3 PPC myths you can’t afford to carry into 2026

PPC advice in 2025 leaned hard on AI and shiny new tools. 

Much of it sounded credible. Much of it cost advertisers money. 

Teams followed platform narratives instead of business constraints. Budgets grew. Efficiency did not.

As 2026 begins, carrying those beliefs forward guarantees more of the same. 

This article breaks down three PPC myths that looked smart in theory, spread quickly in 2025, and often drove poor decisions in practice. 

The goal is simple: reset priorities before repeating expensive mistakes.

Myth 1: Forget about manual targeting, AI does it better

We have seen this claim everywhere: 

AI outperforms humans at targeting, and manual structures belong to the past. 

Consolidate campaigns as much as possible. 

Let AI run the show.

There is truth in that – but only under specific conditions. 

AI performance depends entirely on inputs. No volume means no learning. No learning means no results. 

A more dangerous version of the same problem is poor signal quality. No business-level conversion signal means no meaningful optimization.

For ecommerce brands that feed purchase data back into Google Ads and consistently generate at least 50 conversions per bid strategy each month, trusting AI with targeting can make sense. 

In those cases, volume and signal quality are usually sufficient. Put simply, AI favors scale and clear outcomes.

That logic breaks down quickly for low-volume campaigns, especially those optimizing to leads as the primary conversion. 

Without enough high-quality conversions, AI cannot learn effectively. The result is not better performance, but automation without improvement.

How to fix this

Before handing targeting decisions entirely to AI, you should be able to answer “yes” to all three of the questions below:

• Are campaigns optimized against a business-level KPI, such as CAC or a ROAS threshold?
• Are enough of those conversions being sent back to the ad platforms?
• Are those conversions reported quickly, with minimal latency?

If the answer to any of these is no, 2026 should be about reassessing PPC fundamentals.

Do not be afraid to go old school when the situation calls for it. 

In 2025, I doubled a client’s margin by implementing a match-type mirroring structure and pausing broad match keywords.

It ran counter to prevailing best practices, but it worked. 

The decision was grounded in historical performance data, shown below:

Match type	Cost per lead	Customer acquisition cost	Search impression share
Exact	€35	€450	24%
Phrase	€34	€1,485	17%
Broad	€33	€2,116	18%

This is a classic case of Google Ads optimizing to leads and delivering exactly what it was asked to do: drive the lowest possible cost per lead across all audiences. 

The algorithm is literal. It does not account for downstream outcomes, such as business-level KPIs.

By taking back control, you can direct spend toward top-performing audiences that are not yet saturated. In this case, that meant exact match keywords.

If you are not comfortable with older structures like match-type mirroring – or even SKAGs – learning advanced semantic techniques is a viable alternative. 

Those approaches can provide a more controlled starting point without relying entirely on automation.

Myth 2: Meta’s Andromeda means more ads, better results

This myth is particularly frustrating because it sounds logical and spreads quickly. 

The claim is simple: more creative means more learning, which leads to better auction performance. 

In practice, it far more reliably increases creative production costs than it improves results – and often benefits agencies more than advertisers.

Creative volume only helps when ad platforms receive enough high-quality conversion signals. 

Without those signals, more ads simply mean more assets to rotate. The AI has nothing meaningful to learn from.

Andromeda generated significant attention in 2025, and it gave marketers a new term to rally around. 

In reality, Andromeda is one component of Meta’s ad retrieval system:

• “This stage [Andromeda] is tasked with selecting ads from tens of millions of ad candidates into a few thousand relevant ad candidates.”

That positioning coincided with Meta’s broader pivot from the metaverse narrative to AI. It worked. 

But it also led some teams to conclude that aggressive creative diversification was now required – more hooks, more formats, more variations, increasingly produced with generative AI.

Similar to Google Ads’ push around automated bidding, broad match, and responsive search ads, Andromeda has become a convenient justification for adopting Advantage+ targeting and Advantage+ creative. 

Those approaches can perform well in the right conditions. They are not universally reliable.

---

How to fix this

Creative diversification helps platforms match messages to people and contexts. That value is real. It is also not new. The same fundamentals still apply:

• Creative testing requires a strategy. Testing without intent wastes resources.
• Measurement must be planned in advance. Otherwise you’re setting yourself up for failure.
• Business-level KPIs need to exist in sufficient volume to matter.

This myth breaks down most clearly when resources are limited – budget, skills, or time. In those cases, platforms often rotate ads with little signal-driven direction.

When resources are constrained, CRO is a better use of your resources:

• Review tracking. More tracked conversions improve performance.
• Improve the customer journey to increase conversion rates and signal volume.
• Map higher-margin products to support more efficient spend.
• Test new channels or networks using budget saved from excessive creative production.

The pattern is consistent. Creative scale follows signal scale, not the other way around.

Myth 3: GA4 and attribution are flawed, but marketing mix modeling will provide clarity

Can you think of 10 marketers who believe GA4 is a good tool? Probably not. 

That alone speaks to how poorly Google handled the rollout. 

As a result, more clients now say the same thing: GA4 does not align with ad platform data, neither feels trustworthy, and a more “serious” solution must be needed. 

More often than not, that path leads to higher costs and average results. 

Most brands simply do not have the spend, scale, or complexity required for MMM to produce meaningful insight. 

Instead of adding another layer of abstraction, they would be better served by learning to use the tools they already have.

For most brands, the setup looks familiar:

• Media spend is concentrated across two or three channels at most – typically Google and Meta, with YouTube, LinkedIn, or TikTok as secondary options.
• The business depends on a recurring but narrow customer base, which creates long-term fragility.
• Outside that core audience, marketing is barely incremental, if incremental at all.

In those conditions, MMM does not add clarity. It adds abstraction. 

With such a limited channel mix, the focus should remain on fundamentals. 

The challenge is not modeling complexity, but identifying what is actually impactful. 

How to fix this

The priorities below deliver more value than MMM in these scenarios:

• Differentiate clearly from competitors.
• Increase margins, even basic budget planning can move the needle.
• Build a solid data foundation, including tracking, CRO, and conversion pipelines.
• Diversify channels or ad networks.
• Lock creative execution to real customer pain points.
• Fix marketing execution wherever it breaks.

MMM – like any advanced tool – becomes useful once complexity demands it. Not before. 

Used too early, it replaces accountability with abstraction, not insight.

The reality behind the myths

The common thread across these three myths is not AI, creative, or analytics. It is misuse. 

Platforms do exactly what they are asked to do. They optimize against the signals provided, within the constraints of budget and structure.

When business fundamentals break, AI cannot fix the problem. 

2026 is not about chasing the next abstraction. It is about business and ops focus, paired with disciplined execution, to scale profitably.