The Analytics Power Hour – Episode 282 Summary

Using (and Creating!) Data to Understand Pop Culture with Chris Dalla Riva

Release Date: October 14, 2025
Hosts: Tim Wilson, Julie Hoyer, Val Kroll
Guest: Chris Dalla Riva, Senior Product Manager (Data & Personalization at Audiomack), music analytics newsletter author ("Can’t Get Much Higher"), musician, and forthcoming author ("Uncharted Territory").

Episode Overview

This episode delves into how data can illuminate trends in pop culture—particularly music—using guest Chris Dalla Riva’s epic side project as an example. Chris developed a massive, hand-crafted dataset by listening to every Billboard #1 song since 1958, analyzed patterns from musical trends to cultural inflection points, and distilled lessons about both the art of data creation and the squishy reality of measuring human interests. The conversation explores the practical, philosophical, and sometimes comical realities of tracking pop phenomena through data.

Key Discussion Points & Insights

1. The Genesis of a Personal Data Odyssey (03:23–06:11)

• Chris’s Journey: Started as a nightly wind-down by learning and rating #1 hits with a friend, evolved into a sprawling spreadsheet and ultimately, a book and newsletter.
  • “To write a book, you have to be a little bit insane… it started as a daily way to wind down.” (03:56, Chris)
• Organic Data Structure: Chris’s dataset grew column-by-column (attributes added as needed), often requiring him to revisit and retag hundreds of songs.

2. Data Modeling and Manual Work (06:11–09:02)

• Data Backfilling: New columns/analyses often required relistening or research (e.g., tagging song structure or demographic data).
• Immersion Value: Manual tagging deepened Chris’s understanding—sometimes, being close to the data trumps automation:
  • “Manually filling all this out actually got me so deeply in touch with this data…” (08:13, Chris)

3. Early Insights: Patterns in Pop Culture (09:07–10:32)

• "Teenage Tragedy" Songs: Discovered clusters like 1960s car-crash tearjerkers—revealing how pop music themes reflect social undercurrents.
  • “There’s maybe something deeper going on in the world when these songs get popular.” (09:20, Chris)

4. How the Billboard #1 Works & Its Limitations (10:32–20:20)

• Songs that hit #1 often spend multiple weeks at the top—but Chris only counted each song once.
• Unit of Analysis Debate: Is focusing on #1s representative of trends?
• Methodology Evolution: Criteria for “popularity” and chart rankings have changed—from radio calls and sales to Soundscan and streaming APIs.
  • “It’s a tough task… It’s more accurate now, but… as soon as you have a metric, someone tries to manipulate it.” (13:20, Chris; echoed by Tim)
• Proxy Nature: Charts are imperfect proxies for movements in taste; analyzing trends works because #1s usually reflect larger shifts.

5. Data Collection Toolkit: APIs, Metadata, and the Web (21:10–26:29)

• Pulled from BMI/ASCAP songwriter databases, Spotify metadata (BPM, danceability, etc.), Wikipedia, and lyrics websites.
• Spotify API praised: “It was very easy to just take a playlist of songs and ask the API to spit back certain metadata.” (23:17, Chris)
• Most of Chris’s data entry was manual—because he only processed one song per day.

6. Hands-On Analytics: The Fuzziness of Pop Data (27:03–35:16)

• “Good, Bad & Ugly”: Pop history is full of forgotten gems and dreadful novelties (e.g., "Want Ads" by Honey Cone and "Disco Duck").
• Squishy Definitions: Qualitative choices (e.g., song genre, “two-hit wonder” status) rarely have clean cutoffs.
  • “You do have to wrestle with stuff like that throughout the process…” (31:37, Chris)
• Iterative Refinement: Hypotheses often fall apart; analysis requires constant definition-tweaking.

7. When the Data Falls Short (37:14–39:57)

• Some pop-culture ideas can’t be measured reliably (e.g., "Are punk musicians’ parents more often divorced?").
  • “You have to know when to say, I can’t write about this because I don’t have the information…” (38:31, Chris)

8. Publishing Rhythm & Topic Inspiration (40:19–43:09)

• Weekly Schedule: Chris publishes his newsletter every Thursday—almost always working under deadline.
  • “Put stuff out, even if it’s not perfect or finished… you never know what people are going to connect with.” (40:52, Chris)
• Hypothesis Sourcing: Mix of reader questions, personal observation, and music industry immersion.

9. What Makes Pop, Pop? The Limits of Prediction (43:09–44:29)

• No “formula” for the perfect hit, but popular songs often cluster in stylistic trends—what’s popular is usually of its time.

10. Technology as a Pop Music Inflection Driver (44:29–47:59)

• Tech Drives Trends: Drum machines, hip-hop lyricism, and microphone technology each reshaped the pop landscape.
  • “Musical innovation is often downstream of technological change.” (44:29, Chris)
• Examples: 80s drum machines, hip-hop's rise (words/minute increased), vocalist-as-star equals better microphones.

11. Favorite (and Frustrating) Analyses (49:46–52:20)

• Top Analyses:
  • Annual “new Christmas classics” series (e.g. "Santa Tell Me" by Ariana Grande, "Underneath the Tree" by Kelly Clarkson).
  • Attempts to quantify supermarket music—thwarted by lack of data.
• Some analyses start as analytical, end up narrative: Acceptance of data’s limits is key; some stories can’t be quantified.

Notable Quotes

• On Starting the Project:
  “If I had some grand scheme, this listening journey would have never started… I just came up with this idea I was going to listen to every number one hit…” (03:56, Chris Dalla Riva)

• On Qualitative Data:
  “There’s a fuzziness to popular culture… as much as I would want to be able to categorize certain things perfectly…” (29:54, Chris)

• On Data Limitations:
  “You have to know when to say, I can’t write about this because I don’t have the information…” (38:31, Chris)

• On Musical Innovation:
  “Musical innovation is often downstream of technological change.” (44:29, Chris)

• On Pop Song Alchemy:
  “If I discovered the secret, I would’ve written the song instead of the book… but there’s no real secret sauce.” (43:09, Chris)

Memorable Moments & Lighthearted Exchanges

• Val’s “Top Nine at Nine” Radio Memory:
  “I would always record it. So I’m like, this is it. This is the top nine. Like, they know.” (20:44, Val)

• Fun with Data Definitions:
  Chris tells the tale of finding Pink Floyd as the “greatest two-hit wonder” in a draft analysis and the importance of testing definitions.
  “That cannot be. How is that possible?... Then I had to adjust my criteria.” (33:13, Chris)

• Julie on Analyst Development:
  “Music really shows the squishiness of the data around it… you have to think through and refine definitions and your assumptions.” (35:41, Julie)

Timestamps for Key Segments

• Chris’s Origin Story & Project Start: 03:23–06:11
• Why/How the Data Expanded: 06:11–09:02
• First Surprising Trend (Teenage Tragedy Songs): 09:07–10:32
• How the Billboard #1 Chart Works: 10:32–20:20
• Pulling in External Data/APIs: 21:10–26:29
• Song Structure & Qualitative Analysis: 29:54–35:16
• Data Gaps & When to Stop: 37:14–39:57
• Weekly Publishing & Creative Process: 40:19–43:09
• Tech Changes Drive Pop Trends: 44:29–47:59
• Favorite Analyses & Revisiting Old Ones: 49:46–52:20

Resources & Links Mentioned

• Chris’s Newsletter “Can’t Get Much Higher": [link to be added]
• Google Sheet Data Set (All #1 Songs): Will be linked in show notes
• Recommended Sites:
  • Every Noise At Once (music genre visualizer): everynoise.com
  • Various music APIs: Spotify, Wikipedia, BMI, ASCAP
• Newsletter Crossover:
  • BI Bytes article on time series data: "Same Data, Different Questions Transforming Time Series Data for Better Insights"
  • Ben Stancil’s article on SQL comma conventions: "A dispassionate examination of the empirical evidence regarding positional punctuation in SQL"
• Other Podcasts:
  • 99% Invisible episode on the Roland TR-808 drum machine

Final Thoughts

Chris’s story is a testament to the messiness and wonder of building data sets to decode culture: patience, passion, and flexibility are more important than perfection. Trends in music reflect broader technological and social shifts—but the data always demands humility.

For anyone curious about how the music we hear mirrors our world—or trying to wrangle “squishy” data in any domain—this episode is an inspiring case study in analytics, persistence, and curiosity.

For additional resources and the open Google Sheet of #1 songs, see the show notes.