HOW WE WRITE DOWN THE REPORT

This formula serves as a conceptual and abstract framework to aid understanding. In practice, STARGLOW draws on data from agencies and artists to produce customized reports, thereby indirectly reflect

1. Formula Overview

$$\text{MOV}_t = \text{FV}_t + \text{PFV}_t + \text{PCV}_t + \text{MRV}_t \\$$

$$\begin{align*} t &: \scriptsize\text{Time in months (e.g., t=1 for January 2025, t=2 for February 2025, etc.)} \\ FV_t &: \scriptsize\text{Fandom (Fan) Value} \\ PFV_t &: \scriptsize\text{Recorded Music (Portfolio) Value} \\ PCV_t &: \scriptsize\text{Production and Content Value (Concerts/Contents/MD)} \\ MRV_t &: \scriptsize\text{Management (Broadcast/Appearances/Trademarks) Value} \end{align*}$$

When summing up all values for a given month t, you can readily see the total value the artist generated in that period.

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2. Main Components

2.1. FV (Fandom Value)

$${\text{FV}_t =\text{FB}_t \times {\text{ER}_t}^{w_\beta} \times \text{G} \times\text{I}^{w_\tau} }$$

• FB (Fan Base)

  • Represents the fundamental strength/foundation of the artist’s fandom.

• ER (Engagement Ratio)

  • Quantifies how actively the fandom interacts

• G (Fandom Economic Power)

  • Assesses the fandom’s purchasing power and potential revenue contribution.

• I (Trends Index)

  • Tracks the timing and scale of public attention via search volume data.

2.2. PFV (Portfolio Value)

$$\text{PFV}_t=\sum_{\text{e} \in \mathcal{E}} (\text{SV}_e + \text{APV}_e + \text{RV}_e)\times(\text{UDI}_e)^{w_\tau}$$

• SV (Streaming Value)

  • Combines track-level streaming counts into an overall music value.

• APV (Album Popularity Value)

  • Represents how popular an album is, based on each track’s popularity, scaled by the artist’s overall fan base.

• RV (Retail Album Sales Value)

  • A product of physical album sales and a discounted album price.

• UDI (Uniform Distribution Index)

  • Evaluates how evenly an album’s key metrics (streams, likes, etc.) are distributed across tracks.

2.3. PCV (Production & Content Value)

$$\text{PCV}_t=\text{CEV}_t+\text{MCV}_t+\text{MDS}_t$$

• CEV (Concert/Event Value)

  • Sums actual and estimated revenues from concerts/events, applying time-based discounts.

• MCV (Media/Contents Value)

  • Weighs and sums up metrics from media/social platforms (YouTube, Twitter, Instagram, TikTok, etc.).

• MDS (Merchandise Value)

  • Combines fandom (FV), recorded music (PFV), and engagement (ER) with time-based decay to gauge goods/MD sales.

2.4 MRV (Management & Rights Value)

$$\text{MRV}_t=\sum_{\text{e} \in \mathcal{E}}\Bigl[ \Bigl( (\text{PFV}_t + \text{FV}_t) \times \text{ER} \Bigr)^w + (\text{AF}_e \times N_e )\Bigr]\times\text{DF}_t$$

• Converts monthly broadcast, ad, or brand-collaboration income into a present value.

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3. Data Collection

Our system incorporates both quantitative and qualitative information.

• Quantitative Data focuses on publicly available metrics (through official APIs, charts, etc.) as well as proprietary figures provided by the artist’s label/agency.

• Qualitative Data refers to expert assessments that fill the gaps where numerical stats alone may not capture an artist’s intangible value or deeper market insights.

3.1. Quantitative Data

3.1.1. Fandom Data

• Platform Follower Counts

  • Source: YouTube, Instagram, Twitter, TikTok, etc. via official APIs or verified third-party analytics.

  • Agency Input: In some cases, we receive official fan club membership data or consolidated stats directly from the label if the artist has special promotional events or fan club sales.

  • Use: Helps determine FB, capturing how widespread and potentially influential the artist’s supporter network is.

• Engagement Statistics

  • Source: Social platforms’ engagement endpoints or scrapers.

  • Agency Input: Sometimes, the label provides internal engagement insights, e.g., private fan community participation levels.

  • Use: Contributes to ER, indicating the real intensity of fan interaction beyond passive follows.

3.1.2. Recorded Music Data

• Streaming Metrics

  • Source: Spotify API, YouTube Music, MelOn, etc. for track-level streams.

  • Agency Input: Official figures from the label, especially where streaming services don’t offer public breakdowns or where fast-tracked daily updates are needed.

  • Use: SV to quantify the scale of listening activity.

• Popularity Scores

  • Source: Spotify API, YouTube Music and local chart stats (e.g., MelOn, Circle Chart).

  • Agency Input: Some labels maintain internal sentiment scores or advanced listening data.

  • Use: Fed into APV, reflecting how strongly each track/album resonates with listeners.

• Physical Sales

  • Source: Retail album charts (e.g., Circle Chart), retailers like Hanteo.

  • Agency Input: The label often provides direct daily/weekly sales data, which can be more up-to-date than public sources.

  • Use: Drives RV, representing genuine album sales in physical format.

3.1.3. Concert/Event Data

• Revenue & Attendance

  • Source: Sometimes partial info from ticketing platforms or media reports.

  • Agency Input: Most accurate data typically comes directly from the agency, including box office results, sponsorship, merchandise sales at the venue, etc.

  • Use: Forms CEV after time discounting older events.

• Schedules & Dates

  • Source: Both public event listings and official label calendars or press releases.

  • Use: Ensures we apply discount factors correctly based on how far in the past an event occurred.

3.1.4. Media/Contents Data

• Social Media Content

  • Source: Post frequency, reach, and impressions from YouTube Creator Studio, Twitter Analytics, Instagram Insights.

  • Agency Input: Some labels share private data about paid social campaigns or region-specific ad performance.

  • Use: Combined into MCV, weighting each platform’s magnitude.

• Merchandise/Goods Sales

  • Source: In many cases, merchandise data is not publicly revealed in detail, so we rely on aggregated e-commerce stats if available.

  • Agency Input: The label usually supplies exact figures on goods sold at concerts or through official fan sites, often the only source for real-time merch data.

  • Use: Summed into MDS after factoring in discounting (time-based) and synergy with fan engagement.

3.1.5. Management/Appearance Data

• Broadcast & Advertising

  • Source: Occasional PR announcements or third-party trade articles.

  • Agency Input: Predominantly from the label, who tracks brand deals, cameo appearances, variety show contracts, etc.

  • Use: Consolidated monthly to produce MRV, capturing intangible brand synergy.

3.1.6. Search & Trend Analytics

• Search Volume

  • Source: Google Trends, YouTube search analytics, or local equivalents.

  • Agency Input: Rarely direct input here, but the agency might share “private” search data from exclusive platforms or sponsor partnerships.

  • Use: Affects FV by adjusting the “Trends Index,” indicating real-time audience interest.

3.1.7. Country-Level Data

• Listener/View Shares

  • Source: Usually from streaming platforms’ user distribution data (Spotify for Artists, Apple Music for Artists, etc.).

  • Agency Input: Certain labels track geo-specific fan club signups or offline sales patterns.

  • Use: combining local GDP info and audience share for each region.

3.2. Qualitative Data

Qualitative data addresses intangible factors or nuances that cannot be fully captured by raw numbers. It primarily comes from industry experts with firsthand knowledge:

• Professional Evaluation & Interviews

  • Music critics, A&R managers, or production staff provide insights into areas like musical artistry, fan loyalty, or market trends that are not reflected in raw statistics.

• Field Surveys

  • Occasional anonymous interviews in fan communities, or internal feedback from marketing teams, help gauge audience sentiment at a deeper level.

These qualitative inputs supplement or interpret the quantitative findings, allowing us to cross-check for biases or anomalies before integrating them into key metrics

By combining hard data with expert perspectives, we strive to deliver an objective yet comprehensive representation of the artist’s current standing and potential.

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4. Realistic Modeling Logic

We incorporate several advanced adjustments to make our model more realistic:

4.1. Realistic Streaming Patterns

• Exponential/Piecewise: Sometimes we use a piecewise function to sharply discount sudden one-day surges, yet still employ an exponential decay factor to reflect the natural decline in streaming over time.

4.2. Concert/Event Impact Curves

• Extended Tail: A modified exponential function may be introduced so that a highly successful concert continues to generate residual “buzz” for multiple weeks or months instead of dropping off immediately.

4.3. Popularity Synergy

• Piecewise: Albums with consistently moderate popularity across several tracks might receive an additional synergy boost, while a single-track “wonder” may fade more quickly.

4.4. Media Momentum

• Cross-Influence: Viral posts or TV appearances can briefly boost streaming or merch sales—often captured through piecewise intervals of influence.

4.5. Tiered Retail Value

• Exponential Discount: Older album sales are discounted more heavily the further in the past they occurred, but premium or “deluxe” editions start with a higher baseline price factor.

By applying these advanced steps, each category is modeled more faithfully, preventing oversimplification of real-world behaviors.

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5. Time Decay

Artist activities (album releases, concerts, etc.) typically decrease in value as time elapses:

$$DF_t = X_0 \times e^{-\lambda (t - t_0)}$$

Often, the value peaks for the first few months post-release, then gradually declines.

Instead of purely exponential decay, piecewise (initial, mid, long-term) or linear decay might be used if that better suits the project.

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6. Weighting (Weight)

Each metric—fandom, recorded music, concerts, merchandise, management—carries a different weight to show its relative importance.

these weights are established by expert judgement, public opinion and internal review, allowing adjustments to reflect real-world scenarios.

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7. Report Making Process

STEP 1 | Data Gathering

1. Identify Data Sources (both public and agency-provided).

2. Cross-check overlaps for consistency.

STEP 2 | Value Assessment

1. Gather Metrics for Fandom(FV), Recorded Music(PFV), Concerts/Contents(PCV), Management(MRV).

2. Calculate each metric individually.

3. Apply simulation-based modeling for Advanced Adjustments.

4. Finalize Results with appropriate weighting and discount rates.

This snippet demonstrates how various value components—fan data (FV), portfolio (PFV), production & content (PCV), and management (MRV)—are merged into a single timeline (timeline_df). Each category (pfv, pcv, mrv, fv) is first calculated or integrated, and finally summed up to produce the monthly overall value (MOV_t). By aggregating data from different event records and date ranges, the code exemplifies a step-by-step approach to unify all relevant metrics into one data structure for further analysis or plotting.

STEP 3 | Visualization

Present results in Graphs/Charts for intuitive understanding.

STEP 4 | Interpretation

• Pinpoint Why the artist’s value rose or fell month over month

• Focus on the Key Drivers (fan engagement, streaming spikes, event cancellations, etc.)

STEP 5 | Final Reporting

Deliver a report.

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