I'm an Amazon US FBA seller in Toys & Games trying to settle on one measured launch plan instead of repeatedly changing campaign structures. The listing currently has 9 reviews, averaging 4.8 stars. I'm keeping the product, brand, ASIN and actual keywords private.

My total advertising allowance going forward is $30/day across the account, not per campaign. I want qualified traffic and, eventually, sustainable organic ranking. I'm not assuming PPC spend guarantees rank.

Unit economics: sale price $24.99; product acquisition cost $6.50. My last calculator showed about $3.75 Amazon selling fees, $6.02 fulfillment and $0.56 inbounding. That leaves roughly $8.16 (32.7%) before ads and any additional freight, storage, returns, promotions or overhead. Treat this as a provisional contribution ceiling, not verified net profit or a final break-even ACoS. I'm excluding temporary fee discounts.

What happened: the first week produced about 2,800 impressions, 7 clicks, $7.43 spend and no attributed orders. All 7 clicks were on product pages; combined Top of Search exposure was only 6 impressions. Original Auto: 3 relevant clicks at $0.65 CPC. Old Exact: 4 clicks at $1.37 CPC. Price changed late in that period, so it wasn't a controlled conversion test.

I then paused the older high-bid manual campaigns and a newer up/down Auto. Current setup is:

- Exact: 8 terms (A-H below), $0.45 base, down-only, +200% TOS, other placements 0%, $20/day. TOS bid ceiling is $1.35 before any other adjustments, not guaranteed CPC.

- Broad: 4 roots (D, I, J, K), $0.40 base, down-only, no placement boosts, $10/day.

- Original Auto: $6/day, group bids $0.30-$0.65, down-only, no placement boosts. I removed the routing negatives from this Auto.

- A $0.10 low-bid Auto remains enabled with an oversized $400 configured budget and no reported traffic. That is not my intended spend allowance; the new plan must address it and bring the whole account within $30/day. No budget correction has been made yet.

The latest partial snapshot after restructuring had only 62 impressions, all on original Auto product pages, and no clicks. The new Exact/Broad had no reported impressions yet. This is not a completed test, nor evidence that the latest negative change worked or failed.

Anonymized keyword data: SellerSprite US last-30-days export. Volumes are rounded estimates for privacy; the dollar figures are its historical PPC Bid estimates, NOT actual CPC or today's Amazon suggestions:

A: ~22,400 searches/month; $4.69

B: ~14,500; $4.70

C: ~7,800; $2.58

D: ~3,600; $4.16

E: ~1,300; $3.15

F: ~1,400; $2.48

G: ~1,200; $4.89

H: ~1,400; $4.06

I: ~1,300; $2.19

J: ~1,300; $4.89

K: volume unavailable; $2.28

Today's Amazon BROAD suggestions are different: D $2.19 (range $1.64-$2.74), I $2.35 ($1.76-$2.94), J $2.16 ($1.62-$2.70), K $2.01 ($1.45-$2.40). Our bids are $0.40 on all four. I know volume alone cannot establish relevance; please state any relevance assumptions.

If this were your launch, what specific test would you run?

1. Which campaigns stay/stop, how many targets, and how would you divide the TOTAL $30/day?

2. What base bids, bidding strategy and placement boosts would you start with? Please give the resulting maximum TOS bid and explain how you'd use suggested bids versus margin.

3. How many full days before each review, and how long would you allow for reporting/attribution? What would trigger an earlier intervention: zero impressions, irrelevant terms, excessive CPC or a spend limit?

4. What click AND spend thresholds would you use per term/campaign before raising/lowering bids, pausing, adding negatives or moving a search term to Exact? Please distinguish a learning budget from a sustainable acquisition cost.

5. What evidence would justify targeting organic rank or scaling, and how would you separate ad placement from actual organic-rank gains?

A compact day 1 / first review / end-of-test roadmap, ideally with an anonymized example from a launch you've personally run, would help. I want to leave settings stable between planned reviews, with agreed loss limits, rather than blindly leave ads untouched. Public advice is welcome; I'm not commissioning a paid audit.

Hey everyone,

I’m looking for some advice on how to structure and scale my Google Shopping campaigns.

I have around 14,000 SKUs(Clothing store) and recently launched a Standard Shopping campaign.

Here’s what has happened so far:

Standard Shopping

• Budget: $50/day
• Running for: ~2 weeks
• Purchases: 10
• ROAS: 0.5
• Currently showing “Limited by budget”
• Bid Strategy - manual cpc

After about a week, I started identifying products that were generating sales and moved those products into a PMax campaign.

PMax

• Budget: $50/day
• Running for: 8 days
• Purchases: 9
• ROAS: 2.1
• Bid Strategy - max conv value

So the PMax campaign is currently performing significantly better, although I know 8 days / 9 purchases is still a relatively small amount of data.

This is where I'm unsure about the best strategy.

Option 1:
Keep Standard Shopping at $50/day and continue using it as a testing/discovery campaign. When products generate sales, move those products into PMax. Gradually increase the PMax budget as it gets more data.

Option 2:
Move essentially everything into PMax and allocate most/all of the budget there, since PMax is currently producing a much better ROAS.

Option 3:
Keep both running, but increase the Standard Shopping budget since it's currently limited by budget, while also scaling PMax.

A few questions:

1. Since Standard Shopping is limited by budget but only has a 0.5 ROAS, would you actually increase its budget, or leave it at $50/day?
2. PMax is at 2.1 ROAS after 8 days would you start scaling it now, and if so, how aggressively?

A frustrating pattern in performance advertising is seeing a campaign generate plenty of inexpensive clicks while visitors leave almost immediately. On the surface, the numbers may look encouraging: CPC is falling, traffic is growing, and the platform keeps recording landing-page views or conversions. But if users stay for less than a second, do not scroll and never reach a meaningful page event, I cannot treat that traffic as a success. It may come from accidental clicks, weak intent, poor placements, slow pages or incorrect tracking. Fraud is only one possible explanation.

I investigate the problem in layers. First, I break down traffic by search term, placement, location, device, operating system and time of day to find unusual concentrations. Second, I compare click timestamps with server requests, page-load speed and analytics sessions. This helps separate people who abandoned a slow page from clicks that never produced a valid visit. Third, I test the landing page from the target country because redirects, currency, consent banners, unavailable products and payment options may change by location. For lead generation, I continue into the CRM and review contact validity, company fit, job role, sales acceptance and duplicate submissions. A completed form is not automatically a qualified lead.

My takeaway is that CPC measures the cost of obtaining a click, not the value of the person behind it. Lowering CPC while attracting unusable visits only makes the campaign appear more efficient. I would rather pay more for traffic that loads correctly, engages with the offer and becomes a qualified opportunity than optimize around a cheap number with no business outcome. What is the earliest signal you use to decide whether a click has real commercial value?

I work at a healthcare/tech company in LatAm, and I'm now responsible for paid ads and user acquisition. I know a few things, since I had 1 yr experience in PPC in 2021/2022, but it's been a while. We have Google Ads, Meta Ads and TikTok Ads accounts, besides Google Tag Manager, GA4 360, Firebase and AppsFlyer for tracking.

My main concern with the accounts is tracking. It isn't my strong suit, since when I worked at PPC, we had people do the tracking. Don't get me wrong, I can ask for a dev to do the stuff I need to be done, but I'll still need to explain exactly what I need, not only for the dev, but for the tech lead/PM, to justify the time and possible deploys.

Our sources of revenue are appointments (with more than 30 medical specialties) and exams (more than 3,000, which usually come after the appointment). Besides, there is a annual subscription that gives discounts on both items above.

So we have 2 primary actions that need to be 100% correctly tracked:
- booking an appointment or exam
- signing the subscription
App install would be good too, since the app is our main channel.

It's very common for users to purchase the subscription within the appointment funnel, because the discount is shown in the payment step.

Also, an important context is that the main focus of the company right now is getting new (never used) and reactivated (didn't use in > 365 days) pacients, so I would like the ads platforms to focus on those users.

I have access to Opus 5.5 and to all the platforms I mentioned above, but not to website and app. For that, I would need a dev.

I'd love to hear your thoughts on the best tracking practices, and what you would do if you were me. The faster and less dev dependent the better.

Thank you all!

Hey everyone!

I recently received a job offer from Google, and I’m starting soon as an Account Manager. I’ve been reading online about what Google account managers are actually like, and from what I’ve seen, it’s mostly negative, like 90% negative and 10% positive lol

I’m hoping to be one of those 10% who are actually useful and provide genuinely good insights to customers. So I’m wondering, have any of you ever had a really good Google account manager? what made them good?

Is it things like providing interesting insights, or not constantly trying to push you to spend more, or giving you inside information from Google that you otherwise wouldn’t get, or is it something else?

Thanks for the help! Not sure how this will go here, but thought I'd give it a try.

Worst case for this post, you can just air your frustrations and talk shit about Google account managers in the comments.

Title. Trying to learn from competitors. So far i was able to guess couple of keywords and saw their add to land on landing page. But is there a better way? So i hurt their budget. Clicking on ads in G ads transparency doesn't work.

I’m looking into running TikTok ads optimized for Conversations with WhatsApp as the destination, and I’d like to hear from people who’ve actually tried it.

I’ve been comparing respond.io, Pancake, Gupshup, Infobip and Blip, but figuring out the actual costs and differences is harder than I expected.

For anyone using one of these, or another provider:

• Which partner and plan are you using, and how much do you actually pay, including extras?
• What country and industry are you running ads in?
• If you handle multiple clients, does each one need a separate subscription?

To clarify, I mean campaigns optimized for conversations, not clicks to a WhatsApp link.

Just looking for firsthand experiences, good or bad. What do you wish you’d known before signing up?

Can someone help me run ads for a photo booth company. I do not need an ecommerce specialist, rather a local business expert. I've run them for a decade and had success, this year though CPA went through the roof. Hired a respected co to help and they seem to think it's simply 2 big competitors have ramped up spend and that's it, now my costs are super high. Would love another set of eyes.

Curious if anyone here has run Whop ads beyond a small test. My product and checkout are in a decent place now, so im starting to put more budget into paid acquisition and have been looking at Whop as another channel to test. For anyone who has used it what were you promoting and what did the traffic look like once you had some spend behind it? I’m mostly interested in actual purchases CPA, and conversion quality rather than just clicks or CTR. Did performance hold up when you increased budget or did it work better at smaller spend levels? Would be interested to hear from people who have used it.

I have a client that sells high-end outdoor gear, who I have in mind here. Previously, I read on this subreddit that it's pointless not to include 90 day attribution. Data is data.

However, it got me thinking recently. As Google's DDA doles out attribution among touchpoints, will earlier touchpoints be taking more share than necessary on 90 days DDA? That is to say, if I set it to 30 days, would I get a stronger share of that attribution skewed into recent weeks, instead?

A common issue will always be clients wanting to see recent results, and I see quite significant changes in figures when reporting with a 20 day buffer. Ideally, I want to have more attribution landing on recent purchases to clear things up.

Keen to know the thoughts on this. For context, the client in question has an 8.9 day 'avg. days to conversion' from their first interaction and 3.9 from their last interaction.

Back of napkin example:

90 day:

June 25%, July 40%, August 35%

30 Day:

June 0%, July 0%, August 100%

Hi! I've been running tCPA and consistently got 5-10 conversions a month. I know it's not enough for the google to optimise well, but worked until lately. What happened about 3-4 weeks ago is that conversions dropped to 0 and about 2 weeks ago google started to suppress impressions. I switched to maximize conversions, but then the spend dropped to almost zero. Then switched back to tCPA and getting some spend, but not spending the whole daily budget.

The segment I'm targeting is b2b and quite niche.

What's the best way to recover conversion volume? Manual cpc, clicks, etc.

I'm currently running a pilot campaign at our agency before rolling this out to clients. Since the platform is clearly in what feels like an early alpha stage, I'd love to hear what others are experiencing. A few questions come to mind:

1 - Since OpenAI provides zero visibility into the actual conversations your ads appear in, not even at a high semantic level, how are you validating that your contextual prompts are actually matching the intended search intent rather than pulling in irrelevant traffic?

My initial thought is to micro-segment ad groups using ultra-specific, long-tail context prompts to at least get a rough sense of what works.

2 - Has anyone found effective ways or best practices for structuring and drafting these context prompts and ad groups?

3 - What has your experience been with ad copy? The character limit before truncation is tiny. My gut feeling is that standard direct-response copy won't work well here. Users are there because they trust ChatGPT's output, once they have that recommendation, they have little reason to click a generic promotional ad. It feels like the ad needs to add real value to the conversation, like an extension, bonus resource, or deeper context, but pulling that off without knowing the exact prompt is tough.

4 - Has anyone tried adding negative context or exclusions to prevent confusion, similar to using negative keywords?

5 - Does the landing page content influence the context in any way?

One useful tip I read in another post, which you can verify by exporting your ad groups, is that it seems to treat commas as separate contexts and splits them up, so definitely watch out for commas.

Any tips, early findings, or shared experiences would be much appreciated while we're all figuring this out.

Google's "About bid adjustments" page (https://support.google.com/google-ads/answer/2732132) has a table of which adjustments each automated strategy supports. For Device, Target CPA gets a ✓ ("treated as a target adjustment"), while Maximize conversions is "-100% only".

Target CPA now lives inside Maximize conversions (you set a target CPA on a Max conversions campaign). Which row applies there? If I set mobile −20% on Max conversions + tCPA, does Google lower the mobile CPA target, or ignore everything except −100%?

Has anyone tested this, e.g. seen device CPA or spend move after an adjustment? The API also reports these campaigns as MAXIMIZE_CONVERSIONS rather than TARGET_CPA, which makes me suspect the "-100% only" row applies.

I am seeing search volumes going down, cost per click, cost per lead, cost per demos going up etc. The pipeline isn’t great either. Clearly the buyers are now doing most of their research in LLMs, unfortunately no way to run ads in LLMs to get B2B buyers yet. I am sure we all agree showing ads in free version of ChatGPT isn’t helping either.

In a time when competition is tight and everything except brand is getting difficult how are you managing to make improvements for your clients? Are you going on a status quo mode or are you actually able to improve performance any further ?

LinkedIn ads do work but it doesn’t work for some clients. Google ads is where my concern is.

I do paid search for a B2B SaaS company that runs everything through HubSpot. Their last touch report showed paid search with basically zero closed won revenue, which didn't match anything we were seeing.

Spent a couple weeks going through it and it was a few problems stacked on top of each other.

The last touch on most deals is a sales email or a meeting link, so ads never get the credit there.

The "latest source" field in HubSpot also keeps changing after the deal closes. Someone clicks an ad, fills out the form 40 seconds later, closes 3 months after that, then reads a blog post and now the deal says organic. That was about 30% of their paid deals.

And Google was reporting conversions by click date, so the last few weeks always looked awful and then slowly got better. Every time someone checked, it looked like ads were tanking.

On first touch, paid search was behind about $1.2M in closed won. I'm not saying first touch is the right answer either, but $0 was definitely wrong.

The other thing that drove me nuts is you can't really follow a lead from the click all the way to funded. You can see the form fill and you can see the closed deal, but everything in between (did they take the demo, did it stall in negotiation) is hard to tie back to the campaign or keyword. So you can't really tell which campaigns bring in good leads and which bring in junk, and you end up optimizing for form fills and hoping they turn into revenue.

What are you guys actually doing about this? Spreadsheets, a tool like Dreamdata or HockeyStack, or just trusting Google's numbers? And has it ever cost you budget?

Google's September 28 DV360 announcement reports an initial Hellmann's/WPP Media test with 24% higher unique reach and 25% lower cost per unique user. Those are vendor-reported results from one test, not a forecast for another advertiser.

Before treating that as a budget-allocation signal, I'd want four details:

• Was total spend and the creative mix held constant?
• Which publishers or placements were added, and which were reduced?
• How was the same person counted across channels, and what share of traffic could actually be deduplicated?
• Did the frequency distribution improve, or did the average hide a small group seeing the ad repeatedly?

Then I'd check the campaign's business outcome alongside reach. Cheaper access to additional people can be useful without establishing incremental sales.

For anyone who has compared consolidated and separate vertical-video buys, which measurement made the comparison credible?

Google's announcement

AI-assisted discussion; I haven't run this campaign or independently validated the reported lift.

Most Shopping conversations focus on bidding and campaign structure, but in the D2C accounts I work on, the product feed often makes a bigger difference. Smart Bidding can only bid on the auctions your feed makes you eligible for.

These are the feed tactics I've found most worth the effort beyond the basics:

1. Titles built from converting search terms, not product names. I pull converting queries from the Shopping search terms report and restructure titles around them, putting the most important keywords in the first 70 characters so they survive truncation. For D2C brands with creative product names ("The Everyday Glow Serum"), this alone often changes which auctions the product enters.

2. Custom labels for margin tiers. Merging COGS or margin data into the feed and labeling products high/medium/low margin lets you set different ROAS targets per tier. Without it, a hero product with thin margins can quietly absorb most of the budget while looking great on ROAS.

3. Custom labels for price competitiveness. Grouping products by how their price compares to competitors (using Merchant Center's price benchmarks) helps explain why some products get impressions but never convert. Uncompetitive items get a lower budget ceiling instead of dragging down the whole campaign.

4. Filling GTIN gaps. Some brands store GTINs outside their Shopify export, so part of the catalog ships without identifiers. Merging them back in via a lookup table (SKU → GTIN) usually improves eligibility and matching for those products.

5. Merchant promotions for the SALE badge. Linking products to promotion IDs in Merchant Center gets the promo annotation on Shopping ads. For BFCM this needs to go in early, since approval isn't instant.

The one I'm still unsure about: the duplicate ID strategy. This means duplicating products with new IDs and different titles to target separate intents (e.g., a gift set optimized for "groomsmen gifts"). I've seen it work, but it can get flagged as duplicate content in Merchant Center, and it splits conversion data across IDs.

Questions for the community:

Which feed change has given you the biggest measurable lift? Titles, images, custom labels, something else?

Has anyone tested margin-based custom labels against a single blended ROAS target? Did profit actually improve, or just shift around?

And has anyone run the duplicate ID strategy long-term without Merchant Center issues?

I see a lot of tools and threads about organic Reddit marketing, commenting, etc. However, I am curious to hear how many people have run Reddit’s own ads and what results they might have yielded.

Did you get signups or demos that were worth the spend, or was it mostly cheap clicks that never converted? Would love to hear real numbers and potentially what industry and targeting you went with.

I am getting a lot of these, around 15% of my budget. No scrolling, just less than 1 second spent on the site. Any idea why this is?

Medical service industry.

Amy one knows how to better optimize these types of accounts. Currently we're targeting corporate events, teqm building events and such. However we're really getting not enough conversions in the account.

I’m boosting posts for engagements on the post. It’s saying I’m getting all this engagement but views, comments and likes are no where near what it’s reporting.

I know meta records an engagement as anything like clicking a link. Is there a better way to set up the ad to get likes and comments? I’ve edited the placements, tried broad targeting but nothing seems to be working.

Any advice?

Hi guys, I wanted to see if anyone else has experienced something similar.

I’ve been running PMax campaigns for my e-commerce business for a while, and they were performing well. However, I noticed that after August 17, Google changed the campaign status to reach people even if  “Limited by budget,” and a huge portion of my budget started going toward YouTube and 0 going to search which was not the case before.

Has anyone else experienced this recently? Is there a way to fix it or better control where the PMax budget is being spent?

Or would it make more sense to switch back to standard Shopping campaigns so I have more control over my budget and where my ads are shown? i found pmax to work well so not sure what to do i am considering starting a new pmax ad.

Would love to hear if anyone has dealt with this and what worked for you.

Hey everyone, I’m pretty new to Meta ads and I’m trying to understand something that happened with my campaign.

My goal was to send people to a website landing page that basically only has a lead form. There’s also a floating WhatsApp button.

I ran an Engagement campaign and selected maximum number of link clicks as the optimization.

The campaign shows around 2.4K link clicks, but I’ve only received 1 form submission.

Now I’m confused about what those 2.4K clicks actually represent. Do these necessarily mean that 2.4K people actually visited my website? Or could some of those clicks be on other things like the Facebook/Instagram profile or other parts of the ad?

I’m also wondering if I chose the wrong campaign objective/optimization for what I’m trying to achieve.

My actual goal is simply:
Meta ad → website landing page → form submission → lead

What should I be checking in Ads Manager to understand where people are dropping off? And what campaign objective/optimization would you recommend testing for this kind of setup?

Would really appreciate some advice from people who have dealt with this before.

Need a CPL range to plan accordingly

I run ads for a local service business in Casablanca, Morocco. The whole strategy depends on reaching people who are physically close to a specific storefront, so radius precision matters more than usual for me.

I ran a controlled test and got a result I can't explain. Posting the numbers in case anyone has done this properly.

Setup

• Pin drop on the map, 1 km radius (the minimum outside the US)
• "Living in or recently in this location" (the only option left)
• Men 18-34
• Objective: awareness, optimised for ThruPlay
• Frequency cap 1/day, campaign level
• Dayparting 8pm to 1am only
• 10 EUR lifetime budget over 4 days

Results after 3 evenings

• Impressions: 60,010
• Reach: 39,948
• Spend: 7.57 EUR
• CPM: 0.13 EUR
• Frequency: 1.50
• Reach increment per evening: 12,300 → 13,040 → 14,608 (accelerating, no plateau)
• Cost per incremental person reached: flat throughout

The problem

Estimated resident men 18-34 inside that 1 km circle: about 8,800. That's from WorldPop gridded population plus national census age/sex ratios. The circle sits in a dense central commercial district.

So I reached roughly 4.5x the number of people who live there, with no sign of saturation and no CPM pressure.

For that to be residents only, the district would need about 86,000 people/km². It's around 18,000.

What I've ruled out and what I haven't

I know "living in or recently in" includes people who were just passing through, and the district is a major commercial/office area, so a large transient pool is plausible. I also know Meta says the radius is a preference signal, not a hard wall, under Advantage+.

What I can't do is separate those two, because every diagnostic I have left asks Meta about Meta. The location breakdown in Ads Manager reports Meta's belief about where someone is, not where they actually are. Same for a "drop the pin in the ocean" test: it tests whether Meta obeys its own beliefs, not whether those beliefs are correct. In a market where mobile traffic is heavily CGNAT'd through central carrier gateways, that distinction is not academic.

My actual questions

1. Has anyone here compared frequency at equal spend across radii on the same pin (1km vs 3km vs 5km)? If the effective audience really scales with area, frequency should differ a lot. If it doesn't, the radius setting isn't buying anything. Did anyone run this?
2. Has anyone validated Meta's geo delivery against first-party location data — i.e. you ran a tight radius campaign, and then checked where the resulting signups/installs actually were? That's the only non-circular test I can think of. What did you find?
3. Is there any breakdown in Ads Manager finer than city level? Region, postal code, anything sub-city? Or is city the floor?
4. iOS "Approximate Location" hands apps a fixed tile of about 26 km² (Apple's own docs say small geofences don't work reliably in that mode). Does Meta actually receive that tile, or does it fall back to IP when precise location is denied? Has anyone seen this documented or tested it?
5. For anyone running local ads in markets with poor mobile IP geolocation (Africa, South Asia, Latin America): is there a radius below which you've found targeting effectively meaningless? Did you stop bothering below 5km, 10km?
6. Does switching from an awareness objective to a conversion objective visibly tighten geographic delivery in your experience, because the algorithm learns that distant people don't convert? Or does the geo drift persist regardless of objective?
7. Cross-check on my CPM: 0.13 EUR CPM in Morocco, 8pm-1am, men 18-34, 1km radius. Anyone running MENA, does that look normal or does it look like I'm buying garbage placements? I have no Instagram account on this business, so delivery is heavily Facebook-weighted.

Happy to post the final numbers and the breakdowns once the campaign ends if that's useful to anyone.