Used When:

Typical Use Cases:

Example KPI: Sales per day (incl. VAT) shows the full turnover generated, useful for retail managers or frontline decision-making.

Used When:

Typical Use Cases:

Example KPI: Net sales per labor hour focuses purely on operational efficiency, excluding tax artifacts.

Used When:

Typical Use Cases:

Example KPI: Average basket size (incl. returns) helps see what customers initially chose, regardless of post-sale changes.

Used When:

How It Works (Example: Avg. Purchase Value No VAT excl. Returns):
This metric is calculated by dividing Sales (No VAT) by the number of orders, filtered to include only those orders that:

This method ensures that returns do not skew the performance of the specific date or person that did not actually handle the return. For example:

Aggregation: Average
​Formula: net_sales / order_count (filtered for non-cancelled orders)

Use Cases:

Industry Insight:

Explanation: Total revenue from product sales, including or excluding VAT/Returns.

Use Cases: Used for gross and net revenue reporting. Sales (No VAT) is particularly useful for margin calculations and comparisons across countries.

Symbol: Currency

Variations:

Metric keys:

Explanation: Total number of product units sold. Useful for tracking sales volume and inventory movement.

Formula: sum(orders.products_quantity)

Symbol: -

Variations:

Metric keys:

Explanation: Total value of sales from transactions marked as voided.

Use Cases: It helps monitor cancellations or reversed sales and can highlight operational issues, mistakes, or potential misuse.

Symbol: Currency

Metric key: voided_sales

Explanation: The total amount of tax collected on sales, calculated as the difference between gross sales and net sales, excluding internal sales, either in value or percentage.

Use Cases: It helps show how much of reported revenue is tax rather than actual business income.

Symbol: Currency / %

Metric key:

Explanation: Total weight of products sold in kilograms, excluding internal sales.

Use Cases: Useful for tracking volume sold for weight-based items such as produce, meat, or bulk goods.

Symbol: kg

Metric key: sales_tax

Explanation: Total revenue calculated using each product’s reference price multiplied by the quantity sold, excluding internal sales.

Use Cases: It helps compare actual selling performance against a standard or list-price baseline.

Symbol: Currency

Metric key: reference_sales

Explanation: Total net sales generated from transactions linked to campaigns, excluding VAT and internal sales.

Use Cases: It helps measure how much revenue marketing or promotional campaigns are driving.

Symbol: Currency

Metric key: reference_sales

Explanation: Calculated by dividing the net sales generated from campaigns by the total Sales (No VAT).

Aggregation: Average

Use Cases: This metric provides insight into the effectiveness of marketing campaigns in driving sales.

Symbol: %

Metric key: campaign_sales_ratio

Explanation: Shows the latest date in the selected period when sales is recorded, excluding internal and cancelled transactions.

Symbol: Currency

Metric key: inventory_lastsales

Explanation: Shows the first date in the selected period when sales is recorded, excluding internal and cancelled transactions.

Symbol: Currency

Metric key: inventory_firstsales

Symbol: -
​Benchmarks: Retail: 2–5 items, Restaurant: 1.5–3 items per transaction.

Metric keys:

Explanation: Captures the diversity of products per sale.​

Aggregation: Average
​Use Cases: Helps track cross-selling effectiveness.

Symbol: -

Metric key: unique_item_count

Explanation: The average revenue generated per transaction (per receipt).

Aggregation: Average
​Use Cases: Key for profitability assessment and AOV (Average Order Value) tracking. Can be used to assess spending behavior and upsell/cross-sell and price increase success.

Symbol: Currency
​Benchmarks: Retail: €20–100, Restaurant: €8–25.

Variations:

Metric keys:

Explanation: Shows the average cost or sales value per unit sold, helping with pricing and profitability analysis.

Aggregation: Average

Use Cases: When used on a product level, metric will help estimate the total effects of discounts given (especially when compared with the list price, ie. Avg. product sales price)

Symbol: Currency

Variations:

Metric keys:

Explanation: Proportion of transactions with more than one item, or the opposite (transactions with only one item)

Aggregation: Average

Use Cases: To analyse customer buying behaviour and success of cross-selling campaigns.

Symbol: %

Metric keys:

Explanation: Proportion of number of identified customer receipts.

Aggregation: Average

Use Cases: To analyse customer buying behaviour and diverse in customer type.

Symbol: %

Metric keys:

Explanation: Shows the average number of purchases made by each unique identified customer who has valid IDs in our database (eg., registered/loyalty customers).

Aggregation: Average

Use Cases: To analyse customer engagement, loyalty and the growing sales behavior.

Symbol: -

Metric key: customer_avg_purchase

Explanation: Shows the number of unique orders, excluding canceled or internal sales.

Variations:

Metric keys:

Explanation: Counts the total number of distinct item lines sold across transactions, excluding cancelled and internal sales.

Use Cases: It helps you understand product variety sold and how broad customer purchases are over the selected period.

Metric key: unique_items

Explanation: Total number of product units from transactions marked as internal sales or voided sales activity.

Use Cases: It helps monitor non-regular sales movement and control how much stock is affected by internal or canceled transactions.

Metric key: voided_sales_pcs

Explanation: Counts the number of distinct products sold in the selected period.

Use Cases: It helps you understand product range performance and how broadly sales are distributed across your assortment.

Metric key: sales_products

Explanation: Shows the number of distinct product models sold in the selected period.

Use Cases: Use this metric to analyze inventory at the product model level rather than by individual SKUs. It is especially useful in fashion retail, where products are available in multiple sizes and colors but belong to the same model.

Metric key: sales_models

Explanation: Counts the number of distinct stores that recorded sales in the selected period.

Use Cases: It helps show how widely sales activity is distributed across your store network.

Metric key: sales_stores

Explanation: Counts the number of distinct salespeople who recorded sales in the selected period.

Use Cases: It helps show how many staff members actively contributed to selling activity.

Metric key: sales_person_count

Explanation: Total number of items sold across all transactions including returns, excluding internal sales.

Use Cases: It helps you understand sales volume and how much product is moving through your business.

Metric key: transaction_item_count

Explanation: Counts the number of completed, non-internal transactions that contained exactly one item.

Use Cases: It helps you understand how often customers make single-item purchases, which can be useful for analyzing shopping behavior and basket size.

Metric key: single_order_count

Explanation: Counts the number of completed, non-internal transactions that included more than one item.

Use Cases: It helps you understand how often customers make single-item purchases, which can be useful for analyzing shopping behavior and basket size.

Metric key: multiple_order_count

Explanation: Total number of sales line items across all valid transactions, excluding cancelled and internal sales over the selected period.

Use Cases: It helps you understand how many individual product rows were sold, which is useful for analyzing basket composition and sales activity.

Variations:

Metric keys:

About CoGS in general: The price of the goods sold is determined by the inventory valuation logic of the PoS. Methods vary from FiFo, to moving average.

Explanation: Shows the average cost per unit sold, helping with pricing and profitability analysis.

Formula Examples:

Aggregation: Average

Variations:

Metric keys:

Explanation: Shows the average purchase cost of products based on their latest recorded buy price.

Aggregation: Average

Metric key: product_purchase_price

Explanation: Shows the average selling price per product unit.

Aggregation: Average

Variations:

Metric keys:

Explanation: Represents profitability. Gross margin includes VAT, net margin excludes it.

Use Cases: Profitability assessment, pricing strategy, and supplier evaluation. Also a great KPI to analyze across products and product categories.

Metric keys:

Explanation: Indicates how much profit is made per transaction, either as a value or a percentage.

Aggregation: Average

Use Cases: Tracks profitability per sale and helps identify pricing issues.

Variations:

Metric keys:

Explanation: Indicates how much profit is made per product, either as a value or a percentage.

Use cases: It helps you understand how much margin each product generates on average or percent and supports pricing and product profitability analysis.

Variations:

Metric keys:

Explanation: Measures revenue performance relative to operational hours or labor input.

Formula Examples:

Aggregation: Average

Use Cases: Tracks productivity, staff efficiency, and operational tempo.
​Benchmarks: Retail: €100–€400/h, Restaurants: €40–€150/h.

Variations:

Metric keys:

Explanation: Tracks how much revenue is generated per square meter of store space.

​Aggregation: Average
​Use Cases: Helps evaluate location productivity and rent efficiency.
​Benchmarks: Retail: €5,000–€15,000/m² annually.

Variations:

Metric keys:

Explanation: Revenue amount per customer seat

Aggregation: Average

Use Cases: Sales efficiency by seats available

Variations:

Metric keys:

Explanation: The total Sales (VAT) by the number of Identified Customers, considering only customers with a valid ID and excluding internal sales

Examples:

Aggregation: Average

Use Cases: CRM analysis, campaign performance, customer segmentation, marketing analysis.

Metric key: customer_average_value

Explanation: The number of customers with a valid ID and excluding internal sales.

Metric key: customer_count

Explanation: The number of customers with a valid ID that have the creation date after the selected period.

Metric key: new_customers

Explanation: The number of customers with a valid ID that have the creation data before the selected period.

Metric key: total_customers

Explanation: Counts the number of distinct identified customers who made a repeat purchase within 90 days of an earlier purchase, excluding cancelled and internal sales.

Use cases: It helps you understand short-term customer retention and how many customers are coming back to buy again.

Metric key: customer_repeat_count

Explanation: A proportion of identified customers who made more than one purchase within the selected time period.

Aggregation: Average

Use cases: Identifying loyal customers, customer retention analysis

Metric key: customer_repeat_purchase

Explanation: Shows the percentage of customers from the beginning of the selected period who are still active at the end.

Aggregation: Average

Use cases: It helps you understand how well the business keeps existing customers over time, which is key for loyalty and long-term sales growth.

Metric key: customer_retention_rate

Explanation: Counts the number of unique receipts linked to identified customers, excluding internal sales.

Use cases: It shows how many transactions can be attributed to known customers, helping you track customer engagement and loyalty activity.

Metric key: identified_customer_receipts

Explanation: The propotion of the transactions in which a customer was identified.

Aggregation: Average

Use cases: Measuring customer recognition capabilities, measuring success of customer loyalty programs.

Metric key: identified_customer_ratio

Explanation: Counts the number of distinct identified customers who had a valid starting purchase frequency segment in the selected period.

Use cases: It helps you understand the size of the customer base included at the beginning of customer frequency or loyalty analysis. This metric is also base metric to calculate other metrics.

Metric key: customer_start_count

Explanation: Counts the number of distinct identified customers at the end of the selected period, based on customers who remain active in the customer segmentation data.

Use cases: Useful for understanding the current size of your active customer base for retention and loyalty analysis. This metric is also base metric to calculate other metrics.

Metric key: customer_end_count

Explanation: Counts the number of customers classified as Guest in the selected period.

Use cases: Combining with other sales metrics, it helps you understand how much of your traffic comes from non-registered or anonymous shoppers compared with identified customers.

Metric key: guest_customers

Explanation: Total sales/net sales generated from purchases made by guests, meaning transactions not linked to an active registered customer account.

Use cases: It helps you understand how much revenue comes from anonymous or one-time shoppers versus identified customers.

Variations:

Metric keys:

Explanation: Counts the number of customers who are marked as registered in your customer database.

Use cases: It helps you track the size of your known customer base for loyalty, CRM, and targeted marketing activities.

Metric key: registered_customers

Explanation: Total revenue generated from customers who are currently registered and active at the time of purchase (the registered date is before the ordered date), excluding internal sales.

Use cases: It helps track how much revenue comes from known, enrolled customers versus the broader customer base.

Variations:

Metric keys:

Explanation: Counts the number of customers marked as unregistered in the selected period.

Use cases: Combining with other metrics, it helps show how many purchases come from customers without a registered profile, useful for understanding identification coverage and loyalty program opportunities.

Metric key: unregistered_customers

Explanation: Counts the number of customers who are marked as subscribed.

Use cases: It helps you track the size of your active subscriber base for marketing, loyalty, or membership-related analysis.

Metric key: subscribed_customers

Explanation: Counts the number of customers marked as unsubscribed in your customer subscription data.

Use cases: Use it to understand the size of the audience no longer receiving your communications and to track changes in customer engagement over time.

Metric key: unsubscribed_customers

Explanation: Total number of loyalty points earned or used in customer transactions, excluding internal sales.

Use cases: It helps track customer engagement with the loyalty program and the overall impact of rewards activity on sales.

Metric key: loyalty_points

Explanation: Total number of products returned by customers due to order cancellations, excluding internal sales.

Metric key: product_returns

Explanation: Measures the total number of return transactions. A transaction is considered a return transaction only when the total transaction value is negative. Transactions containing a mix of positive and negative rows (e.g., a sale and a return on the same transaction) are not counted as return transactions.

Metric key: receipt_returns

Explanation: Total cost or sales value of returned products.

Aggregation: Sum

Variations:

Metric keys:

Explanation: Measures the proportion of returned value relative to total sales.

Aggregation: Average

Metric key: return_percent

Explanation: Measures the proportion of returned items relative to the total items sold.

Aggregation: Average

Metric key: return_items_percent

Explanation: Measures the proportion of total number of return receipts by the total number of orders.

Aggregation: Average

Metric key: return_receipt_percent

Explanation: Total cost value of returned items, based on their purchase price and excluding internal sales.

Use Cases: It helps show how much inventory cost is coming back through returns, excluding internal sales.

Variations:

Metric keys:

Explanation: Total discounts from orders. If there are no discount, the result defaults to zero.

Variations:

Metric keys:

Explanation: Measures the proportion of the total discounts (VAT) by the sum of total discounts plus gross sales.

Aggregation: Average

Variations:

Metric keys:

Explanation: Calculated by dividing the total Discount (VAT) by the total Sales (VAT) with or without returns, providing insight into the percentage of sales that have been discounted.

Aggregation: Average

Variations:

Metric keys:

Explanation: Total revenue before discounts or rebates are deducted, excluding internal sales.

Variations:

Metric keys:

Explanation: Total value of discounts granted through campaigns or promotions, excluding internal sales.

Variations:

Metric keys:

Explanation: Counts the number of days in the selected period when the store was open for business.

Use cases: Useful for comparing sales performance across periods by accounting for differences in trading days.

Metric key: business_days

Explanation: Counts the number of days in the selected period when the store was open and recorded sales.

Use cases: It helps put sales results in context by showing how many active selling days contributed to performance.

Metric key: open_sales_days

Explanation: Counts the number of distinct days in the selected period when sales were recorded.

Use cases: It helps users understand how many active selling days are included in the analysis and compare performance across time periods.

Metric key: sales_days

Explanation: Counts the number of distinct hours in which at least one sale was recorded during the selected period.

Use cases: It helps show how sales are distributed over trading time and can be used to compare store activity and sales productivity by hour.

Metric key: sales_hours

Explanation: Total number of customer seats available across the selected departments, excluding internal sales.

Use cases: Useful for comparing seating capacity with sales performance, such as revenue per seat.

Metric key: department_customer_seats

Explanation: Total number of customer seats available across the selected stores.

Use cases: Useful for analyzing capacity and comparing revenue or traffic performance relative to seating, especially when used in metrics like sales per seats.

Metric key: customer_seats

Explanation: The total selling area of the selected store locations, measured in square meters.

Use cases: It helps compare sales productivity across stores, especially when used in metrics like sales per m².

Metric key: store_area

Explanation: The total floor space allocated to the selected departments, measured in square meters.

Use cases: It helps compare how much selling area each department uses against its sales performance and space efficiency.

Metric key: department_area

Explanation: Total purchase cost of inventory on hand at the last/first day of the specified period.

Use Cases: Helps monitor the value of inventory remaining at the end/start of a period, enabling better inventory planning and help to calculate other metrics.

Variations:

Metric keys:

Explanation: The total expected revenue of inventory remaining on hand at the last/first day of a specified period, based on its retail selling price.

Use Cases: Helps estimate the potential revenue of remaining/opening inventory and identify overstocked or understocked inventory and help to calculate other metrics.

Variations:

Metric keys:

Explanation: Measures the average daily inventory value at purchase cost by summing the daily inventory snapshots and dividing by the number of days in the selected period.

Use Cases: Helps measure the average inventory investment over a period and supports inventory planning and replenishment decisions.

Variations:

Metric keys:

Explanation: Measures the average daily retail value of inventory on hand by summing the daily inventory snapshots at retail selling price and dividing by the number of days in the selected period.

Use Cases: Helps measure the average inventory retail price over a period.

Variations:

Metric keys:

Explanation: The total number of inventory units (pieces) on hand at the last/first day of the specified period.

Use Cases: Useful for tracking inventory movement, identify stock shortages or overstock and inventory planning.

Variations:

Metric keys:

Explanation: Measures the average inventory units (pieces) on hand by summing the daily inventory snapshots and dividing by the number of days in the selected period.

Use Cases: Helps measure the average inventory units over a period.

Metric key: avg_inventory_count

Explanation: The first/latest date in the selected period when a valid inbound purchase (inventory receipt) was recorded in the system. A valid inbound purchase is one that has not been cancelled and includes at least one product item.

Symbol: mm/dd/yyyy

Use Cases: Helps for inventory planning.

Variations:

Metric keys:

Explanation: Calculated by dividing the total Inventory Unit Value (either including or excluding VAT) by the total number of Inventory Items.

Aggregation: Average

Variations:

Metric keys:

Explanation: Shows the purchase cost per unit of inventory, based on the value recorded for items in stock.

Use Cases: It helps assess stock cost levels and supports inventory valuation and margin analysis.

Variations:

Metric keys:

Explanation: Shows the retail selling value per unit of inventory, based on the current unit sales price.

Variations:

Metric keys:

Explanation: Calculated by dividing the total Inventory Retail Unit Value by the number of Inventory Items, providing an average price per item in the inventory.

Aggregation: Average

Variations:

Metric keys:

Explanation: Shows the total value of inventory on hand based on each product’s reference price rather than its selling price or cost.

Use Cases: It helps estimate the standard value of stock available and monitor how inventory value changes over time.

Metric key: reference_inventory_value

Explanation: Total purchase cost of currently available inventory on hand at the last/first day of the specified period. The item is consider available if it is in stock and not reserved.

Use Cases: Helps monitor the value of available inventory remaining at the end/start of a period, enabling better inventory planning and help to calculate other metrics.

Variations:

Metric keys:

Explanation: Measures the average daily available inventory value at purchase cost by summing the daily inventory snapshots and dividing by the number of days in the selected period. The item is consider available if it is in stock and not reserved.

Use Cases: Helps measure the average inventory investment over a period and supports inventory planning and replenishment decisions.

Variations:

Metric keys:

Explanation: Shows the number of items currently available in inventory at the last day of selected period. The item is consider available if it is in stock and not reserved.

Use Cases: It helps users monitor stock availability and identify whether inventory levels are sufficient to meet demand or restock.

Metric key: inventory_available_count

Explanation: Shows the number of items available in inventory at the first day of selected period. The item is consider available if it is in stock and not reserved.

Use Cases: It helps users monitor stock availability and identify whether inventory levels are sufficient to meet demand or restock.

Metric key: start_inventory_available_count

Explanation: Measures the average available inventory units (pieces) on hand by summing the daily available inventory snapshots and dividing by the number of days in the selected period.

Use Cases: Helps measure the average available inventory items over a period.

Metric key: avg_inventory_available_count

Explanation: Shows the number of days during the selected period when at least one inventory item was available.

Use Cases: Helps monitor inventory availability over time, identify stockout periods.

Metric key: inventory_available_days

Explanation: Shows the inventory reorder point, which is the minimum stock level that triggers a replenishment order. When inventory falls to or below this level, the item should be reordered to avoid stockouts. This metric is used mostly for product level, and the value show the latest reorder point within selected period, for category level, the value is average.

Use Cases: Helps identify products need to be reordered, prevent stockouts.

Metric key: inventory_reorder_point

Explanation: Shows the inventory max reorder quantity, which is the upper limit of stock you should hold. This metric is used mostly for product level, and the value show the latest reorder point within selected period, for category level, the value is average.

Use Cases: Helps prevent overstocking.

Metric key: inventory_max_reorder_quantity

Explanation: The total number of distinct products (count by Product ID) currently recorded in inventory.

Metric key: inventory_items

Explanation: Shows the latest date in the selected period when inventory quantity increased.

Metric key: inventory_last_received

Explanation: Total number of product units sold over the selected period. It helps track sales volume and understand how quickly inventory is moving.

Metric key: nogs

Explanation: Shows the total number of distinct product models in inventory during the selected period.

Use Cases: Use this metric to analyze inventory at the product model level rather than by individual SKUs. It is especially useful in fashion retail, where products are available in multiple sizes and colors but belong to the same model.

Metric key: inventory_models

Explanation: Counts the number of distinct products currently in stock, considering only items with a quantity greater than zero.

Use Cases: It helps you understand the product types of your inventory and how many different products are available for sale.

Metric key: inventory_products

Explanation: Shows the number of distinct stores that currently have this item in stock, counting only stores with a quantity greater than zero.

Use Cases: It helps you understand product availability and how widely inventory is distributed across your store network.

Metric key: inventory_stores

Explanation: Calculated by taking the number of days in a year (365) and dividing it by the product of the average daily rate of inventory turnover (nogs divided by Average Inventory Amount (pcs)) and the number of days in the period. This metric provides an estimate of how many days the current inventory will last based on the current sales rate.

Aggregation: Average

Metric key: days_of_supply

Explanation: Calculated by taking the difference between Sales (No VAT) and Cost of Goods Sold (No VAT), dividing it by the Avg. Inventory Value (No VAT), and then multiplying by the ratio of 365 to the Day_count. This metric provides insight into the profitability of inventory over a specific period.

Aggregation: Average

Variations:

Metric keys:

Explanation: Calculated by subtracting the Period Start Inventory Amount (pcs) from the Period End Inventory Amount (pcs), providing insight into the change in inventory over the specified period.

Aggregation: Sum

Metric key: inventory_change_pcs

Explanation: Calculated by taking the Sales Margin (No VAT)(incl. Returns) and dividing it by the absolute value of Sales (No VAT), then multiplying by 100. This result is further adjusted by the ratio of Cost of Goods Sold (No VAT) to Period End Inventory Value (No VAT), scaled to a 365-day period based on the total number of days in the reporting period.

Aggregation: Average

Variations:

Metric keys:

Explanation: Calculated by dividing the Sales (pcs) by the sum of Sales (pcs) and the Period End Inventory Amount (pcs). This metric provides insight into the efficiency of inventory movement during a specific period.

Aggregation: Average

Metric key: inventory_sellthrough

Explanation: Calculated by dividing the Sales (pcs) by the Period End Inventory Amount (pcs). This metric provides insight into how effectively inventory is being sold over a specific period.

Aggregation: Average

Metric key: inventory_sellthrough_simple

Explanation: Calculated by dividing the Average Inventory Value (No VAT) by the average daily Sales (No VAT) over the period, which is derived by multiplying net sales by 365 and dividing by the number of days in the period. This metric provides insight into how efficiently inventory is being converted into sales.

Aggregation: Average

Variations:

Metric keys:

Explanation: Calculated by dividing the Cost of Goods Sold (No VAT) by the Period End Inventory Value (No VAT) and then multiplying by 365 divided by the Day count. This metric provides insight into how efficiently inventory is being managed over a specific period.

Aggregation: Average

Variations:

Metric keys:

Explanation: Calculated by dividing the Cost of Goods Sold (No VAT) by the sum of the Cost of Goods Sold (No VAT) and the Period End Inventory Value (No VAT). This metric provides insight into the efficiency of inventory turnover relative to the cost of goods sold.

Aggregation: Average

Metric key: inventory_value_sellthrough

Explanation: Calculated by dividing the Cost of Goods Sold (No VAT) by the Period End Inventory Value (No VAT). This metric provides insight into how efficiently inventory is being sold relative to its remaining value.

Aggregation: Average

Metric key: inventory_value_sellthrough_simple

Explanation: Calculated by dividing the total Inventory Unit Value by the number of Inventory Items. This metric represents the average cost per unit currently held in inventory.

Aggregation: Average

Variations:

Metric keys:

Explanation: Calculated by dividing the total Inventory Retail Unit Value by the number of Inventory Items. This metric provides the average retail selling price per unit currently in inventory.

Aggregation: Average

Variations:

Metric keys:

Explanation: Calculated by adding the Cost of Goods Sold to the difference between the Period End Inventory Value and the Period Start Inventory Value. This metric estimates the purchasing capacity required to maintain inventory levels.

Aggregation: Sum

Variations:

Metric keys:

Explanation: Calculated by adding the No. of Goods Sold (NOGS) to the difference between the Period End Inventory Amount (pcs) and the Period Start Inventory Amount (pcs). This metric estimates the number of units that need to be purchased to maintain inventory levels.

Aggregation: Sum

Metric key: open_to_buy_pcs

Explanation: Calculated by estimating the annual inventory turnover rate based on the No. of Goods Sold (NOGS), the Period End Inventory Amount (pcs), and the selected period length. This metric estimates how many weeks the current inventory is expected to last at the current sales rate.

Aggregation: Average

Metric key: weeks_of_supply

Explanation: Calculated by dividing the Period End Inventory Value (No VAT) by the average daily Cost of Goods Sold (No VAT), then converting the result into days. This metric estimates how many days the current inventory would last based on the sales rate during the selected period.

Aggregation: Average

Metric key: inventory_age

Explanation: Calculated by dividing the number of days inventory was available by the total number of days in the selected period. This metric shows the percentage of time inventory was available for sale and helps identify potential stockout risks.

Aggregation: Average

Metric key: inventory_availability

Explanation: Calculated by dividing the Period End Inventory Value (No VAT) by the average daily Cost of Goods Sold (No VAT), using a yearly sales rate. This metric estimates how many days the current inventory would last based on annualized cost of goods sold.

Aggregation: Average

Variations:

Metric keys:

About Inventory Transfer: Inventory Transfer is the movement of inventory from one warehouse or storage location to another. Transfers may occur within the same facility (between internal storage locations) or between different facilities, such as warehouses, distribution centers, or manufacturing sites, to ensure inventory is available where it is needed.

Use Cases: Use this metric to monitor inventory transfers between locations, helping balance stock levels, reduce overstock and stockouts, and improve inventory distribution efficiency.

Explanation: The total of product purchase costs of all sent transfer items in the selected period.

Variations:

Metric keys:

Explanation: The total purchase cost of all inventory transfer items successfully received in the selected period. Items are considered received once they have been delivered and fully processed into inventory.

Variations:

Metric keys:

Explanation: The total of unit retail price of all sent transfer items in the selected period.

Variations:

Metric keys:

Explanation: The total of unit retail price of all received transfer items in the selected period.

Variations:

Metric keys:

Explanation: The total of number of all sent transfer items in the selected period.

Metric key: transfer_pcs

Explanation: The total of number of all received transfer items in the selected period.

Metric key: transfer_received_pcs

Explanation: The total number of inventory units (pieces) successfully delivered from the sending location as part of completed inventory transfers.

Metric key: transfer_delivered_pcs

Explanation: Total value of stock transfers that have been delivered from the sending location as part of completed inventory transfers, calculated from delivered quantities and unit prices. It helps track the financial volume of completed inventory movements between locations.

Variations:

Metric keys:

Explanation: The total number of inventory units (pieces) successfully delivered to the destination location as part of completed inventory transfers. This value may be higher than Inventory Transfer Received (pcs) because delivered items may still be awaiting processing before they are officially recorded in inventory.

Metric key: transfer_received_delivered_pcs

Explanation: Total value of delivered stock transfers received at destination location as part of completed inventory transfers. This value may be higher than Inventory Transfer Received because delivered items may still be awaiting processing before they are officially recorded in inventory. It helps track how much inventory value has been moved into a location through completed transfer deliveries.

Variations:

Metric keys:

Explanation: Calculated by dividing the number of Inventory Transfer Sent Delivered (pcs) by the total number of Inventory Transfer Sent (pcs). This metric shows the percentage of transferred inventory that has been successfully delivered, providing insight into the accuracy and completion rate of inventory transfers.

Aggregation: Average

Metric key: transfer_accuracy

About Inventory Adjustment: An inventory adjustment is an increase or decrease in a company's inventory to explain theft, broken products, loss or other errors. Positive values indicate inventory added through adjustments, while negative values indicate inventory removed through adjustments.

Explanation: The total number of inventory units (pieces) added to or removed from inventory through inventory adjustments during the selected period.

Metric key: adjustment_pcs

Explanation: The total purchase cost of inventory added to or removed from inventory through inventory adjustments during the selected period.

Metric key: adjustment_value

Explanation: The total retail (selling) value of inventory added to or removed from inventory through inventory adjustments during the selected period.

Metric key: adjustment_retail

Explanation: The total sales value of all sales orders placed within the selected period, excluding cancelled orders.

Variations:

Metric keys:

Explanation: The total quantity of product units ordered during the selected period.

Metric key: order_intake_pcs

Explanation: The total purchase costs of all sales orders placed within the selected period, excluding cancelled orders.

Variations:

Metric keys:

Explanation: Shows the number of sales order in the selected period.

Metric key: salesorder_count

Explanation: Shows the number of distinct identified customer who have valid customer ID in the system in the sales order in the selected period.

Metric key: salesorder_customer_count

Explanation: Total number of sales line items across all valid transactions, excluding cancelled orders that have been placed after the start of the selected period.

Use Cases: It helps you understand how many individual product rows were ordered over time, which is useful for analyzing basket composition and sales activity.

Metric key: salesorder_row_count

Explanation: Indicate the discount in the sales order, either as a value or a percentage.

Use Cases: Tracks discount of sales orders, focus on campaign and discount related.

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Explanation: The average of the difference in days between the order date and the invoice date for all sales order that are not cancelled. The calculation only includes sales order where the order date is less than or equal to the invoice date.

Metric key: order_avg_lead_time

Explanation: The total sales value of all sales orders ordered for delivery by the end of the selected period, including both delivered orders and orders planned for delivery.

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Explanation: The total quantity of product units ordered for delivery by the end of the selected period, including both delivered orders and orders planned for delivery.

Metric key: ordered_quantity

Explanation: The total purchase costs of all sales orders ordered for delivery by the end of the selected period, including both delivered orders and orders planned for delivery.

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Explanation: Indicates how much profit is made from new sales orders, either as a value or a percentage.

Aggregation: Average

Use Cases: Tracks profitability of new sales orders.

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Explanation: Total sales value of orders that have been successfully delivered, excluding cancelled orders.

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Explanation: Total profit of orders that have been successfully delivered, excluding cancelled orders.

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Explanation: Indicates the percentage of profit made from delivered sales orders.

Aggregation: Average

Use Cases: Tracks profitability of delivered sales orders.

Metric key: delivered_orders_profit_percent

Explanation: This metric is used for Sales Orders and approximation of pieces delivered to the consumer. The delta vs previous period is the real delivered quantity, so this metric purely in itself does not tell the delivered quantity. This metric is designed for Tilroy customer.

Use Cases: It helps you track your delivery performance.

Metric key: qty_delivered

Explanation: The total number of distinct products (unique product IDs) that have been delivered.

Use Cases: It helps you track your delivery performance.

Metric key: delivered_items

Explanation: Measures the share of ordered items that were delivered correctly, based on delivered items versus total delivered quantity.

Aggregation: Average

Use Cases: It helps you monitor delivery quality and fulfillment accuracy, with higher percentages indicating fewer delivery mistakes.

Metric key: delivery_accuracy

Explanation: Shows the percentage of ordered items that were actually delivered for non-cancelled orders.

Aggregation: Average

Use Cases: It helps you monitor delivery quality and fulfillment accuracy, with higher percentages indicating fewer delivery mistakes.

Metric key: delivery_accuracy_oversales

Explanation: The total sales value of all sales orders that are still open (not delivered) placed within the selected period, excluding cancelled orders., excluding cancelled orders.

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Explanation: The total quantity of product units ordered that are still open (not delivered) during the selected period.

Metric key: open_ordered_quantity

Explanation: Indicates revenue of sales ordered that are still open (not delivered) during the selected period, either in value or percentage.

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Explanation: The total sales value of all sales orders per delivery date (both open and delivered), excluding cancelled orders.

Aggregation: Average

Use cases: This metric helps to track the delivery performance to access the sales in specific selected delivery date.

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Explanation: The total quantity of product units of all sales orders per delivery date (both open and delivered), excluding cancelled orders.

Use cases: This metric helps to track the delivery performance and keep track if there is any potential issues in delivery.

Metric key: total_ordered_quantity

Explanation: Indicates how much profit is made from new sales orders, either as a value or a percentage.

Aggregation: Average

Use Cases: Tracks profitability of sales orders, focus on delivery date.

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Explanation: Shows average of sales value from all sales orders in the selected period.

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Explanation: Shows average of sales value from all sales orders in the selected period.

Metric key: avg_order_size

About Order book: This metric is useful for forecast the future revenues, especially in the business where the delivery time is long.
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Explanation: The total sales value of all sales orders placed at the first/last day of the selected period, excluding cancelled orders.

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Explanation: The total purchase costs of all sales orders placed at the first/end day of the selected period, excluding cancelled orders.

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Explanation: Calculated by adding Sales to the Sales Order Book Value at the Start of Two Periods Ago and subtracting the Sales Order Book Value at the End of Two Periods Ago. This metric provides a combined view of realized sales and changes in the order book.

Aggregation: Sum

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Explanation: Calculated by subtracting the Sales Order Book Value at the End of the Period from the Sales Order Book Value at the Start of the Period. This metric shows the change in the value of the sales order book over the selected period.

Aggregation: Sum

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Explanation: Calculated by subtracting the Sales Order Book Purchase Value at the End of the Period from the Sales Order Book Purchase Value at the Start of the Period. This metric shows the change in the purchase cost of the sales order book over the selected period.

Aggregation: Sum

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Explanation: Total number of sales line items across all orders that were created from an offer, excluding cancelled orders that have been placed after the start of the selected period.

Use Cases: It helps you understand how many individual product rows were ordered over time, which is useful for analyzing basket composition and sales activity.

Metric key: offer_order_count

Explanation: Counts the number of orders that were created from an offer, excluding cancelled orders.

Use Cases: It helps you track how often offers are converted into actual orders.

Metric key: offer_order_value

Explanation: Total revenue generated from offer linked to an ordered, excluding cancelled orders.

Use Cases: It helps you understand how much revenue your offers or quotations are converting into actual ordered business.

Metric key: offer_order_item_count

Explanation: The total quantity of product units that have been planned.

Metric key: planned_quantity

Explanation: The total quantity of product units that have been over-sale.

Metric key: oversold_quantity

Explanation: Total value of deposits collected from customer orders, excluding cancelled orders.

Metric key: deposit_amount

Explanation: Total value of payments received from completed transactions, excluding cancelled orders. It helps track how much money customers actually paid across all tenders during the selected period.

Metric key: tender_amount

Explanation: Total gross weight of all non-cancelled orders in the selected period.

Use Cases: Useful for understanding shipment volume and supporting logistics, freight cost, and capacity planning.

Metric key: order_gross_weight

Explanation: The total purchase cost of goods purchased from suppliers, excluding cancelled purchases.

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Explanation: Total number of product units purchased. Useful for tracking purchasing volume, inventory replenishment plan.

Metric key: purchase_pcs

Explanation: Total number of product units purchased that have been delivered and fully processed into inventory in the selected period.

Metric key: purchase_delivered_pcs

Explanation: Shows the total number of distinct product models purchase, excluding cancelled purchase records, during the selected period. It helps users understand the variety of products being sourced from suppliers over the selected period.

Use Cases: Use this metric to analyze purchase at the product model level rather than by individual SKUs. It is especially useful in fashion retail, where products are available in multiple sizes and colors but belong to the same model.

Metric key: purchase_models

Explanation: Counts the number of distinct products purchase, excluding cancelled purchase records.

Use Cases: It helps users understand the variety of products being sourced from suppliers over the selected period.

Metric key: purchase_products

Explanation: Total value of delivered inventory purchases calculated at retail selling price, excluding cancelled purchase lines.

Use cases: It helps estimate the potential revenue of incoming stock rather than its actual cost.

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Explanation: Calculated by dividing the Sales (pcs) by the Purchases (pcs). This metric shows the percentage of purchased inventory that has been sold during the selected period.

Aggregation: Average

Metric key: purchase_sellthrough

Explanation: Calculated by dividing the Cost of Goods Sold (No VAT) by the Purchases (No VAT). This metric shows the percentage of purchased inventory value that has been sold based on the cost of goods sold.

Aggregation: Average

Metric key: purchase_value_sellthrough

About Purchase Order Intake metrics: Purchase Order Intake metrics are based on the purchase order date. They include purchase orders with an order date that falls within the selected period, regardless of whether the delivery date falls inside or outside the selected period.

Explanation: The total purchase costs of all purchase orders placed within the selected period, excluding cancelled orders.

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Explanation: The total quantity of product units included in purchase orders created during the selected period, excluding cancelled purchase orders.

Use cases: Helps track purchase order volume and inventory planning.

Metric key: purchase_order_intake_pcs

Explanation: Counts the number of distinct purchase orders, excluding any that were cancelled.

Use cases: It helps track purchasing activity and monitor how many supplier orders were actually placed over a selected period.

Metric key: purchase_order_count

Explanation: Counts the total number of non-cancelled purchase order lines in the selected period.

Use cases: It helps track purchasing activity volume and how many individual items or entries have been ordered from suppliers.

Metric key: purchase_order_row_count

Explanation: Shows the total number of distinct product models included in non-cancelled purchase orders during the selected period.

Use Cases: Use this metric to analyze purchase at the product model level rather than by individual SKUs. It is especially useful in fashion retail, where products are available in multiple sizes and colors but belong to the same model.

Metric key: purchase_order_models

Explanation: Counts the number of distinct products included in non-cancelled purchase orders.

Use Cases: It helps show the variety of items being ordered from suppliers over the selected period.

Metric key: purchase_order_products

Explanation: The average number of days between the order date and the delivery date for all non-cancelled purchase orders. The calculation includes only purchase orders where the order date is on or before the delivery date. When a time range is selected, only purchase orders with an order date within the selected period are included.

Use cases: Use this metric to identify changes in supplier delivery times, optimize purchasing schedules, and reduce the risk of stock shortages.

Metric key: purchase_order_avg_lead_time

Explanation: The total purchase costs of all purchase orders ordered for delivery by the end of the selected period, including both delivered orders and orders planned for delivery.

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Explanation: The total quantity of product units ordered from suppliers through purchase orders created for delivery by the end of the selected period, including both delivered orders and orders planned for delivery.

Metric key: purchase_ordered_quantity

About Delivered Purchase Order metrics: Purchase Order Delivered metrics are based on the delivery date. They include delivered purchase orders with an expected delivery date that falls within the selected period, regardless of when the purchase order was created.

Explanation: Total purchase costs of purchase orders that have been successfully delivered, excluding cancelled orders.

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Explanation: Total retail selling price of purchase orders that have been successfully delivered, excluding cancelled orders.

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Explanation: The total number of product units purchased with a delivery date that falls within the selected period. This value may be higher than Purchases Delivered (pcs) because delivered items may not yet have been processed and recorded in inventory during the selected period.

Use cases: Helps track delivered purchase quantities and identify delays between delivery and inventory processing.

Metric key: purchase_delivered_quantity

Explanation: The earliest delivery date among all non-cancelled purchase orders scheduled for delivery within the selected period.

Use cases: Use this metric to identify the earliest expected supplier delivery within the selected period, helping plan inventory replenishment and receiving activities.

Metric key: purchase_order_first_received

Explanation: The latest delivery date among all non-cancelled purchase orders scheduled for delivery within the selected period.

Metric key: purchase_order_last_received

About Open Purchase Order metrics: This metric considers purchase orders that have created but not yet delivered. Open Purchase Order metrics are based on the delivery date. They include open purchase orders with an expected delivery date that falls within the selected period, regardless of whether the order date falls inside or outside the selected period.

Explanation: The total purchase cost of open purchase orders that have not yet been delivered, excluding cancelled orders. Selecting a future time range helps estimate future purchasing costs and supports budget planning.

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Explanation: The total retail selling price of open purchase orders that have not yet been delivered, excluding cancelled orders. Selecting a future time range helps estimate future profit.

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Explanation: The total quantity of product units of open purchase orders that have not yet been delivered, excluding cancelled orders. Selecting a future time range helps estimate future profit.

Metric key: purchase_open_ordered_quantity

About Total Purchase Orders: Total Purchase Orders per Delivery Date metrics are based on the delivery date. They include both open and delivered purchase orders with an expected delivery date that falls within the selected period.

Explanation: The total purchase cost of open and delivered purchase orders, excluding cancelled orders. The metric includes purchase orders scheduled for delivery within the selected period, making it useful for forecasting future purchasing costs. This metric can also be used to see the total purchase orders across selected delivery date.

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Explanation: The total retail selling price of open and delivered purchase orders, excluding cancelled orders.

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Explanation: The total quantity of product units of open and delivered purchase orders, excluding cancelled orders.

Metric key: purchase_total_ordered_quantity

Explanation: The total purchase costs of all purchase orders placed at the first/last day of the selected period, excluding cancelled orders.

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Explanation: Calculated by subtracting the Purchase Order Book Value at the End of the Period from the Purchase Order Book Value at the Start of the Period. This metric shows the change in the value of outstanding purchase orders over the selected period.

Aggregation: Sum

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Explanation: Total discount of all non-cancelled purchase orders that have delivery date within the selected period. (not calculate when the delivery date is blank)

Use Cases: Tracks discount of purchase orders, focus on cost planning.

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Explanation: Calculated by dividing the Purchase Order Rebate (No VAT) by the sum of the Purchase Order Rebate (No VAT) and the Total Purchase Orders per Delivery Date (No VAT). This metric shows the percentage of the total purchase order value that was discounted through rebates.

Aggregation: Average

Metric key: purchase_order_rebate_percent

Explanation: Total gross weight of all non-cancelled purchase orders in the selected period. Useful for tracking inbound goods volume and planning logistics, storage, and handling needs.

Metric key: purchase_order_gross_weight

About Basket Metrics: These metrics evaluate cross-selling behavior by analyzing the combinations of products purchased together in a customer's basket. They help identify product affinities and purchasing patterns, supporting more effective product recommendations, promotions, and merchandising strategies.

About Web Metrics: These metrics are used to evaluate customer activity and behavior on an e-commerce website. They help businesses understand how customers interact with the online store, supporting improvements in user experience, conversion rates, and online sales performance.

Explanation: The total number of web visitors using the number of unique users when available, or the number of sessions otherwise in the selected period. Depending on how the web data is structured, it either sums visitor counts across the selected period or uses the maximum value to prevent duplicate counting.

Metric key: web_visits

Explanation: The total number of new web visitors during the selected period. The definition of a new web visitor depends on the website or web analytics platform (e.g., a visitor making their first visit during the selected period, or defined by Google Analytics). Depending on how the web data is structured, the metric either sums visitor counts across the selected period or uses the maximum value to avoid duplicate counting.

Metric key: new_web_visits

Explanation: The total number of returning web visitors during the selected period. This metric is calculated as the difference between the total number of web visitors and the number of new web visitors.

Metric key: returning_web_visits

Explanation: The total number of users or sessions from the web traffic data during the selected period. Depending on how the web data is structured, the metric either sums visitor counts across the selected period or uses the maximum value to avoid duplicate counting.

Metric key: web_traffic_visits

Explanation: Total number of pages viewed on your website during the selected period. It helps you understand overall browsing activity and how much visitors are engaging with your online store.

Metric key: web_page_views

Explanation: The total number of item view events recorded for products in the web category during selected period.

Metric key: web_product_views

Explanation: The total number of all instances of 'add to cart' actions during selected period.

Metric key: web_product_cart_adds

Explanation: The total value of the advertising costs associated with web campaigns.

Metric key: web_campaign_cost

Explanation: The total number of clicks recorded for web campaigns.

Metric key: web_campaign_clicks

Explanation: The total number of impressions from the campaigns data.

Metric key: web_campaign_impressions

Explanation: The total number of conversions from the campaigns data.

Metric key: web_campaign_conversions

Explanation: Measures how much sales you earn for the cost you spend on ads. A higher ROAS means your advertising is working well.

Aggregation: Average

Metric key: web_campaign_simple_roas

Explanation: Measures how many people click your campaign compared to the total number of people who see it. A high CTR means your campaign is engaging.

Aggregation: Average

Metric key: web_campaign_click_through_rate

Explanation: Measures the percentage of visitors who complete a specific goal. A goal can be a purchase, sign-up, or download. It shows how well your Conversion Rate works.

Aggregation: Average

Metric key: web_campaign_conversion_rate

Explanation: Measures the cost per click in the campaign. It is an online advertising model where you only pay when a user clicks your ad.

Aggregation: Average

Metric key: web_campaign_cost_per_click

Explanation: Measures the cost per conversion in the campaign. It is the average amount you pay to get a user to complete a specific goal, like a purchase or sign-up.

Aggregation: Average

Metric key: web_campaign_cost_per_conversion

Explanation: Measures the conversion rate of web visits into orders.

Aggregation: Average

Metric key: web_hitrate

About Visitor Metrics: These metrics are used to evaluate physical visitor activity at retail locations, shopping centers, and other monitored areas such as parking facilities. They help businesses understand visitor traffic, occupancy, and movement patterns.

Explanation: The total 'visit_count' from the visits records with labeled "in" during selected period.

Metric key: visits_out

Explanation: The total 'visit_count' from the visits records with labeled "out" during selected period.

Metric key: visits

Explanation: Calculate by dividing the total sum of visit counts (coming in) from the data records and dividing it by the number of distinct organizations (stores) that have recorded visits. The result provides the average number of visitors per store.

Metric key: avg_visitors_per_store

Explanation: Counts the total number of people who passed by the location during the selected period. It helps assess external foot traffic and the store’s potential to attract visitors inside.

Metric key: visits_passby

Explanation: Total number of cars entering the location during the selected period. It helps estimate vehicle-based traffic and assess how many visitors arrive by car.

Metric key: cars_in

Explanation: Total number of cars leaving the location during the selected period. It helps measure vehicle traffic flow and can indicate customer turnout or site activity levels.

Metric key: cars_out

Explanation: Total number of cars recorded passing by the location during the selected period. It helps estimate roadside traffic exposure and the potential opportunity to attract passing customers.

Metric key: cars_passby

Explanation: Total number of bicycles detected entering the location during the selected period. It helps estimate bike-based visitor traffic and understand how many customers arrive by bicycle.

Metric key: bicycle_in

Explanation: Total number of bicycles counted leaving the location during the selected period. It helps estimate bike traffic and customer mobility patterns around the store or venue.

Metric key: bicycle_out

Explanation: Total number of bicycles counted leaving the location during the selected period. It helps estimate bike traffic and customer mobility patterns around the store or venue.

Metric key: bicycle_passby

Explanation: Shows the average time visitors spend in the store during a visit. It helps you understand customer engagement and how effectively the space keeps people inside.

Metric key: avg_visit_time

Explanation: Shows the average number of people present in the location during the selected period based on occupancy counter data. It helps assess how busy the space typically is and supports staffing, capacity, and in-store experience planning.

Metric key: avg_occupancy

Explanation: Shows the absolute difference between counted entries and exits for the selected period. It helps monitor traffic-count accuracy and identify possible issues such as sensor errors, congestion, or people remaining in the store.

Metric key: visits_diff

Explanation: Total number of visitor faces detected and tracked by the demographics system during the selected period. It helps measure overall foot traffic entering or passing the monitored area.

Metric key: tracked_faces_count

Explanation: Counts the total number of male visitors detected by the demographics tracking system. It helps you understand male foot traffic volume and analyze audience composition over time or by location.

Metric key: tracked_males_count

Explanation: Counts the total number of female visitors detected by the tracking system. It helps you understand the gender composition of store traffic and analyze how well your location attracts female shoppers.

Metric key: tracked_females_count

Explanation: Shows the average age of visitors based on available demographic data. It helps you understand the typical age profile of your audience and supports more targeted product, marketing, or store decisions.

Metric key: avg_visitor_age

Explanation: Shows the average time a visitor is tracked in the location, measured in seconds. It helps indicate how long people typically stay or move within the space, supporting analysis of engagement and traffic behavior.

Metric key: avg_track_length

Explanation: Total number of visitors detected and tracked during the selected period. It helps you understand store traffic and evaluate how effectively visits are converted into sales.

Metric key: tracked_visitor_count

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Explanation: The percentage of the number of visits in and out difference and the total in and out visit. The smaller the error the better, when it is zero, it means there is no error.

Metric key: count_error

Explanation: Measures the percentage of visitors who place an order. The including returns version accounts for returned orders, while the excluding returns version measures the gross conversion rate before returns are considered.

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Explanation: Shows the percentage of people/cars/bicycles that passed by and actually entered the store. It helps assess how effectively the storefront, location, and external marketing turn traffic into visits.

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Explanation: Shows the average number of visitors for each hour/day the store is open. It helps evaluate customer traffic intensity and compare how busy locations or time periods are relative to opening time.

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Explanation: Shows the share of tracked visitors identified as male/female out of all tracked faces. It helps you understand the gender mix of store traffic for better assortment, marketing, and staffing decisions.

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Explanation: The total of hours recorded in the work logs during the selected period. If no hours are recorded, the metric defaults to zero.

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Explanation: The total number of distinct occurrences of shift identifiers from the dataset. This count only includes shifts that are associated with valid organization IDs (excluding any with an ID of -1), and do not have an associated absence code (absence_code is null). If no shifts meet these criteria, the result defaults to zero.

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Explanation: The total number of the distinct IDs of salespersons from the worksheet data source, ensuring that only those records belonging to valid organizations (excluding those with an ID of -1) are considered. If no records meet these criteria, the result defaults to zero.

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Explanation: The total hours from the work records where the absence code is not null, the organization ID is valid (not -1). If no records are found, the result defaults to zero.

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Explanation: The total of staff cost values from the relevant data, ensuring that only records with valid organization IDs are included in the calculation.

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Explanation: The proportion of time employees are absent relative to their total available working hours.

Metric key: absence_percent

Explanation: The average salary per hour for the staff.

Metric key: average_salary

Explanation: Calculated by dividing the total Working Hours by the product of business days and 8, which represents the standard full-time work hours in a day.

Metric key: full_time_employees

Explanation: Estimates the sales required to cover the current staff cost based on the configured or actual staff cost percentage of net sales.

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Explanation: The proportion of the total sales by the total working hours, providing insight into sales efficiency relative to the hours worked.

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Explanation: Shows how much sales is generated for each scheduled employee work hour. It helps evaluate staffing efficiency by comparing sales performance to planned labor time.

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Explanation: The difference between the Scheduled Staff Cost and the Unscheduled Staff Cost, providing insight into the variance between actual staffing expenses and the planned staffing budget.

Metric key: staff_cost_vs_scheduled

Explanation: The proportion of the Unscheduled Staff Cost by the Sales (No VAT and VAT), providing insight into the proportion of sales revenue that is allocated to staff expenses.

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Explanation: The proportion of the No. of Orders (excl. Returns) by the Unscheduled Working Hours, providing insight into the efficiency of transactions processed during work hours.

Metric key: transactions_per_work_hour

Explanation: The total of product purchase costs of items in storage where the transaction type is labeled as "Waste" and the transaction status is not canceled during selected period.

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Explanation: The total of retail selling price of items in storage where the transaction type is labeled as "Waste" and the transaction status is not canceled during selected period.

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Explanation: The total of product units in pieces in storage where the transaction type is labeled as "Waste" and the transaction status is not canceled during selected period.

Metric key: waste_pcs

Explanation: The total quantity of products stored in kilograms. The calculation includes only those records where the transaction type is labeled as "Waste" and the transaction status is not canceled during selected period. Additionally, it ensures that only quantities measured in kilograms are considered, using a conditional statement to filter out other units of measure. If the unit of measure is not specified or is not in kilograms, it defaults to zero for that entry.

Metric key: waste_weight

Explanation: The proportion of the total Waste (No VAT) by the total Sales (No VAT), providing a percentage that reflects the proportion of waste relative to sales.

Metric key: waste_percent

Explanation: The total quantity of product units from the inventory data where the transaction type is labeled "Stock Takes" and the transaction status is not canceled.

Metric key: stock_take_pcs

Explanation: The total expected quantity of product units from the inventory data where the transaction type is labeled "Stock Takes" and the transaction status is not canceled.

Metric key: stock_take_expected_pcs

Explanation: The difference between the Stock Take Result (pcs) and the Stock Take Expected (pcs). Use this metric to identify inventory discrepancies by comparing the expected stock quantity with the actual stock count, helping detect shrinkage, errors, or stock adjustments.

Metric key: stock_take_difference_pcs

Explanation: The total value of product units from the inventory data where the transaction type is labeled "Stock Takes" and the transaction status is not canceled, excluding VAT.

Metric key: stock_take_value

Explanation: The total expected value of product units from the inventory data where the transaction type is labeled "Stock Takes" and the transaction status is not canceled, excluding VAT.

Metric key: stock_take_expected_value

Explanation: The difference between stock Take Result (No VAT) and Stock Take Expected (No VAT).

Metric key: stock_take_difference_value

About Stock Sale metrics: Stock Sale Metrics are designed for businesses, such as restaurants and bars, where the items sold to customers differ from the inventory items consumed. Instead of tracking finished products, these metrics measure the raw materials or ingredients used to fulfill sales, helping businesses monitor inventory consumption, improve replenishment planning, and reduce waste.

Explanation: Total quantity of inventory items (e.g., ingredients or raw materials) consumed as a result of sales during the selected period.

Metric key: stock_sale_pcs

Explanation: Total purchase cost (without tax) of inventory items consumed as a result of sales during the selected period.

Metric key: stock_sale_value

About Sales Offers metrics: Sales Offers metrics are based on the offer date. New offers are considered when offer date fall into the selected period. Open offers are considered when the offer's status is still active.

Explanation: Total value of newly created offers or quotations in the selected period. It helps track potential future sales and the level of sales opportunities being generated.

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Explanation: Total profit generated from offer sales, calculated as the offer sales value minus the purchase cost. It shows how much earnings these offers contribute and helps assess the profitability of promotional or special offer activity.

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Explanation: Shows the profit margin on new sales offers as a percentage of their total value.

Metric key: new_offers_profit_percent

Explanation: Total value of sales offers that are still active and not cancelled. It helps track the current potential revenue in your pipeline from quotations that have not yet been completed or closed.

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Explanation: The total expected profit from all active, non-cancelled offers by subtracting purchase cost from offer value. It helps estimate the profit currently sitting in the sales pipeline before offers are converted into actual sales.

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Explanation: Shows the profit margin on open sales offers as a percentage of their total value.

Metric key: open_offers_profit_percent

Explanation: Total number of product units included in offers during the selected period. It helps track the volume of items proposed to customers through quotations or offers, regardless of whether they were converted into sales.

Metric key: offer_quantity

Explanation: The total number of product units included in sales offers that are still open, excluding cancelled offers. It helps you understand the current potential demand in the sales pipeline and how much quantity is still awaiting confirmation or conversion to sales.

Metric key: open_offer_quantity

Explanation: Counts the number of unique sales offers created in the selected period. It helps track how many quotes or offers were issued to customers and monitor sales pipeline activity.

Metric key: offer_count

Explanation: Counts the total number of offer lines recorded in the selected period. It helps track how many individual items or entries are included in offers, giving insight into offer volume and variety.

Metric key: offer_row_count

Explanation: Counts the number of distinct sales offers or quotations that are still open and not yet completed or closed. It helps track the current sales pipeline and shows how many potential deals are still in progress.

Metric key: open_offer_count

Explanation: Counts the number of unique customers associated with offers in the selected period. It helps show how many individual customers were reached or engaged through your offers.

Metric key: offer_customer_count

Explanation: Measures the percentage of offers which turn into an order.

Metric key: offer_conversion

Explanation: Shows the percentage of the value of offers that was converted into actual order value. It helps assess how effectively offers turn into sales revenue, with a higher percentage indicating stronger offer performance.

Metric key: offer_monetary_conversion

Explanation: Shows the percentage of the items in the offers that was converted into actual order items. It helps you understand how effectively offers turn into real demand at the item level.

Metric key: offer_item_conversion

Explanation: The total of 'receipt_value' from the accounting records where the 'cancelled' status is 0 (false) and the 'receipt_type' is labeled "income". If there are no valid records, the result defaults to 0.

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Explanation: The total of 'receipt_value' from the accounting records where the 'cancelled' status is 0 (false) and the 'receipt_type' is labeled "cost". If there are no valid records, the result defaults to 0.

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Explanation: EBITDA, short for earnings before interest, taxes, depreciation, and amortization, measures a company’s operating performance by excluding financing costs, taxes, and certain non-cash expenses. It can be either value or percentage.

Variations:

Metric keys:

Explanation: The total value of gift card payments where the payment type is 'activation'. If there are no such payments, the result defaults to zero.

Metric key: giftcard_deposit

Explanation: The total value of gift card payments where the payment type is 'reloading'. If there are no such payments, the result defaults to zero.

Metric key: giftcard_reload

Explanation: The total value of gift card payments where the payment type is 'redeeming'. If there are no such payments, the result defaults to zero.

Metric key: giftcard_redeemed

Explanation: The total value of gift card payments where the payment type is 'voiding' or 'expiring'. If there are no such payments, the result defaults to zero.

Metric key: giftcard_expired

Explanation: The maximum value of the gift card balance from the payment records.

Metric key: giftcard_balance

Explanation: Total number of item units taken from stock for internal business use rather than sold to customers. It helps track non-sales inventory consumption such as staff use, samples, waste, or operational needs.

Metric key: internal_use_pcs

Explanation: Total value of products taken for internal use rather than sold to customers, based on without VAT/tax price when available, otherwise using with VAT/tax price. It helps track non-sales stock consumption and understand its impact on inventory and profitability.

Metric key: internal_use_value

Explanation: Shows the total number of hours the business was open during the selected period, based on recorded opening schedules. It helps users compare trading time across days, weeks, or locations and gives important context for interpreting sales and traffic performance.

Metric key: hours_open

Explanation: Counts the number of unique days the store was open during the selected period. It helps users understand actual trading days and provides context for comparing sales, traffic, or productivity over time.

Metric key: days_open

About Queue related metrics: These metrics are designed specifically for customers who have the queue data (eg., camera related data).

Explanation: Counts the total minutes when queue levels stayed within the low range based on the defined queue threshold. The low queue data and logic usually comes from customer. It helps assess how often customer waiting conditions were light and whether staffing or service capacity was sufficient.

Metric key: low_queue

Explanation: Counts the number of minutes when queue levels were above the defined high-queue threshold. The high queue data and logic usually comes from customer. It helps identify peak congestion periods and assess whether staffing or service capacity needs adjustment.

Metric key: high_queue

Explanation: Counts the number of visits with no queue, based on visits where the queue stayed at or below the low-queue threshold. It helps you understand how often customers experience immediate or very fast service.

Metric key: no_queue

Explanation: Shows the total number of queue times (in minutes) recorded in the selected period. It helps you understand how often customers had to wait, giving a simple view of queue pressure in the store or service area.

Metric key: total_queue

Explanation: Shows the average number of people waiting in line during the selected period. It helps you understand queue pressure and service efficiency, so you can spot busy times and improve staffing or checkout flow.

Metric key: avg_people_queue

Explanation: Shows the highest number of people waiting in line at any single point during the selected period. It helps identify peak queue pressure and whether staffing or service capacity is sufficient.

Metric key: max_people_queue

Explanation: Measures the percentage of no/low/high queue time with respect to total queue time.

Metric keys:

Explanation: Counts the total number of customer feedback entries received in the selected period. It helps track how much feedback your business is collecting and can indicate customer engagement with surveys or review requests.

Metric key: feedback_count

Explanation: Total of all customer feedback scores collected in the selected period. It helps track overall feedback volume and sentiment, with higher values indicating more positive customer responses overall.

Metric key: sum_feedback

Explanation: Shows the net balance of customer feedback by adding 1 for each promoter score (9–10), subtracting 1 for each detractor score (0–6), and ignoring passive scores (7–8). It helps indicate whether overall customer sentiment is more positive or negative over the selected period.

Metric key: nps_base

Explanation: Shows how much customer feedback scores vary around the average rating using standard deviation calculation. A higher value means opinions are more mixed, while a lower value indicates more consistent customer experiences.

Metric key: stddev_feedback

Explanation: Measures the average feedback scores among all customer's feedbacks during selected period.

Metric key: avg_feedback

Explanation: This metric provides a percentage representation of the Net Promoter Score feedback received.

Metric key: nps_feedback

Explanation: The total monetary amount recorded across cashier shifts in the selected period. It helps track how much cash or balance value was handled during shifts and supports cash control and reconciliation.

Metric key: shift_amount

Explanation: Total number of transactions recorded in cashier balance data. It helps monitor cashier activity levels and supports reconciliation of cash register operations.

Metric key: cashier_transaction_count

Explanation: Shows the total difference between expected cash and the actual cash counted at the register, summed across the selected period. Positive amounts indicate an overage, while negative amounts indicate a shortage, helping monitor cash handling accuracy and identify discrepancies.

Metric key: over_short

Explanation: Total value recorded across all cashier shifts in the selected period. It helps monitor the amount handled during shifts and supports cash control and reconciliation.

Metric key: shift_total

Explanation: Total amount of cash removed from tills and deposited during the selected period. It helps monitor cash handling activity and reconcile register balances against expected cash on hand.

Metric key: cash_dropped

Explanation: Total amount of cash removed from the register, such as payouts for expenses, refunds, or safe drops. It helps track non-sales cash movements and reconcile expected cash balance differences.

Metric key: cash_paid_out

About Weather Metrics: These metrics are available for locations with weather data and customers provide location data. They can be used to identify how factors such as temperature, precipitation, or wind may influence customer traffic, sales, and purchasing behavior.

Explanation: Shows the average temperature for the selected period and location. It helps users understand how weather conditions may influence customer traffic, product demand, and overall sales performance.

Symbol: ℃

Metric key: temperature

Explanation: Shows the average share of the sky covered by clouds during the selected period. It helps users understand weather conditions that may influence customer traffic, outdoor activity, and sales patterns.

Symbol: %

Metric key: cloud_cover