Use Case: Subscriber Lead Scoring

Category: Marketing

GPT Model Used

Custom-built AI model for analyzing ticket buyers and identifying high-potential subscribers.

Data Structure

To effectively score subscriber leads, the model requires the following data:

  • Customer Data: Name, Email, Customer ID

  • Purchase History: Number of tickets purchased, frequency, recency, average order value

  • Engagement Metrics: Email open/click rates, event attendance, website visits

  • Subscription Interest Signals: Past subscriptions, survey responses, membership inquiries

  • Demographic & Behavioral Data: Age, location, preferred genres/shows, donation history

  • USE AOi CORE DATA MODEL

PDF Strategic Knowledgebase

  • Best practices for subscriber conversion

  • Customer segmentation strategies

  • Historical subscriber retention analysis

  • Benchmark reports on subscription trends in the performing arts

Model Description

The AI model assigns a lead score to each ticket buyer based on their likelihood to subscribe. It evaluates purchase behavior, engagement with marketing efforts, and past interactions. The model outputs:

  • High-Potential Leads (strong interest, frequent buyers)

  • Medium-Potential Leads (engaged but inconsistent)

  • Low-Potential Leads (occasional buyers, low engagement)

This allows marketing teams to prioritize outreach and tailor subscription offers accordingly.

Model Parameters

  • Temperature: 0.3 (keeps responses deterministic but allows slight flexibility)

  • Scoring Algorithm: Weighted ranking based on recency, frequency, and monetary (RFM) analysis

  • Prompting Strategy: Uses a structured prompt to analyze user behavior and generate actionable insights

  • Thresholds:

    • 80+ Score → High Priority Subscriber Lead

    • 50-79 Score → Medium Potential Lead

    • Below 50 → Low Priority

Example Output

"Based on purchase frequency and engagement, this customer (John Doe) has an 85% likelihood of subscribing. Recent attendance patterns suggest interest in contemporary dance performances. A targeted email with a discounted subscription package may increase conversion."

Real-World Example: Weston Theater Company (Placeholder for now)

[Insert how Weston Theater Company used Subscriber Lead Scoring to increase subscriptions, with real data on conversion rates, campaign effectiveness, and audience insights.]

Recommended Action Plan

To implement Subscriber Lead Scoring, follow these steps:

1️⃣ Prepare Your Data

  • Gather customer purchase history, engagement metrics, and survey responses.

  • Ensure data consistency by standardizing fields across CRM and marketing systems.

2️⃣ Train Your AI Model

  • Use a structured dataset with historical subscriber conversion data.

  • Fine-tune the model to adjust lead-scoring weightings based on your audience.

3️⃣ Integrate with Marketing Efforts

  • Set up automated workflows:

    • High-potential leads receive targeted subscription offers.

    • Medium-potential leads get engagement-focused content.

    • Low-potential leads receive soft touchpoints (e.g., newsletter).

4️⃣ Monitor & Optimize

  • Track conversion rates and refine the model based on real-time results.

  • A/B test different outreach methods to improve effectiveness.