AI-Powered Data Driven REI Lead Generation: Revolutionizing and Unlocking More Opportunities For Investors

AI Smart ZIP Code Data

  • All deed, mortgage, and legal information in one place.
  • Delinquent tax activity
  • Find potential pre-foreclosures and foreclosures
  • Discover zombie and vacant properties
  • Uncover bored investors
  • Tap into potentially inherited properties
  • Updated vacancy data daily
  • Explore flipped properties and long-term owners
  • Target active investors and create super lists with our list stacking feature.
  • Cash Buyers
  • Absentee Owners
  • All equity positions
  • High
  • Low
  • Free n’ Clear
  • MLS Listings (Active, Pending, Sold)
  • Find potential pre-foreclosures and foreclosures
  • Discover zombie and vacant properties
  • Uncover bored investors
  • Tap into potentially inherited properties
  • Updated vacancy data daily
  • Bankruptcy
  • Private Lenders (all 50 states)
  • Public Record Information, Transaction History, Foreclosure History
  • Owner Types (individuals, business, trust, financial, government)
  • 40 years of real estate transactional data provides access to a vast amount of historical data, enabling investors to gain insights into trends and patterns over time.
  • Analysis of 136 Billion+ data points allows for more accurate and detailed insights, enabling investors to make more informed decisions.
  • Increase from 9 to 56 different regions based on geography, demographics, socioeconomic data, etc., provides more detailed regional breakdowns, helping investors make decisions that are tailored to specific markets.
  • Continual optimization of data ensures that the insights provided are up-to-date and relevant, enabling investors to stay ahead of the competition.
  • Inclusion of out-of-the-box data points like magazine subscriptions provides a wider range of insights, helping investors make decisions based on a more complete understanding of their target audience.
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Data Warehouse

This is the central repository where transformed, ready-to-analyze data is stored. Analyzed data was traditionally held in on-premise data warehouses. Modern data stacks leverage infinitely scalable cloud data warehouses like Snowflake, BigQuery, and Redshift.

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Data Transformation Tools

Data transformation tools like dbt and Dataform help clean up and organize raw data from different sources into the required formats. These tools can be used to create data models, track where data comes from, and check the data’s quality before it’s moved into the warehouse.

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A/B testing tools

A/B testing tools like Optimizely, VWO, and Adobe Target help evaluate and optimize digital experiences by serving different variants of a page or app to users. They provide capabilities to target test segments, analyze performance metrics, and integrate testing data with the broader data stack.

The Role Of Web And App Analytics In The Modern Data Stack

  • Understanding user behavior flows and funnels.
  • Analyzing feature usage and adoption.
  • Attribution modeling and ROI measurement.
  • Segmenting users to understand needs.
  • Personalizing experiences to drive engagement.
  • Predicting churn risk.
  • Generating actionable user insights.
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Benefits Of The Modern Data Stack

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Scalability

The cloud data warehouse can scale up or down on demand, providing flexibility as data volumes and analytics needs change. There is no longer a need for costly on-premise data warehouse infrastructure.

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Agility

New data sources can be quickly ingested and transformations and analyses applied without disrupting existing flows. Faster iteration means faster insights.

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Single Source of Truth

All data lives in the centralized data warehouse, providing a unified view across units and functions – no more siloed data repositories giving conflicting numbers.

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Deeper Analytics

Large amounts of granular data enable more sophisticated analytics using techniques like machine learning. You can drive better insights and decisions.

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Productivity

Less time spent collecting and reconciling data means more time focused on value-added analysis. Self-service analytics reduces dependency on IT and data teams.

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Cost Efficiency

Consolidated architecture and leveraging the cloud reduces costs compared to traditional fragmented systems. It lets you free up resources for high-impact projects.

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