FILTERS - ALL DASHBOARDS
FILTER | EXPECTED BEHAVIOR |
Current -> This Week | Data will show current week, Sunday-Saturday ex. 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 ex. 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 ex. 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 ex. 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 ex. 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 ex. 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 ex. 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 ex. 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 ex. 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 ex. 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 ex. 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 ex. 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 ex. 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 ex. 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 ex. 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 ex. 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 ex. 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 ex. 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 ex. 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 ex. 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
KPI | CALCULATION |
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 patients with a PatientNow record who have not yet completed their first appointment. This includes patients with or without a scheduled appointment. Once they complete their first appointment, they are considered a New Patient |
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 marka |
Cancelled Memberships | Total unique members that have cancelled their membership |
Member vs. Non-Member | Total net sales / Count of unique customers, shown |
Average Spend Over Time | 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
KPI | CALCULATION |
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) |
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
KPI | CALCULATION |
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 |
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 |
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
KPI | CALCULATION |
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
KPI | CALCULATION |
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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ACCOUNTING DASHBOARD
KPI | CALCULATION |
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 |
MEMBERSHIPS DASHBOARD
KPI | CALCULATION |
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
KPI | CALCULATION |
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 |
CLIENT DEMOGRAPHICS DASHBOARD
KPI | CALCULATION |
# of Unique Patients | Count of distinct PatientUniqueID |
Average Age | Average age (in years), across all patients |
% of Female Patients | Count of distinct PatientUniqueID with a Sex listed as female or trans female/Count of all distinct PatientUniqueID |
% of Male Patients | Count of distinct PatientUniqueID with a Sex listed as male or trans male/Count of all distinct PatientUniqueID |
Top Cities | Count of distinct PatientUniqueID, shown by city and Sex |
Ethnicity Ratio | Count of distinct PatientUniqueID, shown by ethnicity |
Age Range Distribution | Count of distinct PatientUniqueID, shown by age range (in years) of the patient and Sex The age range groupings are as follows:
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Appointments by Age Range | Count of distinct appointments made by patients in each age range grouping, shown by age range and Sex The age range groupings are as follows:
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Gender by Referral Source | Count of distinct PatientUniqueID, shown by RefSourceID and Sex |
Demographics Detail |
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PREDICTIVE INSIGHTS DASHBOARD
KPI | CALCULATION |
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:
The higher the risk level, the more likely that patient is to miss their next appointment based on their historical behavior. The more historical data your practice has, the more accurate this classification becomes. TABLE COLUMNS:
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PREPAIDS REPORT DASHBOARD
KPI | CALCULATION |
Prepaids Balance Log | Monitors outstanding prepaid revenue and fulfillment obligations, enabling effective management of customer account balances and revenue recognition. TABLE COLUMNS:
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Prepaids Redemption Log
| Tracks customer redemption of prepaid services by location and employee, enabling verification of fulfillment activity and remaining prepaid liabilities against original sales. TABLE COLUMNS:
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ENTERPRISE HEALTH - SALES DASHBOARD
KPI | Calculation |
Total Revenue | Sum of total net sales (sales minus refunds) |
Total Payments | Sum of total payments made, excluding gift cards |
Retail Revenue | Sum of total retail net sales (sales minus refunds) value |
Service Revenue | Total net sales (sales minus refunds) for services invoiced |
AVG Revenue Per Invoice | Sum of total net sales (sales minus refunds) / count of invoices |
Revenue Over Time | Sum of total net sales (sales minus refunds) shown by date |
Total Revenue by Type | Sum of total net sales (sales minus refunds) shown by Type (Gift Card, Membership, Retail, Service) |
Unearned Liability Log | Tracks prepaid sales (gift cards, packages, credits) by region, location, and employee, monitoring sold value and remaining unearned liability balance. TABLE COLUMNS:
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Top Selling Products by Region | Retail product sales performance by region, enabling identification of top sellers and regional market preferences. TABLE COLUMNS:
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Top Selling Services by Region | Service sales performance by region, enabling identification of top sellers and regional market preferences. TABLE COLUMNS:
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Avg Invoice Spend per Age Group by Region | Analyzes customer spending patterns by age group and region, enabling identification of high-value demographics and regional performance variations to inform targeted marketing and service strategies. |
Average Spent per Invoice by Region | Compares average transaction value across regions, identifying high-performing locations and revenue variation to guide pricing strategy and resource allocation. |
ENTERPRISE HEALTH - CUSTOMERS DASHBOARD
KPI | CALCULATION |
Number of Customers | Count of unique customers across the selected date range and filters |
AVG Customer Age | Average age of all active customers during the selected period |
New Customers by Region | Count of customers with first appointment in the selected date range, grouped and displayed by region. Where the “New” group is made up of customers who have had 1 appointment and the appointment has been completed. |
Existing Customers by Region | Average |
Ethnicity Breakdown by Region | Distribution of customers by ethnicity category, grouped by region |
Gender Breakdown by Region | Distribution of customers by gender, grouped by region |
Age Group Breakdown by Region | Distribution of customers by age group, grouped by region |
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 |
Referrals Breakdown by Region | Count of appointments attributed to each referral source, grouped by region |
Memberships Over Time | Count of active memberships by month over the selected period, grouped by region |
Membership Revenue Over Time | Total value attributed to membership dues over time |
New Members Over Time | Count of unique members with a membership start date within the selected time period over time |
Cancelled Members Over Time | Total unique members that have cancelled their membership over time |
ENTERPRISE HEALTH - APPOINTMENTS DASHBOARD
KPI | CALCULATION |
Appointments Over Time | Count of completed appointments by month over the selected period, grouped by region |
Appointments Prebooked Over Time | Count of appointments booked in advance (prebooked) by month over the selected period, grouped by region |
Appointments by Type | Count of appointments grouped by service type, displayed by region and location |
Prebookings vs. Walk-ins | Distribution of appointments comparing prebooked appointments to walk-in appointments, grouped by region and location |
Key Appointment Metrics by Region | TABLE COLUMNS:
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