Lina Hope
A significant portion of traffic generated by Google Ads campaigns may not be from new customers, but rather from individuals who already have a relationship with the business. This phenomenon, driven by branded searches, can inflate campaign performance metrics, leading to potentially flawed strategic decisions regarding budget allocation, agency evaluations, and automated bidding strategies. Understanding and accurately segmenting this branded traffic is crucial for businesses seeking to truly measure and optimize their customer acquisition efforts.
The core issue lies in how Google Ads often aggregates data. While branded campaigns serve a vital purpose in brand protection and control, their performance is frequently merged with non-branded campaigns. This "blended" reporting can obscure the actual cost of acquiring truly new customers, presenting a rosier picture of efficiency than reality warrants. Without a clear distinction, businesses risk making investment decisions based on data that doesn’t accurately reflect their ability to attract fresh clientele.
The Deceptive Allure of Blended Metrics
Two detailed analyses of e-commerce Google Ads accounts, one for a U.S. collectibles retailer and another for a European direct-to-consumer watch brand, highlight the magnitude of this discrepancy. Over a 90-day period, branded searches accounted for just over one-third of the ad spend in both accounts. However, these searches were responsible for a disproportionately larger share of conversions – more than half in both instances.
Table 1: Branded Search Impact on Spend and Conversions
| Account | Brand Share of Spend | Brand Share of Conversions | Non-Brand CPA vs. Blended |
|---|---|---|---|
| US Collectibles Retailer | 38% | 67% | 2.1x |
| European DTC Watch Brand | 34% | 58% | 1.5x |
Source: Author’s audits of two client accounts, search terms report, 90 days to October 28, 2026.
The cost per acquisition (CPA) further illustrates the impact of this blend. When the blended CPA for each account was indexed to 100, branded searches consistently showed a lower CPA (57 and 59, respectively). In stark contrast, non-branded searches revealed a significantly higher CPA, reaching 209 for the collectibles retailer and 149 for the watch brand. This means that acquiring a customer through non-branded channels was approximately twice as expensive as the blended figure suggested for the collectibles account and about 50% more expensive for the watch brand.
The Return on Ad Spend (ROAS) metric also reveals similar distortions. In the watch brand’s account, the blended ROAS appeared acceptable. However, branded searches were yielding returns more than three times that of non-brand searches. This inflated the blended figure by nearly 50%, potentially leading to misguided decisions about increasing generic ad budgets based on a return that non-branded traffic never actually delivered.
It is important to note that neither of these accounts were poorly managed; both had dedicated brand campaigns in place. The issue stemmed from the reporting methodology rather than a fundamental flaw in campaign execution.
The Stealthy Infiltration of Brand Searches
The problem is exacerbated by the fact that branded searches can often infiltrate campaigns not explicitly designated for them, particularly within campaign-level reports. In the collectibles retailer’s account, the exact brand name appeared across six different campaigns, including one intended to target competitors and another explicitly labeled "non-brand."
Queries such as "[brand] + product type" were being matched within a product-focused campaign. Similarly, searches for the brand name combined with "discount code" were being captured by Performance Max campaigns. These instances go unnoticed when only comparing overall campaign totals, as each campaign is reported under its own designated, often non-brand, label.
A recent examination of the collectibles account’s search terms report revealed two variations of brand searches – one with a misspelling and another including a specific product – being matched within a campaign ostensibly designed for generic searches.
Figure 1: Google Ads Search Terms Report Excerpt (Illustrative)
(Image depicting a Google Ads search terms report showing brand variations in generic campaigns)
Source: Author’s analysis, Google Ads search terms report, October 1-25, 2026. Brand names and costs redacted. Screenshot by the author.
Performance Max campaigns are particularly susceptible to this issue. Without explicit brand exclusions, these campaigns can inadvertently bid on searches for the company’s own name. Given that branded searches typically convert at a higher rate, they can misleadingly portray Performance Max as a robust prospecting tool. In one analyzed account, Performance Max had spent a substantial five-figure sum on branded searches within a single month, a significant portion of which was directed towards individuals already familiar with the business.
The advent of AI-driven optimizations and broad match keywords further complicates this. These systems can capture branded searches through close variants and misspellings. These matches are easily overlooked, especially if analysts are only looking for exact brand name matches. In the collectibles account, a single missing letter in the brand name resulted in hundreds of dollars in wasted ad spend, a leak that would have been undetectable through campaign name filters alone.
Further complicating matters are brand names that overlap with common product terms. For instance, a company named "Garden Tools Co." might find searches for "garden tools" to be ambiguous. Such searches could indicate brand intent or a general interest in the product category. Manual review is often necessary to discern the user’s true intent before classifying these queries.
Strategic Segmentation: A Path to Clarity
To effectively combat this reporting issue, a multi-pronged approach to data segmentation is required. The foundational step involves consolidating all search term data. A recommended practice is to pull at least 90 days of data from all relevant campaign types, including Search, Shopping, Performance Max, and AI Max campaigns, into a single spreadsheet. Each query should then be meticulously classified as branded, non-branded, or competitor-related. This granular classification enables performance comparisons without the distorting effect of blended metrics.
For Performance Max campaigns, leveraging the search term insights within Google Ads is crucial for identifying branded activity. It is advisable to keep the "Other" category separate from non-brand classifications, as it can harbor unclassified traffic that might skew results.
Developing a robust brand filter is paramount. This filter should extend beyond the exact company name to encompass common misspellings, variations in spacing, abbreviations, and even the company’s website domain. This comprehensive approach prevents branded searches from inadvertently falling into the non-brand bucket. A second filter for competitor brands is also recommended. In the analyzed collectibles account, competitor terms exhibited the worst CPA, exceeding the blended figure by more than threefold.
Once search terms are properly grouped, key performance indicators such as spend, conversions, CPA, and ROAS should be calculated for each category independently. While the blended figure can remain on a dashboard for historical context, it should not be the primary metric for setting performance targets. This detailed breakdown provides a far more accurate basis for strategic budget allocation. Tools such as Swishiy’s free Google Ads auditor can automate this segmentation process, offering a significant time-saving advantage.
Following segmentation, it is essential to address identified inefficiencies. If a Search campaign is already effectively capturing brand demand, consider implementing brand exclusions within Performance Max. Similarly, add branded terms as negative keywords to non-brand campaigns where appropriate. Ensuring the brand campaign possesses adequate budget and impression share is critical to capturing its intended audience; otherwise, this valuable traffic can be siphoned off by more expensive, less targeted campaigns.
Evaluating the True Value of Brand Spend
Beyond accurate reporting, a deeper question emerges: is the expenditure on brand searches truly necessary? A branded CPA that appears significantly lower than the account average can be alluring, but it’s crucial to question how many of those customers would have arrived via organic search anyway.
The most effective method to ascertain this is through controlled testing. Pausing brand Search campaigns in specific regions or for a defined period allows for observation of total order volume in those areas, not just paid conversions. If order numbers remain largely unaffected, it suggests that a considerable portion of the ad spend was likely securing clicks that would have occurred organically. Conversely, if competitors are actively bidding on the brand name and order volume drops significantly when ads are paused, it indicates that brand spend plays a protective role in maintaining revenue.
Analyzing the distinction between new and returning customers provides further insights. In the collectibles account, periodic ad pauses offered a natural comparison. On days when brand ads were active, the retailer acquired approximately 50% more new customers daily compared to days when ads were paused. This tangible evidence justifies ad expenditure, a nuance often lost when new and returning buyers are aggregated in blended ROAS calculations.
Treating Brand and Non-Brand as Distinct Entities
Fundamentally, branded and non-branded search campaigns serve different strategic objectives. Brand search capitalizes on existing demand generated through a company’s overall marketing efforts and brand reputation. Non-brand search, conversely, actively seeks out individuals who may have no prior awareness of the business. Merging these distinct functions into a single reporting metric obfuscates the performance of each.
This analysis is not an indictment of brand spend. In both audited accounts, brand campaigns proved to be the most cost-effective component of the overall budget by a considerable margin. However, the segmentation shifted the strategic conversation. Instead of merely questioning overall CPA fluctuations, the dialogue evolved to address two separate, critical questions: Is the non-brand strategy becoming more effective at acquiring genuinely new customers? And is the brand campaign primarily protecting existing revenue, or is it simply recapturing demand that would have manifested organically?
The imperative is clear: begin by meticulously examining search term reports, accurately tagging branded traffic, and isolating non-brand CPA. This granular approach may reveal a higher cost of acquiring new customers than initially perceived through blended reporting. Armed with this clarity, businesses can make more informed decisions about budget increases, ensuring that investments are based on genuine performance rather than inflated metrics influenced by pre-existing brand recognition.
Reynand Wu
Evan Lee Salim
