Rate
Object Type: Metric Team: E-commerce Domain: Product Analytics Used In: E-commerce product performance dashboards (returns and product satisfaction analysis) Description: Return Rate is the percentage of sold products that are returned by customers. It’s an important measure of product performance and customer satisfaction, especially in e-commerce (for example in apparel, where fit issues can drive returns). A high return rate can signal issues such as product quality problems, inaccurate descriptions/sizing, or customer dissatisfaction. For example, if 100 units were sold and 5 were returned, the return rate is 5%. Managing return rate is crucial, as returns directly reduce net sales and incur additional handling costs. Calculation: Return Rate = (Number of Units Returned / Number of Units Sold) × 100%. (This can also be calculated on an order basis, but unit-based is common for product return analysis.)
Returning Customer Count
Object Type: Metric Team: E-commerce Domain: Customers Used In: Customer analytics dashboards (retention and repeat purchase tracking) Description: The number of existing customers who made an additional purchase in the given period. These customers had purchased at least once before (prior to the period) and came back to buy again. This metric shows the volume of repeat customers in that timeframe and is related to customer retention and loyalty. Calculation: Count of unique customers in the period who had also made at least one purchase before the period. (In practice, filter customers who purchased this period by those with a prior purchase history.)
Sell-Through Rate
Object Type: Metric Team: Omnichannel Domain: Inventory Used In: Inventory management dashboards and stock level reports Description: Sell-Through Rate measures the percentage of inventory sold through to customers out of the inventory available or received. It’s commonly used to evaluate product performance in retail and wholesale contexts. For example, in a wholesale scenario, if a brand ships 100 units to a retailer and 80 units are sold to consumers over the season, the sell-through is 80%. A higher sell-through rate indicates strong consumer demand relative to supply. This metric helps identify how well products are selling and can inform production and replenishment decisions. Calculation: Sell-Through % = (Units Sold to Consumers / Units Available or Initially Shipped) × 100%.
Stockout Rate
Object Type: Metric Team: Omnichannel Domain: Inventory Used In: Inventory management dashboards and stock level reports Description: Stockout Rate is the frequency or percentage of time that a product is out-of-stock and unavailable for sale. It can be measured as the percentage of total demand that could not be filled because inventory was not available. A high stockout rate indicates that the product often runs out of stock, leading to missed sales opportunities and potentially unhappy customers. Companies aim to minimize stockouts through better forecasting and inventory management. Calculation: Stockout Rate = (Number of stockout incidents or unfulfilled demand instances / Total demand opportunities) × 100%. (For example, if out of 100 order attempts, 5 couldn’t be filled, the stockout rate = 5%.)
Stores Selling
Object Type: Metric Team: Retail Domain: Distribution Used In: Retail distribution metrics in syndicated data explores (e.g., Circana/Nielsen reports) Description: Stores Selling is the number of retail stores that sold the product during the period. It is a count of distinct store locations where at least one unit of the product was sold. This shows the breadth of distribution in terms of outlets (unweighted by store size). A higher number of stores selling indicates broader availability of the product. Calculation: Count of unique store locations with recorded sales of the product in the period.
Total Distribution Points (TDP)
Object Type: Metric Team: Retail Domain: Distribution Used In: Retail distribution metrics in syndicated data explores (e.g., Circana/Nielsen reports) Description: Total Distribution Points is a combined measure of distribution breadth and depth for retail products. It accounts for how widely a product is distributed and how many product variants are carried on average. TDP is often calculated by summing the % ACV for each item (SKU) of a brand in a market, or equivalently as % ACV × average number of SKUs carried per store. A higher TDP means broader and/or deeper distribution. This metric allows comparison of overall distribution presence, especially when a brand has multiple SKUs. Calculation: Example approach: TDP ≈ % ACV Distribution × Average number of SKUs carried in those stores. (For example, if a brand has one SKU with 50% ACV and another with 40% ACV, total distribution points ≈ 90.)
Trade Promotion Spend (Budget Spent)
Object Type: Metric Team: Retail Domain: Trade Promotion Used In: Description: Total monetary investment dedicated to a promotion, covering off-invoice discounts, bill-backs, MCB, and scan-back deals. Calculation: Sum of all promotional costs associated with the event.
Traffic (Sessions)
Object Type: Metric Team: E-commerce Domain: Site Performance Used In: Website analytics dashboards (traffic and conversion reports, e.g., Google Analytics integration) Description: Traffic (sessions) refers to the number of visits to the website or online store. In digital analytics this is often measured in sessions, where each session represents a single visit by a user (from entry to exit on the site). This metric indicates how many times people are coming to your site and is a basic measure of your online reach or audience size. Note: A single user can have multiple sessions. Calculation: Typically provided by web analytics tools (e.g., Google Analytics) as the count of sessions in the period. (It’s generally a direct metric from analytics rather than calculated manually.)
Units per Point of Distribution
Object Type: Metric Team: Retail Domain: Velocity Used In: Retail velocity analysis in syndicated reports (comparing sales normalized by distribution) Description: Average units sold per 1% of ACV distribution. Similar to the dollar metric above, this measures how many units are sold for each point of ACV distribution. It indicates unit velocity normalized by distribution (i.e., how well the product sells relative to how widely it’s distributed). Calculation: Units per ACV Point = Total Units Sold / % ACV.
Units per Store per Week
Object Type: Metric Team: Retail Domain: Velocity Used In: Retail velocity analysis in syndicated reports (comparing sales normalized by distribution) Description: Average unit sales per store selling, per week. This represents how many units of the product, on average, each store sells in a week. It’s another way to express product velocity at the store level, in terms of unit movement. Calculation: Units/Store/Week = Total Units Sold / (Number of Stores Selling × Number of Weeks in period).
Units Sold
Object Type: Metric Team: E-commerce Domain: Sales & Profitability Used In: E-commerce sales performance dashboards (e.g., Shopify/Amazon sales reports) Description: The total quantity of individual items sold through the e-commerce channel in the period. This counts all units across all orders. It measures the volume of product sold online. Together with the number of orders, it can indicate the average items per order. Calculation: Sum of all units/items included in online orders.
Unit Sales
Object Type: Metric Team: Omnichannel Domain: Sales Used In: Total Company (Omnichannel) sales dashboards and explores Description: The physical volume of product sold, expressed as the number of units (packages) purchased by consumers. In short, this is the total number of individual items sold in the period. Calculation: Sum of all units sold.
Weeks of Supply (WOS)
Object Type: Metric Team: Omnichannel Domain: Inventory Used In: Inventory management dashboards and stock level reports Description: Weeks of Supply estimates how long current inventory will last, given the current rate of sales, expressed in weeks. For example, if you have 8 weeks of supply of a product (and no new inventory arrives), it would take 8 weeks to sell out completely at the current sales pace. WOS helps in inventory planning: too high WOS might indicate overstock, while too low could warn of potential stockouts. Calculation: WOS = Current Inventory On Hand / Average Weekly Units Sold. (If using revenue, it can also be calculated in terms of weeks of forward cover on sales value.)
Wholesale Case Sales
Object Type: Metric Team: Omnichannel Domain: Wholesale Used In: Wholesale channel sales reports and analytics Description: Total number of cases sold to wholesale partners. (Many brands sell products in case packs, with multiple units per case.) This metric tracks how many cases were shipped, indicating volume sold in terms of cases – useful for logistics and supply chain planning. Calculation: Sum of all cases shipped in wholesale orders.
Wholesale Dollar Sales
Object Type: Metric Team: Omnichannel Domain: Wholesale Used In: Wholesale channel sales reports and analytics Description: Total sales revenue from selling products to wholesale partners (e.g., retail accounts, distributors). This is the amount the brand receives from wholesale orders, based on wholesale pricing (not end-consumer retail pricing). Calculation: Sum of all wholesale invoice dollars (gross wholesale revenue, before any deductions like allowances, if not already netted).
Wholesale Unit Sales
Object Type: Metric Team: Omnichannel Domain: Wholesale Used In: Wholesale channel sales reports and analytics Description: Total quantity of units sold to wholesale partners. This counts how many individual units were shipped out in wholesale orders to retailers/distributors in the period. Calculation: Sum of all units sold via wholesale orders.
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