The short version
A conversation with a client about catalogue valuation turned to a name we know well: Tinie Tempah, now firmly back in business. Using Viberate's music data and Claude, we built a transparent, demand-led valuation proxy for his catalogue at two points in time: before his 2026 revival and after it. On a like-for-like, Spotify-derived basis, the base case rises from roughly $2.9 million to roughly $4.2 million. We show exactly how much of that comes from more streams, and how much from a judgement call about how fast demand will fade.
This is a demand-led screen, not a rights-checked price. The point of the piece is the method, not the headline: analysis that used to need a data analyst, weeks of work and bespoke code, done in an afternoon through conversation, with every number traceable back to the data.
Full circle
Around 2010, a young rapper from Plumstead in south-east London was about to release his first album. The label believed he was a future household name. The early audience research told a more careful story: at the start, only one segment really connected with the music. So rather than spend everything chasing the mainstream on day one, the launch was built in stages. We measured the audience every three months and widened the marketing only as receptiveness grew. Each group was a potential next audience, not yet a now audience, until the songs were out and the data said otherwise.
That sequenced, audience-led approach became one of the ideas behind our Now+Next work. The record was Disc-Overy. The singles were Pass Out and Written in the Stars. They are still being streamed by the million today.
Fifteen years on, Tinie is back: a new album, festival dates through 2026, and a clear lift in the data. The question this time is not how to build an audience from scratch. It is what that audience, and the catalogue it keeps returning to, is now worth. The tools to answer it have changed beyond recognition.
The question a client put to us
We had been talking with a client about catalogue valuation, the now-large business of buying and selling rights to recorded music. Tinie's name came into the conversation. In the end they did not take it forward. We are sharing the analysis with the commercial context stripped out: no client, no deal, no brief. What remains is a real catalogue and a real question, which is all a case study needs.
It also let us do something cleaner than a live deal usually allows. With no negotiation to colour the result, we could simply ask the data a fair question and show our working.
What we measured, and what we did not
We pulled Tinie's data directly from Viberate's API and handed it to Claude inside Claude Code. No spreadsheets by hand, no bespoke pipeline. The model explored the data, assembled the signals that matter for valuation, and ran the arithmetic, while the judgement stayed with us.
One distinction matters before any number appears. We valued demand, not a defined set of rights. A real acquisition is a specific bundle: which masters, which publishing share, recoupment status, territory, term and exclusions. Our analysis knows none of that. It reads how much the world is listening and turns that into an indicative figure. Treat it as a screen that tells you whether something is worth a closer look, not as the closer look itself.
The method follows the recipe Viberate's own catalogue estimator uses, so the case study doubles as a real-world test of their tool. Annual Spotify streams become a gross royalty at a blunt global rate, are reduced to a net figure, and are scaled up for publishing and sync income. That annual royalty is then run through a ten-year discounted cash flow, with an assumed annual decay and a terminal value at the end. We rebuilt every step ourselves so it could be inspected, and checked our model against a known result from the estimator before trusting it.
We worked out roughly how much money the music earns each year, then estimated what someone might pay today for all the earnings still to come. It is a sensible ballpark, not a checked-and-signed price, because we did not confirm who legally owns each song.
Before and after: the demand actually moved
The catalogue is substantial: 232 tracks and about 2.4 billion lifetime Spotify streams, anchored by Girls Like, Pass Out, Miami 2 Ibiza and Written in the Stars. Through 2024 and 2025 the weekly streams were broadly flat, sitting around 3.0 to 3.3 million a week. The profile of a fifteen-year-old catalogue gently coasting.
Then 2026 changed the picture. Weekly streams in the second quarter ran about 38 per cent above the 2025 average. Monthly listeners peaked at 6.76 million in May. Shazam tags, a good proxy for fresh curiosity, jumped from under 1,800 a week to a peak above 84,000. Trailing-year streams finished 16.9 per cent up on the year before.
One honest note. Viberate's own composite rank, which blends platforms and is slow to move, was broadly flat over the year (around #3,160 to #3,275, fractionally worse). The momentum shows up first in the things that move fast: streams, Shazam, listeners. We have not dressed the rank up as rising, because it was not.
The valuation bridge
Here is the part most write-ups skip. The same catalogue is worth more after the comeback for two quite different reasons, and only one of them is a hard fact.
The first is simply that there are more streams now, so the annual royalty proxy is larger: about $849,000 before, about $976,000 after. Hold every assumption constant and that extra income alone lifts the base-case valuation from about $2.9 million to about $3.3 million, a gain of roughly $435,000.
The second is a judgement. Before the revival, a buyer would reasonably assume a fading catalogue decays fairly quickly, so we used a 14 per cent annual decay. After it, with demand clearly rising, a shallower 9 per cent decay is defensible. That single change, our judgement reflecting the trend reversal, adds about another $885,000 and takes the base case to roughly $4.2 million.
So the comeback re-rates the base case by about 45 per cent, but most of that move is the decay assumption rather than the streams already banked. That is not a flaw in the analysis, it is the analysis being honest. Demand that is rising today is worth more only if you believe it will keep rising tomorrow, and that belief is a human call, not something the data proves. Across the full range of assumptions, the catalogue screens at roughly $2.2 to $3.7 million before, and roughly $3.3 to $5.1 million after.
More people are listening, which is a fact and is worth about $0.4 million more. The bigger jump comes from betting the renewed interest will last. That bet is reasonable given the comeback, but it is a judgement, so we show it separately rather than hiding it inside one headline number.
What a buyer would challenge next
A screen earns its keep by being clear about its own weaknesses. If this were heading towards a real offer, these are the things we would dig into before trusting any figure.
Where the number is soft
- Spotify only. The royalty base ignores Apple, Amazon, YouTube and the rest, so it understates total recorded income. It is a proxy, deliberately.
- Features muddy ownership. About 38 per cent of the past year's catalogue streams come from tracks where Tinie is a featured artist, not the lead. Who earns what on those is unknown without the paperwork.
- Concentration. The top five tracks carry about 57 per cent of recent streams, and the top ten about 76 per cent. The catalogue leans on a handful of hits.
- Casual listening. There are roughly seven monthly listeners for every committed follower. Plenty of people hear the songs on playlists; fewer have chosen to follow. That is normal for a hits catalogue, but it is reach, not loyalty.
- Blunt constants and demand, not rights. The per-stream rate and the publishing multiplier are single global numbers applied to everyone, and the whole exercise values demand rather than a defined, recouped, term-limited bundle of rights.
What actually changed: the tools
None of the individual ingredients here are new. Streaming data has existed for years. Discounted cash flows are older than streaming. What is new is that a non-specialist can now assemble the data, run the model and stress-test the assumptions in an afternoon, by talking to a language model, with the working visible at every step.
Viberate supplied the raw material: a deep, structured view of an artist's streams, listeners, playlists, Shazam tags and more, reachable through an API and, now, through a connector that lets tools like Claude query it directly in plain language. Claude did the fetching, the arithmetic and the first draft of the interpretation. We supplied the questions, the judgement and the honesty about limits. Neither half would have produced this alone.
Fifteen years ago we used audience data to help launch this catalogue, carefully, one segment at a time. Today the same kind of data, with a language model alongside, can tell you in an afternoon what a comeback has done to its value. The skill that matters now is knowing which questions to ask and which answers to trust. David Boyle, Audience Strategies
Three things to take away
- A comeback shows up in the numbers, and you can value it. Tinie's listening jumped in 2026, and on a like-for-like basis his catalogue screens at roughly $4.2 million now versus about $2.9 million before, up around 45 per cent.
- Be honest about what is fact and what is judgement. The extra streams are real and add about $0.4 million. The rest rests on assuming the revival lasts. We separate the two so no one mistakes a forecast for a certainty.
- The tools have changed who can do this. Pulling the data, running the model and stress-testing it took an afternoon and a conversation with a language model, not a specialist team and a fortnight. The scarce skill now is knowing which questions to ask and which answers to trust.
Check, Edit, Own
One thing did not change. A language model can find patterns, run a discounted cash flow and draft an interpretation. It cannot decide whether the answer is meaningful, whether the assumptions are fair, or whether the number is one you would stand behind. That is the human job, and it is the whole job.
We call it the CEO principle: Check the maths and the method, Edit the story down to what is true and useful, and Own the result. AI made this analysis fast and cheap. It did not make the judgement for us, and it should not make it for you.
About Viberate. Viberate is a music intelligence platform tracking streaming, social, radio and live data for well over a million artists, festivals and venues worldwide. Founded in Ljubljana by Vasja Veber, it now offers an API and an MCP connector, so AI tools can query its catalogue directly in natural language. This case study uses Viberate data; Tinie Tempah is named, while the client context is kept anonymous. The underlying figures are Viberate's; the model and its caveats are ours.
This is a collaboration between David Boyle (Audience Strategies) and Claude (Anthropic), using data from Viberate. It is an indicative, demand-led screen for discussion, not financial advice or a rights-checked valuation.
Method, in brief: annual Spotify streams × a gross per-stream rate × a net factor × a publishing and sync multiplier gives an annual royalty proxy; this is run through a ten-year discounted cash flow at a 10 per cent discount with an assumed annual decay and a terminal value. The "before" view uses trailing-year streams to end-2025 with a 14 per cent decay; the "after" view uses trailing-year streams to June 2026 with a 9 per cent decay. Our model was validated against Viberate's own estimator output before use. Figures are a Spotify-derived proxy and are not comparable to headline catalogue-deal multiples without normalising the income base.