A Guide to AI Powered Skip Tracing Software Development for Real Estate Investors

Imagine being the first investor to contact the owner of a high-value off-market property while others still dig through tax rolls and drive for dollars. Today, more investors are turning to AI skip tracing software development for real estate investors to find tough-to-reach property owners faster than ever.

In fact, systems that deliver accurate contact data and quick match rates are now core to every top investor’s toolkit because quality leads are the lifeblood of profitable deals. According to industry data, skip tracing consistently increases reach and connection rates for investors working off-market lists where traditional methods fail.

This trend explains why more companies are looking for AI skip tracing software development services that tap into multiple data sources, compare patterns, and surface verified owner details at scale. Instead of running manual searches, you can build workflows that automate lead enrichment and accelerate outreach with tools that develop AI skip tracing tools for real estate investment platforms.

In this blog, we will break down how to build AI powered skip tracing software that serves real estate investors and property acquisition teams.

Understanding AI Skip Tracing Solutions for Real Estate Investors and Wholesalers

Real estate teams have always tried to track down property owners who are hard to reach. Skip tracing became the go-to-method because it gathers clues from multiple sources and helps investors connect with sellers before anyone else.

Traditional Skip Tracing Explained

Before we explore the AI side, it helps to understand how skip tracing originally worked.
Here is a simple breakdown.

• Manual searches on public records
• Phone directories and outdated databases
• Social profiles checked one by one
• Repetitive data entry
• Accuracy limited by time and access to data

These methods still work in some cases, but they rarely keep up with competitive markets where every minute counts. Investors who rely only on manual tracing often face low match rates and slow response times.

This comparison shows why more firms are searching for AI skip tracing software development services to support lead pipelines and improve deal flow.

How AI Reinvents Skip Tracing

Instead of searching one source at a time, modern systems run multiple checks in seconds and unify that data into a clear owner profile. Here are the core AI capabilities that reshape skip tracing:

• Intelligent identity matching
• Data clustering for name and address variations
• Pattern based owner prediction
• Automated enrichment from reliable data providers
• Confidence scoring for cleaner lead quality
• Error reduction through continuous learning

This way, the experience becomes smoother because the system handles heavy lifting while teams focus on communication, negotiation and conversions.

Essential Data Sources That Power AI Driven Skip Tracing

Every strong AI model depends on the quality of the data behind it. When companies plan to develop intelligent skip tracing applications, they often overlook how diverse and structured their data needs to be. This list summarizes the most common and effective sources.

• Public records and assessor data
• MLS feeds
• Utility and telecom datasets
• Credit header information when legally allowed
• Postal change records
• Web presence and digital activity
• Property tax history
• Prior transaction records

Good skip tracing platforms combine these sources without overwhelming the user. Smart data pipelines organize information and highlight only what matters.

This entire section forms the foundation of what investors need to know before adopting smarter tracing tools. Traditional methods opened the door, but AI-powered systems now help investors close more deals, increase match rates and reach owners that others miss.

Why Investors Should Focus on AI Real Estate Data Tracing Software Development?

More investors are competing for the same off-market properties, which means the fastest and smartest lead pipelines win. AI-driven skip tracing has moved from an optional add-on to a core acquisition engine because slow research drains opportunities.

Rising Volume of Off-Market Opportunities

Off-market deals remain the most attractive category for investors because they offer higher margins and much less bidding pressure. According to reports, investors bought one-third of all single-family residential properties sold in the second quarter of 2025, highlighting the hunger for faster property discovery.
Growing deal volume increases competition, which pushes more investors toward AI skip tracing software development services to reach owners first.

Persistent Pain Points That Slow Down Real Estate Teams

Investors know the struggle of researching leads manually. The process feels long, repetitive and inconsistent. Below is a quick overview of the most common roadblocks:

• Data scattered across different systems
• Low accuracy when owners change addresses
• Manual research taking hours
• Limited visibility into owner intent
• Harder outreach and lower response rates
• Difficulty scaling with growing lists

These challenges encourage more companies to develop scalable AI skip tracing solutions for investors who handle large volumes of records.

Benefits That Create Real Competitive Advantage

Modern skip tracing offers more than verified phone numbers. AI models transform lead intelligence and help teams act with confidence. The table below highlights key benefits of adopting AI powered systems.

Investors want stronger data, faster turnarounds, and scalable systems that open more doors and reduce wasted time. AI driven platforms offer that edge and make growth more predictable in competitive real estate markets.

Use Cases for Different Segments in AI Skip Tracing Software Development for Real Estate Investors

Different groups rely on AI skip tracing systems for different reasons, but all of them benefit from cleaner data, faster lead processing and better outreach timing. Below are the core use cases arranged in a simple numbered format for clarity.

1. Use Case for Wholesalers and High-Volume Lead Generators

Wholesalers rely on fast turnaround. Their business depends on reaching distressed or off-market owners before competing investors. AI driven tracing helps them manage large lists without losing momentum because automated verification reduces time wasted on dead leads.

2. Use Case for Fix and Flip Investors

Fix and flip operations demand a steady pipeline of motivated sellers. These investors often work across multiple zip codes and need precise owner information to evaluate opportunities quickly. When a property sits vacant or tax delinquent, they want accurate contact data without hours of research.

3. Use Case for Buy and Hold Investors and Portfolio Builders

Long-term investors focus on stability and predictable returns. They want solid rental assets, growing neighborhoods and owners who are open to selling but hard to reach. AI tracing helps these teams discover key segments such as absentee owners, inherited properties, pre-probate cases and long-time owners with equity.

4. Use Case for Proptech Platforms That Serve Real Estate Users

Proptech platforms often integrate skip tracing as a value-added feature for agents, investors and acquisition teams. These companies want scalable engines that deliver accurate data for thousands of users without downtime. AI-driven tracing boosts customer satisfaction because users get better match rates and deeper insights.

Portfolio Spotlight: AI-Powered Real Estate Platform

This AI real estate platform represents a real-world scenario where AI enhanced property discovery and communication align with skip tracing style workflows.

This platform:

• Helps buyers find properties that match their financial and location criteria
• Supports communication between buyers and sellers when traditional contact channels fail
• Uses MLS and GPS data, which mirrors the multi-source approach used in modern skip tracing
• Enhances remote and in person visits for better acquisition decisions

These capabilities show how AI infused platforms improve user experience and trust in modern real estate ecosystems.

5. Use Case for Property Management Companies

Property managers occasionally need owner insights when dealing with third party listings, past tenants or complex ownership structures. AI tracing helps them identify absentee owners, decision makers and responsible parties without phone tag.

6. Use Case for Tenant and Owner Verification Workflows

Some investment strategies rely on background checks, credibility signals and rental history insights. AI driven skip tracing supports these workflows with identity validation, cross referenced records and historical activity indicators.

Portfolio Spotlight: Ratings Platform for Rental Agreements

This review and ratings platform for rental agreements demonstrates a powerful example of verification workflows that align with skip tracing enhancements.

How it supports investor style verification:

• Provides structured reviews for tenants, landlords and properties
• Helps users understand the credibility and behavior of involved parties
• Uses secure data handling that mirrors the trust requirements of AI tracing
• Enhances decision making for rental and acquisition scenarios

The platform offers clarity and transparency, which improves the quality of real estate interactions.

7. Use Case for Acquisition Teams in Large Real Estate Firms

Large investment groups have dedicated teams that operate across multiple geographic regions. They need data accuracy, custom filtering options, progress tracking and automated follow ups. AI tracing provides the infrastructure that matches this scale.

Better data brings better deals. AI powered skip tracing improves accuracy, reduces manual workload and multiplies outreach results. It helps every team move faster in markets that reward speed and clarity.

Also read: 6 use cases of AI software for real estate

Core Features Required for AI Skip Tracing Software Development Services

Investors and acquisition teams want tools that organize complex information, surface accurate contacts and eliminate repetitive work. The table below highlights the must have features that shape high performance systems built for real estate professionals.

These features form the foundation of reliable and scalable skip tracing systems. When applied together, they help investors uncover opportunities faster, reduce wasted calls and build predictable acquisition pipelines.

Advanced Features That Strengthen AI Real Estate Data Tracing Software Development

Advanced features bring clarity to complex owner histories, help teams prioritize their workflow and reduce time spent guessing. The following list explores these deeper capabilities and how they improve deal discovery.

1. Predictive Owner Likelihood Modeling

This feature evaluates a wide mix of signals to determine the likelihood that a specific number or email belongs to the current property owner. Instead of relying on single touchpoints, the model studies movement patterns, digital footprints and historical matches. The final output helps investors identify which contact points are worth calling first.

Also read: Real estate AI predictive analytics software development guide

2. Context Based Lead Scoring

Advanced skip tracing systems use context rather than simple data matches. Signals like property ownership history, neighborhood trends, equity patterns and related behavior create a score that tells investors which leads deserve priority.

3. Automated Data Enrichment from Multiple Streams

This feature blends diverse data sources into a single, useful owner profile. Enrichment layers often include tax details, listing history, utility shifts, licensing information and changes in residency. These upgrades turn raw data into actionable intelligence.

4. Natural Language Understanding for Unstructured Inputs

In real estate, not all data arrives in perfect database format. Email notes, scanned documents, uploaded PDFs and fragments from third party platforms often hold valuable details. Natural language understanding tools read these inputs and extract what matters, such as owner clues, alternative addresses or related contacts.

5. Behavioral Pattern Detection

Modern systems look for unusual activity patterns that indicate ownership transitions, vacancy signs or financial distress. While these patterns are not guarantees, they guide investors toward leads that may be more open to selling.

6. Conversational Intake Through AI Powered Assistants

This capability helps platforms collect structured owner or buyer preferences through natural chat interactions. It makes data entry smoother and turns complex questionnaires into simple messages. Investors using this feature often experience better user engagement and cleaner data.

Portfolio Spotlight: AI Property Management Application

This AI-based property management application shows how conversational intelligence strengthens real estate workflows.

What it does:

• Uses guided questions to understand buyer preferences
• Organizes property insights that help users make informed decisions
• Supports interactive filtering so users receive more relevant results
• Enhances lead qualification through structured conversation flows

When applied to skip tracing, similar methods help gather missing owner clues, validate details and support more refined matching.

Advanced features transform skip tracing from a lookup tool into a strategic intelligence platform. They elevate accuracy, streamline workflows and give investors a clearer path to meaningful leads.

Tech Stack and Compliance Needs in AI Skip Tracing Software Development for Real Estate Investors

Building a modern skip tracing platform depends on the right combination of tools, frameworks and safeguards. The better the foundation, the smoother the performance, accuracy and long term scalability.

Recommended Tech Stack

This table breaks down the core elements developers rely on when creating scalable systems for AI skip tracing software development for real estate investors.

Many companies prioritize these components when planning AI skip tracing software development services for long term stability.

Security and Regulatory Compliance Essentials

Investors reduce risk when their platform follows consistent security practices and regulatory guidelines. Below is a simple and clear list of the essentials.

• Data encryption applied during transit and storage to protect owner information
• Role based access controls to limit internal misuse
• Detailed audit logs for accountability and troubleshooting
• Secure document handling for uploaded files and enriched data
• Compliance with TCPA for communication outreach
• Awareness of FCRA rules for permissible data use
• Alignment with CCPA for user data rights and privacy obligations
• Vendor and licensing review to ensure legal data sourcing
• Regular system monitoring to prevent unauthorized activity
• Periodic penetration testing to validate system integrity

These safeguards allow teams to handle high value property data without exposing investors to unnecessary risk. Strong compliance is often a decisive factor for firms comparing internal builds with external vendors.

Portfolio Spotlight: Real Estate Contract Management Tool

This real estate contract management tool offers a clear demonstration of secure system design that aligns well with skip tracing compliance expectations.

How this project supports secure and compliant workflows:

• Uses safe document storage models that keep contracts and related data fully protected
• Applies structured permissions for agents, buyers and sellers to reduce exposure risk
• Introduces AI powered document summaries that maintain clarity without compromising security
• Runs activity tracking that helps users monitor deadlines, milestones and sensitive actions
• Offers controlled financial mappings and secure payment flows through trusted gateways

These capabilities show how platforms built with rigorous security and compliance can scale confidently. When applied to skip tracing, similar frameworks strengthen the integrity and reliability of every lead.

A thoughtful tech stack combined with responsible compliance practices creates a strong foundation for any AI powered skip tracing platform.

Also read: How to build AI real estate tracking app to monitor properties?

Step-by-Step Process for AI Real Estate Data Tracing Software Development

The development journey moves through structured phases that define the product vision, shape the user experience, build the intelligence engine and prepare the platform for real world use. The steps below outline how development teams bring these systems to life in a smooth and predictable way.

Step 1. Requirement Analysis and Product Discovery

This step defines the purpose of the platform and clarifies what the investor or company truly needs. Discovery covers use cases, data sources, volume expectations, feature priorities and AI integration services. Teams also identify compliance requirements, scalability plans and user roles.

Step 2. Data Strategy and Source Mapping

Skip tracing depends heavily on accurate and diverse datasets. This step focuses on selecting legal sources, establishing licensing needs, designing ingestion rules and building mapping logic. Teams outline how public records, utility data, MLS information or digital footprints will merge into a unified structure.

Step 3. AI Model Selection and Matching Logic

In this step, developers build the identity resolution engine. The model studies patterns, variations and historical traces to match names, addresses and owner signals. It extracts clues from structured and unstructured data, applies matching logic and generates confidence scores.

Step 4. UI and UX Design for Clean and Productive Workflows

User experience shapes how well acquisition teams adopt the tool. UI/UX design company creates intuitive dashboards, simple list uploads, clear scoring displays and fast search capabilities. The goal is to make the platform easy to learn, easy to navigate and reliable for daily operations.

This step focuses on

• Clean data presentation
• Straightforward navigation patterns
• Minimal click paths
• Simple lead management
• Clear indicators for verified data and priority leads

Also read: Top 15 UI/UX design companies in USA

Step 5. Core Development and System Integration

This phase brings the product design and intelligence model together. Developers connect databases, queues, APIs, enrichment partners, communication tools and CRM systems. They also implement the scoring engine, identity resolver, bulk processing logic and data normalization layers.

Step 6. MVP Build and Controlled Testing

The MVP stage delivers a functional version of the product containing essential skip tracing capabilities. Teams test match accuracy, data ingestion speed, bulk processing, lead scoring clarity and integration stability. The goal is to validate real world performance without overwhelming features in the first release.

Developing an MVP helps investors

• See early value
• Test core flows
• Share feedback
• Identify enhancements
• Plan for scale with confidence

Also read: Top 12+ MVP development companies in USA

Step 7. Security, Optimization and Full Deployment

In the final step, teams refine performance, address bottlenecks and prepare the system for real world traffic. Security layers, role controls, encryption, monitoring, backup routines and compliance checks are implemented fully before deployment. Load testing ensures the platform performs smoothly at high volumes.

Once stable, the platform goes live. Continuous optimization begins immediately, supporting accuracy improvements and long-term scalability for investors using the system.

Also read: How to build real estate AI software?

How Much Does It Cost to Create AI Driven Skip Tracing Solutions?

Building a strong AI powered skip tracing platform involves careful planning, smart engineering and a clear understanding of where budget is spent. The overall cost typically ranges from $25,000-$100,000+, depending on the scope, feature depth, data strategy and scaling requirements.

The table below gives a quick snapshot of what companies can expect at different build levels.

These stages give real estate teams flexibility. You can start with something targeted and functional, then expand as your acquisition pipeline grows.

Primary Cost Drivers in AI Skip Tracing Software Development

Every skip tracing platform contains components that influence the budget. The table below explains the biggest cost drivers and why they matter.

These drivers vary based on how much automation, intelligence and customization an investor expects.

Hidden Costs That Investors Should Consider

Some costs remain invisible at the start but influence long term performance. These elements support reliability, uptime and compliance which are critical for investors who rely on continuous lead discovery.

1. Ongoing AI Model Retraining

Every AI system benefits from retraining because owner behavior, data patterns and public records change over time. Typical range is $3,000-$10,000 every enhancement cycle.

2. Data Refreshing and Integrity Management

Skip tracing depends on the freshness of data. As data ages, accuracy drops, which affects lead quality. Data refresh schedules often cost $2,000-$12,000 yearly depending on frequency and volume.

3. Security and Compliance Upgrades

Regulations shift regularly, especially in communication and consumer data sectors. Staying compliant may require additional development. Annual security or compliance upgrades typically cost $1,500-$8,000.

4. Infrastructure Scaling as Lead Volume Grows

As your platform handles larger lists and supports more users, infrastructure costs grow. Scaling with AWS, Azure or Google Cloud often adds $1,000-$6,000 per year depending on usage.

5. Integration Maintenance for Third Party Systems

Integrations with CRMs, dialers, data providers and outreach platforms require periodic updates. Maintenance usually ranges from $1,200-$6,000 yearly.

Understanding these costs helps investors make financial decisions with clarity. As we move to the next section, we will look at whether you should choose custom development or an off-the-shelf tool based on your goals.

Choosing Between Custom and Off-the-Shelf for Build AI Powered Skip Tracing Software

Should you invest in a custom platform or subscribe to an off-the-shelf tool? Both options serve different needs, so this comparison table helps clarify which path aligns better with your long-term goals.

If your business relies on consistent, high quality owner discovery and long-term expansion, custom AI powered skip tracing offers far more control and higher ROI.
If your needs are light and short term, an off-the-shelf tool may be enough.

Risks and Mistakes in AI Real Estate Data Tracing Software Development with Practical Solutions

Developing an AI powered skip tracing platform delivers real advantages, but the journey also presents challenges that teams must handle with care. Knowing these hurdles early helps investors plan better, avoid expensive setbacks and build systems that perform with accuracy and consistency.

Challenge 1: Low Quality or Unreliable Data

When the incoming data has gaps, duplicates or outdated entries, the system produces weak matches and low scoring accuracy.

Solutions

• Use multi source data blending to reduce dependency on any single dataset
• Apply cleansing layers that correct formats and remove inconsistent entries
• Add confidence scoring checks to filter out weak matches

Challenge 2: Overdependence on Generic AI Models

Some teams rely on generic matching models without tuning them to real estate specific patterns. This leads to average results and inconsistent performance across markets.

Solutions

• Train models on region specific behavior for address shifts and ownership signals
• Test variations of matching logic for different property types
• Evaluate model outputs regularly to pinpoint weak areas

Challenge 3: Misaligned User Experience

When acquisition teams struggle to upload lists, read scoring signals or navigate the dashboard, adoption slows down.

Solutions

• Conduct user interviews before designing the interface
• Keep workflows simple with minimal steps
• Use clean labels and clear scoring indicators

Challenge 4: Integration Breakdowns with Existing Systems

Real estate teams rely on CRMs, dialers and automation platforms. Without stable integrations, workflows become fragmented.

Solutions

• Use standardized API patterns with version control
• Test integrations across real world scenarios
• Add monitoring to detect failures instantly

Challenges are normal when building advanced AI platforms, but each one becomes manageable with planning and structure. The next section explores the future of AI-driven tracing and how technology will continue shaping investor workflows.

Also read: AI real estate valuation software development guide

Future Trends Shaping AI Skip Tracing Software Development for Real Estate Investors

The next wave of skip tracing technology brings deeper accuracy, smarter prediction and smoother workflows. These trends are already shaping the future of property data intelligence and will continue to influence how investors plan their acquisition strategies.

1. Multi-Layer Identity Intelligence

Skip tracing is moving toward layered identity ecosystems that combine structured data, behavioral patterns and real time indicators. Instead of relying on static records, platforms will merge movement data, digital traces and lifestyle signals into stronger profiles. This shift will help investors reach owners faster with fewer attempts and much more confidence.

2. Automatic Lead Narratives

Future systems will assemble brief narratives that explain why a lead matters. These narratives summarize ownership events, movement clues, prior transactions and neighborhood activity without overwhelming the user. Acquisition teams will skim these summaries to make quick decisions, improving outreach timing and negotiation prep.

3. Neighborhood Level Prediction Models

Advanced platforms will evaluate entire neighborhoods to pinpoint micro trends that influence owner motivation. These models will blend property age, market activity, equity patterns and demographic movement to predict which areas hold hidden opportunities.

4. Fully Synced Outreach Pipelines

Skip tracing will no longer stop at finding owners. Future tools will push verified contacts into outreach sequences that sync with CRMs, dialers and AI automation services instantly. This creates a straight line from discovery to communication, reducing gaps that slow down momentum and lowering the chances of losing deals to faster competitors.

5. Privacy Centric AI Structures

Privacy expectations are rising across industries. Future skip tracing engines will adopt permission based data ecosystems, encrypted storage flows and transparent insight logs. These elements help maintain user trust while allowing investors to work with sensitive data responsibly.

6. Real Time Data Refreshing

Platforms will shift toward live or near live updating of key datasets. Instead of waiting for bulk imports, systems will refresh tax updates, utility shifts and ownership events as they happen. This creates sharper lead quality and earlier detection of new opportunities that were previously hidden in outdated files.

The future of skip tracing is guided by smarter intelligence, deeper context and cleaner user experiences. Investors who embrace these innovations will operate with more accuracy and reduced guesswork.

Why Trust Biz4Group LLC for AI Skip Tracing Software Development in the USA?

Real estate businesses across the USA need technology partners who understand the fast pace of acquisitions, the pressure of competition and the importance of building tools that deliver measurable returns. Biz4Group LLC stands in this space with years of deep experience in real estate AI software development.

We are a USA-based software development company that helps investors, wholesalers, acquisition managers and proptech founders build AI products to move faster and work with more confidence.

We operate with a simple but powerful philosophy. Success comes from blending technical strength with industry clarity. When companies want to design custom AI skip tracing platforms, lead intelligence engines or advanced real estate applications, they reach out to us because we understand both the tech and the industry.

Our portfolio includes conversational AI platforms, contract automation engines, property discovery tools, MLS powered systems, and end-to-end real estate websites. These projects highlight our ability to turn complex requirements into smooth, reliable and high performing solutions.

Why Businesses Choose Us

Companies count on Biz4Group LLC for several reasons that set us apart from typical development vendors.

• We build custom AI models that improve accuracy, scoring and prediction for property and owner intelligence
• We design clean and intuitive interfaces that keep teams efficient and productive
• We create scalable cloud architectures ready for high data volume and multi market expansion
• We offer strong compliance support to help businesses operate safely across the USA
• We deliver projects with consistency, structure and a transparent communication process

Our expertise covers AI development, user-centered design, scalable infrastructure and compliance aligned development. Whether your goal is to build a lean MVP or a full enterprise AI solution, we craft solutions that support long-term success.

Working with our team means hiring AI developers that values precision, speed, transparency and quality. When your business depends on reliable data and fast decision making, you need a partner that understands how to build platforms that carry that responsibility with care.

Biz4Group is that partner. And we’re ready to build when you are.
Let’s talk.

Wrapping Up

A strong skip tracing engine has become one of the most reliable ways for real estate investors to uncover more opportunities, reach motivated owners and streamline acquisition workflows.

With AI powered capabilities, tracing no longer depends on slow manual research or fragmented data sources. It transforms into a fast, scalable and accurate system that helps real estate teams operate with clarity and speed.

Biz4Group LLC helps companies across the USA build these powerful platforms with expertise in AI app development services, data architecture, real estate workflows and scalable product development. Our experience allows us to design solutions that match real business needs and deliver long term value.

If you want your real estate business to reach property owners faster and run with a stronger competitive edge, partner with Biz4Group LLC and build a platform that helps you win more deals. Let’s turn your idea into the next industry leading solution.

Get in touch.

FAQs

How long does it take to develop a functional AI skip tracing platform?

Most projects reach a working MVP in 6-10 weeks. Biz4Group, however, can deliver a functional MVP in 2-3 weeks. Our team uses reusable components, pre-engineered modules and proven architectural patterns that cut down both development cost and time while maintaining quality and stability.

Can AI skip tracing platforms work with international property data?

Yes, as long as the data sources you choose are legally accessible and structured for analysis. International tracing requires additional normalization layers to align formats and address standards, but it can be built into the system if global expansion is part of your roadmap.

Do AI skip tracing tools require continuous monitoring?

They benefit from routine monitoring because data environments evolve and owner records change. Light monitoring ensures consistent performance, stable integrations and fresh scoring outputs. Most teams automate this process for convenience.

Who typically manages an AI skip tracing system once it is built?

Most companies assign management to an operations lead, data manager or acquisitions coordinator. The platform itself requires minimal day-to-day handling once automation is in place, and technical teams step in only during major updates.

What type of training do teams need before using an AI skip tracing platform?

Training usually involves short sessions focused on list uploads, lead interpretation and workflow navigation. Because modern platforms emphasize clean UI design, onboarding is quick and users become productive without heavy instruction.

Can AI skip tracing platforms help reduce outreach costs?

Yes. By filtering weak leads and sharpening contact quality, teams spend less on calls, texts, direct mail and follow ups. Better targeting lowers overall outreach expenses and increases the return on each campaign.

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