How to Run Google Ads and Meta Ads Together Without Doubling Your Work

Most performance teams run Google Ads and Meta Ads as two separate jobs. Each platform has its own dashboard and its own attribution model. Campaigns are named differently on each, so every report gets built twice. This guide shows how to set up both platforms so you can manage and report on them as one.

Why manage Google and Meta as one system

Google and Meta are the two largest ad platforms in the world. In 2025 Google earned about $214 billion in ad revenue and Meta about $196 billion. The gap is small enough that eMarketer expects Meta to pass Google for the first time in 2026 (Marketing-Interactive / eMarketer). Almost every performance team with a serious budget advertises on both. Very few manage them as one system.

The two platforms do different work. On Google, the person has already typed a search, and your ad answers it. On Meta, your ad reaches people in the feed before they have decided to search, so it creates demand that did not exist yet. Both serve the same goal: getting a qualified buyer at a cost you can afford. That is why it makes sense to plan and fund them together.

Teams usually keep them apart for practical reasons. Each platform has its own dashboard and attribution model, campaigns follow different naming rules, and someone exports two reports every Monday. This is what doubles the work, and all of it can be fixed.

The rest of this guide covers five decisions: use one budget for both platforms, measure both in one place, use the same naming, UTMs and conversion definitions, review both on one schedule, and decide what automation handles. None of them needs a tool you don’t already have. You make each decision once and apply it the same way on Google and Meta.

Budget: one pool for both platforms

Start with the budget, because the other decisions depend on it. A common mistake is to fix a split at the start of the month, for example 60% Google and 40% Meta, and leave it there. That split is a guess. It stops matching reality as soon as results change.

Set one blended target for the result you care about, such as cost per qualified lead or blended ROAS across both platforms. Then give each extra dollar to the platform that delivers that result more cheaply. If Meta’s cost per result falls below Google’s for the same outcome, move budget to Meta. Move it back when that changes. This is how cross-channel budget pacing works: the total budget stays fixed, and the split between platforms changes with results.

An example. Your monthly budget is $50,000 and your target is $60 per qualified lead. Early in the month Google is at $54 per lead and Meta is at $71. You could leave the split as it is. The better option is to add budget to Google until its cost per lead gets close to Meta’s. Then stop adding and watch the numbers, because search demand on Google is limited and costs go up as you push more volume. A week later a new Meta creative goes live and Meta becomes the cheaper platform. Now the budget should move back toward Meta. The split should change when the results change, whatever the date. Doing this by hand in two accounts is repetitive work with a lot of data, and it takes up a large part of an operator’s week.

Measurement: one source of truth

You cannot split budget fairly if you rely on each platform’s own reporting. Google and Meta count conversions with their own models and default attribution windows. As a result, the same sale is often counted by both, and each platform’s numbers tend to look better than they are. Raw ROAS from the two ad managers is calculated in two different ways, so you cannot compare the two numbers.

This is a common problem. In one industry survey, 80% of marketers said they were unhappy with how well they could reconcile results across different tools. Roughly half of US brand and agency marketers rank attribution as a top investment priority (Basis Technologies). To fix it, pick one source of truth, such as GA4, your CRM or a data warehouse, and judge both platforms by its numbers.

This only works if each platform receives accurate conversion data. Server-side tracking helps. Google reports a median conversion lift of about 5% on Search after advertisers turn on enhanced conversions (Google Ads Help). Meta has reported a cost per action about 13% lower for advertisers who use the Conversions API together with the pixel (Meta, via Cometly). Sending first-party data to both platforms in the same way is what makes their numbers comparable. Our guide to conversion tracking with first-party data explains the setup.

Standardize naming, UTMs, and conversion definitions

This part is dull, but it saves the most time. If a campaign is called us_search_leadgen_offerA on Google and US - Prospecting - Lead - Offer A on Meta, no report can group them until someone matches them by hand. When names follow one format, the two exports combine into one report.

Set three conventions and use them the same way on both platforms:

1. Naming template. Choose a field order (market, funnel stage, objective, offer) and one separator. Use it for campaigns, ad sets or ad groups, and ads. Example: us_prospecting_leadgen_offerA on both platforms.
2. UTM scheme. Put the same fields into your UTMs, so your analytics tool groups Google and Meta traffic by the same market, stage and offer. Set utm_source and utm_medium per platform. Make utm_campaign exactly the same as the name from your naming template.
3. Conversion definitions.Decide once what counts as a “lead” or a “purchase”. Set both platforms to count the same event with the same attribution window, so a lead on Google means the same thing as a lead on Meta.

Matching campaign names are what let one report combine data from Google and Meta.

How Google and Meta compare on benchmarks

Once both platforms are measured the same way, you can compare what each one does well. The 2025–2026 industry benchmarks show the difference. Google costs more per click, but the clicks come from people who are already searching. Meta costs much less per click and works higher in the funnel, where it creates demand.

Sources: Google figures from WordStream by LocaliQ’s 2026 Google Ads benchmarks (13,474 US campaigns); Meta figures from WordStream’s 2025 Facebook Ads benchmarks. Numbers vary a lot by industry, so use them only as a rough guide. In the latest benchmark data, average cost per lead across Google and Microsoft Ads fell for the first time in five years (Search Engine Land).

The two platforms are priced for different work, so a lower CPC on Meta says little on its own. Use one blended target across both platforms to decide where the money goes. Separate targets per platform do not give you a fair comparison.

One review schedule for both platforms

Most of the time operators waste goes into checking dashboards too often while making too few decisions. Plan your reviews around how often a decision can actually change results.

• Weekly, cross-channel. One review of Google and Meta against the blended target. This is where you make structural decisions, such as pausing an offer that misses the target, scaling one that works, or changing the budget minimums.
• Monthly, strategic. Creative direction, tests in new markets and changes to the channel mix. This is also where you review creative testing against ROAS. On Meta, creative usually has the biggest effect on results, and it matters more and more on Google too.
• Hourly and intraday. Small changes to bids and budgets. This work is repetitive and has to continue overnight, so a person should not be doing it.

A common mistake is the reverse: doing the hourly work by hand and the strategic work rarely. It is reasonable to doubt attribution data. The answer is better measurement and fewer, more focused reviews. Refreshing dashboards all day does not fix it.

Where automation takes over

Split the work clearly. People decide the things that need judgment: the offer, the creative, the targets and the budget limits. Automation handles work that has to be fast and repeated, such as moving budget every hour toward the platform with better results at that moment.

Give each task to whoever does it better. A person is good at choosing which offer to run and which creative angle should get budget. A person is bad at watching two ad accounts around the clock and adjusting spend every hour without getting tired or making mistakes. Once the work is split this way, the extra work of running a second platform goes away. Reporting and targets already cover both platforms, and the intraday changes happen even when you are away from your desk.

First-party data is worth the setup work because automation makes its decisions from that data. Forrester found that adding first-party behavioral data to marketing raised conversions by 73% (StackAdapt / Forrester). When the conversion data is accurate, you can trust the budget changes that automation makes.

Adriva is built for this split. It runs Google Ads and Meta Ads from one workspace and rebalances budget across both platforms every hour against your blended target. You keep control of the offer, the creative and the budget limits. Adriva handles the hourly changes.

Where to start

To run Google and Meta together, you need five things: one budget, one source of conversion data, one naming standard, one review schedule, and a clear split between what you decide and what automation handles. Once these are in place, the second platform no longer doubles your work.

Start with naming, UTMs and matching conversion definitions. Reporting across both platforms depends on them, and so does automation. Once they are set up, you no longer need to export two spreadsheets every Monday.