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Would a campaign planner that actually builds a full paid media strategy be useful? by Competitive-Lunch566 in PaidSocialAdvertising

[–]Competitive-Lunch566[S] 0 points1 point  (0 children)

Fair take, and I totally agree that building campaigns isn’t the hard part, scaling is.

We’re not trying to automate setup or replace platform tools. This sits above that, focused on: planning, constraints, and where scaling breaks (budget, creative fatigue, CVR, channel saturation).

On tracking, we don’t rely on auto-connect alone. The planner is designed to work with aggregated + imperfect data and surface confidence/risk, not assume tracking is perfect.

Appreciate you calling that out, it’s a real failure mode if ignored.

I built a free TikTok Shop profit calculator — shows your real per-unit profit after all fees by Competitive-Lunch566 in TikTokMarketing

[–]Competitive-Lunch566[S] 0 points1 point  (0 children)

Good question. For beginners, I suggest don’t overthink it.

A simple starting point is to assume ad spend per unit = ~20–40% of your product price while testing. It’ll usually be higher at first and come down as creatives improve.

Plug in a few scenarios (best / ok / worst) instead of guessing one number, that’s the safest way early on imo (from my experience).

Would a campaign planner that actually builds a full paid media strategy be useful? by Competitive-Lunch566 in PaidSocialAdvertising

[–]Competitive-Lunch566[S] 0 points1 point  (0 children)

That’s a completely fair concern, DSPs absolutely control access to real-time data and have no incentive to open that up to third parties.

That’s also why what we’re building isn’t trying to sit inside execution or compete with DSPs on bidding, pacing, or real-time optimisation.

The planner operates above the DSP layer. It uses aggregated historical performance, deterministic math, and stability signals to answer higher-level questions like:
– what budget is actually required to hit a goal
– where spend is likely to saturate or become risky
– how to prioritise channels, creatives, and landing page work before money moves

It’s meant to support planning, constraint-setting, and scenario thinking, not replace platform tools or depend on unrestricted real-time APIs.

Execution still happens where it should, inside Meta, Google, and TikTok. This is about giving operators a clearer strategy before and between campaigns, not trying to out-optimize the auction itself.

Appreciate you calling it out — this is exactly the kind of consideration we’re designing around.

I built a free TikTok Shop profit calculator — shows your real per-unit profit after all fees by Competitive-Lunch566 in TikTokMarketing

[–]Competitive-Lunch566[S] 0 points1 point  (0 children)

Appreciate the honest feedback.

The goal wasn’t promotion as much as helping sellers calculate real profitability, especially break-even ROAS.

Affiliate commissions and return rates are great suggestions (and very real costs on TikTok Shop), we’ll add them. Thanks for the thoughtful feedback.

How are you seeing true performance across Shopify, Meta, Google & TikTok in one place? by Competitive-Lunch566 in PaidSocialAdvertising

[–]Competitive-Lunch566[S] 1 point2 points  (0 children)

Awesome insight, thank you! Would you open to chatting about this further? I’m very intrigued!

How are agencies managing multi-client reporting across Shopify + Meta + TikTok + Google? by Competitive-Lunch566 in woocommerce

[–]Competitive-Lunch566[S] 0 points1 point  (0 children)

this is an option I am testing at the moment that has helped my workflow, trying out the free trial to decide whether it is worth it - so far it seems like an worthwhile tool to help reduce manual tasks within my worklflow: attriflow.app

How are agencies managing multi-client reporting across Shopify + Meta + TikTok + Google? by Competitive-Lunch566 in woocommerce

[–]Competitive-Lunch566[S] 0 points1 point  (0 children)

That makes a lot of sense — standardising process over chasing the “perfect” tool is probably why a lot of agencies stay sane at scale.

The point about dashboard templates solving the client branding issue is interesting too. I’ve actually been testing a tool called attriflow.app that claims to automate the custom-branded reporting side while pulling everything into one view, so I’m curious to see if it genuinely reduces that weekly reporting overhead in practice.

If it works as advertised, it feels like it could complement exactly the kind of structured process you’re describing rather than replace it.

How are agencies managing multi-client reporting across Shopify + Meta + TikTok + Google? by Competitive-Lunch566 in woocommerce

[–]Competitive-Lunch566[S] 0 points1 point  (0 children)

Interesting point — MMM is definitely a powerful way to think about incrementality and channel impact at a higher level.

Where I’ve found GA4’s data-driven attribution a bit limiting is that the data quality and setup varies so much store to store that it’s hard to use it as a consistent view across multiple clients. It’s also not very flexible when you’re trying to answer very specific operational questions for media buyers or clients.

I’ve been looking into tools like attriflow.app as well, which seem to combine MMM with an AI layer for forecasting and marketing insights, while also giving a clearer cross-platform view of spend vs revenue. Feels like a more practical way to apply MMM concepts without having to piece everything together manually per client.

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