How do I set up a listener analytics dashboard for episode-level performance across a podcast network?

To set up a listener analytics dashboard for episode-level performance across a podcast network, start by defining the decisions the dashboard should support, then build a data pipeline that standardizes episode, show, and network metrics from your hosting platform(s), analytics vendor(s), and marketing sources.

1) Define the core use cases

Common questions a network dashboard should answer:

• Which episodes perform best by downloads, listeners, starts, completion rate, and retention?
• How do episodes perform by show, host, genre, release day, and publish time?
• What is the growth trend week over week and month over month?
• Which episodes drive subscribes/follows, conversions, ad impressions, and revenue?
• How do performance metrics compare across platforms, geographies, and devices?

2) Standardize the data model

Create consistent identifiers for:

• Network
• Show
• Episode
• Publisher/host
• Release date/time
• Season
• Category/genre
• Campaign/source if applicable

A good schema usually includes:

• episode_id
• show_id
• network_id
• episode_title
• publish_datetime
• duration_seconds
• download_count
• unique_listeners
• starts
• completes
• completion_rate
• avg_consumption
• day_1, day_7, day_28 performance
• traffic_source
• platform
• country
• device_type

3) Pull data from the right sources

Typical inputs:

• Podcast hosting/analytics platform: downloads, listeners, starts, retention, geography
• Ad server / monetization platform: impressions, fill, CPM, revenue
• CRM / marketing tools: email clicks, referrals, campaign attribution
• Web analytics: site traffic to episode pages
• RSS/feed logs if you need deeper feed-level reporting

If you have multiple hosting platforms across the network, normalize them into one central warehouse.

4) Build the warehouse layer

Use a data warehouse like:

• BigQuery
• Snowflake
• Redshift
• Postgres for smaller setups

Create a pipeline that:

1. Extracts raw data from each source
2. Transforms it into a unified episode-level table
3. Aggregates it for dashboard queries
4. Refreshes daily or hourly, depending on need

A useful approach is:

• Raw tables for source-specific data
• Staging tables for cleaned and standardized fields
• Mart tables for dashboard-ready metrics

5) Choose the right KPIs

For episode-level analytics, the most useful KPIs are:

• Downloads
• Unique listeners
• Starts
• Completions
• Completion rate
• Average consumption / retention
• Day-1, day-7, day-28 performance
• Subscriber/follower growth
• Revenue per episode
• Ad impressions per episode
• Geo distribution
• Platform/device split

If you care about content quality, retention metrics are often more useful than downloads alone.

6) Design the dashboard views

Include these views:

Network overview

• Total downloads/listeners
• Trend lines over time
• Top shows and top episodes
• New vs returning audience
• Revenue summary

Show performance

• Rankings by show
• Average episode performance
• Growth over time
• Audience composition

Episode detail

• Downloads over time since publish
• Retention curve
• Platform and geography breakdown
• Comparison vs show average
• Traffic source breakdown

Publishing performance

• Best day/time to publish
• Episode length vs completion
• Topic/category performance
• Host or talent performance

7) Add comparisons and context

Raw numbers can be misleading. Add:

• Per-show benchmarks
• Rolling averages
• Percent change vs prior episode
• Performance normalized by release age
  For example: compare episodes at 24 hours, 7 days, and 28 days after publish
• Seasonality adjustments

This lets you compare a new episode fairly against older ones.

8) Make it filterable

Let users filter by:

• Date range
• Show
• Host
• Season
• Episode type
• Genre
• Platform
• Geography
• Campaign

This is essential for a network with many shows.

9) Pick visualization types

Use:

• Line charts for trends over time
• Bar charts for top episodes/shows
• Retention curves for consumption
• Heatmaps for publish time/day performance
• Tables with sortable columns for episode rankings
• Geo maps for regional listening

10) Automate refreshes and alerts

Set:

• Daily refresh for standard reporting
• Hourly refresh if teams need near-real-time monitoring

Add alerts for:

• Unusual drops in downloads
• Spike detection
• Episodes underperforming after 24 hours
• Ad inventory changes
• Tracking failures or missing data

11) Ensure governance and consistency

Since it’s across a network, define:

• A single metric glossary
• Standard attribution rules
• One source of truth for episode metadata
• Access control for show-level vs network-level users
• Data quality checks for duplicate episodes, missing IDs, or date mismatches

12) Tools you can use

A common stack:

• Ingest/ELT: Fivetran, Airbyte, custom scripts
• Warehouse: Snowflake, BigQuery
• Transformations: dbt
• BI/dashboard: Looker, Tableau, Power BI, Mode, Metabase
• Orchestration: Airflow, Dagster, Prefect

13) Suggested dashboard layout

A practical first version:

1. Top KPI row

   • Downloads
   • Unique listeners
   • Starts
   • Completion rate
   • Revenue

2. Trend section

   • Daily downloads/listeners
   • Week-over-week growth

3. Ranking section

   • Top 10 episodes
   • Top 10 shows

4. Performance breakdown

   • By platform
   • By geography
   • By release age

5. Episode deep dive

   • Individual episode selector
   • Retention curve
   • Comparison to show average

14) Build iteratively

Start with:

• One warehouse table for episodes
• One dashboard for network overview
• One drill-down page for episode detail

Then expand to:

• monetization
• audience segmentation
• attribution
• predictive insights

If you want, I can also provide:

1. a sample dashboard wireframe,
2. a database schema for episode analytics, or
3. a SQL/dbt model example for the metrics.