Lecture Notes: Geofencing, Dataflick, and Real Estate

Introduction to Geofencing and Its Applications

• Geofencing/Geo-targeting can be applied in various ways.
• Importance of understanding costs and avoiding waste in marketing.

Speaker Background

• Speaker's journey into real estate began with a business partner.
• Initial work with Fortune Builders and experience in Columbus, Ohio.
• Transitioned to working independently in Knoxville, TN, discovering issues with data quality.

Dataflick Introduction

• Emergence of Dataflick due to poor data experiences.
• Aim: To create a better predictive model using AI and machine learning.

Data Providers Comparison

• Competitors: Prop Stream, Batch Leads, Property Radar, etc.
• Dataflick focuses on predictive modeling rather than just hitting broad lists.
• Importance of narrowing down data to increase chances of success.

Predictive Machine Learning Model

• Dataflick's model scores indicate:
  • Likelihood of property selling (on-market/off-market).
  • Likelihood of selling to an investor.
• Use of seller profiles based on historical data to predict behaviors.

Seller Profiles

• Profiles built using 900 unique data points: household demographics, marital status, financial relationships, etc.
• Historical and current data informs machine learning models to generate accurate scores.

Understanding Machine Learning

• Definition: Utilizing large datasets to produce outcomes through models.
• The goal: Improve accuracy over time with more data.

Ideal Users for Dataflick

• Target users include both wholesalers and realtors.
• Marketing channels must be in place for effective data utilization.
• Importance of an omni-channel approach in marketing.

Model Scoring

• Higher scores indicate greater distress factors (bankruptcy, foreclosure, etc.).
• Use of distress lists as data points rather than standalone lists.

Geofencing and Digital Ads

• Geofencing allows targeted advertising based on specific locations.
• Applications in various fields, including law enforcement and gaming.
• Dataflick uses geofencing to send ads to devices in targeted households.
  • Types of ads: streaming commercials, pre-roll videos, banner ads, social media ads.
  • Ads follow user behavior across the internet for maximum exposure.

Cost Structure

• Pricing based on county size, ranging from $750 to $2500.
• Digital ads have a minimum spend of $5,000 for effectiveness.

Common User Mistakes

• Short-sightedness in marketing strategy can lead to poor data utilization.
• Need for proper marketing channels to complement data-driven approaches.

List Stacking

• Definition: Combining multiple distress factors to create a targeted list.
• Dataflick's approach differs by assessing overall context rather than just stacking lists.

Future Developments

• Upcoming features like Flick ID to enhance digital advertising effectiveness.
• Potential expansion into other sectors, such as mortgage and solar, after solidifying real estate foundation.

Conclusion

• Dataflick aims to create precise, effective marketing strategies for real estate professionals.
• Emphasis on continuous improvement in data model accuracy and user experience.