Live Entertainment Marketing & Revenue Intelligence

Live entertainment teams have to make decisions quickly. Ticket inventory has a fixed deadline, marketing performance can change week to week, and strong traffic does not always translate into ticket sales.

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Tech: Python • SQL • Forecasting • Marketing Analytics • Data Visualization • AI APIs

Problem

Managing performance across multiple events, venues, and marketing channels makes it difficult to see what is actually driving ticket sales.Some of the key questions are:

• Which events are falling behind sales targets?
• Where are customers dropping out of the purchase funnel?
• Which marketing channels are driving efficient conversions?
• Is weak performance caused by traffic, audience quality, or checkout friction?
• Where should the remaining marketing budget be focused?

👉 The challenge is not just reporting performance — it is turning the data into a clear next step.

Solution

I built LivePulse AI, a live entertainment analytics dashboard that brings ticket sales, marketing performance, conversion behavior, and event-level revenue into one view.The project uses synthetic multi-venue data to simulate common business scenarios and show how an analyst could monitor performance, identify risks, forecast ticket demand, and support marketing decisions.The platform is designed around a simple workflow:Monitor performance → diagnose the problem → evaluate options → recommend action

Key Features

1. Ticket Sales & Forecasting

• Tracks sales against expected pace
• Forecasts final ticket sell-through
• Highlights events that may miss capacity or revenue targets
• Shows sales trends as the event date approaches

2. Marketing Funnel Analysis

• Tracks the customer journey from impressions to ticket purchase
• Identifies conversion drop-offs
• Compares performance by channel, device, campaign, and audience
• Helps distinguish traffic problems from conversion problems

3. Channel Performance

• Tracks ROAS, CPA, CPM, CTR, and conversion rate
• Compares performance across paid search, social, email, and retargeting
• Shows which channels are driving traffic versus actual purchases

4. Multi-Event Performance

• Provides an executive view across multiple events and venues
• Compares sales, revenue, marketing efficiency, and risk level
• Supports both individual live events and multi-show productions

5. Budget Scenario Planning

• Tests different marketing budget allocations
• Estimates potential impact on ticket sales and revenue
• Helps evaluate where additional spend may be most effective

6. Executive Summary

• Turns the underlying analysis into a concise business summary
• Highlights key performance changes, risks, and recommended actions
• Provides supporting metrics and assumptions behind each recommendation

About the Data

Because internal entertainment data is not publicly available, I created a synthetic dataset designed around realistic scenarios such as:

• Declining mobile checkout conversion
• High traffic with weak purchase conversion
• Strong search performance with limited budget
• Retargeting saturation
• Underperforming weekday showtimes

The goal of the project is to demonstrate how I approach a business analytics problem — from defining KPIs and structuring the data to identifying performance issues and translating the findings into actionable recommendations.

One-line positioning

Turning ticketing and marketing data into clearer business decisions.