IP Targeting: Stop Paying For Traffic That Will Never Convert

Every traffic buy includes waste.

Wrong geography. Bot traffic. Users on networks that never convert for your offer.

You’re paying for this right now.

IP targeting filters it out before you spend a dollar on it.

Most marketers skip IP targeting completely, or they grab some public blacklist, paste it in, and wonder why nothing changes.

That’s not how this works.

Here’s exactly how to use AI to build, manage, and optimize IP targeting lists that cut unprofitable traffic from your campaigns. We’ll cover how IPs actually work, how to find profitable ranges, and how to implement filtering that protects your ad spend.

This is one piece of the larger campaign optimization system - but it’s one of the most overlooked.

What An IP Address Actually Is

“IP Address” stands for “Internet Protocol Address.”

It’s a unique number linked to all online activity.

I put “unique” in quotes because things get complicated.

In the US, most cellphone users have a unique IP while on their cellular network. The moment they connect to WiFi? Their phone and computer share the same public IP.

On a home network with a router, you have an internal IP controlled by that router. Every device - phone, laptop, tablet, smart TV - shares the same public IP address.

Unless you specifically have multiple public IPs from your ISP (unlikely unless you’re a business), everything in your household looks like one address to the outside world.

Why this matters for your ads:

IP targeting delivers ads to entire network connections. When you target an IP, you hit every device connected to that network. Powerful for household targeting, but you need to understand what you’re actually reaching.

Most consumer traffic comes from Class A, B, and C ranges. Class D is multicast, Class E is reserved. You’ll rarely target those directly.

Cell Tower IPs: A Hidden Edge

Think of a cell tower as a “router” for wireless traffic.

Multiple phones connect to the tower. All devices get assigned either the same IP for that tower or a unique IP within a specific range.

This varies by country and carrier.

Some traffic sources still use IP-based frequency capping. If everyone on a specific tower shares the same IP and you set a 1 impression per hour frequency cap…

You just killed all the carrier traffic in that region.

One impression per hour across potentially thousands of users.

Not as common anymore. But when you find a traffic source that still works this way? That’s an edge your competitors don’t know about.

Proxy Servers: Where Geography Gets Weird

In some parts of the world, internet traffic routes through multiple countries before reaching its destination.

The IP address identifies the location of the ISP or proxy server - and that can be halfway around the world from where your visitor actually sits.

I had a Qatar campaign running for months with almost no competition.

The reason? The carrier traffic I needed routed through a proxy server in Austria.

If you targeted Qatar + Carrier on most traffic sources, you wouldn’t get all the carrier traffic “in that country.” The IPs showed as Austrian.

When I noticed those specific IP addresses converting on one traffic source, I IP-targeted them on my other sources with no country targeting at all.

My traffic stats showed Austria. My conversions came from Qatar.

That’s the kind of edge you build with IP targeting.

How AI Changes This

The old way: Pull converting IP addresses from your tracker. Manually look up each one. Find the CIDR range. Convert it. Build a list. Repeat.

The new way: AI handles the grunt work. You focus on strategy.

Here’s what AI can do for IP targeting right now:

1. Automated IP Analysis

Feed your conversion data to Claude. Ask it to identify patterns in your converting IP ranges. It clusters IPs by carrier, geography, and ISP faster than you ever could manually.

Prompt example:

Analyze this list of converting IP addresses. Identify:
1. Common CIDR ranges (group IPs that share the same /24, /16 blocks)
2. ISP patterns (which providers appear most frequently)
3. Geographic clustering (any concentration in specific regions)

Here's my data: [paste IP list]

2. CIDR Range Conversion at Scale

Instead of using online tools one IP at a time, give AI a batch of IPs and have it return the CIDR ranges.

Convert these IP addresses to their CIDR /24 ranges:
192.168.1.45
192.168.1.78
192.168.2.101
10.0.0.55

Return format: IP -> CIDR Range -> First IP -> Last IP

3. Blacklist Pattern Recognition

AI analyzes your non-converting traffic and identifies patterns in wasted spend. Feed it your high-impression, zero-conversion IPs and ask it to find what they have in common.

4. Cross-Reference With Known Bot Ranges

Public databases of known datacenter and bot IP ranges exist. AI cross-references your traffic against these lists and flags suspicious patterns.

AI doesn’t replace your judgment. It handles the data processing so you can make decisions faster.

Finding Profitable IP Ranges (Step-By-Step)

Many of you are thinking: if I can find a whitelist of IP addresses I can make some bank!

Not so fast.

Just like every other optimization, fewer people with your specific data means more edge for you. Using someone else’s public blacklist or whitelist? That’s already priced into the market.

Build your own.

What you need:

1. A tracking tool that shows IP data (almost every tracker has this - see our tracking setup guide if you need help)
2. Active campaigns generating conversions (even if unprofitable)
3. An offer that relies on specific traffic demographics

Step 1: Pull Your Converting IP Data

Export all conversions from your tracker. You want the IP address for each conversion, plus any other data points you have: carrier, device, time of day.

Step 2: Identify Patterns

Look for IP addresses that appear multiple times. Better - look for IP ranges that perform consistently.

With AI, you do this in seconds:

Here's my conversion IP data for the last 30 days.
Identify which /24 CIDR ranges have:
- 3+ conversions
- Conversion rate above my campaign average of X%

[paste data]

Step 3: Look Up The CIDR Range

Take your profitable IPs and find the route (also known as CIDR).

You can use IP lookup services:

• IPInfo.io
• WhatIsMyIPAddress.com
• DB-IP

The CIDR tells you the full range of IPs in that block.

Step 4: Convert CIDR to IP Range

Take the CIDR (example: 192.168.1.0/24) and convert it to first and last IP.

/24 = 256 addresses (192.168.1.0 to 192.168.1.255) /16 = 65,536 addresses /8 = 16,777,216 addresses

For quick conversion, ask AI:

What is the IP range for CIDR 103.45.67.0/22?

Step 5: Build Your Whitelist

Compile all your profitable CIDR ranges into a single list. This becomes your targeting whitelist.

Why this works:

1. You’re not relying on the traffic source to maintain accurate carrier databases
2. You can do “carrier targeting” on sources that don’t offer it by simply IP targeting
3. Your list is based on YOUR conversion data, not generic industry data

IP Targeting Performance: What The Numbers Show

According to El Toro, leading IP-targeting platforms achieve 95% confidence rates in connecting IP addresses to individual households.

Average display click-through rates sit around 0.07%. IP targeted campaigns? Consistently 2x to 10x higher, with CTRs at or above 0.7% (SEO Design Chicago).

Case study results from El Toro:

• Home furnishing company: Digital ads produced ROAS of 4,572% - about 80% higher than direct mail alone
• B2B tech targeting: 17 of 465 targeted companies converted (3.66% conversion rate), including Goldman Sachs, 3M, and News Corp
• Political campaigns: 19.5% increase in voter turnout using targeted IP advertising
• Fitness center reactivation: 22 new signups and 54% lift in sales in 27 days from targeting former members

Precision makes the difference.

Traffic Sources With IP Targeting

More sources offer IP targeting every year. Here’s the current landscape:

Major Platforms:

• Google Ads - IP exclusions available; IP targeting via Customer Match and DV360
• The Trade Desk - Enterprise-level IP targeting and device graph matching
• DV360 - Full IP targeting capabilities for enterprise advertisers
• Amazon DSP - IP-based household targeting with purchase data overlay

Self-Serve DSPs:

• Choozle - Self-serve DSP with integrated IP-based targeting and custom audience segments
• Simpli.fi - Known for granular geo-fencing down to GPS coordinates
• StackAdapt - AI-powered optimization with IP targeting capabilities

Performance Networks:

• PropellerAds - IP targeting in advanced settings
• RichAds - IP whitelisting/blacklisting
• Adsterra - Request IP lists through your AM

Specialized Providers:

• El Toro - The OG of IP targeting. They invented and patented the technology for matching physical addresses to IPs
• Semcasting - Smart Zones IP targeting
• Digital Element - IP intelligence and geolocation

Note: Some networks that offered IP targeting previously have restricted features due to privacy regulations. Verify current capabilities with your account manager.

Using AI For Real-Time IP Optimization

Instead of building static lists, use AI to analyze performance and adjust targeting dynamically.

Set up an AI optimization workflow:

1. Daily IP Performance Export

Export your daily traffic data: IP, impressions, clicks, conversions, spend.

2. AI Analysis Prompt

Analyze today's IP performance data. Identify:

1. IPs with 50+ impressions and 0 conversions - add to blacklist
2. IPs with conversion rate 2x campaign average - add to whitelist
3. New CIDR ranges appearing in conversion data
4. Any IPs that should be removed from current whitelist (performance dropped)

Current campaign average conversion rate: X%
Current blacklist: [list]
Current whitelist: [list]

Today's data: [paste]

3. Implement Changes

Update your targeting based on AI recommendations. Review the suggestions - don’t blindly trust automation - but let AI handle the data processing.

4. Track Incremental Impact

Measure before/after metrics on each list update. Build a feedback loop.

Pro tip: Platforms like Blueshift and Customer.io now offer AI segment builders where you describe your goal in plain language and the system generates the targeting logic. If your stack supports it, you can describe IP-based segments naturally: “Target IPs that have converted in the last 30 days but haven’t seen an ad in 7 days.”

IP Targeting Strategies That Work

A few ways to use this in practice.

Trade Show Targeting

Specific IP addresses link to specific locations.

Say you’re a company at a trade show with free public WiFi.

Connect to the WiFi. Find the public IP address. Then run CPM ads targeting ONLY that IP address with no frequency cap.

A simple banner: “Come visit us at booth #1282.”

Everyone on that WiFi network sees your ad. Repeatedly. At a fraction of what you’d pay for trade show sponsorship.

Auto Mall Domination

In the US, most dealerships cluster together in “auto malls.”

If there’s a cell tower near the auto mall - or if dealers have public WiFi - you can IP target just that area.

For affiliates: run auto insurance offers to people actively shopping for cars. The intent is there.

Competitor Location Targeting

Know your competitor’s office IP? Target their employees with recruitment ads. Or competitive product messaging.

This works for B2B. If you can identify which IP ranges belong to target accounts, you can run account-based marketing without expensive ABM platforms.

Event Venue Targeting

Concerts. Conferences. Sports events.

Any venue with a known IP range becomes a targeting opportunity. Reach attendees in real-time with relevant offers.

Former Customer Reactivation

If you have customer addresses, services like El Toro can match them to household IPs. Run “We miss you” campaigns to exactly the right households.

No wasted impressions on people who’ve never heard of you.

This pairs well with retargeting strategies - use IP targeting for first touch, retargeting for follow-up.

Privacy: What You Need To Know

Before building IP whitelists, understand the regulatory landscape.

GDPR (Europe): IP addresses are personal data under GDPR. If you’re targeting EU traffic, you need a lawful basis for processing this data. The ICO has raised concerns about IP targeting as a fingerprinting technique.

CCPA (California): Similar requirements for California residents. Users have the right to know what data you collect and opt out. IP status as personal data under CCPA is somewhat ambiguous, but err on the side of compliance.

Google’s 2025 Policy Shift: Google now allows advertisers to target audiences using IP addresses and no longer prohibits fingerprinting techniques - but requires disclosure when these practices are used.

Best practices:

• Use IP data from your own campaigns (first-party data)
• Don’t store IP data longer than necessary
• Include IP collection in your privacy policy
• Be cautious with third-party IP lists of unknown origin
• Implement proper consent mechanisms for EU/UK traffic

Research from Harvard Business Review suggests consumers are more comfortable with IP-based targeting than cookie tracking, with 64% considering it less invasive when properly implemented with transparency (AdMonsters).

The question isn’t whether to use IP targeting. It’s how to implement it responsibly.

IP Targeting vs. Cookie-Based Targeting

With third-party cookies dying, IP targeting matters more.

Safari already blocks third-party cookies. Chrome has eliminated them. Match rates on cookie-based targeting have fallen to 20-25% as more users opt out or use privacy-focused browsers (eMarketer).

IP targeting advantages:

• Works across all browsers
• No cookie consent required (though privacy laws still apply)
• More stable than cookies that get cleared
• Reaches all devices on a network
• Better for household-level targeting

IP targeting limitations:

• Less precise for individual user targeting
• Dynamic IPs change (especially mobile)
• VPNs and proxies can mask true location
• Shared networks mean you’re targeting households, not individuals
• Accuracy varies by ISP and geography

For most performance marketers: use BOTH. IP targeting for broad reach and fraud filtering. First-party data and contextual targeting for precision.

The tools that win in 2026 combine multiple signals - not rely on any single identifier.

If you’re running Google Ads or Facebook Ads, IP targeting becomes an additional layer on top of the platform’s native targeting - not a replacement.

Common Mistakes

Mistake 1: Using Public Blacklists Without Verification

Someone else’s blacklist is based on their campaigns, their offers, their traffic. What doesn’t convert for them might convert for you.

Build your own lists from your own data.

Mistake 2: Targeting Too Narrow

Yes, IP targeting is precise. But if you target a single IP address, you’ll get minimal reach.

Balance precision with volume. Start with /24 ranges and narrow from there.

Mistake 3: Not Accounting For Dynamic IPs

Residential ISPs frequently reassign IPs. A whitelist from 6 months ago might be completely stale.

Refresh your lists regularly. Set up automated processes to validate and update.

Mistake 4: Ignoring Mobile

Mobile carriers use dynamic IP addressing. Your carefully crafted IP whitelist might miss 60%+ of your audience.

For mobile campaigns, combine IP targeting with other signals: carrier targeting, device targeting, geo-targeting.

Mistake 5: Set And Forget

IP performance changes. Networks get reassigned. Fraud patterns shift.

Review your lists monthly at minimum. Build in automation to flag underperforming ranges.

Implementation Checklist

Ready to implement? Here’s your action plan:

Week 1: Foundation

• Verify your tracker captures and exports IP data
• Pull last 30-60 days of conversion data with IPs
• Run initial AI analysis to identify patterns
• Research IP targeting capabilities on your traffic sources

Week 2: Build Initial Lists

• Create whitelist from top-performing IP ranges
• Create blacklist from high-impression, zero-conversion ranges
• Set up CIDR conversion workflow (manual or AI-assisted)
• Document your baseline metrics

Week 3: Implement

• Add IP targeting to one test campaign
• Run A/B test: targeted vs. non-targeted
• Monitor for reach impact (don’t over-filter)
• Track conversion rate changes

Week 4: Optimize

• Analyze test results
• Refine lists based on performance
• Set up automated daily/weekly list updates
• Roll out to additional campaigns

What To Do Next

IP targeting is an edge most competitors don’t use, or use incorrectly.

Combined with proper tracking, you find profitable traffic segments that others miss. With AI handling the data processing, you do this at a scale that wasn’t possible before.

A few things to keep in mind:

• Privacy regulations have changed the game. Use first-party data, be transparent, verify compliance
• Build your own lists. Public whitelists are already priced into the market
• Keep lists fresh. IPs change, performance shifts, fraud patterns evolve
• Combine with other signals. IP targeting works best alongside other filters, not as your only one

The marketers who win aren’t the ones with the biggest budgets.

They’re the ones who cut waste faster than their competitors.

Your next step: Export your conversion data from the last 30 days with IP addresses included. Run it through Claude using the analysis prompt above. You’ll find patterns you didn’t know existed.

Once you’ve got IP targeting dialed in, the next step is scaling your campaigns without losing the efficiency you built.

Sources:

• SEO Design Chicago - IP Targeting Guide
• El Toro - What Is IP Targeting
• El Toro - Case Studies
• AdMonsters - IP Address Targeting: Privacy Myth or Marketing Must?
• eMarketer - How Cookie Deprecation Affects IP Targeting
• Improvado - Programmatic Advertising Platforms 2026
• Blueshift - AI Audience Segmentation