Auction Excellence - Parking Lot Management & Quality Inspection

Overview

The Analytics Module is a unified hub for all data analysis, reporting, and staffing tools. Access everything from a single location with seamless navigation between dashboards.

Query Builder

Ask questions in natural language and get instant visualizations

Staffing Dashboard

Plan CR writer staffing with arrival patterns

Saved Queries

Create, save, and share custom queries

Backlog Dashboard

Real-time backlog visibility and staffing status

Engagement Analytics

Team engagement trends, badge distribution, and leaderboards

Accessing the Analytics Module

The Analytics Module is available to administrators and owners from the sidebar.

Open Analytics

Click Analytics in the sidebar navigation to open the unified module

Choose Your Dashboard

Use the tab navigation at the top to switch between:

Query Builder (default)
Import
Reports

Select Date Range

Use the date picker to set your analysis window (shared across tabs)

Explore Your Data

Each dashboard provides specialized tools for different analysis needs

The Analytics Module uses URL state persistence - your date range, dataset selections, and filters are saved in the URL, making dashboards shareable via link.

URL Structure

The Analytics module is accessible at /auctions/[auctionId]/analytics/ with the following sub-routes:

Route	Dashboard
`/analytics/query-builder`	AI-powered Query Builder
`/analytics/import`	Data Import (CSV to native tables or custom objects)
`/analytics/reports`	Reports (Staffing, Backlog, Engagement, Saved Queries)
`/analytics/reports/staffing`	Staffing Model Dashboard
`/analytics/reports/backlog`	Backlog Dashboard
`/analytics/reports/engagement`	Engagement Dashboard

Legacy URL Support: If you have bookmarked old URLs like /staffing-model or /reports, they automatically redirect to the new unified Analytics module location.

Query Builder

The Query Builder is the heart of the Analytics module - ask questions in plain English and get instant visualizations powered by AI.

How It Works

Select Your Data Source

Choose from three categories of data sources:

Standard Tables: Native tables like Check-In Records, CR Inspections, and Vehicle Journey
Custom Objects: Your auction-specific objects (Dealerships, Fleets, etc.) created in Custom Objects
Imported Datasets: CSV datasets with metadata templates applied

Join with Other Objects (Optional)

After selecting a primary data source, optionally join with related objects via the Join with other objects section. Available joins are determined by the Data Model associations.

Ask Your Question

Type a natural language question like “Show me arrivals by hour for the last 3 weeks”

Review Results

The AI generates SQL, executes it, and displays results with an appropriate visualization

Customize & Save

Adjust the chart type, modify the query, or save as a report

Example Queries

Staffing Analysis

Query: “What are the peak check-in hours over the last 3 weeks?”Result: Bar chart showing average arrivals by hour with peak hours highlighted

Query: “Compare lease vs consignment arrivals by day of week”Result: Grouped bar chart with sale type breakdown

Operational Metrics

Query: “Show total car count by location for the last 30 days”Result: Bar chart comparing location volumes

Query: “What’s the trend of submissions this month vs last month?”Result: Line chart with comparison and percentage change

Quality Insights

Query: “What are the top 5 defect categories this quarter?”Result: Pie chart of defect distribution

Query: “Show defect trends by week for the last 3 months”Result: Line chart with weekly defect counts

Cross-Object Analysis

Query: “What’s the average time from check-in to CR completion?”Result: Summary statistic with trend line (requires Vehicle Journey or joined tables)

Query: “Show CR completion rate by sale type”Result: Bar chart comparing Lease vs Consignment CR rates

Custom Object Queries

Query: “Show me total dealerships by region”Result: Bar chart of dealership counts grouped by region field

Query: “List vendors with contract value over 50000”Result: Data table with matching vendor records

Cross-Object Joins

When your Data Model has associations between objects, you can query across multiple objects in a single question.

Select Primary Object

Choose your main data source (e.g., Check-In Records)

Expand Join Section

Click Join with other objects below the table selector

Select Secondary Objects

Check the objects you want to join (up to 4). Available objects are determined by your Data Model associations.

Choose Join Type

Toggle between LEFT JOIN (default, includes all primary records) and INNER JOIN (only matching records)

Query Across Objects

The column reference panel shows fields from all joined objects, grouped by object name. Ask questions that reference fields from any joined object.

Cross-object joins use the association graph from the Data Model. Only objects with a direct association to your primary object appear as join options.

Data Source Selector

The data source selector provides consistent dataset selection across the Query Builder and report editor.

Category	Description
Standard Tables	Native tables (Check-In Records, CR Inspections, Vehicle Journey) with typed columns
Custom Objects	Auction-specific objects defined in Custom Objects. Shown with a distinct icon and record count
Imported Datasets	CSV datasets with template-based type identification

Click Dataset Selector

Click the “Data Source” dropdown in the Query Builder

Browse Categories

Categories are grouped into Linked Datasets and Datasets

View Dataset Info

Each dataset shows row count, date range, and applied template (if any)

Select Dataset

Click to select the dataset you want to query

Linked Datasets combine multiple datasets using configurable join columns (VIN, stock number, etc.), enabling powerful cross-dataset queries like “time from check-in to CR completion.”

Data Source Icons

Icon	Source Type	Description
Table	Standard Table	Native tables with typed columns
Boxes	Custom Object	Auction-specific custom objects
🚗 Car	Check-In Detail	Check-in/arrival data (template)
📋 Clipboard	Inspector Detail (CR)	Condition report data (template)
📊 Chart	Other Dataset	General imported datasets

Ask AI Button Integration

The Ask AI button appears throughout the Analytics module when you’re viewing data:

Location	What Happens When Clicked
Staffing Dashboard	Opens Query Builder with the current dataset pre-selected and date range
Datasets Tab	Opens Query Builder with selected dataset pre-loaded
Saved Queries Tab	Opens Query Builder to create a new query

This provides seamless context-aware navigation - your current filters and selections carry over to the Query Builder.

Analytics Dashboard

The Analytics Dashboard provides pre-built visualizations for common operational metrics.

Dashboard Sections

Section	Purpose
Quick Stats	Key metrics at a glance (top of page)
Occupancy Trends	Lot count submissions over time
Submission Type Breakdown	Distribution by vehicle category
Location Comparison	Side-by-side location performance
User Activity	Team member submission frequency
Defect Trends	Quality inspection patterns
KPI Impact Summary	A3 rock impact visualization

Dashboard Sections

Section	Purpose
Quick Stats	Key metrics at a glance (top of page)
Occupancy Trends	Lot count submissions over time
Submission Type Breakdown	Distribution by vehicle category
Location Comparison	Side-by-side location performance
User Activity	Team member submission frequency
Defect Trends	Quality inspection patterns
KPI Impact Summary	A3 rock impact visualization

Quick Stats Cards

Quick stats provide immediate visibility into your most important metrics. These cards appear at the top of the Analytics page.

Understanding Quick Stats

Stat	What It Shows	Calculation
Total Submissions	Lot count submissions in selected period	Sum of all submissions
Total Cars Counted	Vehicles tracked across all submissions	Sum of car counts from all entries
Active Locations	Locations with submissions in period	Count of unique locations
Active Users	Team members who submitted data	Count of unique submitters
Inspections Completed	Quality inspections performed	Count of inspections
Open Rocks	Unresolved A3 rock reports	Count of open/in-progress reports

Reading Quick Stat Trends

Each quick stat card includes a trend indicator:

Indicator	Meaning
↑ Green	Value increased compared to previous period
↓ Red	Value decreased compared to previous period
— Gray	No significant change

The comparison period matches your selected time range. If you select “Last 7 days,” the trend compares to the 7 days before that.

Clicking Quick Stats

Quick stat cards are interactive:

Click any card to navigate to the detailed view for that metric
Total Submissions → Opens Submissions page with date filter applied
Inspections Completed → Opens Inspections page
Open Rocks → Opens Rocks page filtered to open status

Time Period Selection

Control the date range for all analytics visualizations using the time period selector.

Using the Date Picker

Click Date Range

Click the date range button in the top-right corner of the Analytics page

Select Preset or Custom

Choose a preset range or select custom dates

Apply Selection

Click Apply to update all charts with the new date range

Preset Time Ranges

Preset	Date Range
Today	Current day only
Yesterday	Previous day only
Last 7 Days	Past week including today
Last 30 Days	Past month including today
This Month	Current calendar month
Last Month	Previous calendar month
This Quarter	Current fiscal quarter
This Year	Current calendar year
Custom Range	Manually select start and end dates

Custom Date Range

For specific analysis periods:

Click Custom Range in the date picker
Select the start date from the calendar
Select the end date from the calendar
Click Apply to update the dashboard

Custom date ranges can span up to 365 days. For longer historical analysis, use the Reports module.

Filtering Capabilities

Narrow your analytics view to focus on specific locations, submission types, or team members.

Filter	Options	Use Case
Location	All locations or specific locations	Analyze individual lot performance
Submission Type	All types or specific categories	Focus on particular vehicle categories
User	All users or specific team members	Review individual performance

Applying Filters

Click Filter Button

Click the Filter button (funnel icon) near the date picker

Select Filter Type

Choose which dimension to filter: Location, Type, or User

Choose Values

Select one or more values to include

Apply Filters

Click Apply to update all charts

Filters apply to all charts on the page simultaneously. Active filters are shown as badges near the filter button.

Clearing Filters

To reset to the default view:

Click the Filter button
Click Clear All to remove all active filters
Or click the X on individual filter badges

Charts and Visualizations

Occupancy Trends

The Occupancy Trends chart shows how lot counts change over time, helping you identify patterns in vehicle volume.

Reading the Line Chart

Element	Description
X-Axis	Time periods (days, weeks, or months depending on range)
Y-Axis	Total car count across all submission entries
Line	Shows the trend of total vehicles over time
Data Points	Hover to see exact values for each period

Time Period Comparisons

The chart automatically adjusts granularity based on your selected range:

Selected Range	Chart Granularity
1-7 days	Hourly data points
8-30 days	Daily data points
31-90 days	Weekly data points
91+ days	Monthly data points

Identifying Trends

Upward Trend

Indication: Consistently increasing car counts over timePossible causes:

Increased inventory intake
Seasonal auction volume increase
New location or expanded capacity

Action: Ensure adequate staffing and lot capacity

Downward Trend

Indication: Consistently decreasing car counts over timePossible causes:

Successful sales clearing inventory
Reduced intake
Seasonal slowdown

Action: Review if trend is expected or needs investigation

Cyclical Pattern

Indication: Regular peaks and valleys on consistent intervalsPossible causes:

Weekly auction schedule (peaks before sales, valleys after)
Seasonal patterns (quarterly, monthly)

Action: Use patterns for staffing and capacity planning

Spike or Anomaly

Indication: Sudden increase or decrease outside normal rangePossible causes:

Large vehicle shipment arrival
Major auction event
Data entry error (investigate)

Action: Verify data accuracy, document cause if known

Submission Type Breakdown

Understand the distribution of vehicle categories across your submissions.

Pie Chart Interpretation

The pie chart shows the percentage distribution of vehicles by submission type:

Element	Meaning
Slice Size	Proportion of total car count for that type
Percentage Label	Exact percentage of total
Legend	Color key for each submission type
Hover	Shows exact count and percentage

Click on a pie slice to filter the page to that submission type and see detailed data.

Stacked Bar Charts

Stacked bar charts show type distribution over time:

Each bar represents a time period (day, week, or month)
Colored segments represent different submission types
Bar height indicates total volume for that period
Segment height shows volume per type

Type Distribution Analysis

Use this chart to answer questions like:

Question	How to Analyze
”What’s our most common vehicle status?”	Find the largest pie slice or tallest segment
”Has our mix of types changed over time?”	Compare segment proportions in the stacked bar chart
”Which types are growing or declining?”	Track segment sizes across time periods
”Are certain types concentrated at specific times?”	Look for patterns in when segments appear

Location Comparison

Compare performance metrics across your locations to identify high performers and areas needing attention.

Comparison Metrics

Metric	Description
Total Submissions	Number of lot count submissions per location
Total Cars	Total vehicles counted at each location
Average Submission Size	Average car count per submission
Submission Frequency	How often submissions are made

Identifying Outliers

Outliers may indicate issues requiring investigation or simply reflect legitimate differences in location size and volume.

Pattern	Possible Meaning
Location far below average	Lower volume, potential staffing issue, or data entry gap
Location far above average	Higher volume, busier lot, or multiple submitters
Sudden change from historical	New circumstances, staffing change, or data issue

Benchmarking Locations

Use location comparisons to:

Set expectations - Establish baseline metrics for each location
Identify best practices - High performers may have processes worth replicating
Allocate resources - Adjust staffing based on relative volume
Track improvement - Monitor changes after process improvements

When comparing locations, consider normalizing by capacity or staff count for fair comparisons between different-sized facilities.

User Activity

Track team member performance and engagement through the User Activity section.

Activity Leaderboard

The leaderboard ranks team members by submission activity:

Column	Description
Rank	Position based on selected metric
User	Team member name
Submissions	Number of submissions in period
Cars Counted	Total vehicles across all submissions
Last Active	Most recent submission timestamp

Submission Frequency

Understand how consistently team members submit data:

Metric	Calculation
Daily Average	Submissions per day when user is active
Active Days	Days with at least one submission
Consistency Score	Percentage of expected submissions completed

User Engagement Metrics

High Engagement

Indicators:

Regular, consistent submissions
Submissions during expected hours
Complete data entry (no missing fields)

Action: Recognize and potentially have them mentor others

Variable Engagement

Indicators:

Irregular submission patterns
Some days active, some days not
May be affected by schedule or assignment changes

Action: Review if pattern matches work schedule; clarify expectations

Low Engagement

Indicators:

Few submissions relative to expectations
Long gaps between activities
May indicate training needs or access issues

Action: Check in with user; verify location assignments and app access

Defect Trends

Track quality inspection patterns to identify common issues and improvement opportunities.

Quality Inspection Patterns

Visualization	Shows
Defect Category Bar Chart	Most common defect types found
Trend Line	Total defects over time
Heat Map	Defects by location and category

Most Common Defects

The defect category chart highlights which issues appear most frequently:

Defect Category	Example Issues
Windows	Streaks, spots, incomplete cleaning
Vacuuming	Debris, crumbs, incomplete coverage
Cupholders	Sticky residue, debris
Dashboard	Dust, fingerprints
Exterior	Water spots, bird droppings

Defect categories are configurable per auction and per location. Administrators can create custom criteria and assign specific criteria to each location via Settings > Quality Criteria. Analytics include both system and custom criteria for your auction.

Trend Analysis

Use defect trends to:

Identify systemic issues - Consistently high defects in one category
Measure improvement - Track defect reduction after process changes
Compare locations - Find which locations have quality challenges
Set targets - Establish defect reduction goals

KPI Impact Summary

Visualize the impact of A3 rock reports on key performance indicators.

A3 Rock Impact Visualization

Each A3 rock report tracks impact across five KPI categories:

KPI Category	What It Measures
Safety	Incidents, near-misses, compliance issues
Quality	Defects, errors, rework required
Delivery	Delays, missed deadlines, schedule impacts
Cost	Financial impact, additional expenses
Engagement	Team morale, process adherence

Category Breakdown

The KPI Impact chart shows:

Bar chart - Total impact per category across all rocks
Trend line - How KPI impacts have changed over time
Rock count - Number of rocks affecting each category

Use KPI impact data to prioritize which rocks to address first. High-impact categories may need immediate attention.

Data Tables

Beyond charts, the Analytics section provides detailed data tables for in-depth analysis.

Submissions Table

View all lot count submissions with detailed information.

Column Descriptions

Column	Description
Date/Time	When the submission was created
Location	Parking lot where submission was made
User	Team member who submitted
Entries	Number of submission type entries
Total Cars	Sum of car counts across all entries
Image	Thumbnail of attached photo (if any)

Multi-Entry Display

Each submission can contain multiple entries (one per submission type). The table shows:

Entry count badge - Total number of types included
Expandable row - Click to see individual entry details
Entry breakdown - Type name, car count, and entry image for each

Find Submission

Locate the submission row you want to inspect

Click to Expand

Click the row or the expand icon to reveal entries

View Entry Details

See each submission type entry with its individual count and image

Entry Images Viewing

When entries include photos:

Thumbnail - Small preview in the table row
Click thumbnail - Opens full-size image in lightbox
Lightbox controls - Navigate between images, zoom, download
Close - Click outside image or press Escape

Entry images help verify accuracy and provide visual documentation for auditing purposes.

Filtering Options

Filter	Options
Date Range	Select start and end dates
Location	Filter to specific locations
Submission Type	Show only entries of certain types
User	Filter by submitter
Has Image	Show only submissions with/without photos

Sorting

Click any column header to sort:

Click	Action
First click	Sort ascending (A-Z, oldest-newest)
Second click	Sort descending (Z-A, newest-oldest)
Third click	Clear sort, return to default

Quality Inspections Table

Review quality inspection records with defect details.

Viewing Defect Details

Find Inspection

Locate the inspection in the table

View Defect Count

The “Defects” column shows total issues found

Expand Row

Click to see individual defect categories and notes

View Photos

Click defect thumbnails to see inspection photos

VIN Search

Quickly find inspections for a specific vehicle:

Enter the full or partial VIN in the search box
Results filter instantly as you type
VIN search checks both the VIN field and barcode field

VIN search is case-insensitive and supports partial matching. Enter at least 4 characters for best results.

Date Filtering

Filter inspections by date range:

Use the same date picker as the main Analytics page
Or enter specific dates directly in the table filter
Inspections are shown newest-first by default

Rock Reports Table

Track A3 rock reports and their resolution status.

Status Filtering

Status	Meaning
Open	New rock, not yet being addressed
In Progress	Active work to resolve the rock
Closed	Rock resolved and verified

Use the status dropdown to filter the table by current status.

Action Tracking

Each rock report contains action items. The table shows:

Column	Description
Actions	Number of action items created
Completed	Actions marked as done
Pending	Outstanding actions needing completion
Overdue	Actions past their due date

Overdue actions are highlighted in red. Review these regularly to ensure timely rock resolution.

KPI Impact View

The table can display KPI impact scores:

Enable the “KPI Impact” column from column settings
See color-coded impact levels for each category
Sort by total impact to prioritize high-impact rocks

Exporting Data

Export analytics data for offline analysis, presentations, or integration with other tools.

CSV Export

Export tabular data in CSV format for spreadsheet analysis:

Navigate to Data Table

Go to the table you want to export (Submissions, Inspections, or Rocks)

Apply Filters

Set any filters to narrow your export (optional)

Click Export

Click the Export button above the table

Select CSV

Choose Export as CSV

Download

Your browser downloads the CSV file

What Data Is Included

CSV exports include:

Included	Not Included
All visible columns	Image files (URLs only)
All filtered rows	Deleted records
Formatted dates/times	Nested entry details (flattened)
Current sort order	User IDs (names only)

For multi-entry submissions, CSV export creates one row per entry, not per submission. This provides the most detailed data for analysis.

PNG Chart Export

Save chart visualizations as images:

Hover Over Chart

Move your mouse over the chart you want to export

Click Export Icon

Click the camera or download icon in the chart corner

Select PNG

Choose Download as PNG

Save File

Choose a location to save the image file

Chart Export Options

Option	Result
PNG	High-quality image suitable for presentations
SVG	Vector format for scalable graphics
Copy to Clipboard	Paste directly into documents

Exported charts include the current date range and any active filters in the image title for reference.

Best Practices

Effective Analytics Usage

Establish Baselines

Before trying to improve metrics:

Run analytics for a typical 30-day period
Note average values for key metrics
Document any seasonal factors
Use these baselines to measure future changes

Regular Review Cadence

Set up a regular review schedule:

Daily: Check quick stats for anomalies
Weekly: Review trends and user activity
Monthly: Deep-dive into location comparisons
Quarterly: Analyze long-term trends and set goals

Act on Insights

Analytics are only valuable when they drive action:

Identify patterns or issues in the data
Investigate root causes
Implement changes
Measure results in subsequent periods

Share with Stakeholders

Common Analysis Scenarios

Scenario	Recommended Approach
”Why did submissions drop yesterday?”	Filter to that day, check user activity, compare to typical pattern
”Which location needs more staffing?”	Compare submission volumes and frequencies across locations
”Are our quality issues improving?”	Look at defect trends over 30/60/90 days
”Who are our top performers?”	Review user activity leaderboard
”What vehicle types are increasing?”	Analyze submission type breakdown over time

Troubleshooting

Common Issues

Charts not loading

Solutions:

Check your internet connection
Refresh the page
Try a shorter date range (large ranges may be slow)
Clear browser cache and try again

Data seems incorrect

Solutions:

Verify your date range and filters are correct
Check if you’re viewing the right auction (use auction switcher)
Confirm the specific submissions exist in the data table
Contact support if discrepancy persists

Export not downloading

Solutions:

Check browser download settings
Disable popup blockers for this site
Try a different browser
Reduce the export size with filters if file is very large

Time periods don't match expectations

Solutions:

Verify your auction’s timezone setting
Check that your local computer time is correct
Note that day boundaries are based on auction timezone

Backlog Visibility

The Analytics module integrates with real-time backlog calculations to help you monitor staffing effectiveness.

Backlog in Analytics

Feature	Description
Dashboard Card	Quick backlog status summary on the main dashboard
Submissions Table	Backlog column showing hours per submission
Query Builder	Ask questions about backlog trends and patterns

Backlog Queries

Use the Query Builder to analyze backlog data:

“Show average backlog hours by location this week”
“What’s the trend of backlog over the last 30 days?”
“Which submission types have the highest backlog?”
“Compare head count to backlog hours”

For detailed backlog configuration and understanding, see the dedicated Backlog Dashboard documentation.

Dataset Intelligence

The Analytics module includes powerful tools for enriching your datasets with business context, enabling more accurate AI-powered queries.

Column Intelligence Features

Feature	Purpose	Documentation
Column Metadata	Add display names, descriptions, and categories to help the AI understand your data	Column Metadata
Calculated Fields	Create derived columns with SQL formulas	Calculated Fields
Metadata Templates	Apply pre-configured metadata for common data sources	Metadata Templates

Why Configure Metadata?

When you import a CSV, the Query Builder sees column names like Inspector_Detail_Date_stop_date. Without context, the AI doesn’t know this represents “CR completion date.” By adding metadata:

Display names make columns human-readable
Descriptions provide business context for accurate AI queries
Categories group related columns together
Calculated fields derive new values from existing data

Well-configured metadata dramatically improves Query Builder accuracy. See the Column Metadata guide to get started.

Templates for Common Data Sources

For CR Simplified reports and other common data sources, use pre-built templates that automatically configure:

48-72 column definitions with automotive-specific descriptions
Calculated fields like days_since_checkin, days_to_cr, cr_time_minutes
Smart auto-detection during CSV import

See Metadata Templates for available system templates and how to create custom templates.

Engagement Analytics

The Engagement Analytics dashboard provides administrators with visibility into team member activity and participation trends across the auction.

Accessing Engagement Analytics

Navigate to Analytics in the sidebar, then select the Engagement tab. The dashboard is available to owners and admins.

Summary Cards

The top of the dashboard displays key metrics:

Card	Description
Active Users	Number of users active in the current period
Average Streak	Mean login streak length across team members
Badges Earned	Total badges awarded in the current period
Video Completion	Average video completion percentage

Engagement Trend Charts

Five time-series charts visualize engagement patterns:

Chart	Type	What It Shows
Daily Active Users	Line	Number of unique users active per day
Submissions per Day	Area	Daily lot submission volume
Inspections per Day	Area	Daily quality inspection volume
Video Completions per Week	Bar	Weekly knowledge base video completions
Average Streak Length per Week	Line	Weekly average login streak across team

Use the time range selector (30, 60, or 90 days) to adjust the analysis window.

Per-User Engagement View

On the user detail page (accessed from User Management), an Engagement section shows:

Streak overview cards — current and longest streaks per type
Earned badges grid — all badges the user has earned with dates
Activity timeline — 90-day daily activity from engagement_daily_stats
Leaderboard position — user’s rank per metric for the current month

Monthly Leaderboard

The leaderboard tab shows team rankings for the current month. The top 3 users per metric are highlighted. Select different metrics (submissions, inspections, login streak) using the tab selector.

Custom Objects

Define custom data types and query them in the Query Builder

Data Model

Create associations between objects for cross-object joins

Column Metadata

Configure column display names, descriptions, and categories

Calculated Fields

Create derived columns with SQL formulas

Metadata Templates

Apply pre-configured metadata for common data sources

Backlog Dashboard

Real-time backlog visibility and status thresholds

Staffing Dashboard

Detailed guide for the Staffing Model Dashboard within Analytics

Saved Queries

Complete guide for creating and sharing queries

Need Help?

Getting Started

Complete admin dashboard overview

Contact Support

Reach out to our support team

Getting Started

User & Location Management

Operations

Analytics Module

Account

Data Management

Configuration

​Overview

Query Builder

Staffing Dashboard

Saved Queries

Backlog Dashboard

Engagement Analytics

​Accessing the Analytics Module

​Navigation

​URL Structure

​Query Builder

​How It Works

​Example Queries

​Cross-Object Joins

​Data Source Selector

​Data Source Icons

​Ask AI Button Integration

​Analytics Dashboard

​Dashboard Sections

​Dashboard Sections

​Quick Stats Cards

​Understanding Quick Stats

​Reading Quick Stat Trends

​Clicking Quick Stats

​Time Period Selection

​Using the Date Picker

​Preset Time Ranges

​Custom Date Range

​Filtering Capabilities

​Filter Options

​Applying Filters

​Clearing Filters

​Charts and Visualizations

​Occupancy Trends

​Reading the Line Chart

​Time Period Comparisons

​Identifying Trends

​Submission Type Breakdown

​Pie Chart Interpretation

​Stacked Bar Charts

​Type Distribution Analysis

​Location Comparison

​Comparison Metrics

​Identifying Outliers

​Benchmarking Locations

​User Activity

​Activity Leaderboard

​Submission Frequency

​User Engagement Metrics

​Defect Trends

​Quality Inspection Patterns

​Most Common Defects

​Trend Analysis

​KPI Impact Summary

​A3 Rock Impact Visualization

​Category Breakdown

​Data Tables

​Submissions Table

​Column Descriptions

​Multi-Entry Display

​Entry Images Viewing

​Filtering Options

​Sorting

​Quality Inspections Table

​Viewing Defect Details

​VIN Search

​Date Filtering

​Rock Reports Table

​Status Filtering

​Action Tracking

​KPI Impact View

​Exporting Data

​CSV Export

​What Data Is Included

Overview

Accessing the Analytics Module

Navigation

URL Structure

Query Builder

How It Works

Example Queries

Cross-Object Joins

Data Source Selector

Data Source Icons

Ask AI Button Integration

Analytics Dashboard

Dashboard Sections

Dashboard Sections

Quick Stats Cards

Understanding Quick Stats

Reading Quick Stat Trends

Clicking Quick Stats

Time Period Selection

Using the Date Picker

Preset Time Ranges

Custom Date Range

Filtering Capabilities

Filter Options

Applying Filters

Clearing Filters

Charts and Visualizations

Occupancy Trends

Reading the Line Chart

Time Period Comparisons

Identifying Trends

Submission Type Breakdown

Pie Chart Interpretation

Stacked Bar Charts

Type Distribution Analysis

Location Comparison

Comparison Metrics

Identifying Outliers

Benchmarking Locations

User Activity

Activity Leaderboard

Submission Frequency

User Engagement Metrics

Defect Trends

Quality Inspection Patterns

Most Common Defects

Trend Analysis

KPI Impact Summary

A3 Rock Impact Visualization

Category Breakdown

Data Tables

Submissions Table

Column Descriptions

Multi-Entry Display

Entry Images Viewing

Filtering Options

Sorting

Quality Inspections Table

Viewing Defect Details

VIN Search

Date Filtering

Rock Reports Table

Status Filtering

Action Tracking

KPI Impact View

Exporting Data

CSV Export

What Data Is Included

PNG Chart Export

Chart Export Options

Best Practices

Effective Analytics Usage

Common Analysis Scenarios

Troubleshooting

Common Issues

Backlog Visibility

Backlog in Analytics

Backlog Queries

Dataset Intelligence

Column Intelligence Features