# How To Configure Google Demand Gen Audience Targeting

> Tower33 breaks down how to configure Demand Gen signals, ad groups, and seed lists so your budget works harder. Get the practitioner's guide.

**Published:** 2026-10-09 | **Updated:** 2026-10-31 | **Author:** victorsoares
**URL:** https://tower33.com/articles/google-demand-gen-audience-targeting/
**Type:** post

**Categories:** Google Ads

![How To Configure Google Demand Gen Audience Targeting](https://tower33.com/articles/wp-content/uploads/sites/107/2026/10/1791454084650-96bf0fbf2af1-1024x572.webp)

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## Content

*Written by: Anthony Miller, Founder, Tower 33*

## Key Takeaways

- Demand Gen treats selected audiences as signals for AI expansion rather than hard targeting boundaries, so signal quality determines whether the system finds the right people.
- The strongest audience signals for lead-gen D2C brands come from Customer Match lists, high-intent website visitors, and custom segments built around specific buyer intent phrases.
- Optimized targeting should be turned off when conversion tracking is polluted, seed lists are stale, or when testing a specific audience in isolation to keep data clean.
- Seed lists for lookalikes must prioritize recency, engagement quality, and list hygiene; a stale or noisy list teaches the AI to find more of the same low-quality prospects.
- Tower33 rebuilds measurement and conversion signals before refining Demand Gen audience strategy, so performance reflects real qualified leads rather than platform-reported volume.

[Get Tower33’s Demand Gen Strategy Updates](https://tower33.com/join-strategy-newsletter/?utm_term=google-demand-gen-audience-targeting&utm_campaign=ai_growth_agent)

## What Google Demand Gen Audience Targeting Actually Is (And What It Is Not)

Google Demand Gen is a campaign type that [serves visual ads across YouTube, YouTube Shorts, Discover, Gmail, and the Google Display Network](https://needmomentum.com/optimize-google-ads-demand-gen), using Google AI to match assets with audiences most likely to convert. Unlike Search, there is no keyword targeting. That reality creates a strategic constraint for every advertiser. In Search, intent is expressed by the query. In Demand Gen, intent has to be inferred entirely from the signal set you provide. The audience signal is the only lever you have for intent.

[Google announced in May 2026 that standalone Display campaigns are being retired](https://blog.google/products/ads-commerce/google-display-ads-demand-gen), with the Google Display Network folding into Demand Gen and migration expected to complete by 2027. Demand Gen is now the only route to Discover, Gmail, Shorts, and eventually the full Display Network. Teams that run paid media on Google now need a working understanding of how Demand Gen treats audiences.

The audiences-as-signals model works like this: when you select an audience in Demand Gen, you are feeding the AI a starting point, not drawing a fence. [Google’s own guidance states that optimized targeting combined with first-party data seed lists looks beyond manually selected audience segments to find high-intent users who exhibit the same conversion-driving behaviors as an advertiser’s existing customer base.](https://business.google.com/us/accelerate/resources/articles/do-the-youtube-four-to-drive-conversions) The system is designed to expand. Your job is to make sure it expands in the right direction by giving it a clean, accurate signal to start from. That starts with knowing which audience families are available and what each one actually signals to the AI.

## Demand Gen Audience Families And Where The Real Intent Lives

[Google Ads Help groups audience segments into ten families](https://deepclick.com/resources/blog/google-ads-audience-targeting), including affinity, custom, detailed demographics, life events, in-market, your data, website and app visitors, Customer Match, lookalike, and Google-engaged audiences. The table below covers the full taxonomy with practical notes for lead-gen D2C brands. The pattern to watch: segments built from your own data carry the strongest intent signal, while interest-based segments work better as supporting signals than as primary targeting.

| Segment Name | What It Is | Best Use Case |
| --- | --- | --- |
| Customer Match | [CRM-based lists hashed and uploaded to Google; matched to signed-in Google users](https://deepclick.com/resources/blog/google-ads-audience-targeting) | Strongest signal for lead-gen brands in travel, health and wellness, and home services; past bookers, qualified leads, and closed customers are high-quality seeds |
| Website And App Visitors (Remarketing) | [Users who visited specific pages or took specific actions on your site or app](https://deepclick.com/resources/blog/google-ads-audience-targeting) | High-intent retargeting for any vertical; segment by page depth and recency for cleaner signals |
| YouTube Engagers | [Users who interacted with your YouTube channel or videos, including viewing any video from a channel, viewing certain videos, subscribing to a channel, visiting a channel homepage, liking any video from a channel, or adding a video to a playlist](https://support.google.com/google-ads/answer/2545661?hl=en) | Useful for travel and education brands with meaningful video content; weaker for home services with thin video libraries |
| In-Market Segments | [Estimated by Google based on interests, intent, demographics, Google activity, and third-party website behavior](https://needmomentum.com/optimize-google-ads-demand-gen) | Research-stage prospecting; strongest for travel and health and wellness where Google’s in-market categories are well-developed |
| Affinity Segments | [Users grouped by long-term interests, habits, and passions](https://deepclick.com/resources/blog/google-ads-audience-targeting) | Upper-funnel awareness; less precise for lead-gen conversion goals; useful when paired with a soft CTA like a brochure download |
| Detailed Demographics | [Long-term life facts, such as parental status](https://deepclick.com/resources/blog/google-ads-audience-targeting) | Home services and education brands benefit most; note that [US housing, employment, and consumer finance ads face restrictions on certain demographic targeting](https://deepclick.com/resources/blog/google-ads-audience-targeting) |
| Custom Segments | [Built from keywords, URLs, and apps; the closest thing to intent targeting outside of Search](https://deepclick.com/resources/blog/google-ads-audience-targeting) | Strong for any lead-gen vertical; build around actual buyer intent phrases, not broad category terms |
| Lookalike Segments | [Available only in Demand Gen](https://deepclick.com/resources/blog/google-ads-audience-targeting); reaches users sharing characteristics with a seed list; [converted from hard targeting to audience suggestions in February 2026](https://ppc.land/demand-gen-feed-use-hits-35-as-pmax-advertisers-face-q4-overlap-smec) | Prospecting for any vertical when seed list quality is high; output quality is entirely determined by seed quality |

For lead-gen D2C brands in travel, health and wellness, home services, and education, the strongest segments are Customer Match, website visitors segmented by intent depth, and custom segments built around specific buyer intent. In-market segments help with prospecting when conversion signals are clean enough to teach the AI what a qualified lead looks like. Affinity segments support awareness but rarely carry a strong enough signal for lead-gen conversion goals on their own.

[Get The Demand Gen Audience Playbook](https://tower33.com/join-strategy-newsletter/?utm_term=google-demand-gen-audience-targeting&utm_campaign=ai_growth_agent)

## How Demand Gen Treats Audiences As Signals Instead Of Boundaries

When you select an audience in Demand Gen, you are not restricting delivery to that audience. You are feeding the AI a signal about who might convert. The system reads that signal, combines it with live conversion data from your account, and decides who actually sees the ad, including people well outside your selected segments.

The strategic consequence is direct: the real lever is signal quality, not simple audience selection. A clean, well-structured signal set produces useful expansion. A noisy or stale signal set produces cheap, low-quality reach. [Google frames this plainly: the campaign “is only as powerful as the quality of the data fueling it,” and accurate measurement is the “primary performance multiplier” for Demand Gen bidding, creatives, and targeting.](https://business.google.com/us/accelerate/resources/articles/do-the-youtube-four-to-drive-conversions)

This connects directly to a pattern Tower33 sees repeatedly in inherited accounts: automation running without strategic oversight. Over-reliance on automation without oversight, as documented in A Methodology for Paid Media Success, is one of the most common reasons campaigns fail. The AI will happily spend the budget. It will not tell you it is optimizing toward the wrong signal. Performance differences in Demand Gen come from fluency in understanding an audience and using networks correctly based on goals, not from obscure setting tweaks.

[Read The Methodology Behind This Approach](https://tower33.com/join-strategy-newsletter/?utm_term=google-demand-gen-audience-targeting&utm_campaign=ai_growth_agent)

## How To Decide What Counts As Materially Different For Ad Group Architecture

Ad group structure should follow audience meaning, not personal preference. Audiences are materially different when they imply a different buying cycle, a different value proposition, a different creative treatment, or a different conversion action.

Consider a concrete lead-gen example. A travel brand’s Customer Match list of past bookers carries high intent, a short decision cycle, and familiarity with the brand. An in-market affinity segment for vacation planning sits in the research stage, needs a brochure-style CTA, and often includes people who have never heard of you. These belong in separate ad groups. The creative should differ. The call to action should differ. Combining them in one ad group produces blended data that hides what is working for each audience.

[Splitting Demand Gen audiences into separate ad groups makes it possible to tell what worked.](https://ivitskiy.com/blog/en/demand-gen-discovery-ads) Remarketing, similar interests, in-market intent, and demographics each point at their own audience signal, so you can read performance by segment instead of guessing at a blended average. Dumping every signal into one group makes that analysis impossible.

The trade-off is real. More ad groups create more distinct signals but less conversion data per group for the AI to learn from. Consolidation becomes the right call when individual ad groups fail to generate enough conversions to exit the learning phase. Treat this as a decision based on data volume and intent clarity.

## How To Decide When To Turn Off Optimized Targeting In Demand Gen

Optimized targeting lets Google expand beyond your selected audiences to find additional conversions. When it works well, it finds users your signal set pointed toward but did not explicitly include. When it works poorly, it spends budget on audiences that never convert and makes your reporting look fine while your CRM tells a different story.

The decision framework is straightforward: optimized targeting is only as good as the conversion signal feeding it, so the question is whether your signal is clean enough to trust it.

| Scenario | Recommendation | Rationale |
| --- | --- | --- |
| Conversion tracking is clean, seed audiences are high quality, campaign has 50+ conversions | Leave it on | [Google recommends waiting at least two weeks or accumulating 50 conversions before judging optimized targeting performance](https://needmomentum.com/optimize-google-ads-demand-gen), because the system needs data to expand intelligently |
| Conversion tracking is polluted (multiple conversion actions, wrong optimization goals) | Turn it off until tracking is fixed | Optimized targeting learns from your conversion signal, so a polluted signal teaches it to find the wrong people |
| Seed lists are stale or built from unqualified leads | Turn it off, rebuild seed lists first | Expansion from a bad seed produces bad reach at scale |
| Testing a specific remarketing list or seed audience in isolation | Turn it off for the test window | [Turning it off separates seed performance from algorithmic expansion](https://stackmatix.com/blog/google-demand-gen-campaigns), which produces clean data |
| Expanded audiences are consistently converting at acceptable CPL (cost per lead) | Leave it on | The system is working, so restricting it costs incremental volume |

Turning off optimized targeting remains a reversible choice. The reason to test it against cost per qualified lead rather than raw CPL is that raw CPL rewards cheap, unqualified volume, so a campaign can look efficient while filling your CRM with leads that never close. When you measure against qualified leads, the tradeoff becomes visible. You may lose impression volume, but you gain a clearer picture of whether the expansion is actually producing customers.

[Get The Demand Gen Signal Checklist](https://tower33.com/join-strategy-newsletter/?utm_term=google-demand-gen-audience-targeting&utm_campaign=ai_growth_agent)

## How To Build Seed Lists For Lookalikes Without Getting Burned

This is where lead-gen D2C brands in travel, health and wellness, and home services lose the most money, and it is the section most ranking articles skip. The reason is structural: the AI learns from who is in your seed list, so a list full of unqualified or dormant leads teaches it to find more of the same. The seed list defines who the system thinks you want.

Strong seed lists for Demand Gen lookalikes share a few traits.

- **Recency:** [Customer email addresses decay at roughly 25–30% per year](https://keepersdigital.com/en-us/resources/paid-search/unified-audience-strategy), so a list that is 90 days old may already have meaningfully reduced match rates. For most lead-gen brands, a 30-day refresh cycle is the practical minimum.
- **Engagement And Conversion Quality:** [The strongest seed sources are high-barrier actions, such as high-value purchasers in the top 20% by average order value or lifetime value, lead form submissions with more than five fields completed, repeat purchasers, and for SaaS or subscription businesses, trial users who reached an activation milestone.](https://keepersdigital.com/en-us/resources/paid-search/demand-gen-audience-expansion) Newsletter signups and page views are too low-barrier to serve as seed signals.
- **List Size:** [Google requires a minimum of 100 users in a Customer Match seed list before lookalike generation becomes available, but 100 is a floor, not a performance target.](https://keepersdigital.com/en-us/resources/paid-search/demand-gen-audience-expansion) A seed list of 1,000 to 5,000 high-quality users produces more reliable lookalikes.
- **List Hygiene:** [Scrub for duplicate email entries, malformed addresses, and generic role-based addresses like “info@” or “sales@” before uploading.](https://keepersdigital.com/en-us/resources/paid-search/demand-gen-audience-expansion) A clean list of 2,000 unique high-value users will outperform a raw list of 5,000 that includes duplicates and noise.

[Google Ads Customer Match match rates typically fall between 29% and 62%](https://searchengineland.com/google-ads-customer-acquisition-retention-goals-475191), which means a list of 1,000 records may still fall short of the eligibility threshold for serving. Providing multiple match keys, such as email, phone, and address together, improves match rates meaningfully over email alone.

When a seed list is stale, the lookalike it generates is stale. The AI is not correcting for the quality of your inputs. It is amplifying them. That amplification is also why Demand Gen behaves differently from the campaign types most advertisers are used to.

## How Demand Gen’s Audience Model Differs From Display And Search

The confusion between Demand Gen and Display audience targeting is understandable. Display historically allowed tighter audience boundaries, and many advertisers carried those expectations into Demand Gen. The models differ in a meaningful way.

In Search, intent is expressed by the query, so the keyword is the signal. In Display, audiences could function as harder targeting constraints, restricting delivery more directly to the selected segments. [In Demand Gen, audience signals function as hints rather than hard targeting constraints, and Google expands beyond those seeds to find similar users across its inventory.](https://stackmatix.com/blog/google-demand-gen-campaigns-guide)

The practical implication is simple. Teams that managed Display campaigns and expected Demand Gen to behave the same way will see different behavior. The absence of keyword targeting in Demand Gen is the design, not a gap to work around. The audience signal is the only intent lever available, which is why signal quality matters more here than in any other Google campaign type.

## How To Measure Whether Your Audience Signals Are Working

Raw CPL (cost per lead) hides the real story for Demand Gen in a lead-gen context. It rewards cheap, unqualified volume. The metrics that matter are cost per qualified lead and cost per converted lead, which require your CRM to close the loop on what happened after the form was submitted.

Focus reviews on a short list of checks.

- Conversion rate by audience segment, so you can see whether remarketing segments convert at a materially higher rate than expanded segments
- Whether the AI is expanding into audiences that convert at acceptable cost, or into audiences that convert on paper but never close in the CRM
- Whether reported platform performance reconciles with CRM outcomes; [attribution gaps between platform reporting and CRM data are a diagnostic signal worth investigating](https://tower33.com/articles/google-search-campaign-revenue-attribution/?utm_term=google-demand-gen-audience-targeting&utm_campaign=ai_growth_agent)
- Impression-to-conversion ratio by segment, reviewed at 7, 14, and 30 days after enabling optimized targeting

Impressions and CTR (click-through rate) without a revenue or cost-per-qualified-lead line will make a Demand Gen campaign look healthy in a platform report while the brand loses money on every order. That is the failure mode to avoid. Tower33 inherited an account where the previous agency’s reporting showed a healthy ROAS (return on ad spend) while the brand was actually losing $200 on every order, driven by measurement that reassured rather than informed.

## Why Tower33 Is The Right Partner For Demand Gen Signal Strategy

Tower33 is a boutique, founder-led paid media firm, small by design and built to serve the underserved middle. Anthony Miller, Founder and Lead Strategist, personally leads strategy and day-to-day management on every account, with more than 15 years managing hundreds of millions in media spend across brands. There is no handoff from the person who ran the pitch to a coordinator who was not on the call.

When Tower33 inherits a Demand Gen account, measurement gets rebuilt before performance is judged. That means fixing conversion signal hygiene and setting correct optimization goals first. From there, brand and non-brand separation, UTM discipline, cross-platform tracking validation, and server-side tracking all work together to prevent attribution loss. The signal quality problem gets fixed before the targeting conversation starts, because changing targeting on top of a broken signal set produces better-looking reports rather than better results.

Base pricing starts at $5,000. There are no contractual lock-in terms. Clients receive a written performance report every Wednesday and a strategy call later that week. The methodology behind the approach is documented in full in [A Methodology For Paid Media Success](https://tower33.com/join-strategy-newsletter/?utm_term=google-demand-gen-audience-targeting&utm_campaign=ai_growth_agent), available free to newsletter subscribers.

If your Demand Gen campaign is spending but the results do not match who you thought you were buying, the signal is the problem. That problem is solvable with better data and structure, not with a single setting change.

[Get Weekly Demand Gen Strategy Breakdowns](https://tower33.com/join-strategy-newsletter/?utm_term=google-demand-gen-audience-targeting&utm_campaign=ai_growth_agent)

## Frequently Asked Questions

### How Long Does Demand Gen Take To Learn From A New Signal Set?

[As noted earlier, Google recommends waiting at least two weeks or 50 conversions before evaluating optimized targeting](https://needmomentum.com/optimize-google-ads-demand-gen). Frequent edits during this window reset the learning phase and delay stable performance. If the campaign is not generating enough conversions to exit the learning phase, the budget or the conversion action definition may need adjustment before you can judge signal quality.

### Should You Use One Ad Group Or Many In Demand Gen?

The right number depends on whether your audiences are materially different, meaning they imply a different buying cycle, value proposition, creative treatment, or conversion action. When they do, separate ad groups produce cleaner data and more relevant creative. When they do not, consolidation gives the AI more conversion data per group and a faster path through the learning phase. Treat this as a decision based on audience intent and data volume.

### What Should You Do When Optimized Targeting Spends Outside Your Audience?

Start by checking whether the expanded audience is converting at an acceptable cost per qualified lead, not just converting on the platform. If expanded segments produce qualified leads at or near your target cost, the expansion is working. If they produce cheap conversions that never close in the CRM, the conversion signal is likely polluted. Fix the conversion tracking first, then decide whether to turn off optimized targeting while the signal is cleaned up.

### How Do You Tell If A Lookalike Seed List Is Stale?

Warning signs include a rising CPA on lookalike segments without a corresponding market shift, declining conversion quality in the CRM, and a match rate below 20% of the uploaded list. The 25–30% annual decay rate mentioned earlier means any list older than 90 days should be treated as potentially degraded. Refresh the seed list, scrub it for duplicates and malformed records, and rebuild the lookalike from current data rather than appending to the old list.

### Does Demand Gen Work For Lead Generation Or Only Ecommerce?

Demand Gen works for lead generation when the measurement setup matches the goal. For lead-gen D2C brands in travel, health and wellness, home services, and education, the relevant metrics are cost per qualified lead and cost per converted lead, not purchases or ROAS. The campaign should be optimized toward conversion actions that represent actual qualified intent, such as completed multi-field forms, booked consultations, quote requests, or calls over a meaningful duration. Optimizing toward page views or low-barrier form fills teaches the AI to find people who will never become customers.

### What Metrics Should You Watch Beyond CPL?

Use cost per qualified lead, cost per converted lead, conversion rate by audience segment, impression-to-conversion ratio by segment, and reconciliation between platform-reported conversions and CRM outcomes. [View-through conversions represent a meaningful share of Demand Gen results and influence downstream search behavior](https://stackmatix.com/blog/google-demand-gen-campaigns), so campaigns should be evaluated over a full sales-cycle window rather than only the first seven days. Impressions and CTR without a revenue or cost line function as activity metrics, not performance metrics.

### How Does Demand Gen Audience Targeting Differ From Search?

As covered earlier, Search expresses intent through the query while Demand Gen has no keywords, so the audience signal is the only intent lever. The practical difference is clear. A weak signal set in Search still gets some benefit from keyword intent, while in Demand Gen the signal set defines the entire problem and the entire opportunity.

[Stay Ahead On Demand Gen Strategy](https://tower33.com/join-strategy-newsletter/?utm_term=google-demand-gen-audience-targeting&utm_campaign=ai_growth_agent)

## Read Next

- [How to Structure Google Demand Gen Campaigns That Convert](https://tower33.com/articles/google-demand-gen-campaign-structure/?utm_term=google-demand-gen-audience-targeting&utm_campaign=ai_growth_agent)
- [Programmatic Advertising Best Practices: A 10-Step Playbook](https://tower33.com/articles/best-programmatic-advertising-practices/?utm_term=google-demand-gen-audience-targeting&utm_campaign=ai_growth_agent)
- [Agency vs In-House Google Ads: A Decision Framework](https://tower33.com/articles/agency-vs-inhouse-google-ads/?utm_term=google-demand-gen-audience-targeting&utm_campaign=ai_growth_agent)
- [How To Structure Google Shopping Campaigns By Category](https://tower33.com/articles/structure-google-shopping-campaigns-category/?utm_term=google-demand-gen-audience-targeting&utm_campaign=ai_growth_agent)
- [How to Run a Paid Media Lead Generation Audit](https://tower33.com/articles/paid-media-lead-generation-audit/?utm_term=google-demand-gen-audience-targeting&utm_campaign=ai_growth_agent)

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    - **Name:** Should You Use One Ad Group Or Many In Demand Gen?
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      - **Text:** The right number depends on whether your audiences are materially different, meaning they imply a different buying cycle, value proposition, creative treatment, or conversion action. When they do, separate ad groups produce cleaner data and more relevant creative. When they do not, consolidation gives the AI more conversion data per group and a faster path through the learning phase. Treat this as a decision based on audience intent and data volume.
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      - **Text:** Start by checking whether the expanded audience is converting at an acceptable cost per qualified lead, not just converting on the platform. If expanded segments produce qualified leads at or near your target cost, the expansion is working. If they produce cheap conversions that never close in the CRM, the conversion signal is likely polluted. Fix the conversion tracking first, then decide whether to turn off optimized targeting while the signal is cleaned up.
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      - **Text:** Warning signs include a rising CPA on lookalike segments without a corresponding market shift, declining conversion quality in the CRM, and a match rate below 20% of the uploaded list. The 25–30% annual decay rate mentioned earlier means any list older than 90 days should be treated as potentially degraded. Refresh the seed list, scrub it for duplicates and malformed records, and rebuild the lookalike from current data rather than appending to the old list.
    **Question:**

    - **Name:** Does Demand Gen Work For Lead Generation Or Only Ecommerce?
      **Answer:**

      - **Text:** Demand Gen works for lead generation when the measurement setup matches the goal. For lead-gen D2C brands in travel, health and wellness, home services, and education, the relevant metrics are cost per qualified lead and cost per converted lead, not purchases or ROAS. The campaign should be optimized toward conversion actions that represent actual qualified intent, such as completed multi-field forms, booked consultations, quote requests, or calls over a meaningful duration. Optimizing toward page views or low-barrier form fills teaches the AI to find people who will never become customers.
    **Question:**

    - **Name:** What Metrics Should You Watch Beyond CPL?
      **Answer:**

      - **Text:** Use cost per qualified lead, cost per converted lead, conversion rate by audience segment, impression-to-conversion ratio by segment, and reconciliation between platform-reported conversions and CRM outcomes. View-through conversions represent a meaningful share of Demand Gen results and influence downstream search behavior, so campaigns should be evaluated over a full sales-cycle window rather than only the first seven days. Impressions and CTR without a revenue or cost line function as activity metrics, not performance metrics.
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    - **Name:** How Does Demand Gen Audience Targeting Differ From Search?
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      - **Text:** As covered earlier, Search expresses intent through the query while Demand Gen has no keywords, so the audience signal is the only intent lever. The practical difference is clear. A weak signal set in Search still gets some benefit from keyword intent, while in Demand Gen the signal set defines the entire problem and the entire opportunity.

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  - **Description:** Tower33 breaks down how to configure Demand Gen signals, ad groups, and seed lists so your budget works harder. Get the practitioner's guide.
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    - **Name:** Anthony Miller
    - **JobTitle:** Founder
    - **Description:** 20+ years in customer acquisition, digital marketing, search, social, analytics, and technology.Anthony has worked with hundreds of brands, managed hundreds of millions in ad spend, and founded both Tower33 and Travel Outside. Today, he works with select partners building and running customer acquisition ad programs through strategy, and hands-on management.
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      - **Name:** Tower 33
      - **Url:** https://tower33.com/
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    - **Url:** https://tower33.com


---

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## Citations

- [Google Local Services Ads Requirements: A Complete Guide](https://tower33.com/articles/google-local-services-ads-requirements/)
- [How To Optimize Google PMax Campaigns That Actually Convert](https://tower33.com/articles/optimize-google-pmax-campaigns/)
- [Best Agency for Google Search Campaigns: A Buyer’s Guide](https://tower33.com/articles/best-agency-google-search-campaigns/)
- [Google Shopping Budget Allocation: A Step-by-Step Guide](https://tower33.com/articles/google-shopping-campaign-budget-allocation/)
- [The Best Google Ads Reporting Agency Lets You Audit First](https://tower33.com/articles/best-google-ads-reporting-agency/)

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