This Reports Data Dictionary is the single, authoritative reference for every metric, calculation, and data field shown across the PatientNow Essentials Insights Hub. Whenever an article says a definition "lives in the Reports Data Dictionary," this is where it links. Agreeing on these definitions is what prevents reporting discrepancies, reinforces clean front-desk data entry, and supports reliable clinical and accounting audits. Definitions are grouped by dashboard, and shared metrics (such as Total Revenue or Total Payments) carry the same calculation everywhere they appear.
Key Terms — a few conventions used throughout this dictionary:
Net sales: sales minus refunds.
Checked out: an appointment or sale marked complete at the register — most metrics only count checked-out activity.
Unique: counted once per customer, invoice, or appointment (no double-counting).
Over Time: the same metric plotted by date rather than as a single total.
How to Use This Dictionary
Every Insights Hub dashboard is built from the metrics defined here. When a dashboard card or report column is unclear, find its dashboard section below and look up the exact calculation. A few reading tips:
Shared metrics repeat by design. Metrics like Total Revenue, Total Payments, and New Inquiries appear on more than one dashboard and use the same calculation in each place, so a number reconciles across views.
"Over Time" variants are the same metric plotted by date. If Total Revenue is a single figure, Revenue Over Time is that figure broken out day by day.
Notes and examples in the third column explain edge cases — future-dated data, "NA" categories, refresh cadence, and the workflow habits (check-out, real-time check-in, inventory class assignment) that keep a metric accurate.
Amber flags mark definitions where the source guides currently disagree. Those are awaiting a subject-matter expert's ruling and should be confirmed before they are treated as final.
Navigating the Insights Hub
Navigate to Reports > Insights Hub, then select the dashboard you want. The Global Controls Bar at the top of every dashboard sets the location and date range for the whole page; the filters in the first section below define exactly what each date range includes.
Global Filters (All Dashboards)
These date-range filters live in the Global Controls Bar and behave identically on every dashboard. They define exactly which days a dashboard counts.
Filter | Expected Behavior | Notes & Examples |
Current → This Week | Data will show current week, Sunday-Saturday | e.g. If today's date is a Friday, this filter would show data from the most recent Sunday to Friday Note: This filter will show all data from this time period, even if it is in the future |
Current → This Month | Data will show from all days in the current calendar month | e.g. If today's date is in January, this filter would show data from January Note: This filter will show all data from this time period, even if it is in the future |
Current → This Quarter | Data will show from all days in the current calendar quarter | e.g. If today's date is in Q2, this filter would show data from Q2 Note: This filter will show all data from this time period, even if it is in the future |
Current → This Year | Data will show from all days in the current calendar year | e.g. If today's date is in 2026, this filter would show data from 2026 Note: This filter will show all data from this time period, even if it is in the future |
To Date → Week-to-Date | Data will show from previous Sunday to current day | e.g. If today's date is a Thursday, this filter would show data from the current Sunday to Thursday |
To Date → Month-to-Date | Data will show from first day of the current calendar month to the current day | e.g. If today's date is the 15th of the month, this filter would show data from the 1st of the month to the 15th |
To Date → Quarter-to-Date | Data will show from first day of the current calendar quarter to the current day | e.g. If today's date is in January, this filter would show data from January |
To Date → Year-to-Date | Data will show from first day of the current calendar year to the current day | — |
Previous → Previous Week | Data will show from the previous week Sunday to Saturday | — |
Previous → Previous Month | Data will show from the previous calendar month | e.g. If today's date is in January, this filter would show data from December |
Previous → Previous Quarter | Data will show from the previous calendar quarter | e.g. If today's date is in Q2, this filter would show data from Q1 |
Previous → Previous Year | Data will show from the previous calendar year | e.g. If today's date is in 2026, this filter would show data from 2025 |
Next → Next Week | Data will show from the upcoming Sunday to the following Saturday | e.g. If today's date is a Thursday, data show from the upcoming Sunday to the following Saturday Note: Phase 1 foundational dashboards do not respond to this filter |
Next → Next Month | Data will show from all days of the next calendar month | e.g. If today's date is in January, this filter would show data from February Note: Phase 1 foundational dashboards do not respond to this filter |
Next → Next Quarter | Data will show from all days of the next calendar quarter | e.g. If today's date is in Q1, this filter would show data from Q2 Note: Phase 1 foundational dashboards do not respond to this filter |
Next → Next Year | Data will show from all days of the next calendar year | e.g. If today's date is in 2025, this filter would show data from 2026 Note: Phase 1 foundational dashboards do not respond to this filter |
Last → Last Hour | Data will show starting from the current hour to as many hours as selected back | e.g. If "Last 3 Hours" is filtered, data will show for the current hour and the 2 hours prior, if "Last 1 Hour" is filtered, data will show for the current hour Note: currently the dashboards update every few hours, so this filter is not recommended at the current refresh cadence |
Last → Last Day | Data will show starting from the current day to as many days as selected back | e.g. If "Last 3 Days" is filtered, data will show for the current day and the 2 days prior, if "Last 1 Day" is filtered, data will show for the current day |
Last → Last Week | Data will show starting from the current week to as many weeks as selected back | e.g. If "Last 3 Weeks" is filtered, data will show for the current week and the 2 weeks prior, if "Last 1 Week" is filtered, data will show for the current week |
Last → Last Month | Data will show starting from the current month to as many months as selected back | e.g. If "Last 3 Months" is filtered, data will show for the current month and the 2 months prior, if "Last 1 Month" is filtered, data will show for the current month |
Last → Last Quarter | Data will show starting from the current quarter to as many quarter as selected back | e.g. If "Last 3 Quarters" is filtered, data will show for the current quarter and the 2 quarters prior, if "Last 1 Quarter" is filtered, data will show for the current quarter |
Last → Last Year | Data will show starting from the current year to as many years as selected back | e.g. If "Last 3 Years" is filtered, data will show for the current year and the 2 years prior, if "Last 1 Year" is filtered, data will show for the current year |
Top KPI Dashboard
The executive cockpit. These KPIs summarize the whole practice; most also appear in more detail on their dedicated dashboards.
Metric / Field | How It's Calculated | Notes & Examples |
Total Revenue | Sum of total net sales (sales minus refunds) | — |
Total Payments | Sum of total payments made, excluding gift cards | — |
Number of Services Sold | Count of total services rendered where the customer has been checked out | — |
Average $ Per Sale | Sum of total sales / Count of unique sales checked out | Note: This metric takes into account valid sales (those that are checked out and not deleted) and not all sales have payments attached to them, which may cause this metric to look inconsistent with the "Total Payments" widget drill-downs |
Revenue Over Time | Sum of total net sales (sales minus refunds) shown by date | — |
Total Revenue by Category | Sum of total net sales (sales minus refunds) shown by inventory class | Note: A category of "NA" means there is no Inventory Class assigned |
New Inquiries | Count of total unique inquiries made to practice | — |
Inquiry to Appointment Conversion | Count of total unique inquiries resulting in appointment / Count of total unique inquiries | — |
First Scheduled Appointments | Count of appointments where customer has never been seen by practice before and their first scheduled appointment is scheduled for the time period selected | — |
Average Appointments Per Customer | Total unique appointments / Total unique customers | — |
New vs. Existing Customer Mix | Unique customers shown by customer grouping over time Where the "New" group is made up of customers who have had 1 appointment and the appointment has been completed, and "ExistIng" group is made of customers who have had more than 1 appointment | — |
Customer Retention | Unique count of retained customers / Unique count of total customers Where retention rate is calculated as the count of unique retained customers/count of total unique customers and a retained customer is defined as a customer that has been seen in the past 120 days or less and it was not their first appointment, or a valid appointment is completed during the timeframe selected that is the customer's 4th+ appointment | — |
Cancellation Rate | Count of cancelled appointments / Count of all appointments | — |
Appointments Over Time | Count of appointments shown by date and appointment classification (i.e. completed, scheduled to be completed, no-shows, cancellations) | Note: Scheduled to be completed is an appointment that is on the schedule but has not been checked out yet and is not a no-show/cancellation |
Appointments Completed | Count of appointments where the appointment was finished as expected (i.e. not a no-show or cancellation and has been checked out) | — |
Average Time Spent in Room | Difference between the sum of the minutes in the Initial and Complement Time associated with the appointment and the Scheduled Room time of the appointment, averaged across all appointments | — |
Average Wait Time | Minutes between scheduled appointment start time and actual check in time, averaged across all appointments | — |
Staff Utilization | Staff time in appointments / Total scheduled working time | Note: Staff time in appointments is calculated as the Room Time of scheduled appointments that is marked as type "Work" ("Lunch" and "Blocked" types are not factored into this number) |
Total Retail Revenue | Sum of total retail net sales (sales minus refunds) value | — |
Number of Units Sold | Total quantity of retail products sold | — |
Inventory Turnover | Cost of goods sold / Quantity on hand * Cost of products on-hand) Where cost of goods sold and cost of products on-hand are defined by unit price | — |
Units on Shelf | Total number of retail product units in inventory | — |
Top 10 Products by Revenue | Total retail net sales (sales minus refunds) shown for the top 10 retail products, ranked by net sales | — |
Invoices Processed | Total unique invoices processed | — |
Services Rendered | Total unique service quantity | — |
Total Tips Paid | Sum of total tips received | — |
Amount of Discounts | Sum of total discounts given on invoices | — |
New Memberships | Count of unique members with a membership start date within the selected time period | — |
New Membership Revenue | Sum of membership dues attributed to new members | — |
Active Memberships | Total unique members with an active membership | Note: An active membership is defined as a membership with the status marked "Active" and a membership Billing Date within the selected timeframe |
Cancelled Memberships | Total unique members that have cancelled their membership | — |
Member vs. Non-Member Average Spend Over Time | Total net sales / Count of unique customers, shown by member vs. non-member classification | — |
Membership Revenue Over Time | Total value attributed to membership dues over time | — |
Revenue by Membership Type | Total value attributed to membership dues, shown by type of membership | — |
Sales Dashboard
Retail and service sales performance, before and after refunds.
Metric / Field | How It's Calculated | Notes & Examples |
Total Sales | Sum of total gross sales before refunds | — |
Total Payments | Sum of total payments made, excluding gift cards | — |
Average $ Per Sale | Sum of total sales / Count of unique sales checked out | — |
Total Revenue | Sum of total net sales (sales minus refunds) | — |
Gross Margin | Sum of total net sales (sales minus refunds)/Sum of total gross sales (sales before refunds) | Gross Margin = Total Net Sales (Revenue) / Total Gross Sales x 100 |
Revenue Over Time | Sum of total sales shown by date | — |
Total Revenue by Category | Sum of total net sales (sales minus refunds) shown by inventory class | Note: A category of "NA" means there is no Inventory Class assigned |
Total Service Revenue | Total net sales (sales minus refunds) for services invoiced | — |
Total Retail Revenue | Total net sales (sales minus refunds) for retail items invoiced | — |
Number of Services Sold | Count of total services rendered | — |
Revenue Mix Over Time | Percentage and total net sales (sales minus refunds), shown for services invoiced and retail items invoiced | — |
Top 5 Services by Revenue | Top 5 services ranked by total net sales (sales minus refunds) Table shows revenue, which is calculated as the total net sales, and average days between service, which is calculated as the average number of days between appointments where the service was completed | — |
Bottom 5 Services by Revenue | Bottom 5 services ranked by total net sales (sales minus refunds) Table shows revenue, which is calculated as the total net sales, and average days between service, which is calculated as the average number of days between appointments where the service was completed | — |
Top 5 Retail Products by Revenue | Top 5 retail products ranked by total net sales (sales minus refunds) Table shows revenue, which is calculated as the total net sales, and average days between sales, which is calculated as the average number of days between invoices for the retail product | — |
Bottom 5 Retail Products by Revenue | Bottom 5 retail products ranked by total net sales (sales minus refunds) Table shows revenue, which is calculated as the total net sales, and average days between sales, which is calculated as the average number of days between invoices for the retail product | — |
Average Revenue by Day and Time | Aggregation of total net sales within the selected date range, grouped by day and hour of the day where the sale occurred | Note: The red and green colors are based on a pre-established reference range for the average net sale values. Currently, the minimum is set to 100 and the maximum to 1,200 |
Revenue Forecast by Month | A projection of total net sales (sales minus refunds) The forecast is calculated by using statistical models that take into account outcomes spanning over 2 years of past data and customer behavior on appointments and revenue, seasonality, and expected error rates to extend the historical patterns 6 months into the future | — |
Enrollment & Retention Dashboard
Lead conversion, new-vs-returning mix, and retention metrics.
Metric / Field | How It's Calculated | Notes & Examples |
Number of Customers | Count of distinct customer IDs | — |
New Inquiries | Count of total unique inquiries made to practice | — |
Consultations | Count of total unique appointments where the appointment is marked as a consultation | — |
Appointments Scheduled | Count of unique appointments that are scheduled to be completed | Note: Scheduled to be completed is an appointment that is on the schedule but has not been checked out yet and is not a no-show/cancellation |
First Scheduled Appointments | Count of appointments where customer has never been seen by practice before | — |
New vs. Existing Customer Mix | Unique customers shown by customer grouping over time Where the "New" group is made up of customers who have had 1 appointment and the appointment has been completed, and "Existing" group is made of customers who have had more than 1 appointment |
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New Customers by First Scheduled Service Type | New customers shown by the Service assigned to their first scheduled appointment Where the "New" group is made up of customers who have had 1 appointment and the appointment has been completed. | — |
Inquiry to Appointment Over Time | Count of total unique inquiries resulting in appointment / Count of total unique inquiries, shown over time | — |
Inquiry to Consultation Over Time | Count of total unique inquiries resulting in a consultation appointment / Count of total unique inquiries, shown over time | — |
% of Appointments Completed | Count of appointments completed / Count of all appointments (scheduled to be completed, no-shows, cancellations, appointments completed) | Note: Appointments completed are appointments where the appointment was finished as expected (i.e. not a no-show or cancellation and has been checked out). Scheduled to be completed is an appointment that is on the schedule but has not been checked out yet and is not a no-show/cancellation |
Avg. Appts Per Customer | Total unique appointments / Total unique customers | — |
No-Show Rate | Count of appointments that were a no-show / Count of all appointments (scheduled to be completed, no-shows, cancellations, appointments completed) | Note: Appointments completed are appointments where the appointment was finished as expected (i.e. not a no-show or cancellation and has been checked out). Scheduled to be completed is an appointment that is on the schedule but has not been checked out yet and is not a no-show/cancellation |
Avg. Days Between Appts | Sum of the days until next scheduled appointment / Count of unique customers with a next appointment scheduled | — |
Cancellation Rate | Count of cancelled appointments / Count of all appointments | — |
Customer Retention | Unique count of retained customers / Unique count of total customers Where retention rate is calculated as the count of unique retained customers/count of total unique customers and a retained customer is defined as a customer that has been seen in the past 120 days or less and it was not their first appointment, or a valid appointment is completed during the timeframe selected that is the customer's 4th+ appointment |
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Customer Retention Over Time | Unique count of retained customers / Unique count of total customers, shown over time Where retention rate is calculated as the count of unique retained customers/count of total unique customers and a retained customer is defined as a customer that has been seen in the past 120 days or less and it was not their first appointment, or a valid appointment is completed during the timeframe selected that is the customer's 4th+ appointment | — |
Customer Retention by Last Appointment Recency | Count of unique customers that have had their most recent appointment completion within different recency groupings:
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Appointments Dashboard
Scheduling, completion, wait time, and utilization metrics.
Metric / Field | How It's Calculated | Notes & Examples |
New Inquiries | Count of total unique inquiries made to practice | — |
Consultations | Count of total unique appointments where the appointment is marked as a consultation | — |
Appointments Scheduled | Count of unique appointments that are scheduled to be completed | Note: Scheduled to be completed is an appointment that is on the schedule but has not been checked out yet and is not a no-show/cancellation |
Appointments Completed | Count of appointments where the appointment was finished as expected (i.e. not a no-show or cancellation and has been checked out) | — |
% Appointments Completed | Count of appointments completed / Count of all appointments (scheduled to be completed, no-shows, cancellations, appointments completed) | Note: Appointments completed are appointments where the appointment was finished as expected (i.e. not a no-show or cancellation and has been checked out). Scheduled to be completed is an appointment that is on the schedule but has not been checked out yet and is not a no-show/cancellation |
Appointments Over Time | Count of appointments shown by date and appointment classification (i.e. completed, scheduled to be completed, no-shows, cancellations) | Note: Scheduled to be completed is an appointment that is on the schedule but has not been checked out yet and is not a no-show/cancellation |
Inquiry to Appointment Over Time | Total number of unique inquiries converted to appointments / Total number of unique inquiries | Note: An inquiry is considered converted when an appointment occurs after the inquiry creation date and the appointment is not a no-show or cancellation |
Inquiry to Consultation Over Time | Total number of unique inquiries converted to consultations / Total number of unique inquiries | Note: An inquiry is considered converted when an appointment marked as "consultation" occurs after the inquiry creation date and the consultation is not a no-show or cancellation |
Avg. Appts Per Customer | Total unique appointments / Total unique customers | — |
Avg. Days Between Appts | Sum of the days until next scheduled appointment / Count of unique customers with a next appointment scheduled | — |
No Show Rate | Count of appointments that were a no-show / Count of all appointments (scheduled to be completed, no-shows, cancellations, appointments completed) | Note: Appointments completed are appointments where the appointment was finished as expected (i.e. not a no-show or cancellation and has been checked out). Scheduled to be completed is an appointment that is on the schedule but has not been checked out yet and is not a no-show/cancellation |
Cancellation Rate | Count of cancelled appointments / Count of all appointments | — |
Avg. Time Spent in Room | Sum of initial and complementary time scheduled for an appointment in minutes, averaged across all appointments | — |
First Scheduled Appointments by Service Type | Count of appointments where customer has never been seen by practice before, shown by the Service assigned to that appointment | — |
Return Appointments by Service Type | Count of appointments where customer has been seen by practice before, shown by the Service assigned to that appointment | — |
Staff Utilization | Staff time in appointments / Total scheduled working time | — |
Average Wait Time | Minutes between scheduled appointment start time and actual check in time, averaged across all appointments | — |
Staff Utilization Over Time | Staff time in appointments / Total scheduled working time, shown over time | — |
Average Number of Appointments by Day and Time of Day | Aggregation of count of unique scheduled appointments within the selected date range, grouped by day and hour of the day where the appointment is scheduled to be completed | — |
Key Appointment Metrics by Service Type |
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Peak Demand Forecasting by Day | A projection of count of appointments completed The forecast is calculated by using statistical models that take into account outcomes spanning over 90 days of past data and customer behavior on appointments, seasonality, and expected error rates to extend the historical patterns 90 days into the future | — |
Inventory Dashboard
Stock, units sold, valuation, and turnover metrics.
Metric / Field | How It's Calculated | Notes & Examples |
Total Retail Revenue | Sum of total retail net sales (sales minus refunds) value | — |
Number of Units Sold | Total quantity of retail products sold | — |
Units on Shelf | Total number of retail product units on-hand | — |
Inventory Turnover | Cost of goods sold / Quantity on hand * Cost of products on-hand) Where cost of goods sold and cost of products on-hand are defined by unit price | — |
Retail Revenue Over Time | Sum of total retail net sales (sales minus refunds) value, shown over time | — |
Revenue Mix Over Time | Percentage and total net sales (sales minus refunds), shown for services invoiced and retail items invoiced | — |
On Hand Valuation | Count of products on hand * Unit price, summed for all active products in inventory | — |
Average Gross Selling Price | Average total selling price of items across all invoices, before discounts | — |
Average Net Selling Price | Average total selling price of items across all invoices, factoring in discounts | — |
Top 10 Products by Revenue | Total retail net sales (sales minus refunds) shown for the top 10 grossing products | — |
Top 10 Products by Volume | Count of units sold shown for the top 10 sold products by volume of units sold | — |
Key Inventory Metrics |
| Note: Represents an estimation of the days of stock remaining for a product
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Estimated Days Left in Inventory | Current Quantity on Hand / average daily unit sales rate | Forward-looking estimate of how many days of stock remain before you need to reorder. (From the Inventory reports; shown here for reference.) |
Reorder Level ("Qty Reorder At") | The minimum unit threshold configured on a product profile | When on-hand quantity falls to this level, the item is flagged on your inventory reorder reports. |
Accounting Dashboard
Payments, invoices, discounts, tips, gift cards, packages, and reconciliation metrics — including invoice-level and line-item detail.
Metric / Field | How It's Calculated | Notes & Examples |
Total Payments Made | Sum of total payments made, excluding gift cards | Note: Not all payments are tied to an employee, which may cause the numbers on the employee drill-down for this card to appear inconsistent with the Total Payments number |
Total Unpaid Balance | Sum of total balance outstanding (amount invoiced minus total payments and gift cards) | — |
Invoices Processed | Total unique invoices processed | — |
Services Rendered | Total unique service quantity where the customer has been checked out | — |
Total Overhead Cost | Sum of the total overhead cost value associated with invoiced sales | — |
Full Register Summary |
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Item Level Transaction Detail |
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Total Payments Over Time | Sum of total payments made (excluding gift cards), over time | Note: Not all payments are tied to an employee, which may cause the numbers on the employee drill-down for this card to appear inconsistent with the Total Payments number |
Number of Gift Cards Sold | Unique count of Gift Card ID | — |
Value on Gift Cards Sold | Sum of face value on gift cards sold | — |
Remaining Gift Card Balance | Sum of stored amount remaining on gift cards sold | — |
Gift Card Redemption | Sum of stored amount remaining on gift cards sold / Sum of face value on gift cards sold | — |
Number of Packages Sold | Count of unique ID of prepaid packages sold | — |
Value on Packages Sold | Sum of total value of prepaid packages sold | — |
Package Value Remaining | Sum of remaining value of prepaid packages sold | — |
Prepaid Package Redemption | Sum of remaining value of prepaid packages sold / Sum of remaining value of prepaid packages sold | — |
Payments by Payment Type | Total net sales shown by method of payment | — |
Total Revenue by Revenue Category | Total net sales shown by inventory class | Note: A category of "NA" means there is no Inventory Class assigned |
Total Tips Paid | Sum of total tips received | — |
Sales Tax Collected | Sum of total tax invoiced | — |
Amount of Discounts | Sum of total discounts given on invoices | — |
Refunds Given | Sum of total net sales where the entry is flagged as a refund | — |
Payments Dashboard (Payrix)
Merchant-processing metrics available only to practices on integrated payments (Payrix). These bridge bank payouts and clinical invoices for reconciliation, fee analysis, and dispute tracking.
Metric / Field | How It's Calculated | Notes & Examples |
Deposit Gross Total | The cumulative credit card payment volume batched together before processing fees are deducted | Deposit Reconciliation card. Also labeled Gross Batch Total. |
Net Payout | The actual net amount deposited into your practice's bank account after merchant processing fees are subtracted | Maps to your bank statement deposit. |
Total Processing Fees | The cumulative sum of interchange, network assessment fees, and processor markups deducted from a batch | Fee Intelligence dashboard. |
Interchange Expense | The wholesale cost charged by card-issuing networks (Visa, Mastercard, etc.) to process a card | Varies by card type; e.g. standard debit vs. premium rewards cards (Interchange Type). |
Assessment Expense | Network assessment fees charged by the card networks | Shown alongside interchange on the Fee Intelligence dashboard. |
Disputed Amount | The value of credit card transactions currently being contested by clients via chargeback | Dispute Intelligence (Dispute Log). |
Dispute Win/Loss Ratio | The percentage of chargeback disputes successfully defended and won by the practice | Dispute Intelligence (Dispute Log). |
Deposit Status | The clearing state of a payout batch | e.g. cleared or pending. |
Transaction ID | The unique merchant identifier assigned to a payment | Ties a clinical invoice to its processor record in the Payment Detail Log. |
Memberships Dashboard
Active/new/canceled membership counts and membership revenue.
Metric / Field | How It's Calculated | Notes & Examples |
Active Memberships | Total unique members with an active membership (i.e. have not cancelled their membership) | — |
New Memberships | Count of unique members with a membership start date within the selected time period | — |
Cancelled Memberships | Total unique members that have cancelled their membership | — |
Membership Revenue | Sum of membership dues attributed to all members | — |
New Membership Revenue | Sum of membership dues attributed to new members | — |
Membership Revenue Over Time | Total value attributed to membership dues over time | — |
Average Total Spend by Membership Type | Total sale / count of unique customers, shown by type of membership | — |
Memberships to Expire in 60 Days | Count of members whose memberships have an end date within the next 60 days | — |
Revenue by Membership Type | Total value attributed to membership dues, shown by type of membership | — |
Average Length of Membership | Average of the difference between last membership billing date and the member's first membership "Sold On" date, shown in days | — |
Member vs. Non-Member Average Spend Over Time | Total sale / count of unique customers, shown by member vs. non-member status | — |
Active Members by Membership Type | Count of unique customers with an active membership, shown by type of membership | — |
Cancellations by Membership Type | Count of unique customers with a cancelled membership, shown by type of membership | — |
Consent Report
Patient contact and consent fields available in the Consent Report.
Metric / Field | How It's Calculated | Notes & Examples |
First Name | First Name listed on the patient's account | — |
Last Name | Last Name listed on the patient's account | — |
Email address listed on the patient's account | — | |
Mobile | Mobile phone number listed on the patient's account | — |
Work | Work phone number listed on the patient's account | — |
Home | Home phone number listed on the patient's account | — |
Email - Appt. Reminder/Medical Info | Flag that denotes whether a patient has opted in to receive emails relating to appointment reminders or medical information | — |
Email - Special Offers | Flag that denotes whether a patient has opted in to receive emails relating to special offers | — |
SMS - Appt. Reminder/Medical Info | Flag that denotes whether a patient has opted in to receive SMS texts relating to appointment reminders or medical information | — |
SMS - Special Offers | Flag that denotes whether a patient has opted in to receive SMS texts relating to special offers | — |
Predictive Insights Dashboard (Catalyst)
Forecasting and risk models available on the Catalyst tier. These are predictions, not historical counts. Learn more: Reports > Insights Hub > Catalyst Tier & AI Analytics Overview
Metric / Field | How It's Calculated | Notes & Examples |
Appointment Demand Forecasting | Count of daily unique appointments, forecasted 90 days into the future
The appointment forecast model analyzes a practice's complete appointment history, including appointment dates, providers, service types, cancellations, and no-shows, to learn the patterns in how your schedule fills over time. It identifies trends like seasonal demand shifts, day-of-week patterns, and how factors like cancellation rates affect actual visit volume. Using those patterns, it projects the number of appointments expected each day for the next 90 days. The more historical data your practice has, the more accurate those projections become. | — |
Revenue Dip Prediction | Total sum of weekly revenue, forecasted 6 months into the future The revenue forecast model analyzes your practice's complete sales history to predict total weekly revenue for the next 90 days.
The model is trained on your historical transaction data, including service and product sales, pricing, discounts, quantities, overhead costs, and payment totals, giving it a detailed picture of how revenue has been generated across your practice over time. It learns patterns from sales history: which weeks tend to be stronger, how pricing and discounts affect total collections, how membership activity correlates with revenue volume, and how different service and inventory categories contribute to your bottom line.
Using those patterns, it projects expected weekly revenue across a rolling three-month window. The more historical data your practice has, the more accurate those projections become. | — |
Inventory Stock Forecast | The inventory stock model analyzes each product's complete sales history to estimate when it will run out of stock, giving your team enough lead time to reorder before a gap in availability affects your patients or revenue.
The model is trained on your practice's full product sales history, learning how demand for each product has behaved over time. It accounts for patterns including seasonal demand shifts, historical fluctuations in purchase volume, and recurring trends across days, weeks, and months. This allows it to produce an out-of-stock estimate for each product, particularly for products whose demand varies throughout the year.
The more historical data your practice has, the more accurate that projection becomes. Each product is flagged for reorder ("Reorder" column in the table) when its estimated out-of-stock date falls within the next seven days, or when current stock has already dropped below the recommended reorder level - whichever comes first.
Table Columns:
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Retention Dip Prediction | Weekly retention rate, forecasted 90 days into the future Where retention rate is calculated as the count of unique retained customers/count of total unique customers And a retained customer is defined as a customer that has been seen in the past 120 days or less and it was not their first appointment, or a valid appointment is completed during the timeframe selected that is the customer's 4th+ appointment The retention forecast analyzes your practice's complete appointment history to predict your weekly patient retention rate for the next 90 days.
The model is trained on your historical weekly retention rates, learning how that metric has moved over time across your practice. It identifies patterns in how retention fluctuates week to week, recognizes periods of stronger or weaker patient loyalty, and uses those trends to project where your retention rate is headed. Using those patterns, it projects expected weekly retention rate across a rolling 90-day window. The more historical data your practice has, the more accurate those projections become. | — |
Retention by Service Type | Retention rate, shown by a patient's last completed ProductServiceType Where retention rate is calculated as the count of unique retained customers/count of total unique customers And a retained customer is defined as a customer that has been seen in the past 120 days or less and it was not their first appointment, or a valid appointment is completed during the timeframe selected that is the customer's 4th+ appointment The retention rate is colored according to the following parameters:
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Clients Most Likely to No-Show/Cancel | The no-show and cancellation risk model analyzes each patient's appointment history to calculate the likelihood that they will cancel or no-show their next scheduled appointment. Rather than forecasting a trend over time, it produces a risk score for each individual patient who has an upcoming appointment, allowing your team to take proactive action before a gap appears in your schedule. The model is trained on your practice's complete appointment history, learning which patterns are most associated with cancellations and no-shows. It considers factors like how frequently a patient has cancelled or no-showed in the past, how recently they were last seen, and their scheduling behavior over time. Each patient is assigned one of three risk levels:
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Clients Most Likely to Purchase a Membership | The membership purchase likelihood model analyzes each patient's transaction and visit history to calculate the probability that they will purchase a membership. Rather than forecasting a trend over time, it produces an individual likelihood score for every active patient, allowing your team to focus membership conversations on the patients most likely to say yes. The model is trained on your practice's complete sales history, learning the behavioral patterns that distinguish patients who have purchased memberships from those who haven't. It considers factors like purchase history, service preferences, spending patterns, discount usage, and visit behavior over time, building a profile of what a likely membership candidate looks like based on real patient data from your practice. Each patient is assigned one of three likelihood levels:
The higher the likelihood level, the more closely that patient's behavior resembles patients who have historically converted to members. The more historical data your practice has, the more accurate this classification becomes.
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Frequently Asked Questions
Why does the same metric appear on more than one dashboard?
Why does the same metric appear on more than one dashboard?
By design. High-level KPIs on the Top KPI Dashboard are repeated in more detail on their dedicated dashboards (Sales, Appointments, and so on). The calculation is identical in each place, so the numbers reconcile.
Why don't my totals match between "Total Sales" and "Total Revenue"?
Why don't my totals match between "Total Sales" and "Total Revenue"?
Total Sales is gross (before refunds); Total Revenue and Net Sales are net (after refunds). Total Payments excludes gift card redemptions to avoid double-counting cash.
What does a category of "NA" mean in a chart?
What does a category of "NA" mean in a chart?
It means items were sold without an assigned Inventory Class or Service Category. Assign a class to every active item so revenue groups correctly.
How current are these numbers?
How current are these numbers?
Dashboards refresh automatically every few hours. Because of that cadence, the shortest "Last Hour" filters are not recommended for real-time decisions.
Why are "New Customer" and "Customer Retention" marked as needing review?
Why are "New Customer" and "Customer Retention" marked as needing review?
The two source guides define these differently, and the correct definition has not yet been confirmed. Until a subject-matter expert rules, treat the flagged calculations as provisional — the amber note in each row states both versions.
Which dashboards require integrated payments?
Which dashboards require integrated payments?
The Payments Dashboard (Payrix) metrics — payouts, fees, interchange, and disputes — appear only for practices on integrated payments. Without Payrix connected, those cards stay locked.