Your Google Ads Leads Look Great, But Are They?

What I mean by “spam leads” (and how they show up)

When I talk about spam leads from paid search, I am not only talking about obvious bots. I am also dealing with fake form submissions, low-intent tire kickers, lead-gen arbitrage, and traffic that looks fine in ad reports but never becomes pipeline.

On the surface, campaigns can look healthy - cost per lead is acceptable and conversions rise. Underneath, the team is chasing ghosts while real buyers wait longer for a reply. For a founder trying to scale, that is not just annoying. It is dangerous, because bad inputs quietly push strategy in the wrong direction.

Typical symptoms include:

• Throwaway emails, invalid phone numbers, or duplicated contact details
• Form fields filled with nonsense, repeated characters, or irrelevant requests
• Leads from countries or regions I do not serve
• Sudden “conversion” spikes without any lift in qualified opportunities
• No-show meetings clustering at odd hours or in patterns that do not match my market

Why paid search starts attracting junk even when performance looks “good”

It is easy to assume spam is mainly a security issue - add a CAPTCHA and move on. That used to be closer to true. Now the bigger driver is commercial: modern ad platforms are designed to find more conversions, and automation will often chase the cheapest, easiest conversions it can locate.

When I scale budgets and lean heavily on automated bidding and campaign types, the system can expand into queries, placements, or user segments that are only loosely related to what I sell. In B2B services - where the audience is narrow and the sales cycle is longer - this creates a painful mismatch: a small volume of junk can drown out real demand and skew decision-making.

Common settings that invite spam or low intent include:

• Broad match keyword coverage that pulls in vague, consumer-style searches
• Automated campaign types that limit visibility into search terms and placements
• Loose geo targeting (or “interest in” locations rather than “presence in” them)
• Network and placement expansion into partner inventory, apps, and low-quality sites

If you are pressure-testing your setup, I like starting with conversion sanity checks before you scale ad spend, then revisiting the bidding approach once tracking and lead quality are stable (see smart bidding in simple words and when to use manual bids).

Where spam and low-quality leads actually come from

I find it helpful to separate the sources into a few buckets, because each one requires a different fix.

Bots and automated scripts can click ads or submit forms at scale, and many now mimic real behavior well enough to pass basic protections. They can create short sessions, odd engagement patterns, and a high volume of “leads” that never respond. The broader trend is well documented in reports like Imperva’s 2024 Bad Bot Report.

Click fraud and click farms are more intentional: competitors, shady publishers, or organized networks can drain spend or trigger conversion events. This often shows up as unusual clusters (similar devices, repeated technical fingerprints, suspicious IP ranges, or strange timing).

Low-quality placements are a quieter killer. If my campaigns serve on apps or sites designed for accidental taps, I can pay for clicks that were never intentional - especially on mobile placements where a user might trigger an ad interaction while trying to close a screen.

Lead brokers and resellers can also distort results in some industries. When someone gets paid per submitted form, bad actors may use scripts or low-paid manual labor to fill forms with made-up details to hit volume targets.

Misaligned human intent is the bucket people forget. Some “spammy” leads are real humans with the wrong goal: job seekers searching careers, students looking for research material, overseas agencies pretending to be prospects so they can pitch their services, or consumers trying to buy something entirely different.

When I suspect paid search is the culprit, I look for patterns rather than one-off weird leads. Location breakdowns, device and OS distributions, time-of-day spikes, and placement or source reports are usually enough to tell me whether this is “a few bad leads” or a structural problem.

The hidden business cost: unit economics, speed-to-lead, and dirty data

The biggest risk is not a few fake Gmail addresses - it is what junk leads do to my operating model.

First, they inflate volume and hide the true CPL. If a meaningful share of submissions are spam or out of target, the reported cost per lead becomes misleading. My “$80 CPL” might really be $115+ for sales-eligible leads once I strip out junk.

Second, they burn sales time. Even a quick triage adds up. For example, if a team handles 200 paid-search leads per month and 30% are spam or off-target, that is 60 leads. At 10 minutes each, that is 10 hours of wasted time monthly. Multiply by fully loaded rep costs and it becomes a real budget line, not an annoyance.

Third, they slow response to real buyers. Speed-to-lead matters in B2B. If my team is buried in junk, real prospects wait longer and win rates suffer quietly.

Fourth, they corrupt forecasting. A CRM full of noise makes channel reporting unreliable. Over time, I can end up scaling spend based on inflated conversion counts rather than pipeline contribution. (This problem is broader than ads - see the study by Validity on CRM data management.)

Finally, they create reputational drag. Reaching out repeatedly to people who were never my customer (or who never intended to submit a real inquiry) creates friction. In tight industries, that kind of friction accumulates.

Reducing spam without killing demand: a layered approach I trust

There is not a single magic switch. What works is layering defenses so that (1) the form blocks obvious junk, (2) campaigns stop buying the worst traffic, and (3) the business learns from every bad lead.

I also avoid changing everything at once. I prefer small batches of changes, tracked by date, and measured not only on lead volume but on sales-accepted rate and opportunity creation.

Make the form act like a gate, not a suggestion

Many teams start by tweaking campaigns and ignore the lead capture experience. I do the opposite. The form is the last checkpoint before a rep invests time, so it should filter.

To increase signal quality, I ask for business context (not just contact info). For example: company name, website, role or title, company size range, and timeline or urgency. When the form clearly looks “B2B,” low-intent users drop off and real buyers self-select in.

I also add validation and friction where it matters. That can include stricter formatting checks, blocking obvious disposable emails, and requiring work email for high-intent requests. On the bot side, basic CAPTCHAs alone often are not enough anymore, so I rely on layered protections like invisible scoring, honeypot fields, rate limiting, and server-side validation.

The key is measuring the right outcome. If I add friction and total leads fall, that is not automatically bad. I judge success by downstream performance: sales-accepted rate, opportunity rate, and revenue per 100 visits - not raw form fills.

Tighten targeting, exclusions, and campaign expansion

Once the form is not wide open, I go upstream and reduce the amount of junk traffic I am buying in the first place.

I review search terms over the last 30-90 days and build negative coverage around predictable magnets (jobs and careers, “free,” templates and examples, and other clearly off-ICP intent). If broad match is pulling in a lot of noise, I do not necessarily abandon it - but I ring-fence it with tighter controls and allocate more spend to phrase and exact on the highest-intent themes. If you need a simple system for this, negative keywords: the cheapest way to cut waste is a strong starting point.

Geo settings matter more than many teams admit. I make sure I am targeting people in the location, not people merely interested in it, and I exclude regions I do not serve (see geo targeting: how tight should your ads go?). I also check device and time-of-day patterns; when spam clusters on certain devices, OS versions, or odd time windows, I restrict those segments carefully and monitor what happens to qualified pipeline (related: ad scheduling: save money by time of day).

For automated campaign types and any inventory that can drift into low-quality placements, I regularly review where traffic is coming from and exclude sources that clearly do not fit a B2B decision-maker context. In higher-risk accounts, tools that specialize in invalid traffic can add coverage beyond native settings - for example, advanced automated solutions designed to detect and block invalid traffic patterns.

Create a feedback loop so the platform learns what “bad” looks like

Spam does not go to zero, so I treat it as a continuous improvement problem.

I make sure sales can quickly tag leads as spam, out of target, or qualified using a lightweight field - no heavy admin, just consistent labeling. When possible, I use those stages to improve measurement and optimization so the ad platform is not rewarded equally for every form fill. A lead that becomes a sales-accepted opportunity should matter more than a lead that never responds or gets disqualified immediately.

I also keep a simple cadence - weekly or biweekly - to review patterns with sales: what changed, where junk came from, which searches or sources are repeating, and what small set of fixes I will deploy next. This turns “random spam” into structured data I can use to sharpen targeting and reduce waste over time.

How I run a practical traffic-quality review (without turning it into a big project)

When paid search quality is in question, I keep the review focused on decisions, not dashboards.

I start by quantifying the problem using CRM outcomes: what share of paid-search leads become sales-accepted, what share become opportunities, and where disqualifications cluster. Then I map that back to campaign variables: search terms, match types, geos, devices, time of day, and placement sources.

From there, I prioritize a short set of changes by impact and effort - typically a mix of query cleanup, geo and network tightening, form validation and qualification improvements, and cleaner conversion definitions that reward the platform for real business outcomes rather than cheap form submissions.

If the result is fewer leads but more qualified conversations and a cleaner pipeline, that is a win. The goal is not to eliminate every bad submission - it is to make paid search reflect real demand, protect sales time, and keep forecasting grounded in reality.

If you want a second set of eyes to pinpoint where spam is entering your funnel, you can Get a Paid Search Audit or see how it works if you prefer a structured, done-with-you cleanup.