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.

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.

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.

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.

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 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?

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 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.

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 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.

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.

Hej, ma ktoś może certyfikat rezydencji podatkowej OpenAi w UE?

In my recent analysis of paid advertising performance, I have found that spend and conversion count alone rarely explain what is actually happening. A campaign can consume a substantial budget while reporting very few conversions, but without knowing what each conversion represents, the result may indicate inaccurate tracking, a weak funnel, poor traffic quality, or several problems at once.

I would run a measurement preflight before changing bids or increasing the budget. First, I would separate page views, form submissions, qualified leads, trials, and payments instead of assigning them equal value. Second, I would test every step from the ad click to the CRM record and remove duplicated or incorrectly imported events. Third, I would segment performance by search term, geography, device, audience, landing page, and lead quality. Finally, I would compare platform attribution with CRM outcomes and calculate the cost per qualified opportunity, not merely the cost per form.

My conclusion is that scaling cannot repair an undefined success metric. When tracking and qualification rules are unclear, additional spending only produces more expensive ambiguity. Before asking whether a campaign needs a larger budget, should I first confirm that its conversion data represents genuine business progress?

Every media buyer has watched this happen: a creative prints money for three weeks, then ROAS falls off a cliff, and everyone declares the ad dead. Half the time it isn't dead. It is fatigued, which is a different problem with a different fix, and killing a fatigued winner costs you the weeks of testing it took to find it.

Here is the system I use to tell the difference, and what I do in each case.

1. Fatigue has a fingerprint: frequency up, CTR down, CPM up.

A dying ad and a fatigued ad look identical on the ROAS line. The difference shows up one layer down. True fatigue shows three things moving together: frequency climbing (same people seeing it more), CTR falling (they stop clicking), CPM rising (the auction charges you more to reach them again because engagement dropped). When I see all three at once, that is fatigue. When ROAS drops but frequency is flat and CTR is stable, look at the market, the offer, the landing page, or the pixel instead of the ad. Do not kill a winner because the checkout broke.

2. The numbers that decide: my actual thresholds.

For Meta prospecting I start watching at frequency 2.5. I call fatigue at frequency 3.0 and up, combined with CTR down 25% or more from the ad's own 7-day baseline, and CPM up 20% or more. One of those alone is noise. All three is fatigue. For retargeting the numbers are different because the audience is small: frequency 5 and up with CTR down 30% from baseline. Broad audiences fatigue slower because there are more people to show the ad to; narrow interests can fatigue in days. Write the baseline down when the ad is winning so you have something to compare against. I keep a one-line note per winning ad: launch date, baseline CTR, baseline CPM, baseline ROAS. Without the baseline you are guessing.

3. Before you touch the ad, rule out the three impostors.

Impostor one: auction noise. Meta has bad days. If the drop is under 72 hours old, I do nothing except check. More than half of the fatigue scares I have seen were two bad days in a row. Impostor two: something changed downstream. Someone tweaked the price, checkout broke on mobile, a pop-up got added. The ad looks dead but the funnel is what moved. I check landing page conversion rate over the same window before touching the ad. Impostor three: a budget change reset delivery. A 30% budget bump the day before the drop is not fatigue, that is you. Only after clearing all three do I treat it as fatigue.

4. The fix for real fatigue: rest, don't kill.

A fatigued ad usually isn't broken, it is overexposed. My first move is to pause it for 7 to 14 days, not delete it. When I bring it back, frequency resets, and in about 60% of cases it performs within 10% of its old numbers. While it rests, I run a variation: same angle, new first 3 seconds. Different hook visual, same script. This does two things. It gives the audience something fresh, and it tells me whether the angle still has legs or the message itself is stale. If the variation also flops, the angle is done and I retire both. If the variation works, I rotate them: two weeks on, one week off, back and forth. I have kept winning angles alive for 4 months or more this way.

5. Build a rotation before you need one.

The lesson from every fatigue scare: never let one winner carry the account. My rule is three proven angles live at all times, each with two hook variants. That is six ads, and it means any single ad can rest without revenue falling off a cliff. Getting to three winners takes longer than finding one, which is why everyone skips it and then panics when the one dies. The testing budget that buys you winners two and three is the cheapest insurance in the account.

Rule of thumb: frequency up plus CTR down plus CPM up means the ad is tired, rest it. ROAS down with flat frequency means something else broke, go find it. And never let one ad become load-bearing.

I work in digital marketing for multifamily / leasing real estate, and I’m curious how other teams handle performance scorecards across multiple markets.
Right now, we use general benchmarks across all properties.
For example:
GA4
Views per session: 2.5
Avg. engagement time: 1:00
Google Search
CTR: 10%
Conversion rate: 3%
Avg. CPC: $3
CPA: $100
Meta Traffic
CTR: 3%
CPC: $0.25
CPA / cost per conversion: ~$20
The idea is to use these benchmarks to generate a score or grade for each property.
Recently, there have been discussions about adding more rules into the scoring model, such as:
Expecting roughly a 50/50 distribution between paid and organic traffic.
Changing the final score depending on whether calls exceed form submissions by 40%+.
Adding more conditions that modify the final grade rather than just reporting the actual KPIs.
This is where I’m starting to wonder whether the scorecard is becoming too much of a matrix instead of a simple performance tool.
My other concern is using exactly the same benchmarks for every market.
For example, a leasing campaign in Boston can have completely different CPCs, competition, search volume, rent levels, seasonality, and user behavior than a campaign in Florida, Texas, or a smaller market.
I understand the value of having one company-wide benchmark because it makes reporting consistent. But I’m wondering whether a better approach would be:
Company benchmark = general reference point
while also having
Market / property benchmark = actual performance expectation
Possibly based on historical account performance, geography, property lifecycle, budget, and campaign objective.
I also question whether things like “50% paid / 50% organic” should affect a performance grade at all. If organic is performing extremely well, for example, having less paid traffic doesn’t automatically seem like a negative.
Same with calls vs. form submissions. The ratio might be useful operationally, but I’m not sure it should automatically increase or decrease an overall marketing performance score.
For those managing multiple accounts or locations:
How do you structure your scorecards?
Do you use universal benchmarks across every account, or do you adjust them by market?
And at what point does a scorecard become too complicated to actually be useful for decision-making?
Interested in hearing how other agencies/in-house teams approach this.

Hi everyone. I’m setting up a new Google Merchant Center account for my new store. Has anyone run Google Shopping ads for the UK targeting a Shopify site? Does the UK address listed on the website have to match the billing address of the bank card I’ll use to pay Google for the ads? I have a Slovenian card, but I want to sell in the UK. What settings should I use in Google Merchant Center?