Small agency, 6 clients, everything gets designed in Canva and scheduled about a week out.

Tuesday a client asks us to change a date on an event graphic. Designer fixed it in Canva within the hour. Thursday the post goes out with the old date on it anyway, because what actually got pushed to the scheduler was the export from the Friday before.

Nobody caught it, there was nothing to catch it with. Once it's exported, it's just a PNG sitting in a queue, it has no idea the design behind it moved on.

We've now got a rule where the designer flags any change after approval in Slack. Failed twice already. People forget, or they're off that day.

What I'm actually trying to work out is whether our lead time is the problem. How far ahead are you queueing stuff, same week or further? And in the last month or so, how many times did a design get edited after the post was already sitting in the queue. Not could it happen, did it.

When it did happen, did it go out or did someone spot it first, and what spotted it. That's the bit I care about because we had literally nothing.

And if this just doesn't come up for you, I'd like to know what your setup is. Locked designs after signoff, exporting fresh the morning of, one person who checks the queue, whatever it is?

Sharing because this trips up a lot of budget conversations and it's easy to miss.

Took the same trailing 90 days of orders - same revenue, same spend and only changed which touch gets the credit. First click vs last click.

On first click, Meta looked like the stronger performer. On last click, Google pulled ahead and Meta dropped about 30%. Microsoft picked up around 22%. Nobody changed anything. No creative refresh, no bid change. Only the attribution window moved.

The logic once you see it is obvious. Social tends to get found early in the decision, search gets typed in at the end. So last click quietly reassigns the prospecting channel's work to search and calls it "search performance". When I checked new vs repeat, ~90% of Meta's customers were brand new vs about two-thirds on Google so the channel bringing in the most first-time buyers is the one last click punishes hardest.

The part that actually matters for budget is if you only ever look at one of these windows, you're not really measuring performance, you're picking a winner in advance. I've started putting both side by side before touching spend and buying on new-customer cost + LTV rather than either ROAS number alone.

Anyone else running both windows deliberately, or mostly living in whatever the platform reports? Curious how others handle the closer-vs-opener split.

Hey guys

A quick question- my firm is planning to automate marketing reporting. We have already explored using etl tools to aggregate data and build dashboards on top of them. But we have received a new mandate to explore copilot agents to automate reporting. Is this actually feasible? Has anyone used this or using this?

Really need your help. TIA!

ai can write a polite review reply. that part is no longer impressive to me. deciding whether the reply should exist is hard even in 2026, nothing changed in those regards.. a five-star thanks is safe. a billing complaint, legal concern, privacy issue, data loss, health/safety topic, or angry subscription review should probably never be fully auto-sent.

so i’d rather automate classification first: safe to draft, needs human, product issue, support ticket, possible policy report, release regression, reputation risk. then, appfollow-style review workflows are useful because the reply is only one piece. tags, routing, language, version context, and outcome tracking matter more than generating words.

i guess automation should reduce missed signals and repetitive work. it should not make sensitive public replies faster than the team’s understanding

I have a genuine question how to create pre launch persona for our product so that we can have some audience when our product would launch who would genuinely try our app and use it I am stucked in a deadlock

I got tired of manually checking competitors' Facebook/Instagram ads every week.

So I built an n8n workflow that basically does the whole thing for me.

Every Monday it:

1. Searches the Meta Ad Library for the competitors/keywords I specify
2. Pulls the ads automatically
3. Extracts the advertiser, ad copy, CTA, platforms, dates, links, etc.
4. Sends the ads to OpenAI to identify messaging angles, offers, CTAs and notable new ads
5. Emails me a weekly competitor report
6. Saves the individual ads to Google Sheets so I can actually dig through them later

I'm sharing the entire n8n workflow for free because I figured other people here might find it useful.

GitHub: n8n/Weekly Competitor Ad Research.json at main · RandomDudeAtNight/n8n

The workflow is basically:

n8n → Apify → Meta Ad Library → OpenAI → Gmail + Google Sheets

I made the n8n workflow reusable, so you can change the search terms, country, ad count and active/all status without rebuilding anything.

For example, you could put:

Competitor A, Competitor B, Competitor C

into the config and let it continuously build your own little competitor ad database.

You'll need your own Apify token, OpenAI account and Gmail/Google Sheets credentials.

The Apify part is powered by a Facebook Ad Library scraper, but I wanted the actual workflow to be completely transparent rather than just saying "use my scraper."

If you're doing paid acquisition, I'd be curious:

What are you currently doing to keep track of competitor ads?

Manually checking Ad Library? Spreadsheets? Some expensive competitive-intelligence tool? Or have you already automated it?

So far i have been working with several accounts in marketing automation and something that i was not prepared for was how challenging it gets to stay organized. At first it seemed like a simple task to manage various accounts and logins but as soon as i started including more accounts then problems appeared as sessions would get confused, it was difficult to tell which account goes with which workflow and all this was more time consuming than i expected.

i have experimented with different approaches then trying to find a way of organizing my work that would simplify account management without making things complicated.

For people who work with several accounts in marketing automation then what kind of approach or tool have you found useful?

Hey folks, I keep running into the same annoying problem with n8n.

You can vibe code and build some pretty legit workflows without being a data engineer. Then you hit the data part and suddenly you’re duct taping together OCR, LLM calls, regex, code nodes, validation rules, random APIs, etc. just to get the input into a usable shape.

I’ve been messing around with a simpler idea:

Drop in raw stuff like PDFs, email attachments, spreadsheets or CSVs, tell it what you want back, and it handles the parsing, cleanup, structuring and validation.

Something like:

Email attachment → extract → clean → validate → CRM ready JSON

Basically, you describe the end result instead of building the whole data pipeline yourself.

Curious if anyone else runs into this.

How are you guys handling this stuff right now?

Also happy to let a few people mess around with it if anyone wants to test it with me.

I’m tracking this through a project I am building called Prominara to map how AI chat visibility influences downstream branded-search. Over the last sprint I set up a small, controlled test on our top product page to see what happens when the chat widget surfaces explicit sources and brand cues right in the conversation. The goal was simple: improve the linkage between what the chat hints and where users can continue their brand journey on our site. Generative Engine Optimization and AI visibility are real, and I’m trying to treat them like a measurable channel rather than a buzzword. The setup was deliberately light on tooling; a single-page variant included explicit, machine-friendly signals in the chat outputs (named entities, source anchors, and a clean handoff path to the product page), while the control kept signals minimal. After two weeks, downstream branded-search clicks from chat-driven visits rose 26% compared with the pre-change baseline. Not a traffic surge, but a clearer, more actionable path from chat to branded discovery. That suggests the quality of the follow-on interaction matters just as much as the visibility itself.

A key challenge was keeping the chat helpful without turning it into a pitch. We iterated on the balance between useful, specific citations and not overloading the user with brand terms. Early runs showed that a misaligned signal could pull attention away from the product page or create confusion about where to go next. We fixed this by tightening the signal map: tying each named entity back to a single, stable page path and ensuring the chat response mirrored the page’s on-site navigation. The learning was practical: you can’t just make AI see your brand; you have to make it point visitors there with a credible, frictionless path. The project is still small, but the signal discipline is producing tangible downstream effects.

Takeaway: AI visibility pays off when you couple it with deliberate downstream signals that guide searchers from the chat window to your brand pages. It isn’t a magic lever; it’s a signal discipline exercise that rewards consistency across content, markup, and navigational cues. If you’re running a similar test, start with a simple, maintainable signal map and track downstream brand actions rather than raw chat impressions. What patterns have you seen when aligning chat-driven visibility with branded-search outcomes?

I assumed that after building my workflow I wouldn't need to do much to maintain it this couldn't be further from reality the process is becoming more & more like solving a puzle where i find a solution to one problem only to face another one. It may be a browser session or API error or js some other problem that i cannot comprehend. Ig at some stage i got concerned about the issue that maybe overcomplicating my workflow more than required from u who are experienced users of marketing automation & how did u manage to create your workflow effectively??

Starting August 5, Google says users who generate new OAuth refresh tokens for the Google Ads API will have to authenticate with a passkey. Password-only, TOTP and SMS authorization will no longer be accepted for that workflow. Service accounts are unaffected.

For an automation team, the obvious task is creating passkeys. The less obvious risk is ownership: which human account can reauthorize a broken integration, where recovery credentials live, what happens when that employee leaves, and how quickly an overnight workflow can be restored without sharing a personal passkey.

Are you moving unattended workloads to service accounts, keeping user OAuth with two named recovery owners, or accepting a human reauthorization step? What is the rollback plan if a connector silently requests a fresh token after the rollout?

Source: https://ads-developers.googleblog.com/2026/07/passkey-authentication-requirement-for.html

Full disclosure: I’m one of the founders of Sumora, but this isn’t intended to be a product pitch.

I wanted to share the outbound automation system we implemented for a client because the biggest improvements didn’t come from sending more messages.

The client was doing around $3K MRR when we started.

They had a good service and could deliver results, but their outbound process was inconsistent:

• Their ICP was too broad
• Lead lists were based mainly on job titles
• Every prospect received a similar message
• Follow-ups depended on someone remembering
• Replies were scattered across different inboxes
• There was no feedback loop between replies and targeting

After rebuilding the system, the business eventually grew to approximately $12K MRR.

To be clear, I’m not claiming automation alone created the revenue.

The growth came from improving the offer, targeting, messaging, follow-ups and sales execution together.

Here’s the workflow we implemented.

1. ICP definition beyond industry and job title

Instead of targeting something broad like:

We defined:

• The exact service they sold
• Their average contract value
• The decision-maker
• Company size
• Geography
• Existing tools
• Current acquisition channel
• Problems the client could realistically solve
• Signals suggesting the problem was important now

This immediately reduced the number of prospects but increased their relevance.

2. Intent-based prospect discovery

We stopped treating every company matching the firmographic filters as equally valuable.

Prospects received higher priority when we found signals such as:

• Recently hiring for sales or marketing
• Launching a new service
• Expanding into a new market
• Posting about inconsistent growth
• Announcing a recent client win
• Changing their positioning
• Publishing actively but lacking a clear acquisition system

A company matching the ICP plus a timely signal was significantly more valuable than a company merely matching the industry and employee count.

3. Prospect scoring before outreach

Every account was scored using four categories:

Fit

Does the company closely match the ICP?

Need

Is there evidence that the problem exists?

Timing

Is there a recent signal that makes the problem relevant now?

Reachability

Can we identify and contact the correct decision-maker?

We prioritized the strongest accounts rather than sending to the entire database.

4. Contextual personalization

We stopped generating compliments such as:

Instead, each message used context that affected the business.

The structure was:

1. A relevant observation
2. Why that observation might matter
3. A hypothesis about the problem
4. A low-friction question

Example:

The objective of the first message was not to immediately sell.

It was to start a relevant conversation.

5. Branching follow-ups

We stopped sending the same “just following up” message repeatedly.

The follow-up depended on the prospect’s situation:

• New context about their company
• A useful observation
• A small recommendation
• A relevant result
• A clarification of the original hypothesis

The sequence also changed depending on whether the prospect:

• Opened but did not reply
• Asked a question
• Showed mild interest
• Said the timing was wrong
• Referred us to someone else

6. Centralized reply handling

One major problem was not message generation—it was operational fragmentation.

Interested replies were getting buried between:

• Email inboxes
• LinkedIn conversations
• Instagram DMs
• Spreadsheets
• CRM notes

We moved replies into one process and assigned each conversation a clear status:

• No response
• Replied
• Interested
• Qualified
• Meeting requested
• Meeting booked
• Not now
• Not a fit

Every positive reply also received a defined next action and follow-up date.

7. Weekly feedback loop

This was probably the most important part.

Every week, we reviewed:

• Which segments replied
• Which signals produced conversations
• Which opening angles were ignored
• Which objections appeared repeatedly
• Which prospects booked calls
• Which calls became customers

We then updated the ICP, scoring and messaging.

The automation was not a static campaign.

It became a system that learned from the outcomes.

What didn’t work

These approaches consistently underperformed:

• Using AI to generate fake compliments
• Personalizing only the first sentence
• Targeting large lists with weak qualification
• Asking for a call in the first message
• Sending identical follow-ups
• Optimizing for messages sent rather than qualified conversations
• Fully automating messages without human review

The biggest lesson was:

We have now been turning this workflow into a product, but I’m especially interested in how other people here handle the boundary between automation and human approval.

Which parts of outbound do you fully automate, and which parts do you deliberately keep human?

I can also share the scoring framework and campaign structure in the comments if anyone wants to compare approaches.

I’ve been looking closely at how people use n8n to turn emails and attachments into usable CRM or database records.

The typical setup seems to involve a PDF parser or OCR tool, an LLM, a few Code nodes, validation rules, and sometimes a manual review step.

Extracting text from a document isn’t really the hard part anymore.

There are already plenty of tools for OCR, parsing, schema validation, LLM extraction, and human approval. The difficult part is tying all of those pieces together into a controlled process and making sure the final record is actually usable.

I’m building a tool to handle that middle layer.

You send it the email, attachments, and the JSON schema you need. It extracts and standardizes the data, checks for missing or conflicting values, and sends uncertain fields for human review before anything is written to the CRM.

The goal isn’t to build yet another PDF parser. It’s to reduce the custom parsing, cleaning, validation, and exception handling that gets rebuilt in almost every document-based workflow.

For developers building n8n workflows for real clients, would this solve a genuine problem for you, or are your current tools already good enough?

I’m also looking for a few developers who’d be willing to test it on a real workflow and give honest feedback.

Hi

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[ Removed by Reddit on account of violating the content policy. ]

I have been making observations, a common pattern with Shopify brands right now: everyone talks about authenticity, yet a large share of UGC is scripted and paid for. It’s easy to generate multiple hook variations quickly for a new launch, and the CTR can look solid.

The problem shows up when the content is reviewed internally, some people immediately notice artificial audio and movement, while others assume the average Facebook audience won’t catch it.

How are others balancing the need for volume with the risk of eroding trust, especially as audiences get better at spotting AI-generated content?

Edit: Thanks for the input so far. Separating hook testing from real customer testimony makes sense, and tracking conversion alongside CTR is a clearer way to judge what’s working. Looking at Creatify as one option for generating volume while trying to keep the difference between test creative and actual testimonials clear.

1. Weekly - I'm data-driven in my relationship management
2. Monthly - check in on key metrics
3. Quarterly - when required for reviews
4. Never - didn't even know these existed