Metrics, Dimensions & Filters
This page explains the core concepts that power everything you do in Watching That — across Reports, Analysis, Inspect, and Monitoring. These concepts apply regardless of which data source you're connected to.
On This Page
Events, Metrics, and Dimensions — how raw data becomes things you can measure and slice
Three Types of Metrics — base, derived, and conditional
Dimensions — the fields you break metrics down by
Filters — narrowing data at report, block, analysis, and monitor levels
Putting It Together — the two-axis mental model
Configuration and Availability — what determines what you see
Data Source References — integration-specific docs
Events, Metrics, and Dimensions
Watching That ingests data as a continuous stream of events. Whether the source is ad server log files, bid-level logs, pixel tracking, or API feeds, the data arrives as individual event records — an impression served, a request made, an error fired, a bid received, and so on.
Two things determine what you can measure and how you can slice it:
Event types → Metrics. Each event type you enable unlocks a set of metrics — what you can count and calculate. More event types = more metrics, especially derived metrics that combine data across event types.
Fields → Dimensions. Each field you enable becomes a dimension — what you can break metrics down by in reports, analysis, and monitoring.
Your specific configuration determines which event types and fields are active for your organisation. Not every metric or dimension will appear in your account — contact your account team if you need something that isn't currently enabled.
Three Types of Metrics
Base Metrics — direct counts
One event in, one count out.
Example: Every impression event increments Gross Impressions. Every request event increments Ad Requests. Every error event increments Errors.
Base metrics require only their parent event type to be enabled.
Derived Metrics — calculated from base metrics
Rates, ratios, averages, and aggregations — often combining data from multiple event types.
Example: Fill Rate (%) divides filled ad slots by total ad slots, requiring both availability and impression event types. Error Rate (%) requires both error and request event types.
Derived metrics unlock progressively as you enable more event types. You can also create your own derived metrics using formulas in Reports and Analysis — the standard ones are just the starting point.
Conditional Metrics — filtered by a dimension value
A subset of a base or derived metric, isolated by a condition.
Example: Gross Invalid Impressions = Gross Impressions where the invalid traffic flag is set. Impressions With Errors = impressions that had associated error events.
Conditional metrics require their parent event types plus the relevant dimension fields to be active.
Dimensions
Dimensions are the fields you break your metrics down by — the rows and columns in your reports and analyses.
Every field your data source provides and your configuration enables is a potential dimension. Common categories:
Category | Examples |
|---|---|
Content & Inventory | What content was playing, which site/section/placement |
Ad Details | Creative IDs, ad unit types, position (pre/mid/postroll), CPM |
Ad Break & Slot | Break structure, slot position, fill capacity, unfilled time |
User & Device | Country, region, device type, platform/player, user agent |
Traffic Quality | Invalid traffic flags and reason codes |
Partners & Value Chain | Distribution partners, selling partners, content providers |
Marketplace & Programmatic | Deal IDs, buyer seats, sales channel type |
Audience & Targeting | Audience segments, matched targeting items, privacy choices |
Which specific dimensions are available depends on your data source and configuration. See your integration's field reference for the complete list.
Filters
Filters narrow the data that metrics and dimensions operate on. You can apply them at multiple levels:
Level | Scope | Example |
|---|---|---|
Report-level | Every visualisation in a report |
|
Block-level | A single chart or table within a report |
|
Analysis | Applied interactively during ad-hoc exploration | Add and remove filters as you investigate |
Monitor | Defines the scope of an anomaly or validation monitor | Monitor error rate only for |
Filters use dimensions as their criteria. They can be combined (AND logic) and many dimensions support multi-value selection (e.g., country IN [US, GB, AU]).
Putting It Together
Think of it as two axes:
Vertical (metrics): What do you want to measure? Impressions? Fill rates? Revenue? Error rates? Each requires specific event types.
Horizontal (dimensions): How do you want to slice it? By device? By partner? By geography? Each requires specific fields.
The intersection — metrics broken down by dimensions, narrowed by filters — is what powers Reports, Analysis, Inspect, and Monitoring.
Configuration and Availability
Most organisations start with a core set of event types and dimensions, then extend over time. What's available to you depends on:
Factor | What it determines |
|---|---|
Your data source(s) | Different integrations provide different event types and fields |
Your configuration | Which event types and fields are enabled for your organisation |
Cross-event dependencies | Some derived metrics require multiple event types to be active |
Your account team can walk you through what's currently active and what's available to add.
Data Source References
For the specific metrics and dimensions available per integration:
Integration | What's documented |
|---|---|
Event types, metrics catalogue, field reference for FreeWheel v4 log file customers | |
Complete standard metrics catalogue organised by event type | |
Step-by-step setup walkthrough with day-one examples |