This is the support page for the Linear Programming analytics feature on Slate Analytics. It provides partners with an overview of the feature, metric definitions, data methodology, dashboard navigation guidance, and answers to frequently asked questions.
Purpose and Product Description
Linear Programming on Slate Analytics is a program-level analytics experience that gives Prime Video Channel and FAST (Free Ad-Supported Streaming Television) partners granular insights into the exact programs and live events that customers are viewing on their linear stations. Previously, partners only had access to station-level data — Linear Programming breaks this down to the individual program level, enabling data-driven decisions across program scheduling, content acquisition, and marketing.
Key Benefits
- Program-level granularity —See exactly which programs customers are watching on your linear stations, not just aggregate station metrics.
- Scheduling optimization —Identify peak viewing hours and top-performing programs to optimize your station’s content schedule and airing times.
- Content acquisition insights —Understand which program genres and formats drive the most engagement to inform content licensing and acquisition decisions.
- Marketing intelligence —Discover your highest-performing programs to create targeted marketing campaigns and promotional materials.
- Fast data delivery —Access program-level data with more than 95% program-level dataless than 24 hours from the time a stream occurs.
- Data accuracy—Over 95% match rate with station-level. Total hours watched calculated through the Playback datasets API and dashboard, ensuring confidence in reporting. The remaining 5% variance falls within our expected range due to program schedule matching data quality. See FAQs.
Access Methods
Three access methods are available for Linear Programming data. Partners should use the method that best fits their workflow:
Option 1: Linear Dashboard |
Option 2: Analytics AI Assistant(Coming Q3 2026) |
Option 3: Slate Datasets API(Coming Q3 2026) |
|
|---|---|---|---|
Best for |
Visual analysis, quick insights, executive reporting |
Natural language questions, ad-hoc exploration |
Enterprise reporting, internal BI systems, programmatic access |
Access |
Insights→Analytics → Linear Dashboard |
Insights→ AI Assistant |
Slate Datasets API endpoint |
Data format |
Interactive visualizations and tables |
Conversational answers with data |
Customer-level viewing data (JSON/CSV) |
Getting Started
Prerequisites
- Active Prime Video Channel or FAST partnership (coming Q3 2026)
- Slate Analytics access (contact your CAM if not yet onboarded)
- Linear station must be live on Prime Video
Accessing the Linear Dashboard
- Log in to Slate (https://slate.amazon.com)
- Navigate to Insights then Analytics from the main navigation
- Select the Linear dashboard
- Use the global filters to select your station, date range, and territory
First-Time User Guide
When you first visit the Linear Dashboard, you will be presented with a guided walkthrough and demo video that covers:
- How to navigate the dashboard
- How to use filters and interactivity features
- How to interpret metric cards and visualizations
- How to drill into program-level details
Dashboard Features
Metric Cards
The Linear Dashboard displays four metric cards at the top of the page:
Metric |
Definition |
|---|---|
Total hours watched |
Total hours of content viewed across all programs on the selected station(s) during the selected time period |
Unique customers (Channels only) |
Count of distinct customers who watched at least one program on the selected station(s) during the selected time period |
Hours per customer (HPC) |
Average hours viewed per unique customer (Total hours watched ÷ Unique customers) |
Visualizations
The dashboard includes the following visualizations:
Visualization |
Description |
|---|---|
Viewership daily trends |
A time-series chart showing daily engagement trends for Total hours watched and Unique customers over the selected date range |
Top 10 programs |
A ranked chart of the top 10 programs, sortable by Total hours watched or Unique customers |
Program data table |
A detailed table with all program and airing-level fields (see Section 4: Data Definitions for the full field list) |
Interactivity & Filters
Partners can interact with the dashboard using the following controls:
- Program filter — Select a program name from the Top 10 chart or data table to filter the entire dashboard for that program’s historical data
- Global filters — Apply filters across the entire dashboard for: Date range, Channel name, Station name, Territory, Day of Week (e.g., Mon, Tues, Wed, Thu, Fri, Sat, Sun)
General Dashboard Controls
The following controls and indicators are common across all Slate Analytics dashboards, including the Linear Dashboard.
- Last Updated indicator — The “Last Updated” tag at the top of the dashboard shows how recently your data was refreshed on the page. Data typically refreshes 4–6 times per day. Note: If the Last Updated time exceeds 24 hours, the Prime Video Slate team is working to resolve an issue. Check back later for updated data.
- Date grouping — Viewing data is grouped by the date on which the viewing event occurred for the customer, anchored to the customer’s local date. For example, if a customer in the PST time zone watches at 11 PM on January 1, that viewing session is anchored to January 1. If a customer in the EST time zone watches at 2 AM on January 2, that session is anchored to January 2.
- Date Range filter — The Date Range dropdown enables you to select the date range for all data shown on the dashboard. All metric cards, visualizations, and data tables update to reflect the selected range.
- Channel/Station filter — Stations are grouped under a single Channel for ease of analysis. The Channel/Station dropdown allows you to filter and multi-select the stations you want to view. Use this control to narrow your analysis to specific stations or compare performance across multiple stations.
Data Methodology
How Data is Collected
Linear Programming data is derived by matching program schedules with streaming event data.
The process works as follows:
- Streaming events — Prime Video captures playback segment data each time a customer watches content on a linear station.
- Program schedule matching — Streaming events are matched against the station’s program schedule to attribute viewing time to specific programs.
- Enrichment — Matched data is enriched with program metadata (title, genre, airing times) and customer engagement metrics.
- Aggregation — Data is aggregated at the program level and made available through the dashboard, AI Assistant, and API.
Data Freshness
Attribute |
Target |
|---|---|
Data freshness |
95% precision < 24 hours |
Definition |
Time between a stream occurring and when the stream’s data is available to the partner |
Refresh cadence |
Intraday refreshes up to 4x per day |
Data Accuracy
Attribute |
Target |
|---|---|
Match rate |
> 95% with station-level Total hours watched |
Why < 100% |
The program-level dataset is derived by matching program schedules with streaming event data. A small percentage of streams may not match to a specific program due to schedule gaps |
Note: Because program-level data is derived from schedule matching, there may be minor discrepancies between program-level totals and station-level totals reported. This is expected behavior and does not indicate a data quality issue.
Data Definitions
Core Fields
The following fields are available in the Linear Programming data table and API
Field |
Definition |
Example |
|---|---|---|
Station |
The name of the linear station |
Amazon News, Amazon Sports, MGM Entertainment |
Program name |
The title of the program as provided in the station’s schedule |
“Morning News Hour” |
Airing start time |
The scheduled start time of the program (UTC) |
2026-06-15T14:00:00Z |
Airing end time |
The scheduled end time of the program (UTC) |
2026-06-15T15:00:00Z |
Territory |
The territory/market where the viewing occurred |
US |
Date |
The calendar date of the airing |
2026-06-15 |
Day of week |
The day of the week of the airing |
Monday |
Total hours watched |
Total hours viewed for this program airing |
1,250.5 |
Unique customers |
Distinct customers who watched this program airing |
3,200 |
Hours per customer (HPC) |
Average hours viewed per customer for this airing |
0.39 |
Channel name |
The channel or partner name associated with the station |
Amazon |
Metric Calculation Notes
Metric |
Calculation |
Notes |
|---|---|---|
Total hours watched |
Sum of all playback segment durations attributed to the program |
Expressed in decimal hours |
Unique customers |
Count of distinct customer IDs attributed to the program airing |
Deduplicated at the customer level |
Hours per customer |
Total hours watched ÷ Unique customers |
Rounded to 2 decimal places |
Analytics AI Assistant (Coming Q3 2026)
Overview
The Slate Analytics AI Assistant is enriched with knowledge and data for Linear Programming. Partners can ask natural language questions about their linear station performance and receive instant, data-driven answers.
Sample Questions
The following are examples of questions you can ask the AI Assistant about your Linear Programming data:
- How many unique viewers tuned into my Linear station this week, and how does that compare to last week?
- What are the peak viewing hours for my Linear station, and how do they vary by day of the week?
- Which programs on my Linear station have the highest average concurrent viewership over the past 30 days?
- What is the average watch time per viewer session on my Linear station, and how has it trended over the past quarter?
- Which geographic regions or markets are driving the most viewership for my Linear station?
Slate Datasets API (Coming Q3 2026)
Overview
Partners with API access can retrieve customer-level Linear Programming viewing data programmatically via the Slate Datasets API. This enables integration with internal BI systems, custom reporting pipelines, and enterprise analytics platforms.
API Data Availability
Attribute |
Details |
|---|---|
Dataset name |
Linear Programming |
Data granularity |
Customer-level viewing data |
Delivery method |
Slate Datasets API (incremental program-level delivery) |
Authentication |
Standard Slate API authentication |
Use Cases
- Building internal enterprise reporting for your linear business
- Integrating program-level data with your existing scheduling systems
- Creating custom dashboards and reports for internal stakeholders
- Combining Linear Programming data with other business data for cross-functional analysis
Quality Standards
Data Quality Targets
Quality Dimension |
Target |
Measurement |
|---|---|---|
Freshness |
95% program level data < 24 hours |
Time from stream occurrence to data availability |
Accuracy |
> 95% match rate |
Variance between Program-level Hours watched vs. station-level Hours watched available in Playback dashboard and datasets API |
Schedule completeness |
>95% schedule coverage |
Percentage of streams successfully matched to a program |
Known Limitations
Limitation |
Description |
Impact |
|---|---|---|
Schedule gaps |
If a station’s program schedule has gaps or missing entries, streams during those periods cannot be attributed to a specific program |
May result in < 100% match rate |
Program boundary streams |
Streams that span two consecutive programs are attributed to the program airing at the stream’s start time |
Minor attribution variance at program transitions |
Late-arriving schedule data |
Schedule corrections or late submissions may cause temporary data gaps |
Self-healing: data is automatically updated on subsequent refreshes |
