# Welcome to ReportLab ReportLab is your centralized reporting suite designed specifically for multi-location, appointment-based businesses. It provides clear, actionable visibility into your sales, customers, appointments, and operational performance - all in one place. This guide introduces how ReportLab is structured, how to navigate each dashboard, and how to use this documentation to understand the meaning behind the data. *** ## **What ReportLab Provides** ReportLab surfaces the most important information about your business in a way that is straightforward and approachable for all users (operators, executives, and customer-facing teams). It is designed to help you: * Understand high-level business performance at a glance * Dive deeper into trends and breakdowns * Compare locations or regions * Identify opportunities for improvement * Make informed decisions based on real data
## **How ReportLab Is Organized** The ReportLab dashboards are grouped into meaningful sections that mirror the structure of most appointment-based organizations. This documentation follows the same structure so you can easily move between the dashboards and the documentation that explains them. ``` ## Available Now ## Executive Operations People ## Coming Soon ## Customer Customer Experience Finance Marketing Product Reference Data Reports System Health Vault ```

:chair-office:

Executive

High-level business performance, including sales, volume and location comparisons/pages/aOjs4gC5WzIpifbjuGsn

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Operations

Appointment volume, completion rates, cancellations, no-shows and provider utilization/pages/vYpI80AuWscNGvMpAU4y

:hands-holding-heart:

People

Labor economics, staffing efficiencies and scheduling insights/pages/TAuT7zbAS029R4PpdzFc
Each dashboard has a matching page in this documentation that explains: * The purpose of the dashboard * How to use it * Definitions for key metrics * How to interpret the data * Common insights and recommended actions \[Insert Image Here of GitBook Sidebar Showing Dashboard Sections] # How dashboards work Every dashboard in reportlab follows a consistent structure so users always know where to look first and how to read the data effectively. ## **1. High-level metrics** At the top of each dashboard, you will see a set of key performance indicators (KPIs). These provide a quick overview of your most important business outcomes. Common examples include: * Total Sales * Completed Appointments * Unique Clients Served * Visit Frequency * Retention Rate The **Reference Data → Metrics** section of this documentation contains the exact definitions, formulas, and clarifications for each metric. **\[Insert Image Here of KPI Tile Row]** *** ## **2. Time-based trends** Below the KPIs, you will find charts that show how performance changes over time (daily, weekly, monthly, quarterly, etc.). These trends help you identify patterns such as seasonality, growth, or changes in demand. \[Insert Image Here of Line Graph or Trend Chart] These visualizations are designed to answer questions like: * Are we improving over time? * Which periods show strong or weak performance? * Are there predictable peaks or slow periods? *** ## **3. Breakdowns and comparisons** {% columns %} {% column %} Further down the dashboard, you will find sections that break your metrics into more specific views, such as: * Location * Region or market * Provider or staff member * Service type * Client type (new vs returning) * Cancellation or no-show reasons {% endcolumn %} {% column %} #### These breakdowns help identify: * Strong and weak performing locations * Provider performance gaps * Service-specific demand * Operational bottlenecks {% endcolumn %} {% endcolumns %} *** #### And the documentation will explain: * What each chart represents * Why it matters * What patterns to look for * How the information can guide decisions **\[Insert Image Here of Bar Chart or Comparison Layout]** *** ## **How to interpret reportlab dashboards** Each dashboard is built to answer a set of core business questions. This helps you move from simply viewing data to understanding what it means. Below is an example of how reportlab frames analysis across business areas. {% tabs %} {% tab title="Executive Perspective" %} * Is revenue increasing or decreasing? * Which regions or locations are the strongest performers? * How are discounts, taxes, and net sales trending? * Are there growth or decline patterns over time? {% endtab %} {% tab title="Customer Perspective" %} * How often clients return * Whether retention is improving * The ratio of new vs repeat clients * Trends in client engagement {% endtab %} {% tab title="Operations Perspective" %} * Appointment volume patterns * Provider utilization * Cancellation and no-show drivers * Scheduling and staffing efficiency {% endtab %} {% endtabs %} *** ## **How this documentation supports reportlab** This documentation acts as the interpretation layer for your dashboards. Use it whenever you want to confirm a definition, understand how data is calculated, or interpret what a trend indicates. | If you need… | Go to… | | ----------------------------------- | --------------------------------------- | | Definition of a metric | Reference Data → Metrics | | Guidance on what a dashboard is for | The dashboard’s introduction section | | Help understanding a chart | The breakdown explanations | | Suggestions on what actions to take | Insights & Recommendations on each page | | Clarification on calculations | Reference Data → Metrics or Formulas | *** ## **Recommended workflow for using reportlab** 1. **Open a dashboard** in reportlab and review the high-level metrics. 2. **Scan the trends** to understand how performance is shifting over time. 3. **Review breakdowns** to identify strong and weak segments. 4. **Open the corresponding documentation page** for explanations and decision-support notes. 5. **Refer to the Reference Data section** for definitions or formulas if needed. This workflow ensures clarity and consistency across your entire organization. # Filters Filtering is one of the most important tools in reportlab. It allows you to control what data appears on the dashboard based on criteria such as time period, location, provider, service type, or client group. Filtering helps you tailor each dashboard to the specific question you are trying to answer. #### Common Filter Types * **Date Range**: Select a specific timeframe (e.g., last 30 days, previous year, quarter-to-date). * **Location or Region**: Focus the data on one or multiple business units. * **Provider or Staff Member**: Compare performance across team members. * **Service or Item Type**: Understand which services or products drive key metrics. * **Client Segment**: Explore new vs returning clients or specific customer groups. #### Why Filtering Matters Filtering allows you to: * Narrow the dashboard to the exact context you need * Avoid misinterpretation caused by irrelevant data * Compare groups or locations accurately * Explore how performance differs across time or segments
#### Filter Best Practices * Apply filters one at a time to see how each affects the results * Reset filters before reviewing a new section or question * Use consistent filters across dashboards when comparing performance * Double-check date ranges, as they impact nearly every metric # Sales and Volume A detailed view of all of your sales data #### :sparkles: Why is this important? This dashboard is meant to help you understand different aspects of your sales performance by looking at your gross sales, how much is given in discounts, what item types #### How to use this dashboard: This dashboard starts with a high level view of sales over time (gross and net) letting you see broader trends in how your business is performing. From there, you can start to look at different lenses of your sales, whether that's by specific location, different regions, or different product types. This gives you a quick comparison to help you identify trends or areas where you want to start to understand in more detail. #### Sections ***
Company
* Gross Sales: All revenue received through your payment processor * Discounts: Amount deducted due to discounts * Taxes: Amount charged for taxes * Vouchers: Value of services covered by vouchers * Net Sales:
Region
***
#### Date Filters {% tabs %} {% tab title="Date Grain" %} Switch between weekly, monthly, quarterly or annually. {% endtab %} {% tab title="Date" %} Only look at data for specific date ranges that you care about * In the last 3 months * On or after January 1, 2025 * Between July 1 and July 6 of 2025 {% endtab %} {% endtabs %} #### Location Filters {% tabs %} {% tab title="Location" %} Only look at the performance of certain locations (select as many as you'd like to see) {% endtab %} {% tab title="Region" %} Only look at the performance of certain regions (select as many as you'd like to see) {% endtab %} {% tab title="Sub Region" %} More granular location data (select as many as you'd like to see) {% endtab %} {% endtabs %} #### Business Filters {% tabs %} {% tab title="Sales Type" %} Only look at certain attributes of your sales (was it a giftcard, was it a product, service?) {% endtab %} {% tab title="Item Type" %} {% endtab %} {% tab title="Item Name" %} {% endtab %} {% endtabs %} # Gross Sales # Net Sales ### Philosophical Vision This metric reflects our belief in location-level accountability and transparency in customer engagement. By measuring active customer counts per unit, operators gain a tangible understanding of how each location is performing relative to expectations and peers. *** ### Definition **Active Customers:** A customer is considered "active" at a location if they have completed at least one transaction or appointment within the reporting period.**Location:** A physical unit or service center where business is conducted. *** ### Metric Formula Active Customers = COUNT(DISTINCT customer\_id)WHERE transaction\_date BETWEEN start\_date AND end\_dateAND location\_id = \[location] *** ### Example | Soho | 243 | 2025-08-31 | | -------- | --- | ---------- | | Flatiron | 195 | 2025-08-31 | | Tribeca | 221 | 2025-08-31 | *** ### Annotation * **Customer ID** must be linked to a valid service or sale within the defined timeframe. * Customers counted more than once if active at multiple locations. * Cancellations or no-shows do **not** count as active participation. *** ### How to Use * Benchmark performance across units. * Identify underperforming or high-performing locations. * Pair with staffing and cost data to analyze efficiency. *** ### Tags `reporting` `core metric` `unit economics` `location-level` # Customer Profile # Customers # Google Ads ## Measures These measures capture the core performance and impact of your advertising spend, from visibility and engagement to revenue-driving outcomes. Together, they show how efficiently your campaigns turn impressions into clicks, and clicks (or views) into measurable business results. **Spend**\ The total amount of money spent on ads during the selected time period. **Clicks**\ The number of times users clicked on your ads, indicating direct engagement and traffic driven to your site or app. **Impressions**\ The total number of times your ads were shown, reflecting overall reach and visibility. **Conversions**\ The number of times users completed a desired action (such as a purchase, signup, or booking) after interacting with your ad. **Conversions Value**\ The total monetary value attributed to those conversions, used to measure revenue and return on ad spend (ROAS). **View-Through Conversions**\ The number of conversions that occurred after a user saw (but did not click) an ad and later converted within the attribution window, capturing the influence of awareness and upper-funnel campaigns. ## Channel Types Advertising Channel Types define the primary way your ads are delivered across Google’s ecosystem, from high-intent search results to immersive visual and video experiences. Each channel serves a different role in the customer journey—capturing demand, generating interest, or driving conversions. Understanding these channels helps you align budget and strategy to where customers are most likely to engage. **Search**\ Text ads shown when users actively search for specific keywords, capturing high-intent demand at the moment of need. **Smart (Smart Campaigns)**\ Automated, goal-driven campaigns that let Google handle targeting, bidding, and placements across its network with minimal setup. **Display**\ Visual banner and rich media ads served across millions of websites and apps to build awareness and retarget audiences. **Performance MAX**\ An all-in-one, AI-optimized campaign type that runs across Search, Display, YouTube, Gmail, and Discover to maximize conversions or revenue. **Demand Generation**\ Upper-funnel, visually rich campaigns designed to spark interest and consideration across YouTube, Discover, and Gmail feeds. **Video**\ Video ad campaigns on YouTube and partner sites, optimized for reach, engagement, or conversions using sight, sound, and motion. # Appointments # Visit Frequency ### Philosophical Vision This metric reflects our belief in *** ### Definition **Active Customers:** *** ### Metric Formula ```sql Active Customers = COUNT(DISTINCT customer_id) WHERE transaction_date BETWEEN start_date AND end_date AND location_id = [location] ``` *** ### Example | Soho | 243 | 2025-08-31 | | -------- | --- | ---------- | | Flatiron | 195 | 2025-08-31 | | Tribeca | 221 | 2025-08-31 | *** ### Annotation * **Customer ID** must be linked to a valid service or sale within the defined timeframe. * Customers counted more than once if active at multiple locations. * Cancellations or no-shows do **not** count as active participation. *** ### How to Use * Benchmark performance across units. * Identify underperforming or high-performing locations. * Pair with staffing and cost data to analyze efficiency. *** ### Tags `reporting` `core metric` `unit economics` `location-level` # Labor Economics This dashboard has three key components, which can be toggled at the top level of the dashboard.
{% tabs %} {% tab title="Scheduling" %} ***What question does this answer?*** Am I scheduling my business in a way that I'm not leaving money on the table (not enough staff to cover the demand) or spending more than I should be (scheduling more staff than service demand) {% endtab %} {% tab title="Demand Utilization" %} ***What question does this answer?*** What is the absolute maximum amount of business I can take based on the location hours, chairs available and number of staff scheduled? What is the amount of business I can realistically expect to handle after deducting business & personal time blocks (breaks, trainings, housekeeping etc.) {% endtab %} {% tab title="Labor Utilization" %} ***What question does this answer?*** {% endtab %} {% endtabs %} ### Philosophical Vision One of the most important pieces of your business is to strategically meet the demand for your services with the proper amount of staffing. It can be very easy to over-tune in either direction, so we thoughtfully crafted a way for you to understand if you've been over-staffing or under-staffing based on what your customers are telling you. *** {% hint style="info" %} For more detail on how to understand this dashboard, select any of the definitions below {% endhint %} ### Definitions * [Overstaffed vs. Understaffed](/people/labor-economics/staffing-ratio) * [Demand Utilization](/people/labor-economics/demand-utilization) *** ### How to Use * Understand your trends of order volume and shifts worked * Identify when you are over or under spending on labor *** # Overview ### North Star: One Daily Question “Do we have the right people on the floor at the right times to deliver the booked + likely-to-happen services—without overpaying for idle time?” # Scheduling Recommendation Engine ## Overview The Scheduling Recommendation Engine analyzes daily staffing data per location and produces an actionable recommendation. It compares actual shifts worked against demand-based targets and flags mismatches as scheduling opportunities. The engine runs as a custom calculation in Metabase, evaluated against aggregated daily metrics per location. *** ### How It Works ``` Raw Data (orders, shifts, targets) │ ▼ Gap Metrics Computed ├── Under Utilization Gap └── Over Utilization Gap │ ▼ Case Logic Evaluated (top → bottom) │ ▼ Recommendation Returned ``` #### Input Metrics | Metric | Description | | ------------------------- | ---------------------------------------------------------------------------------------------------- | | **Under Utilization Gap** | Measures how far below target efficiency the location is running. Higher values = more overstaffed. | | **Over Utilization Gap** | Measures how far above target efficiency the location is running. Higher values = more understaffed. | > **Key insight:** These two gap metrics are the primary decision drivers. They directly encode the magnitude and direction of the staffing mismatch. #### Decision Logic The engine evaluates conditions **top-to-bottom** and returns the **first match**. This means more severe conditions are checked first.
PriorityConditionRecommendation
🔴 HighOver Utilization Gap ≥ 0.75Materially understaffed — add coverage on peak periods first.
🔴 HighUnder Utilization Gap ≥ 0.50Materially overstaffed — reduce coverage on low-demand periods first.
🔴 HighOver Utilization Gap ≥ 0.40 AND Under Utilization Gap ≥ 0.20Demand is uneven — rebalance hours from low to high-demand periods.
🟡 MediumOver Utilization Gap ≥ 0.30Add a small amount of coverage where demand spikes.
🟡 MediumUnder Utilization Gap ≥ 0.25Trim coverage modestly in low-demand periods.
🟢 On Track(default)Staffing is generally aligned to demand.
*** ### reportlab Implementation ```sql case( [Average of Over Utilization Gap] >= 0.75, "High Priority: You are materially understaffed. Add coverage on peak periods first.", [Average of Under Utilization Gap] >= 0.50, "High Priority: You are materially overstaffed. Reduce coverage on low-demand periods first.", [Average of Over Utilization Gap] >= 0.40 AND [Average of Under Utilization Gap] >= 0.20, "High Priority: Demand is uneven. Rebalance hours from low-demand to high-demand periods.", [Average of Over Utilization Gap] >= 0.30, "Medium Priority: Add a small amount of coverage where demand spikes.", [Average of Under Utilization Gap] >= 0.25, "Medium Priority: Trim coverage modestly in low-demand periods.", "On Track: Staffing is generally aligned to demand." ) ``` *** ### Design Principles #### Optimize for false positives The engine is intentionally tuned to **flag aggressively**. A missed opportunity (false negative) costs real margin. A false alarm costs a manager 30 seconds of review. The thresholds reflect that asymmetry. #### Gap metrics as primary signals Earlier versions of this logic gated every branch behind an **Opportunity Score** threshold. This created a problem: locations with clear directional signals (e.g., a 0.57 under-utilization gap) were classified as "On Track" because their composite score fell below the entry threshold. The current version uses gap metrics directly, removing the redundant gate. #### Evaluation order matters The `case()` statement returns the first matching condition. The ordering encodes priority: 1. **Understaffing** is checked before overstaffing at the High tier — this can be swapped if cost savings should take priority over coverage risk. 2. **Rebalance** (both gaps present) is checked after pure directional signals — a location that is clearly understaffed shouldn't get a "rebalance" label. 3. **Medium** conditions use lower thresholds and catch softer signals that don't rise to High priority. *** ### Threshold Reference | Threshold | Value | Rationale | | -------------------- | ------------------------------ | ------------------------------------------ | | High understaffing | `Over Util ≥ 0.75` | Demand significantly exceeds capacity. | | High overstaffing | `Under Util ≥ 0.50` | At least half the capacity is underused. | | High rebalance | `Over ≥ 0.40` + `Under ≥ 0.20` | Both signals present simultaneously. | | Medium understaffing | `Over Util ≥ 0.30` | Noticeable but manageable demand pressure. | | Medium overstaffing | `Under Util ≥ 0.25` | Modest excess capacity worth reviewing. | > These thresholds are starting points. After running in production, review the distribution of recommendations and adjust if a particular tier fires too often or too rarely. *** ### Worked Example **Location:** Lynnfield · **Date:** Saturday, Mar 14 2026 | Metric | Value | | --------------------- | ----------------- | | Shifts Worked | 7 | | Daily Orders | 31 | | Scheduling Ratio | 4.43 orders/shift | | Target Range | 5–7 orders/shift | | Shift Delta vs Mid | -1 | | Under Utilization Gap | 0.57 | | Over Utilization Gap | 0.00 | **Evaluation trace:** 1. `Over Utilization Gap (0.00) ≥ 0.75` → ❌ Skip 2. `Under Utilization Gap (0.57) ≥ 0.50` → ✅ **Match** **Result:** *High Priority: You are materially overstaffed. Reduce coverage on low-demand periods first.* **Action:** Cut \~1 shift (or equivalent hours) to reduce waste. *** ### Changelog | Date | Change | | ---------- | ------------------------------------------------------------------------------------------------------------------------------------ | | 2026-03-13 | **v2.0** — Removed Opportunity Score gates. Simplified to gap-metric-only logic. Lowered thresholds to optimize for false positives. | | — | **v1.0** — Original logic with Opportunity Score ≥ 5/10 gates on all branches. | # Staffing Ratio Shows the relationship between your order volume and shifts that were worked #### Why this metric matters: The point of this metric is to make sure that you are scheduling around your demand in a way that is not leaving money on the table (you have more demand than you are staffed for) and you are not spending additional overhead when the demand is lower. *** #### How to read this metric: The primary chart shows three key pieces of data. The first is your order volume, and how that is changing over time. On top of your order volume, you have two different trend lines - One for how many "Overstaffed" days you had, and one for how many "Understaffed" days you had. You want **both of these lines** to be as close to 0 as possible. * Trending down is a positive indicator on either line. * Trending up is a negative indicator which should spark some intervention or changes to scheduling After looking at the trends, you can look at the table view which aggregates the same metrics by day of the week. This can help you diagnose if you need to adjust staffing holistically, or if you are only off on certain days of the week. The last notch down from there is an hourly view, the same mindset can be used when looking across different hours of the day. *** Scheduling Ratio shows how much of each scheduled hour was actually spent performing services. * 1.0 means staff were fully booked that hour. * Below 1.0 means staff had idle time. * Above 1.0 means multiple services were happening at once, requiring overlapping staff effort. #### Definition:
Understaffed When your daily orders divided by shifts worked is **greater than 14**
Overstaffed When your daily orders divided by shifts worked is **less than 8**
{% hint style="info" %} An order is defined as an item type of "Service" and the status is "Closed" {% endhint %} *** # Demand Utilization What percentage of business are you getting compared if you were operating at full capacity (Location, charis and hours of operation #### Why this metric matters: Based on a given location, the number of chairs, and the hours that are open - how many appointments could you theoretically see. This tells you the absolute maximum amount of business you could do if nothing else was done aside from customer appointments. However, this isn't really how things work. There are time blocks required for other areas of your business, as well as employee lunches, breaks, training etc. This is where we surface the realistic service hours that you could fill with customer appointments. You now have a very accurate picture of how many hours were spent on service appointments, compared to a theoretical and realistic potential capacity. *** #### How to read this metric: *** #### Definitions:
Theoretical Service Hours Based on the location hours, the number of chairs available and the number of employees staffed, how many hours could be filled with appointments. This excludes things like breaks or other business responsibilities outside service appointments
Realistic Service Hours This is the total theoretical service hours ***excluding*** personal and business blocks. This is much closer to what a typical day's availability would look like
Realistic Demand Utilization The utilization is calculated by taking the number of actual appointment hours divided by the realistic service hours. `800 appointment hours / 1000 realistic hours = 80% demand utilization`
{% hint style="warning" %} Business and Personal block hours must be logged directly in Booker. It's recommended that these are only used for true time blocks to help you get the most accurate data {% endhint %} ***