Dario Zannoni

Instant Form campaigns offer a 'Maximize number of conversion leads' performance goal, which they had assumed required CRM lead-scoring data; they say anyone can select it.

• The expected cost is volume: the system avoids cheaper, lower-quality conversions, so total leads likely fall.
• Later posts extend the same argument.
• A Google Sheets integration lets a sales team update each lead's status, and Meta reads those outcomes back into targeting.
• Meta's move from 'Conversion Leads' to 'Qualified Leads' introduces 'Sequenced Events', where the ad is credited only if an initial lead and a higher-value follow-up event both occur in order.
• A separate post describes a manual download option in the campaign results table, which they call fastest for immediate access but not a replacement for CRM integration or automatic export to Sheets.
• They point to Bram Van der Hallen's post on automatic collection as the better ongoing setup.

Zannoni describes switching a Meta Instant Form campaign's performance goal to 'Maximize number of conversion leads' and notes that lead quality should improve while total lead volume likely falls. In Sheets, a sales team updates a lead from qualified to converted, and Meta uses that offline outcome to refine targeting.

The argument moves from a single performance-goal setting to a Google Sheets feedback loop and Meta's Sequenced Events model, which Zannoni says is clearer to implement but still has unclear timing and minimum-volume rules.

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They often close by identifying what remains unclear, including whether a change is new behaviour or was simply undocumented before.

In the Instant Form post, Zannoni opens by saying they had misunderstood the performance goal options and explains what changed after reading Meta's documentation.

• In the Google conversion-signal post, they say they do not know whether the documented behaviour is a recent algorithm change or a long-standing feature.
• They also note that it is still unknown which campaign types benefit beyond Demand Gen.

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Their practical argument: additional meaningful events can improve Smart Bidding, but the setup must not dilute or mislead the main goal.

• They list four conditions: keep only the final outcome as the account-level conversion goal; assign each extra action to the most semantically accurate conversion goal; set actions as primary because the system does not learn from secondary actions; and avoid the 'Other' category and custom conversions because they carry weak semantic meaning.
• Examples include adding Add to Cart or Begin Checkout alongside Purchase for ecommerce, and tracking Pricing or Contact Us page views for lead generation.

For ecommerce, Zannoni suggests keeping Purchase as the account default while collecting mid-funnel events like Add to Cart or Begin Checkout and mapping them to the respective standard goal. For lead generation, where standard goal categories are thinner, they suggest tracking high-value page views through the Page view category or using the Contact category for phone and email clicks.

Zannoni links the change to Google's Journey Aware Bidding update for Search campaigns using tCPA. They note that Demand Gen clearly benefits, while the effect on other campaign types is unconfirmed.

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They list four possible configurations for Shopping Performance Max and describe applying the feed-plus-Search setup to a campaign with erratic performance. Their argument: removing placements, not just adjusting bids, can address sudden spending peaks.

Zannoni applied the products-feed-plus-Search-DSA configuration to a Performance Max campaign with erratic performance. They report that ROAS consistently exceeded targets for Shopping and Search, with no sudden irregular peaks in Display or Video spending.

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They list the requirements: working ecommerce tracking in Analytics, a linked account with personalized advertising enabled, and product IDs matching the feed.

• But they recommend the Google Ads tag where possible because its data is processed faster and covers more users.
• Their recurring point: an easier implementation route can carry a performance cost, and the tradeoff should be clear.

After describing the Google Analytics route for dynamic remarketing, Zannoni says the common observation is that data from the Google Ads tag is processed more quickly and offers broader coverage. They recommend implementing dynamic remarketing events directly through the Google Ads remarketing tag whenever possible.

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Google Ads bulk image edits can enhance up to 100 images at once but cannot send edited images back to Merchant Center.

• Merchant Center's 'Generated for You' selects products automatically, which they see as its main weakness.
• Their argument is not that the tools are useless, but that automation without product-level selection leaves catalog teams doing manual work.

In Merchant Center, 'Generated for You' proposed enhanced images for only 8 of Zannoni's roughly 2,700 products, with no way to choose which items are selected. They describe the tool they would want instead: select multiple products, choose enhancements such as generate scene, remove background, or increase resolution, review the images, and apply them directly to the selected products.

Unlike the lead-campaign posts, this post closes with a product gap and a proposed ideal feature, not an immediate recommendation.

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Zannoni describes Ryan as one of the people with outstanding insight into ecommerce PPC and Performance Max, and links to the session. They point readers to deeper specialist material when the topic calls for it, rather than treating their own posts as the final word.

The post carries the SMX hashtag and links to Mike Ryan's session; Zannoni also calls Ryan a very nice person. The brief recommendation is attributed to Ryan, not presented as Zannoni's own analysis.

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Zannoni shows the answer is no and explains the tradeoff advertisers accept when they switch.

Zannoni opens by admitting they misunderstood one of Meta's performance goal options.

• Instant Form campaigns offer two choices: maximize number of leads or maximize number of conversion leads.
• They had assumed the second option required a connected CRM feeding lead-scoring data back to Meta.
• After checking Meta's help documentation, they found any advertiser can select it.
• Meta does not explain the mechanism, so they assume it draws on aggregated cross-account patterns about which users and placements produce higher-quality leads.
• A CRM connection still helps, but it is not a prerequisite.
• The expected cost is volume: Meta will avoid cheaper, lower-quality conversions, so the total number of leads will likely fall.

For brands planning a campaign

For a lead-gen client with poor-quality Instant Form leads and no CRM integration, this is a low-cost test that requires one setting change. Judge it on downstream conversion rate, not lead count, because lead volume will probably drop.

It is the clearest explanation in the set of how offline lead outcomes feed back into ad targeting.

Zannoni frames two obstacles that have limited Instant Form adoption: getting the lead data out and improving lead quality.

• Downloading CSV files was awkward and CRM connections were hard to implement, so Meta added automatic export of Instant Form leads to Google Sheets.
• The new development is that the same sheet can now carry quality data back.
• Meta sends leads to the sheet; the sales team updates each lead's status as it follows up, for example from qualified to converted; Meta reads the updated statuses and uses them to improve targeting.
• Zannoni calls the implementation simple despite the technical-looking help page and says they are looking forward to trying it.

For brands planning a campaign

This suits small businesses with a sales follow-up process but no engineering capacity for the Conversion API. It also supports a content angle on closing the loop between ad platform and sales outcomes, often the missing step in lead-quality programmes.

Readers who manage lead campaigns need to understand the new event logic before rebuilding anything.

Zannoni reports that Conversion Leads is being replaced by Qualified Leads, with a new optimisation event type called Sequenced Events.

• Under maximize number of qualified leads, Meta asks advertisers to define two events: an initial Lead event, such as a website form submission, and a final higher-value action that happens afterwards.
• The ad is credited only when both occur in order.
• Unlike the previous CRM-based method, the final event does not have to be a CRM or offline event; standard website conversions such as CompleteRegistration appear usable, as do custom conversions.
• Meta requires enough historical data showing the final event follows a lead, with enough qualifying sequences over a 28-day period.
• Zannoni explains the problem being solved: Meta can generally optimise only within seven days of a click, which is enough for the initial lead but not for qualification.
• The maximum lag between events, exact timing rules and minimum volume remain unclear; advertisers should ask Meta for those requirements before changing their setup.

For brands planning a campaign

Account managers should check whether an existing CRM-based lead-quality setup maps onto the new sequence before assuming it still works.

• The wider range of acceptable final events opens the feature to advertisers who could not previously use it.
• Before advising clients, confirm the timing and volume rules with Meta.

The four cautions make the advice practical.

Zannoni noticed updated Google help pages stating that primary conversion actions not used for optimisation may still enhance predictions and help a campaign ramp.

• They cannot tell whether this is new behaviour or previously undocumented, but the practical point is that collecting additional meaningful events as primary conversion actions can help Smart Bidding even when those events are not the campaign's goal.
• Zannoni lists four conditions: only the final outcome you truly want should be set as an account-level conversion goal; additional actions should be assigned to the most semantically correct goal, because the wrong category risks passing misleading data; they must be set as primary, since the system does not learn from secondary actions; and the Other category and custom conversions should be avoided because they carry weak semantic meaning.
• Examples include ecommerce, keeping Purchase as the account default while collecting Add to Cart or Begin Checkout, and lead generation, tracking high-value page views such as Pricing or Contact Us, or using the Contact category for phone and email clicks.
• Zannoni says it is still unknown which campaign types benefit beyond Demand Gen, and links the change to Journey Aware Bidding for Search campaigns using tCPA.

For brands planning a campaign

For accounts with sparse conversion volume, this is a concrete structural recommendation rather than a bidding tweak. It distinguishes enriching the signal from diluting the goal, a distinction most accounts get wrong.

Useful as a quick reference, it also shows where Zannoni draws the line between a convenience and a proper process.

Zannoni describes a new download icon in the results field of Meta lead form campaigns, available at campaign, ad set and ad levels.

• Clicking it lets you choose the period and file format, CSV or XLS, and download the lead data immediately.
• They call it the most direct and fastest way to access leads, but still recommend setting up direct CRM integration or automatic collection into Google Sheets, citing a post by Bram Van der Hallen.

For brands planning a campaign

Useful as a quick-win item in practitioner-focused content. This is a manual export, not a lead-routing system, so do not present it as a replacement for CRM integration in a client recommendation.

It also ends with a genuine question to other practitioners, making it a good conversation starter.

Zannoni writes that the configuration has existed from the start, but they only recently realised it was available: within Google Pmax for Shopping, you can run products feed plus Search, using Dynamic Search Ads, while excluding standard Video and Display ads.

• They applied it to a Performance Max campaign that had been showing erratic performance, and report that ROAS has consistently exceeded targets for both Shopping and Search, with the campaign running smoothly and without the sudden irregular peaks in Display or Video spending often seen in Performance Max.
• Zannoni lists four possible configurations for Shopping Performance Max: full assets plus products feed; full assets plus products feed plus automated text assets and URL expansion; products feed only; and products feed plus automated text assets and URL expansion.
• They ask whether others use the Shopping plus Search DSA-only setup and what results they have seen.

For brands planning a campaign

Relevant to ecommerce advertisers whose Performance Max spend swings unpredictably across Display and Video. The first-hand ROAS claim comes from one account, so treat it as a hypothesis to test, not a benchmark.

It offers a useful model for evaluating a new platform feature against the actual process.

Zannoni describes two generative AI features for bulk image enhancement.

• In Google Ads Manager, the Assets Library AI image editor can enhance up to 100 images at once, with prompt-driven options to replace backgrounds and add objects.
• Images can be regenerated individually and saved for use in Google Ads, but edited images cannot be sent back to Merchant Center.
• Zannoni was not aware of an official announcement, only a new help page, and rarely needs bulk image editing for Google Ads.
• In Merchant Center, Generated for You creates enhanced product images by adding a matching background and increasing resolution.
• It selects products automatically, which Zannoni sees as a weakness: of 2,700 products in the catalog, it suggested images for 8.
• Each proposal can be added to Merchant Center, downloaded or edited, and expires if not approved.
• Zannoni liked the generated images but says the tool falls short for large catalogs.
• They would prefer to select multiple products, choose enhancements such as generating a scene, removing the background or increasing resolution, then review and apply them directly to those products.

For brands planning a campaign

For ecommerce clients with large feeds, this frames a real limitation of Google's AI creative tooling and offers a concrete wish list. It supports content about where AI automation still needs human selection at catalog scale.

Readers deciding how to implement remarketing get both the setup conditions and a recommendation.

Zannoni reports that a December 1 announcement makes it possible to run dynamic remarketing in Google Ads using ecommerce data from Google Analytics, without setting dynamic events through the Google Ads tag.

• Requirements: ecommerce event tracking enabled and working in GA; GA and Google Ads linked with personalised advertising turned on; and product IDs in GA matching those in the product feed.
• Zannoni says the GA route looks convenient and may be easier to implement, but notes the common observation that data from the Google Ads tag is processed more quickly and offers broader coverage.
• They recommend implementing dynamic remarketing events directly through the Google Ads remarketing tag whenever possible.

For brands planning a campaign

Useful for measurement and tracking content aimed at ecommerce advertisers choosing an implementation path. The recommendation gives a clear default: use the Google Ads tag and treat the GA route as a fallback.

A brief X post in Japanese recommending a session on advanced techniques for ecommerce Performance Max by Mike Ryan of Smarter Ecommerce.

• Zannoni calls Ryan one of the people with outstanding insight into ecommerce PPC and Performance Max, and adds that Ryan is also a very nice person.
• The post carries the SMX hashtag and links to the session.

For brands planning a campaign

Useful for mapping the adjacent experts Zannoni points to and identifying a second voice if a campaign needs deeper Performance Max or feed-focused ecommerce content.

Dario Zannoni explains that the "Maximize number of conversion leads" performance goal can be selected on Instant Form campaigns even without a CRM, though total lead volume usually falls.

• A later post covers Meta's Google Sheets integration: the sales team updates each lead's status in the sheet, and Meta reads those outcomes back to refine targeting.
• A third post tracks Meta's shift from "Conversion Leads" to "Qualified Leads" and its "Sequenced Events" setup, in which a lead is credited only when the initial form submission and a higher-value follow-up event occur in order.

Reads Meta's help documentation and campaign interfaces, tests or inspects settings, then explains their practical effects and what remains unclear.

For brands planning a campaign

Useful for a lead-quality brief: it explains how lightweight integrations can improve lead quality and notes the tradeoffs in volume and documentation.

Examples: Source 1 · Source 2 · Source 3

Dario Zannoni calls it the fastest and most direct option, but recommends CRM integration or automatic export to Google Sheets for ongoing work rather than manual downloads.

For brands planning a campaign

Useful for content about the operational side of lead generation: handoff, follow-up, and why a manual download is a stopgap rather than a process.

Examples: Source 1

Zannoni lists cautions before adding such actions: keep only the final outcome as the account-level goal, assign each action to the most semantically accurate conversion goal, set actions as primary rather than secondary, and avoid the "Other" category and custom conversions.

• Examples cover ecommerce (adding Add to Cart or Begin Checkout alongside Purchase) and lead generation, where standard goal categories are thinner and page views of pricing or contact pages may be used.
• An update links the change to Google's Journey Aware Bidding announcement and notes that Demand Gen clearly benefits, while benefits for other campaign types remain unconfirmed.

For brands planning a campaign

Useful for a lead-quality brief: it shows how mid-funnel signals can feed bidding, with concrete setup rules and a clear distinction between documented guidance and assumptions.

Examples: Source 1

The post lists four possible configurations and describes applying the feed-plus-Search setup to a campaign with erratic performance, reporting steadier spending and ROAS above target. It closes by asking readers which configurations they use and what results they have seen.

Applies a configuration to a live campaign, tracks placement-level spending and return, then compares the results with alternatives.

For brands planning a campaign

Useful for ecommerce advertising content: a concrete account of how campaign structure affects spending stability, and a willingness to ask the audience rather than present a settled answer.

Examples: Source 1

Zannoni tests the tools against a catalog of roughly 2,700 products, notes that "Generated for You" proposed images for only eight items, with no way to choose which products are selected, and points out that images edited in Google Ads cannot be sent back to Merchant Center. The post concludes that neither tool meets the needs of large catalogs and describes the tool that would.

The post closes with a stated product gap and a proposal for an ideal feature.

For brands planning a campaign

Good source for content on creative automation at scale, including the case against treating new AI features as complete solutions.

Examples: Source 1

It lists the requirements: working ecommerce tracking in Analytics, a linked account with personalized advertising enabled, and product IDs matching the feed. Despite the easier setup, Zannoni recommends the Google Ads tag where possible because its data is processed faster and covers more users.

For brands planning a campaign

Useful for measurement and data-quality content: it frames a convenience-versus-performance tradeoff advertisers face when choosing tracking implementations.

Examples: Source 1

Occasional curation: amplifies another practitioner's work rather than publishing original analysis.

For brands planning a campaign

Relevant to tone and partnership fit: Zannoni recommends other people's material and works across English and Japanese.

Examples: Source 1

The posts describe and evaluate these products; they disclose no sponsorship, partner status, or client work with Google.

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Zannoni tests and explains these features; no sponsorship, partner status, or client relationship with Meta is stated.

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The post endorses the person and the session, not a stated commercial relationship with the company.

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Akash covers defining the conversion action, installing the tag, adding values, testing before launch and conducting a monthly audit, while flagging common breakages. Zannoni explains extra conversion signals and how to choose a dynamic remarketing data source, including which settings to avoid.

Akash writes about B2B revenue attribution, connecting campaigns to CRM deals and leadership’s budget questions; their posts end with a reporting question. Zannoni focuses on platform behaviour, testing feature changes across Meta and Google and noting what documentation leaves unconfirmed.

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Aman examines a Merchant Center quality score and a makeup store’s traffic-and-orders figures; Zannoni tests Google’s bulk image and "Generated for You" tools against a live product catalog. Both ask what the numbers don’t show.

Aman frames the gaps as questions for brand owners — product data, reviews, shipping, shade guides and checkout — in a marketing-copy style. Zannoni tests platform features against a large catalog, reports where the tools fall short and proposes the feature that would help.

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Irum describes wiring WordPress forms straight into Clio Grow so intake arrives structured and validated; Zannoni describes downloading lead data from the campaign results table and feeding lead outcomes back to Meta.

Irum promotes a paid implementation service to law firms, presenting the tooling as a package on offer. Zannoni analyses platform behaviour, explains a setting or raises an open question, and points readers to other practitioners for deeper material.

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This page needs practitioner content on lead-quality optimization, and the closest alternative work is a documented Fluent Forms to Clio Grow integration for law firms: a visitor submits a form, the data is validated and mapped by field, and the lead lands in the CRM as a contact or intake record without Zapier or manual entry.

• That is the intake half of lead quality: whether a form submission becomes a usable record or sits in an inbox.
• Irum also publishes on website accessibility to WCAG 2.2 AA, including audits, keyboard navigation, form and heading fixes, and contrast work.
• If the buyer task is 'make our lead capture and site usable enough that the leads we already pay for actually reach the sales team', this work is closer to that task than Zannoni's platform-side analysis.

The tradeoff is scope.

• Irum's posts are framed as service offers, with the deliverable list and inquiry route built into each post, so the content reads as a capability statement rather than an explanation of how a platform setting behaves.
• The work stops at the intake boundary: it does not cover Meta performance goals, conversion signals, sequenced events, or how an ad platform learns which leads are worth chasing.
• Commissioning Irum instead would buy you the plumbing but not Zannoni's explanation of the optimization controls that make it matter.

Examples: Source 1 · Source 2

The posts address the retail side of the same buyer decision.

• One walks through a Google Merchant Center score of 'Great', what sits behind it, and which weak signals, such as missing return information, slow mobile pages, thin product data, or unclear shipping, drag the store rating down.
• The other takes a makeup brand's dashboard—15,022 visitors, 992 orders, 626 new customers and over $60K net revenue—and asks what made those visitors trust the brand enough to buy, then lists the levers: swatches, shade guides, user content, reviews, product pages, checkout, retargeting.
• For a campaign about why paid traffic does not convert in a retail catalogue, that is direct material.

The tradeoff is that this is a different buyer problem.

• Neither post addresses lead generation, form-based lead capture, or the quality controls inside a lead campaign, so it does not substitute for Zannoni's platform-change analysis on lead-quality optimization.
• The writing is built around diagnostic questions and checklists rather than a documented platform change with its limits named. jabeen's posts close with a question to the reader rather than what is still unknown about a setting, which is the pattern this campaign needs.

Examples: Source 1 · Source 2

One post sets out the difference between traffic-source reporting, which answers where a click came from, and revenue attribution, which answers which campaign produced money.

• It uses a worked example of a $14.20 click that ends in a $3,500 contract 45 days later, and gives three checks: trace the conversion event back to a real business outcome, verify the attribution path across the actual customer timeline, and identify where the chain breaks.
• The other post is a conversion-tracking audit in steps, from defining the action and installing the tag to value tracking, test mode before launch, and a monthly check of the 'Frequently problematic tags' report, plus three failure modes such as tag manager changes silently destroying tags.
• For a brief about trusting the numbers before optimizing them, this is the closest fit among the three.

The tradeoff is that this is measurement hygiene, not lead-quality optimization.

• The posts do not cover Meta lead form performance goals, the Google Sheets quality feedback loop, or sequenced events, so a content programme built on them would explain how to verify a conversion, not how to improve which leads arrive.
• The framing is general education, with numbered steps and a closing question, rather than the close reading of a specific platform change that this page needs.

Examples: Source 1 · Source 2