The Freedom Ledger Band Analytics App
The Freedom Ledger Band Analytics App
In the fast-paced world of music, data is your greatest hit. Whether you’re an independent artist juggling tours and streams or a powerhouse label managing a roster of stars, staying on top of every metric can feel like an endless remix. That’s where LabelMaster Pro comes in – our proprietary software app designed to synthesize, interpret, and supercharge your music data.
Why LabelMaster Pro?
• Comprehensive Data Mastery: Import your spreadsheets effortlessly to track everything from tour attendance and ticket revenue to music sales, website traffic, social media growth, and beyond. Our app handles granular data points like stream counts by platform, fan demographics, and engagement rates, turning raw numbers into actionable strategies.
• Multi-Level Hierarchy for Growth: Built for scale, LabelMaster Pro supports multiple bands under one label and multiple labels under a single company umbrella. Filter views by artist, label, or enterprise-wide dashboards to spot trends, compare performances, and optimize resources across your entire portfolio.
• AI-Driven Synthesis and Interpretation: Go beyond spreadsheets with intelligent analytics. Our AI engines crunch your data to reveal insights like predictive sales forecasts, audience retention patterns, and ROI on marketing campaigns. Visualize it all with customizable charts, heatmaps, and reports – no coding required.
• Seamless Integration and Automation
: Pull data directly from platforms like Spotify, Google Analytics, and social media APIs. Automate consolidations from individual band files into company overviews, ensuring real-time accuracy without manual hassle.
• Secure and Collaborative: Role-based access keeps sensitive info protected while enabling team collaboration. Export reports for stakeholders or integrate with accounting tools for end-to-end business intelligence.
Testimonial Teaser: “LabelMaster Pro transformed our chaotic spreadsheets into a symphony of insights. We’ve boosted tour revenues by 30% just by spotting hidden trends!” – CEO, IndieWave Records.
Call to Action: Ready to hit play on smarter music management? Sign up for a free trial today and elevate your label to legendary status. Visit [LabelMasterPro.com] or contact steve@monarchliterary.com
LabelMaster Pro: Where Data Meets the beat.
The Freedom Ledger Band Data App
The Freedom Ledger App Source Code
init(date: Date = .now, venue: String = "", city: String = "", attendance: Int = 0, capacity: Int = 0, ticketsSold: Int = 0, pricePerTicket: Double = 0.0, merchSold: Double = 0.0, tips: Double = 0.0, totalRevenue: Double = 0.0, expenses: Double = 0.0, setLengthMinutes: Int = 0, crowdReaction: Int = 0, newMailingSignups: Int = 0, milesDriven: Int = 0, fuelCost: Double = 0.0, gigType: String = "") {
self.date = date
self.venue = venue
self.city = city
self.attendance = attendance
self.capacity = capacity
self.ticketsSold = ticketsSold
self.pricePerTicket = pricePerTicket
self.merchSold = merchSold
self.tips = tips
self.totalRevenue = totalRevenue
self.expenses = expenses
self.setLengthMinutes = setLengthMinutes
self.crowdReaction = crowdReaction
self.newMailingSignups = newMailingSignups
self.milesDriven = milesDriven
self.fuelCost = fuelCost
self.gigType = gigType
}
var netProfit: Double {
totalRevenue - expenses
}
init(date: Date = .now, platform: String = "", trackAlbum: String = "", unitsStreams: Int = 0, pricePerPlay: Double = 0.0, revenue: Double = 0.0, buyerLocation: String = "", payoutDate: Date? = nil, netAfterFees: Double = 0.0, freeDLs: Int = 0, inventoryLeft: Int = 0, reviewRating: Double = 0.0, commentsSentiment: String = "", releaseName: String = "", playlistAdds: Int = 0) {
self.date = date
self.platform = platform
self.trackAlbum = trackAlbum
self.unitsStreams = unitsStreams
self.pricePerPlay = pricePerPlay
self.revenue = revenue
self.buyerLocation = buyerLocation
self.payoutDate = payoutDate
self.netAfterFees = netAfterFees
self.freeDLs = freeDLs
self.inventoryLeft = inventoryLeft
self.reviewRating = reviewRating
self.commentsSentiment = commentsSentiment
self.releaseName = releaseName
self.playlistAdds = playlistAdds
}
init(date: Date = .now, totalVisits: Int = 0, newUsers: Int = 0, pageViews: Int = 0, bounceRate: Double = 0.0, avgSessionMin: Double = 0.0, trafficSource: String = "", topReferrer: String = "", topPage: String = "", downloads: Int = 0, mailingListAdds: Int = 0, cartAdds: Int = 0, checkoutValue: Double = 0.0, device: String = "", location: String = "", keywords: String = "") {
self.date = date
self.totalVisits = totalVisits
self.newUsers = newUsers
self.pageViews = pageViews
self.bounceRate = bounceRate
self.avgSessionMin = avgSessionMin
self.trafficSource = trafficSource
self.topReferrer = topReferrer
self.topPage = topPage
self.downloads = downloads
self.mailingListAdds = mailingListAdds
self.cartAdds = cartAdds
self.checkoutValue = checkoutValue
self.device = device
self.location = location
self.keywords = keywords
}
init(date: Date = .now, platform: String = "", followers: Int = 0, netGainLoss: Int = 0, postType: String = "", viewsImpressions: Int = 0, likes: Int = 0, comments: Int = 0, shares: Int = 0, engagementRate: Double = 0.0, reach: Int = 0, dmsReceived: Int = 0, linkClicks: Int = 0, hashtagUsed: String = "", sentiment: String = "", topContent: String = "") {
self.date = date
self.platform = platform
self.followers = followers
self.netGainLoss = netGainLoss
self.postType = postType
self.viewsImpressions = viewsImpressions
self.likes = likes
self.comments = comments
self.shares = shares
self.engagementRate = engagementRate
self.reach = reach
self.dmsReceived = dmsReceived
self.linkClicks = linkClicks
self.hashtagUsed = hashtagUsed
self.sentiment = sentiment
self.topContent = topContent
}
init(date: Date = .now, itemName: String = "", unitsSold: Int = 0, price: Double = 0.0, revenue: Double = 0.0, costToMake: Double = 0.0, net: Double = 0.0, platform: String = "", inventoryStart: Int = 0, inventoryLeft: Int = 0, bundle: Bool = false) {
self.date = date
self.itemName = itemName
self.unitsSold = unitsSold
self.price = price
self.revenue = revenue
self.costToMake = costToMake
self.net = net
self.platform = platform
self.inventoryStart = inventoryStart
self.inventoryLeft = inventoryLeft
self.bundle = bundle
}
init(date: Date = .now, category: String = "", revenue: Double = 0.0, expense: Double = 0.0, net: Double = 0.0, cumulativeNet: Double = 0.0, roiNotes: String = "", taxNotes: String = "") {
self.date = date
self.category = category
self.revenue = revenue
self.expense = expense
self.net = net
self.cumulativeNet = cumulativeNet
self.roiNotes = roiNotes
self.taxNotes = taxNotes
}
init(date: Date = .now, source: String = "", mailingAdds: Int = 0, opensPercent: Double = 0.0, clicks: Int = 0, unsubs: Int = 0, podcastDLs: Int = 0, youtubeSubs: Int = 0, collaborationLeads: Int = 0, activismImpact: String = "", fanStories: String = "") {
self.date = date
self.source = source
self.mailingAdds = mailingAdds
self.opensPercent = opensPercent
self.clicks = clicks
self.unsubs = unsubs
self.podcastDLs = podcastDLs
self.youtubeSubs = youtubeSubs
self.collaborationLeads = collaborationLeads
self.activismImpact = activismImpact
self.fanStories = fanStories
}
init(date: Date = .now, gigEnergy: Int = 0, songsWritten: Int = 0, restHours: Double = 0.0, mealsOnRoad: String = "", voiceNotes: String = "", notes: String = "") {
self.date = date
self.gigEnergy = gigEnergy
self.songsWritten = songsWritten
self.restHours = restHours
self.mealsOnRoad = mealsOnRoad
self.voiceNotes = voiceNotes
self.notes = notes
}
init(date: Date = .now, summary: String = "", suggestions: [String] = [], impactScore: Double = 0.0) {
self.date = date
self.summary = summary
self.suggestions = suggestions
self.impactScore = impactScore
}
var body: some View {
NavigationView {
VStack {
Picker("Category", selection: $selectedTab) {
Text("Tour").tag("Tour")
Text("Sales").tag("Sales")
Text("Website").tag("Website")
Text("Social").tag("Social")
Text("Merch").tag("Merch")
Text("Financial").tag("Financial")
Text("Fan").tag("Fan")
Text("Health").tag("Health")
}
.pickerStyle(SegmentedPickerStyle())
.padding()
if selectedTab == "Tour" {
TourEntryForm(context: context)
} else if selectedTab == "Sales" {
SalesEntryForm(context: context)
} // Add similar for other categories...
}
.navigationTitle("Data Entry")
}
}
var body: some View {
Form {
DatePicker("Date", selection: $date)
TextField("Venue", text: $venue)
// Add all fields...
Button("Save") {
let newTour = TourData(date: date, venue: venue /*, ... */ )
context.insert(newTour)
}
}
}
var body: some View {
ScrollView {
VStack {
Text("Key Metrics")
// Calculate and display totals, charts
Chart {
ForEach(tours) { tour in
BarMark(
x: .value("Date", tour.date, unit: .day),
y: .value("Revenue", tour.totalRevenue)
)
}
}
.frame(height: 200)
// Add more charts
}
}
.navigationTitle("Dashboard")
}
var body: some View {
List(reports) { report in
VStack(alignment: .leading) {
Text(report.summary)
ForEach(report.suggestions, id: \.self) { suggestion in
Text("- `\(suggestion)")
}
}
}
.navigationTitle("AI Reports")
.toolbar {
Button("Generate Report") {
generateReport()
}
}
}
func generateReport() {
// Fetch data
let analyzer = BusinessAnalyzer(tours: Query<TourData>(), sales: Query<SalesStreamData>() /* etc */ )
let summary = analyzer.generateSummary()
let suggestions = analyzer.generateSuggestions()
let score = analyzer.calculateImpactScore()
let newReport = AIReport(summary: summary, suggestions: suggestions, impactScore: score)
context.insert(newReport)
}
init(tours: [TourData], sales: [SalesStreamData] /* ... */) {
self.tours = tours
self.sales = sales
// ...
}
func generateSummary() -> String {
let totalRev = tours.reduce(0) { $0 + $1.totalRevenue } + sales.reduce(0) { $0 + $1.revenue }
return "Total Revenue: `\(totalRev). Average Attendance: `\(averageAttendance())"
// Add more
}
func averageAttendance() -> Double {
guard !tours.isEmpty else { return 0 }
return Double(tours.reduce(0) { $0 + $1.attendance }) / Double(tours.count)
}
func generateSuggestions() -> [String] {
var suggestions = [String]()
if averageAttendance() < 50 {
suggestions.append("Consider smaller venues or more promotion to boost attendance.")
}
if sales.reduce(0) { $0 + $1.revenue } < 1000 {
suggestions.append("Focus on blues-infused tracks for higher streaming revenue.")
}
// Add heuristic-based suggestions
// Use NaturalLanguage for sentiment analysis on comments if available
return suggestions
}
func calculateImpactScore() -> Double {
// Simple calculation
return Double.random(in: 50...100)
}
import pandas as pd
import os
# Define the base directory where all label folders are stored
base_dir = 'record_company_data'
# Initialize an empty list to hold individual dataframes
dataframes = []
# Loop through each label directory
for label in os.listdir(base_dir):
label_path = os.path.join(base_dir, label)
if os.path.isdir(label_path):
# Loop through each band's spreadsheet in the label directory
for file_name in os.listdir(label_path):
if file_name.endswith('.xlsx') or file_name.endswith('.csv'):
band = os.path.splitext(file_name)[0] # Extract band name from file name
file_path = os.path.join(label_path, file_name)
# Read the spreadsheet (handle both Excel and CSV)
if file_name.endswith('.xlsx'):
df = pd.read_excel(file_path)
else:
df = pd.read_csv(file_path)
# Add columns for label and band to track organization
df['Label'] = label
df['Band'] = band
# Append the dataframe to the list
dataframes.append(df)
# Concatenate all band dataframes into a single company dataframe
company_df = pd.concat(dataframes, ignore_index=True)
# Optionally, perform additional processing, such as aggregating data
# Assuming common columns like 'Date', 'Sales', 'Streams', etc.
# For example, group by label and band to summarize total sales
if 'Sales' in company_df.columns:
summary_df = company_df.groupby(['Label', 'Band']).agg({'Sales': 'sum'}).reset_index()
else:
summary_df = pd.DataFrame() # Placeholder if no 'Sales' column
# Save the consolidated company data to a new spreadsheet
company_df.to_excel('company_consolidated_data.xlsx', index=False)
# Save the summary if applicable
if not summary_df.empty:
summary_df.to_excel('company_summary.xlsx', index=False)
print("Company data from multiple labels and bands consolidated successfully.")
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