Scoop is optimized for analyzing data over time. Comparing metrics across periods reveals whether things are improving, declining, or holding steady—essential insights for business performance. Scoop's time series capabilities go far beyond basic line charts, offering sophisticated date handling, period comparisons, and multi-date analysis that would require extensive coding in traditional BI tools.

Quick Reference

Feature	Purpose	Access
Period Frequency	Daily, weekly, monthly, quarterly, yearly aggregation	Time dropdown
Period End Type	Rolling (relative to today) vs Calendar (fixed dates)	Time settings
Date Selection	Which date column to analyze by	Metric settings
Time Range	How far back to show data	Range selector
Period Comparison	Compare current vs prior periods	Comparison toggle

Time Concepts in Scoop

Every dataset has multiple ways to analyze time. Understanding these unlocks Scoop's full analytical power.

Types of Dates

Date Type	Description	Example
Snapshot Date	When data was captured/ingested	Report run date
Entity Dates	Dates on individual records	Created date, close date
Calculated Dates	Derived from other fields	Days since last activity

Example: Sales Pipeline Dataset

A sales opportunities report ingested daily has three natural time dimensions:

Date	What It Represents	Analysis Use
Snapshot Date	Date each report was uploaded	Pipeline value over time
Created Date	When opportunity was created	New business generation
Close Date	Expected close date	Forecasting, pipeline by expected timing

Key Insight: Scoop tracks all these dates and lets you analyze the same dataset by any of them—no data transformation needed.

Creating Time Series Charts

Step 1: Select a Metric

Click any numeric column to visualize it. Scoop automatically creates a time series:

Action	Result
Click "Amount"	Shows Amount by Snapshot Date, daily
Click "Count"	Shows record count by Snapshot Date, daily
Click any measure	Time series with smart defaults

Step 2: Adjust Period Frequency

Choose how to aggregate time:

Period	Best For	Example Output
Daily	Short-term tracking, operational data	Apr 1, Apr 2, Apr 3...
Weekly	Medium-term trends, less noise	Week of Apr 1, Week of Apr 8...
Monthly	Business reporting, standard KPIs	January, February, March...
Quarterly	Executive reporting, seasonal analysis	Q1 2024, Q2 2024...
Yearly	Long-term trends, year-over-year	2022, 2023, 2024...

Step 3: Set Time Range

Control how much history to display:

Range	Shows	Use Case
Last Day	Most recent 24 hours	Real-time monitoring
Last Week	Last 7 days	Weekly reviews
Last Month	Last 30 days	Monthly reporting
Last Quarter	Last 90 days	Quarterly business reviews
Last Year	Last 365 days	Annual trends
Custom	Any date range	Specific analysis periods

Period End Types

This critical setting affects how periods are calculated.

Rolling Periods

Characteristic	Description
Definition	Periods relative to today
Example	"Last month" = 30 days ago to today
Benefit	No partial periods at edges
Dates shown	Rolling end dates (not calendar boundaries)

When to use: Default choice for most analysis. Ensures you're always comparing complete periods.

Calendar Periods

Characteristic	Description
Definition	Fixed calendar boundaries
Example	"Last month" = Jan 1 to Jan 31
Benefit	Matches standard business reporting
Dates shown	Calendar month/quarter/year ends

When to use: When comparing to financial reports, budgets, or external benchmarks that use calendar periods.

Choosing the Right Period Type

Scenario	Recommended	Why
Daily operational review	Rolling	Avoids partial current period
Month-end financial report	Calendar	Matches accounting periods
Executive dashboard	Rolling	Always current, complete periods
Budget comparison	Calendar	Budgets are calendar-based
Trend analysis	Rolling	Smoother comparisons

Grouping Data by Attributes

Add dimensions to see how metrics break down over time.

Adding a Group By

Step	Action	Result
1	Click "Group By" dropdown	Shows available dimensions
2	Select dimension (e.g., Stage)	Stacked/grouped time series
3	Optionally filter	Focus on relevant groups

Example: Pipeline by Stage Over Time

Group Amount by Stage to see pipeline health:

What You See	What It Tells You
Stage composition	Which stages hold most value
Stage trends	Are early stages growing?
Stage balance	Healthy mix vs. concentration

Common Grouping Combinations

Metric	Group By	Insight
Revenue	Product	Product mix over time
Deals	Sales Rep	Rep performance trends
Tickets	Category	Issue type evolution
Pipeline	Stage	Sales funnel health
Users	Region	Geographic growth

Changing the Analysis Date

Switch which date drives the time axis.

Accessing Date Selection

Step	Action
1	Find your metric in the panel
2	Click the three dots (⋮) menu
3	Select "Change Date Column"
4	Choose desired date

Understanding Different Date Analyses

Date Type	What You Analyze	Typical Values
Snapshot Date	State at each point in time	Higher (cumulative view)
Entity Dates	Current state of records by that date	Lower (deduplicated)

Why values differ:

Snapshot analysis counts a deal in every snapshot it appears
Entity date analysis counts each deal once (its current state)

Example: Three Views of the Same Data

Using the same sales opportunities dataset:

Analysis Date	Shows	Sample Insight
Snapshot Date	Pipeline value over time	"Pipeline grew from $10M to $18M this year"
Created Date	New opportunities by creation	"Created $3M in new deals this month"
Close Date	Expected revenue by timing	"Have $5M forecasted to close in Q2"

Time Comparison Features

Period-over-Period Comparisons

Compare current period to previous:

Comparison	Formula	Use Case
Month over Month	This month vs. last month	Monthly progress
Quarter over Quarter	This Q vs. last Q	Quarterly trends
Year over Year	This period vs. same period last year	Annual trends, seasonality

Creating Comparison Charts

Method	How
Dual axis	Add same metric twice, shift one by period
Calculated KPI	Create "vs. Prior Period" compound KPI
Side-by-side	Two charts with different date ranges

See Creating KPIs for building time-shifted metrics.

Advanced Time Series Techniques

Cumulative vs. Point-in-Time

View	Shows	Example
Point-in-time	Value at each period	Monthly revenue
Cumulative	Running total	YTD revenue

Moving Averages

Smooth out noise with rolling calculations:

Average	Benefit
7-day moving average	Smooth daily volatility
4-week moving average	Smooth weekly patterns
3-month moving average	Identify true trends

Seasonality Analysis

Technique	Purpose
Year-over-year overlay	See seasonal patterns
Same period comparison	Compare like periods
Seasonal indexing	Normalize for seasonality

Chart Type Selection for Time Data

Chart Type	Best For	Example
Line	Trends, multiple series	Revenue over 12 months
Area	Cumulative values, composition	Stacked pipeline by stage
Column	Period comparisons	Monthly vs. monthly
Combo	Different metrics together	Revenue (bars) + margin % (line)

When to Use Each

Scenario	Recommended Chart
Single metric trend	Line
Part-to-whole over time	Stacked Area
Discrete period values	Column
Two scales (value + rate)	Combo with dual axis
Many series comparison	Line (limit to 5-7)

Best Practices

Data Preparation

Practice	Why
Consistent date formats	Accurate time parsing
Complete time coverage	No gaps in trends
Appropriate granularity	Enough data points per period

Visualization Design

Practice	Why
Start Y-axis at zero	Accurate perception of change
Limit series to 5-7	Visual clarity
Use consistent colors	Easy tracking across charts
Add annotations	Highlight significant events

Analysis Approach

Practice	Why
Check data completeness	Partial periods mislead
Consider seasonality	Don't compare Dec to Jan naively
Use rolling periods	Avoid partial period issues
Combine with grouping	Understand drivers of trends

Troubleshooting

Chart Shows Gaps

Cause	Solution
Missing data periods	Check data ingestion schedule
Filtered out values	Review filter settings
Date parsing issues	Verify date column format

Unexpected Values

Issue	Check
Values too high	May be using snapshot date (counting duplicates)
Values too low	May be using entity date (deduplicated)
Wrong aggregation	Verify sum vs. count vs. average

Period Alignment Issues

Issue	Solution
Can't compare periods	Use same period type (both rolling or calendar)
Partial current period	Switch to rolling periods
Dates don't match reports	Verify calendar vs. rolling setting

Performance with Large Date Ranges

Issue	Solution
Slow rendering	Reduce time range or increase period
Too many data points	Use weekly instead of daily
Chart too dense	Limit series or time range

Common Time Series Patterns

Sales Performance

Metric	Date	Period	Insight
Revenue	Snapshot	Monthly, Rolling	Monthly bookings trend
Pipeline	Snapshot	Weekly	Pipeline health tracking
Win Rate	Close Date	Quarterly	Closing efficiency

Customer Analytics

Metric	Date	Period	Insight
Active Users	Event Date	Daily	Engagement trends
New Signups	Created Date	Weekly	Growth rate
Churn	Cancelled Date	Monthly	Retention health

Operations

Metric	Date	Period	Insight
Tickets	Created Date	Daily	Support volume
Resolution Time	Resolved Date	Weekly	Efficiency trends
SLA Compliance	Due Date	Monthly	Performance tracking