Most PPC accounts do not start to slip because a bid was 10% too high or a keyword list was missing a few variations. They get less efficient because the structure stops working as soon as the account grows. New product lines get bolted on, campaigns multiply, Performance Max arrives, and suddenly it is hard to answer basic questions without exporting everything into a spreadsheet and translating what is happening after the fact.
That is when performance becomes harder to defend because budget decisions get made on blended averages, Smart Bidding learns from mixed signals, and testing slows down because every structural change risks unintended consequences elsewhere. You still have campaigns, ad groups, and keywords, but you lose the thing that matters most: clarity.
A scalable PPC account structure is not about being tidy, but rather it is about building a decision-making system. It should give you control over what you need to control, keep reporting clean enough to trust, and stay stable even as spend, coverage, and campaign types expand. Google explicitly frames this as a hierarchy where each level plays a different role, and structure is the way you choose to use that hierarchy in practice.
This guide focuses on how to create a PPC account structure that scales for UK advertisers, with an intermediate to advanced lens. It covers the core hierarchy, the segmentation models that hold up over time, and the practical naming and governance decisions that stop an account from turning into a patchwork.
Before you design anything, be clear on what the hierarchy actually does, because it explains why some structural decisions scale and others do not.
At a high level, the hierarchy is fixed. The way you use it is not:
Themed ad groups still matter because they keep intent clean. When keywords, ads and landing pages sit around a single idea, performance is easier to diagnose and creative testing is more meaningful. In automation-heavy accounts, structure is not about micromanaging every lever. It is about giving the platform a clear set of signals so learning stays stable, and so you are not forced into constant clean-up work later
A useful mental model is this: campaigns exist for control and reporting, ad groups exist for relevance, and keywords exist for intent coverage. If you build structure with that in mind, you avoid the common trap of creating campaigns just to “organise” things.
There is no universal best PPC account structure because the “perfect” structure depends on how the business makes money, how users search, and what you need to report on quickly. A structure that works brilliantly for a single-service lead gen business can be actively harmful for an ecommerce account with varied margins and stock pressure.
A practical definition of “perfect” is simpler: your structure should make decision-making easier as the account grows.
It should let you scale without multiplying admin, and it should prevent performance conversations becoming vague. If the only way to understand performance is to export, filter, and explain caveats, the structure is already costing you time and margin.
The easiest way to sense-check your structure is to ask whether it supports answers to questions your stakeholders will ask every week. For example, can you separate brand demand from non-brand growth, and can you report performance cleanly by commercial segment without creating dozens of one-off views? If the answer is no, you do not need more optimisation. You need better architecture.
Many accounts get built around Google’s menu e.g. Search goes here, Performance Max goes there, and reporting is forced to match afterwards. The result is usually a weekly report that says something like “Search is down, PMax is up”, without any clarity on whether the business is actually growing or whether brand demand has simply shifted between campaign types.
The better approach is to define the reporting splits you need to make decisions at speed, then build campaigns around those splits. That matters because the most meaningful levers sit at campaign level: budgets, bidding strategies, geo settings, and the cleanest boundaries for separating intent.
In most UK accounts, the decision set tends to include:
Once those are locked in at campaign level, ad groups and keywords can do their job without being forced to carry reporting responsibilities they were never designed for.
If you want a wider strategy layer to sit behind these decisions, it should connect back to how you measure performance. The account structure should make it easier to interpret the metrics that actually matter, not inflate them or blend them into something meaningless.
Most scalable accounts rely on a combination of segmentation models. The skill is choosing what belongs at campaign level and what can be handled through ad groups, assets, audiences, and labels.
This model works well when the business has clear commercial segments and different margins, lead values, or operational constraints. Campaigns map to categories or services that you would genuinely fund differently if performance changes.
The main advantage is that budgeting becomes aligned to how the business makes money.
Intent-led structure is often the best fit for lead gen and high-consideration services where product boundaries overlap. Instead of campaigns mirroring what you sell, they mirror how people search and decide.
A common pattern is separating high intent and mid intent, because those groups often have different conversion rates and different lead quality. It also tends to make ad copy and landing page alignment more straightforward, which helps performance without requiring constant structural change.
Geo-led structure scales only when geography changes the economics or delivery. If you serve different regions with different capacity, different conversion rates, or different competitive intensity, geo-level budgets and bidding can make sense.
If you split geography purely for the sake of it, you usually end up with thin data and unstable bidding. Geo segmentation is a control lever, not a formatting preference.
Funnel-led structure is useful when you are deliberately running activity across the customer journey and you need separate constraints. This is more common in larger accounts where prospecting and remarketing need distinct budgets, or where new customer acquisition is tracked separately from retention.
Where teams go wrong is mixing funnel-led segmentation with other segmentation models without a naming and governance system. If you cannot explain the purpose of each campaign quickly, it will not scale across a team.
If you want a structural decision that pays you back quickly, separate brand and non-brand at campaign level.
Brand and non-brand behave differently under pressure.
Separating brand and non-brand makes it easier to:
It also creates a cleaner foundation for measurement. If your reporting is mixing brand demand with non-brand acquisition, you can end up chasing improvements in the wrong place, because the blended number looks healthier than the growth engine underneath it.
Ad groups have changed role, but they have not become irrelevant. In an automation-heavy setup, ad groups are still where you protect relevance, isolate messaging tests, and keep landing page alignment clean.
The common mistake is treating the ad group as a dumping ground for “similar keywords”, then wondering why ads feel generic and landing pages do not match intent. If you want a structure that scales, ad groups should still be intentionally themed.
A practical rule is that 1 ad group should represent 1 primary theme that can reasonably share ad copy and a landing page. Even with Smart Bidding doing more of the heavy lifting, ad groups are still where you protect relevance. They shape how specific your ads can be, how clean your landing page alignment is, and how easy it is to isolate what is driving a performance shift. If an ad group contains multiple intents that would naturally need different messaging or different landing pages, it is too broad, and efficiency usually gets diluted over time.
When an ad group starts needing multiple landing pages to convert properly, it is not a theme anymore. It is a bucket, and buckets are where efficiency goes to die.
Keyword strategy is no longer about building an encyclopaedic list of variants. It is about building coverage around intent clusters, then controlling query drift through query analysis and negatives.
If you are using broader match approaches with Smart Bidding, you are effectively trading manual targeting for better reach and faster learning. That trade only works if your measurement is clean and your structure protects intent boundaries.
Match types and query behaviour have shifted and the scalable approach is to build coverage around intent clusters, then use search terms and negatives to keep boundaries in place. If you are leaning into broader matching with Smart Bidding, the trade-off is simple: you get more reach and faster learning, but you need cleaner structure and cleaner measurement so the system learns from the right outcomes.
A sensible way to keep keyword structure scalable is:
External best practice guides vary on exact numbers, but the consistent message is to keep ad groups thematically tight and avoid stuffing them with dozens of loosely related keywords.
If your naming system does not make reporting easier, it will be ignored and you will end up relying on institutional memory to interpret the account.
A naming convention should let you answer questions without opening the UI. It should also support handovers, because scaling almost always means more people touching the account.
A practical naming pattern many teams use is:
For example:
That structure makes it clear what the campaign is for, where it is targeted, and how it should be reported. If you need more detail, labels are usually better than stuffing naming conventions with multiple extra fields.
Use labels to mark things like testing status, priority tiers, and seasonal focus. Labels are an underused way to maintain reporting flexibility without creating duplicate campaigns just to make a dashboard work.
Performance Max changes what structure means, because many levers move from explicit targeting to inputs and learning conditions. The job becomes less about micromanaging queries and more about ensuring the system is learning from clean signals and the right commercial priorities.
If you are running Search and Performance Max together, structure decisions should reflect what each is meant to do. Search is often your clearest layer for intent and reporting control. Performance Max can expand reach and coverage, but it needs guardrails to stay efficient over time.
Performance Max changes what “structure” means because the levers move from targeting into inputs and learning conditions. The job becomes making sure the system is fed the right priorities: product or service segmentation that reflects value, asset groups that map to real themes and landing pages, and measurement that is clean enough for optimisation to compound. When PMax is running alongside Search, it helps to be explicit about roles. Search usually remains the clearest layer for intent and reporting control, while PMax is best treated as a scaled automation layer with defined guardrails.
One practical point worth staying on top of is reporting visibility. Platform changes can materially affect how you audit and control PMax. Platform-level reporting has improved here as well, which makes governance easier. More placement visibility in the “Where ads showed” reporting reduces blind spots and makes it simpler to audit whether PMax is expanding efficiently or drifting into low-value inventory.
This is the build process that keeps the structure stable while still leaving room for testing and expansion.
Start with what the business would actually fund differently. Segment by product, service, or customer type where value, margin, or capacity changes.
Group queries by how users decide, not just by keyword similarity. If “price” and “best” behave differently, treat them as different intent clusters.
Do this at campaign level so reporting and bidding strategy learning stay clean.
Only elevate something to campaign level when you need separate budgets, bidding strategies, geo controls, or reporting lines. If you do not need those, campaign proliferation will slow you down.
Ad groups should map to themes that can share a landing page and a clear value proposition. If you cannot write specific ad copy for the group, it is too broad.
Treat negatives as structural boundaries. You are designing a system that prevents overlap and query drift, not just launching a list.
Document what each campaign is for, what changes are allowed, and how performance should be interpreted. If the structure cannot be understood quickly by someone new, it will not scale.
For a broader view on how structure ties into ongoing optimisation and ROI, it should sit within a smarter PPC strategy framework rather than being treated as a one-time build.
Splitting campaigns too aggressively can fragment data and make Smart Bidding less stable. The account feels controlled, but performance becomes noisier because each campaign has less learning volume.
Putting everything into a small number of campaigns can make reporting impossible and prevents you from funding what matters. It also increases the risk of mixed intent learning.
If ad groups contain multiple intents but send traffic to a generic landing page, you end up paying for relevance you never convert. This is one of the fastest ways to inflate CPA without noticing immediately.
If reporting depends on someone remembering what a campaign name means, the account will not survive scale. Governance fails quietly, then becomes expensive.
Structure should be stable, not fixed forever. If the measurement model changes, or the business shifts its commercial priorities, the structure needs to evolve deliberately rather than through add-ons.
A scalable PPC account structure is what makes good optimisation possible in the first place. It keeps budgets aligned to commercial priorities, stops reporting becoming a translation exercise, and gives Smart Bidding cleaner signals to learn from. If your account feels harder to manage every month, that is usually not a bid strategy problem. It is a structure problem.
If you want a useful next step, audit your current setup against 3 questions: can you report brand vs non-brand without caveats, can you see performance by commercial segment without manual work, and can you launch a new campaign without creating overlap or confusion. If any of those answers are “not consistently”, fixing structure will unlock more performance than another round of incremental tweaks.
When you want a second pair of eyes, Verkeer’s paid search team can review your current Google Ads setup, highlight what is holding scale back, and recommend a structure that fits how your business actually grows. Explore our Paid Search support or get in touch.
Enough to control budgets and bidding by your most important commercial and reporting segments, but not so many that you fragment learning and create admin overhead. If a campaign does not need its own budget, bidding strategy, geo constraint, or reporting line, it usually does not need to exist as a separate campaign.
Not as a default. Match behaviour and Smart Bidding mean match-type splits often add complexity without adding meaningful control. Split match types when you have a clear reason, such as isolating broad match for a controlled test, or protecting brand coverage with stricter targeting.
Hierarchy is the fixed framework in the platform, such as account, campaigns, ad groups, and keywords. Structure is how you choose to organise within that framework so you can control budgets, maintain relevance, and report performance cleanly as the account scales.
Separate brand and non-brand at campaign level and report them independently. This prevents brand demand from masking non-brand inefficiency and makes performance changes easier to diagnose when auction pressure, seasonality, or messaging shifts affect results.
Use Search to maintain intent clarity and reporting control, and use Performance Max where it delivers incremental value. Structure Performance Max around commercial priorities and keep measurement clean so the system learns from the right signals, while Search continues to provide visibility into demand capture and performance drivers.