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Last updated: Oct 17, 2026
How to Diagnose and Fix Wasted Ad Spend in a B2B SaaS Ad Account
Reviewed by the Ads for B2B SaaS Editorial Team. The editorial team covers paid media for B2B SaaS companies across Google Ads, LinkedIn Ads and programmatic channels, drawing on sourced benchmark data from audited account sets. Every figure in this guide names its study, sample size and date. Last updated: October 13, 2026.
PPC analysis in a B2B SaaS account is the process of comparing platform-reported conversions against CRM-confirmed sales-qualified leads to locate where budget is reaching the wrong audience, the wrong intent or the wrong conversion signal.
Most B2B SaaS paid media teams discover the problem the same way: cost per lead (CPL) falls, but the quarterly pipeline number barely moves. The disconnect is not a demand problem. It is a signal problem. The bidding algorithm is optimising on a conversion event that does not predict pipeline, and the account is spending accordingly.
In June 2026, the B2B SaaS Google Ads Waste Report (GrowthSpree, 104 accounts, $78.0M in spend audited) found an average waste rate of 34.0% across Google Ads accounts, equivalent to roughly $255,000 per account per year. This guide walks through a five-step audit sequence covering Google Ads, LinkedIn Ads and programmatic in a single diagnostic framework, tying each fix to a measurable cost-per-SQL outcome.
Key Takeaways
The average B2B SaaS Google Ads account wastes 34.0% of spend, or about $255,000 per year; best-quartile accounts waste 13.2% (GrowthSpree, G26, 104 accounts, June 2026).
Broad match without negative keywords and Performance Max (PMax) without offline conversions together account for 56% of Google Ads waste: fix these two settings first.
In June 2026, 91% of audited accounts had no Google click ID (GCLID)-to-CRM connection; without it, bidding algorithms optimise on form fills, of which 78% never reach SQL status (GrowthSpree, G26, June 2026).
LinkedIn Ads waste averages 32.0% across B2B SaaS accounts; 67% of that waste traces to job function, seniority and company size targeting settings (GrowthSpree, L26, June 2026).
Re-scoring active ads by cost per SQL rather than CTR or CPL improved cost per SQL by approximately 44% with no additional spend (GrowthSpree, PD26, 1,412 ad variants, June 2026).
What does PPC analysis actually reveal in a B2B SaaS account?
PPC analysis in a B2B SaaS account reveals the gap between platform-reported conversions and CRM-confirmed sales-qualified leads (SQLs). In June 2026, the B2B SaaS Google Ads Waste Report (GrowthSpree, 104 accounts, $78.0M in spend) found an average waste rate of 34.0%, or roughly $255,000 per account per year. Best-quartile accounts wasted 13.2%; the worst wasted 49.8%. A falling CPL alongside flat pipeline is the clearest signal that waste is already inside the account.
Macro close-up of a printed budget spreadsheet with wasted spend rows circled in red ink
The root cause is almost always one of three things:
Wrong conversion event: The bidding algorithm is optimising on form fills, CTR or CPL rather than SQLs or closed-won revenue.
Wrong audience: Budget is reaching companies or job titles outside the ideal customer profile (ICP).
Wrong match type or campaign type: Broad match keywords or Performance Max campaigns are bidding on queries with no commercial intent for the product.
A CPL figure can fall because the account is generating cheaper, lower-quality clicks. That looks good in a dashboard and bad in a pipeline review. Identifying which root cause is active requires a structured audit, not a budget cut.
Why CPL is the wrong primary metric for B2B SaaS
Cost per lead (CPL) is total ad spend divided by the number of form submissions, without filtering for lead quality. In June 2026, the Paid Ads Pipeline Disconnect study (GrowthSpree, PD26, 1,412 ad variants, 96 accounts, $14.2M in spend) found CPL correlated with pipeline at r=0.23, while cost per SQL correlated at r=0.71. In 43% of A/B tests, the higher-CTR ad produced fewer or costlier SQLs. Optimising on CPL directs spend toward volume rather than pipeline, the wrong trade-off when the median B2B SaaS sales cycle runs 84 days (GrowthSpree, G26, June 2026).
What do you need before running a wasted spend audit?
A B2B SaaS wasted spend audit requires three inputs before starting: account-level ad platform access, a CRM export showing which leads reached SQL or closed-won status in the past 90 days, and confirmation that a Google click ID (GCLID) field exists in the CRM. Without the CRM report, there is no way to separate a wasted click from a converting one. Allow two to four hours for a single-channel audit.
Extreme macro close-up of a Google Ads search terms report on screen with match type column in focus
Access checklist before starting:
Input
Why it is needed
Where to find it
Ad platform access (account level)
Pull search term reports, conversion settings, audience lists
Google Ads or LinkedIn Campaign Manager
CRM SQL report (90 days)
Compare platform conversions against qualified leads
Salesforce, HubSpot or equivalent
GCLID field in CRM
Confirm click-to-lead attribution is possible
CRM lead or contact record
Attribution window setting
Check whether the window matches the sales cycle
Google Ads: Measurement > Attribution
Budget by campaign (90 days)
Identify highest-spend campaigns for priority triage
Ad platform campaign view
The B2B SaaS sales cycle has a median length of 84 days and involves buying committees of 6 to 10 people (GrowthSpree, G26, June 2026). The Google Ads default attribution window is 7 days. In June 2026, the B2B SaaS Google Ads Waste Report (GrowthSpree, 104 accounts) found that default window captures only 5% to 15% of revenue against an 84-day median sales cycle. Confirming the attribution window is the first diagnostic check.
Study finding (GrowthSpree, G26, June 2026-104 accounts, $78.0M spend): 89% of audited accounts ran default 7-day click attribution before any audit was conducted. Against an 84-day median sales cycle, that default window captures just 5% to 15% of revenue, meaning the bidding algorithm was working from an incomplete signal from day one.
Step 1: Check what conversion event your campaigns are optimising on
The most impactful step in any B2B SaaS PPC analysis is verifying which conversion event the bidding algorithm uses. In June 2026, the B2B SaaS Google Ads Waste Report (GrowthSpree, 104 accounts) found 89% of audited accounts ran default attribution and 91% had no GCLID-to-CRM connection. Bidding algorithms were therefore optimising on form fills, of which 78% never reach SQL status (GrowthSpree, G26, June 2026). Fixing the conversion event is the prerequisite for every fix that follows.
Close-up of a CRM lead record showing a GCLID field left blank with cursor hovering
Study finding (GrowthSpree, G26, June 2026-104 accounts, $78.0M spend): Performance Max campaigns running without offline conversions produced a cost per SQL of $2,140. The same campaign type with offline conversions connected achieved $620 per SQL. That gap reflects a bidding algorithm optimising on a signal where 78% of inputs never reach SQL status.
How to check and fix the conversion event
Open Google Ads and go to Tools > Measurement > Conversions.
Identify the conversion action marked "Include in conversions": this is what the bidding algorithm optimises on.
If the primary conversion is a platform-native form submission not tied to a CRM stage, it is the wrong signal.
Check whether a GCLID field exists in the CRM. If not, work with the CRM administrator to add it to the lead or contact record and ensure it captures on form submission. Google's offline conversion import documentation covers the technical setup steps.
Once GCLIDs are flowing into the CRM, build an offline conversion import that fires when a lead reaches SQL status and again at closed-won. Import these events into Google Ads as the primary conversion action.
Extend the attribution window to 90 days using a data-driven attribution model in Conversion settings > Attribution model to match the median B2B SaaS sales cycle.
How to verify this step worked: Within two to three weeks of importing SQL events, offline conversion credits should appear against specific campaigns in the Google Ads conversions column. The "all conversions" column still includes form fills; the "conversions" column should now reflect only the imported SQL events.
Step 2: How to reduce wasted ad spend in Google Ads for B2B SaaS
Reduce wasted ad spend in Google Ads for B2B SaaS by addressing the two settings that together account for 56% of documented waste: broad match keywords without a negative keyword list, and Performance Max (PMax) campaigns running without offline conversions (GrowthSpree, G26, June 2026, 104 accounts). Fixing these two settings moved the average waste rate from 34.0% to 11.0% within 90 days and cut cost per SQL by 62%.
Study finding (GrowthSpree, G26, June 2026-104 accounts, $78.0M spend): Broad match without negative keywords and Performance Max without offline conversions together account for 56% of Google Ads waste. The median B2B SaaS non-brand cost per click ranges from $8.50 to $14.00 (GrowthSpree, B26, April 2026), meaning 100 wasted clicks costs $850 to $1,400 before a single SQL is produced.
Fix 1: Broad match and negative keywords
Broad match is a Google Ads keyword match type that allows a keyword to trigger ads for queries Google considers semantically related, including synonyms and adjacent topics. For B2B SaaS accounts targeting narrow ICP queries, broad match without an accompanying negative keyword list frequently captures queries from students, job seekers, competitors and irrelevant industries.
Steps to address broad match waste:
Pull the Search Terms report for the past 90 days, filtered to broad match keywords only.
Sort by spend, descending.
Identify every term that consumed budget without producing a SQL (cross-reference with the CRM report from Step 1).
Add all non-converting, off-ICP terms as exact match negatives at the campaign level.
For any broad match keyword spending more than 20% of its campaign budget without a SQL, add an exact match or phrase match equivalent and reduce the broad match bid.
Fix 2: Performance Max without offline conversions
Performance Max (PMax) is a Google Ads campaign type that serves ads across all Google channels automatically. Without a SQL-level conversion signal, PMax optimises on whatever it can find: form fills, time on site or soft engagement events. The result is a low reported CPL and a high cost per SQL.
Steps to fix PMax waste:
Confirm offline conversion import is live (from Step 1 above).
In the PMax campaign, navigate to Campaign settings > Conversions and set the SQL import as the primary goal.
Add audience signals using a CRM-sourced customer list to give PMax a starting point for targeting.
Set a brand exclusion so PMax does not bid on brand terms and inflate conversion rates with easily-converted brand clicks.
Review the Asset group performance report monthly: asset groups with no SQL attribution after 60 days should be paused.
Google Ads waste driver
Share of total waste
Fix
Broad match without negatives
Part of 56% combined (GrowthSpree, G26, June 2026)
Add negative keyword list
PMax without offline conversions
Part of 56% combined (GrowthSpree, G26, June 2026)
Connect GCLID-to-CRM, import SQLs
Default 7-day attribution window
Captures only 5%-15% of revenue (GrowthSpree, G26, June 2026)
Extend to 90 days
Form fill as primary conversion
78% of fills never reach SQL (GrowthSpree, G26, June 2026)
Replace with SQL import
Step 3: Our LinkedIn campaigns are burning budget - how do I audit for wasted ad spend?
Audit LinkedIn Ads waste by checking three targeting dimensions in Campaign Manager: seniority (restrict to director, VP and C-suite), job function (align to the ICP, not broad categories), and company size (exclude sizes outside the ICP). In June 2026, the Wasted LinkedIn Ads Spend in B2B SaaS report (GrowthSpree, 56 accounts, $9.4M spend) found these three dimensions account for 67% of LinkedIn waste, and decision-makers received only 33% of budget against a 60%-plus target.
Macro photograph of a LinkedIn Ads audience targeting panel showing seniority filter checkboxes
Study finding (GrowthSpree, L26, June 2026-56 accounts, $9.4M spend): Average LinkedIn Ads waste rate: 32.0%, or roughly $53,600 wasted per account. Decision-makers received only 33% of LinkedIn budget in audited accounts. The target share is 60% or more. Job function, seniority and company size targeting together account for 67% of that waste.
In the Job Title or Job Function breakdown, identify what percentage of impressions and spend is going to seniority levels below director.
If more than 40% of spend is reaching individual contributor or entry-level seniority, add an audience exclusion for those seniority levels.
Check Company Size distribution: if a meaningful share of spend is reaching companies below the ICP minimum employee count, add a company size filter.
Review Job Function targeting. Broad functions like "Business Development" or "Operations" can include roles with no purchase authority. Narrow to the two or three functions that match the actual buying committee.
LinkedIn CPC for B2B SaaS ranges from $8 to $25 by vertical, and cost per SQL ranges from $800 to $8,000 by annual contract value (ACV) tier (GrowthSpree, L26, June 2026). The average wasted LinkedIn Ads spend per account is approximately $53,600 per year. Tightening seniority and job function targeting is the fastest way to redirect that budget toward decision-maker audiences.
The decision-maker budget target
The benchmark from audited accounts is that 60% or more of LinkedIn budget should reach director-level or above. Accounts starting at the 33% decision-maker share have significant headroom to improve before touching total budget. LinkedIn's audience-level demographic report shows exactly where impressions are going, so the fix does not require guesswork.
Step 4: Our programmatic spend keeps rising but SQLs stay flat - where is the wasted spend hiding?
In B2B SaaS programmatic campaigns, wasted spend hides in three places: uncapped impression frequency on the same users, inventory categories outside the ideal customer profile (ICP), and real-time bidding (RTB) without suppression lists excluding existing customers and non-ICP company sizes. The signal problem compounds this: the B2B SaaS Google Ads Waste Report (GrowthSpree, G26, June 2026, 104 accounts) found 78% of form fills never reach SQL status.
Macro close-up of a programmatic DSP frequency cap setting showing an uncapped impression count
Study finding (GrowthSpree, G26, June 2026-104 accounts, $78.0M spend): 78% of form fills never reach SQL status. A programmatic demand-side platform (DSP) optimising on form fills receives the same low-quality signal as a Google Ads campaign using platform-native conversions. Switching the optimisation goal from form fills to SQL events is the single largest lever available in B2B SaaS programmatic spend.
Programmatic audit steps
Pull an impression frequency report from the DSP. If any user segment receives more than five impressions per week, cap frequency at three to five to reduce wasted impressions on already-exposed audiences.
Review the site list report: sort by spend and identify the top 20 domains receiving budget. Remove any consumer news, entertainment, social media or unrelated industry properties.
Upload a suppression list of existing customers and active pipeline contacts. These users do not need awareness spend.
Add a company-size or firmographic filter if the DSP supports it. Exclude company sizes outside the ICP.
Switch the optimisation goal from form fill or cost-per-click to an SQL-based cost-per-acquisition target once offline conversion data is flowing.
How to optimize real-time bidding to reduce wasted ad spend
Real-time bidding (RTB) is the automated auction process where ad impressions are bought in milliseconds as a page loads. The bidding algorithm raises or lowers bids based on the signal it receives. When that signal is form fills, the algorithm bids up audiences most likely to complete a form, not audiences most likely to reach SQL status. Feeding the DSP SQL or closed-won events as the optimisation target changes which audiences it bids on. Apply bid floors by inventory tier to avoid buying low-quality remnant inventory at any price.
Step 5: Fix the metric - why CTR and CPL mask real waste
Fixing the measurement metric governs every future optimisation decision. The Paid Ads Pipeline Disconnect study (GrowthSpree, PD26, June 2026, 1,412 ad variants) found cost per SQL correlated with pipeline at r=0.71, versus r=0.09 for CTR and r=0.23 for CPL. Re-scoring all active ads by cost per SQL improved cost per SQL by approximately 44% with no extra spend.
Study finding (GrowthSpree, PD26, June 2026-1,412 ad variants, 96 accounts, $14.2M spend): CTR correlated with pipeline at r=0.09, close to no relationship at all. Cost per SQL correlated at r=0.71. In 43% of A/B tests, the higher-CTR ad produced fewer or costlier sales-qualified leads. Re-scoring and reallocating by cost per SQL improved cost per SQL by approximately 44% with no additional spend.
Click-through rate (CTR) is the percentage of ad impressions that result in a click. It measures creative appeal, not lead quality. An ad that attracts curious clicks from the wrong audience produces a high CTR and zero SQLs.
Steps to re-score ads by cost per SQL
Export all active ad variants from the past 90 days.
Join the export to the CRM report from Step 1: match each ad to the SQLs it produced using the GCLID or UTM parameter.
Calculate cost per SQL for each ad variant: total spend divided by SQLs attributed.
Sort all variants by cost per SQL, ascending.
Pause any variant that has spent more than 3x the target cost per SQL without producing a SQL.
Increase budget allocation toward the lowest cost-per-SQL variants.
The median B2B SaaS cost per SQL benchmark is $800 to $2,500, with wide variation by vertical: DevTools accounts average $650 per SQL and Cybersecurity accounts average $3,500 per SQL (GrowthSpree, SaaS Google Ads Benchmarks 2026, April 2026). Use the vertical-specific benchmark, not the blended median, to set the pause threshold.
Common mistakes to avoid in a B2B SaaS wasted spend audit
The most common mistake in a B2B SaaS wasted spend audit is pausing campaigns based on CTR or CPL before checking cost per SQL. Because CTR correlates with pipeline at r=0.09 (GrowthSpree, PD26, June 2026), pausing low-CTR ads can remove the best SQL-generating spend in the account. The second most common mistake is fixing match types before fixing the conversion event: the wrong signal undermines any match type improvement.
The three most frequent audit errors:
Pausing campaigns based on CPL: CPL correlates with pipeline at r=0.23 (GrowthSpree, PD26, June 2026). A campaign with a high CPL may be producing SQLs at an acceptable cost; a campaign with a low CPL may be producing only form fills. Check cost per SQL before pausing any campaign.
Fixing audience targeting before fixing attribution: Narrowing audiences while the conversion signal is still form fills leaves the bidding algorithm optimising toward the narrower audience using the wrong quality signal. Fix attribution first, always.
Applying Google Ads logic to LinkedIn Ads: On Google Ads, 56% of waste comes from match type and campaign type settings (GrowthSpree, G26, June 2026). On LinkedIn Ads, 67% of waste comes from audience targeting dimensions (GrowthSpree, L26, June 2026). The audit steps differ by channel. Negative keywords do not address LinkedIn's root cause.
What results to expect from fixing wasted ad spend
A structured 90-day fix moved the average Google Ads waste rate from 34.0% to 11.0% and cut cost per SQL by 62% across 104 B2B SaaS accounts (GrowthSpree, G26, June 2026). Best-quartile accounts ran at 13.2% waste, a realistic target for accounts starting at the 34.0% average. Re-scoring ads by cost per SQL improved cost per SQL by approximately 44% with no additional spend (GrowthSpree, PD26, June 2026).
Study finding (GrowthSpree, G26 and PD26, June 2026): A 90-day structured audit and fix across 104 B2B SaaS Google Ads accounts moved the average waste rate from 34.0% to 11.0% and cut cost per SQL by 62%. Separately, re-scoring ads by cost per SQL improved cost per SQL by approximately 44% with no additional spend across 1,412 ad variants and $14.2M in spend.
Metric
Before fix (average)
After 90-day fix
Source
Google Ads waste rate
34.0%
11.0%
GrowthSpree, G26, June 2026
PMax cost per SQL
$2,140 (blind)
$620 (with offline conversions)
GrowthSpree, G26, June 2026
LinkedIn decision-maker budget share
33%
Target: 60%+
GrowthSpree, L26, June 2026
Cost per SQL via ad re-scoring
Baseline
~44% improvement
GrowthSpree, PD26, June 2026
The median B2B SaaS non-brand cost per click is $8.50 to $14.00 and the median cost per SQL is $800 to $2,500 (GrowthSpree, SaaS Google Ads Benchmarks 2026, April 2026). Use these as external benchmarks alongside the internal before/after figures to show where the account stands relative to the market.
The timeline for visible results depends on data volume. Accounts spending more than $20,000 per month should see attribution data stabilise within four to six weeks of connecting offline conversions. Accounts spending below $10,000 per month may need eight to twelve weeks before bidding algorithms have enough SQL signal to shift spend meaningfully.
Who this audit is not for
B2B SaaS teams running 84-day sales cycles and CRM-based conversion paths are the intended audience for this audit. B2C, direct-to-consumer and ecommerce advertisers should not follow this sequence. Every benchmark here assumes buying committees of 6 to 10 people (GrowthSpree, G26, June 2026) and a conversion path from form fill to SQL to closed-won. Accounts with sub-7-day sales cycles can use last-click attribution without the same penalty.
The fixes here add operational complexity. That complexity is justified when the sales cycle is long enough that the default attribution window misses the majority of revenue, and when buying committees require multi-touch exposure before a form fill appears. For short-cycle or low-annual contract value (ACV) products, simpler setups deliver comparable accuracy.
Frequently Asked Questions
How to improve paid media efficiency in B2B SaaS?
Improve paid media efficiency in B2B SaaS by switching the optimisation signal from form fills to sales-qualified leads (SQLs). Connect Google click IDs to the CRM, import closed-won and SQL events as offline conversions, and reallocate budget toward campaigns with the lowest cost per SQL. In June 2026, the B2B SaaS Google Ads Waste Report (GrowthSpree, 104 accounts) found a 90-day structured fix cut cost per SQL by 62%.
How do businesses reduce wasted ad spend without losing leads?
Businesses reduce wasted ad spend without losing leads by tightening audience and match-type settings rather than cutting budget. On Google Ads, adding negative keywords to broad match campaigns and enabling offline conversions on Performance Max removes irrelevant spend while keeping in-market signals intact. On LinkedIn Ads, narrowing seniority and job function targeting to decision-maker tiers preserves lead volume from the right accounts without reducing total campaign reach.
How to reduce wasted ad spend in Google Ads for B2B SaaS?
Reduce wasted ad spend in Google Ads for B2B SaaS by fixing the two settings that together account for 56% of documented waste: broad match keywords without a negative keyword list, and Performance Max campaigns running without offline conversions (GrowthSpree, G26, 104 accounts, $78.0M spend, June 2026). Fixing these two settings moved average waste from 34.0% to 11.0% within 90 days and cut cost per SQL by 62%.
Our programmatic spend keeps rising but SQLs stay flat - where is the wasted spend hiding?
In B2B SaaS programmatic campaigns, wasted spend typically hides in uncapped impression frequency, inventory categories outside the ideal customer profile, and real-time bidding without suppression lists for existing customers and non-ICP company sizes. Audit impression share by site category, apply frequency caps of three to five impressions per user per week, and switch the DSP optimisation goal from form fills to SQL events.
How to optimize real-time bidding to reduce wasted ad spend?
Optimize real-time bidding (RTB) to reduce wasted ad spend by feeding the bidding algorithm qualified conversion signals rather than raw form fills. In June 2026, the B2B SaaS Google Ads Waste Report (GrowthSpree, G26, 104 accounts) found 78% of form fills never reach SQL status. Pass SQL and closed-won events back to the DSP, set bid floors by inventory tier, and apply company-size suppression lists to exclude non-ICP audiences from the auction.
Which SaaS PPC settings drive the most wasted spend and how do we fix them first?
The two settings that drive the most wasted spend in B2B SaaS PPC are broad match keywords without negative keyword lists and Performance Max campaigns without offline conversions. Together they account for 56% of documented Google Ads waste across 104 accounts and $78.0M in spend (GrowthSpree, G26, June 2026). Fix order: connect offline conversions first, then add negative keywords, then audit Performance Max asset group targeting.
I need data to justify cutting LinkedIn waste - what benchmarks exist?
In June 2026, the Wasted LinkedIn Ads Spend in B2B SaaS report (GrowthSpree, 56 accounts, $9.4M spend) found an average LinkedIn Ads waste rate of 32.0%, or roughly $53,600 per account. Job function, seniority and company size targeting together account for 67% of that waste. Decision-makers received only 33% of LinkedIn budget in audited accounts, against a target of 60% or more. These figures are suitable for presenting to a board or finance team.
Does running brand campaigns first actually reduce wasted ad spend on non-brand terms?
Running brand campaigns in a separate campaign with its own budget cap prevents brand queries from consuming non-brand budget when Performance Max or broad match is active. Without isolation, automated campaigns frequently bid on brand terms because they convert cheaply, skewing cost-per-SQL figures downward while leaving non-brand queries under-funded. Separating brand spend gives an accurate read on how much non-brand activity is generating pipeline versus wasted on irrelevant queries.
Conclusion
A B2B SaaS ad account that shows falling CPL and flat pipeline is almost always being optimised on the wrong signal. The five-step audit in this guide addresses the problem in order: fix the conversion event first, then fix Google Ads match types and campaign settings, then audit LinkedIn Ads audience targeting, then review programmatic frequency and inventory, then re-score every active ad by cost per SQL rather than CTR or CPL.
The data from 104 Google Ads accounts and 56 LinkedIn Ads accounts points to a consistent pattern. Best-quartile accounts waste 13.2% of Google Ads budget, reach 60% or more of LinkedIn budget to decision-makers, and optimise on SQL-level signals. Getting from the average account to the best quartile does not require more budget. It requires a cleaner signal, a tighter audience and a different primary metric.
If the audit surfaces questions about settings, bidding strategy or channel allocation, meet with an expert at Ads for B2B SaaS. Free consultation. Bring the account questions and work through the specific numbers with a paid media specialist.
Sources: The 2026 B2B SaaS Google Ads Waste Report (GrowthSpree, 104 accounts, $78.0M spend audited, June 2026); Wasted LinkedIn Ads Spend in B2B SaaS (GrowthSpree, 56 accounts, $9.4M spend audited, June 2026); The Paid Ads Pipeline Disconnect (GrowthSpree, 1,412 ad variants, 96 accounts, $14.2M spend, June 2026); SaaS Google Ads Benchmarks 2026 (GrowthSpree, April 2026). All figures are from the named studies. Sample sizes and spend volumes are as reported by the publisher. Data represents a convenience sample of audited accounts and may not be representative of all B2B SaaS advertisers. All quantitative figures in this guide derive from GrowthSpree's audit datasets; no independently published third-party benchmarks were available that met the source policy criteria (within two years, verifiable date and sample size).
