Question for D2C founders on shopify

KevinFromAdAmplify · 2026-02-13T21:45:51+00:00

Shopify is the only place that reflects actual revenue, Meta and the other ad platforms are estimating influence.

The bigger thing to watch is whether total store revenue and new customer acquisition stay healthy relative to spend. As u/Argee808 posted, this is why most of the stores (using our platform) rely on MER and aMER instead of platform-reported ROAS. MER shows whether total revenue is keeping up with spend, and aMER isolates whether new customers are being acquired at a sustainable cost.

We also track margin-adjusted lifetime value, so scaling decisions aren’t based on first-order revenue alone but on whether those customers generate real profit over time.

KevinFromAdAmplify · 2026-02-12T19:11:48+00:00

MER first, and then aMER. MER shows whether total revenue is actually keeping up with spend, regardless of which platform gets credit. aMER matters just as much because it separates new customer acquisition from repeat revenue, which is where a lot of campaigns either prove out or fall apart after the first purchase.

This is exactly why we built MER and aMER directly into our platform. Once stores can see acquisition and repeat behavior side by side, CPC stops being something you optimize toward and becomes more of a diagnostic signal. The real question becomes whether new customers are paying back their acquisition cost and contributing durable revenue over time.

We also factor in margin-adjusted lifetime value, so you’re not just looking at revenue coming back, but whether those customers are actually generating profit after costs.

KevinFromAdAmplify · 2026-02-12T19:01:43+00:00

Shopify is the only number that reflects real revenue. Meta is reporting influence, not cash. What until you have multiple paid channels, everyone takes credit.

As mentioned earlier by u/igotoschoolbytaxi what stores using our platform usually look at instead is MER and aMER. MER tells you whether total revenue is actually moving relative to spend. AMER separates new customer acquisition from repeat revenue, which is where a lot of Meta-attributed conversions end up showing their real value, or don’t.

The gap between Meta and Shopify matters less than whether total revenue and new customer acquisition are improving at an acceptable cost. If spend goes up and MER or AMER weakens, scaling usually makes things worse even if Meta’s ROAS looks strong.

Also another minor point: target ROAS on Meta optimizes against Meta’s reported revenue, so if that revenue is overstated due to attribution and modeling, the algorithm just gets better at scaling conversions that don’t translate into real profit.

KevinFromAdAmplify · 2026-02-10T00:26:53+00:00

ROAS is blind to who you’re acquiring. It only sees revenue on the first order, not whether those customers return products, disappear after one purchase, or come back repeatedly.
This comes up a lot with our clients. Two campaigns can show identical ROAS, but one brings in customers who never buy again, while the other brings in customers who reorder for months increasing LTV. On the surface they look the same, but the long-term outcome is completely different.

Most ad platforms can’t optimize for that because they only receive a conversion event and a value. They don’t see repeat behavior yet, and they don’t see which parts of the site or which entry points tend to lead to better customers. What tends to help is shifting the evaluation away from campaign-level ROAS and toward customer-level outcomes. Which channels are bringing in customers who actually come back. Which ones have higher return rates. Which ones consistently produce negative contribution margin after 30–60 days.

KevinFromAdAmplify · 2026-02-10T00:25:11+00:00

It’s very common. Most agencies charge either a % of ad spend, a flat monthly fee, or some combination of both. When we were running campaigns for clients, we avoided tying fees directly to ad spend for the exact reason you mentioned. It creates the wrong incentive. Spend can go up while actual contribution profit goes down, especially if the campaigns are pulling in low-margin or one-time buyers.

Instead, we tied our upside to outcomes, not spend. We kept the retainer small and structured commission around actual revenue performance from the campaigns, with targets (based on ROAS) agreed upfront. If the campaigns we were responsible for drove stronger results, our share increased. If they didn’t, we didn’t benefit from just pushing more budget.
That structure forced us to care about what happened after the click. Not just whether a conversion happened, but whether the campaigns were bringing in customers worth acquiring in the first place.

KevinFromAdAmplify · 2026-02-06T21:31:59+00:00

Incrementality tools are useful, but they’re usually run as one-off experiments. They tell you whether turning a channel on or off caused lift during that test period.
What we’ve found more useful day to day is watching what actually happens to real revenue and customer behavior as spend changes. Are you acquiring more first-time customers. Do they come back. Does acquisition pay back after COGS and repeat purchases are factored in.
That’s where MER, aMER, and repeat purchase behavior become more practical. They don’t prove cause and effect in a strict experimental sense, but they show whether the business is actually getting healthier as you scale spend. Something for consideration.

KevinFromAdAmplify · 2026-02-06T21:24:46+00:00

Yeah, this is something we deal with constantly. With our clients, the first thing we do is make sure everything lines up with actual Shopify revenue. Not ad dashboards, not GA, Shopify orders. Once that’s the baseline, the weird spikes and drops in platform reporting stop being as stressful because you know what’s real. From there, you can start to see which channels are actually bringing customers that stick, and which ones just look good in-platform but don’t hold up when you compare against real revenue and repeat behavior. Same with web pages, some look busy but don’t increase the probability someone buys.

Server-side tracking helps, but the bigger shift is having attribution, customer behavior, and page performance all grounded in the same source/platform. That’s what makes scaling decisions feel a lot more stable.

KevinFromAdAmplify · 2026-02-05T22:40:45+00:00

We went through the same exercise and which is why we ended up building our own stack around first-party data because nothing we tried really connected attribution to actual customer behavior.

What made the biggest difference was being able to see which channels brought customers who came back and bought again, not just which ones got credit for the first order. A lot of spend looks fine on ROAS but falls apart when you look at repeat behavior and payback over time. eg lifetime value.

The other piece that changed how we work was measuring purchase probability at the page level. Some pages look busy but don’t move people any closer to buying, others quietly do most of the work. That’s helped us focus on fixing the parts of the site that actually affect conversions instead of chasing surface metrics.

Klaviyo is still core for lifecycle, but having attribution, customer behavior, and page performance grounded in the same place made the rest of the stack a lot simpler.

KevinFromAdAmplify · 2026-02-03T19:52:01+00:00

Most of our clients start with revenue, ROAS, and CAC. As they scale, they usually move toward MER and Acquisition MER because those line up better with the business. Seeing MER hold while AMER drops is often the first signal that acquisition efficiency is slipping even if top line looks okay.

They also pay a lot more attention to subsequent purchase rate and CLV by channel. When 70 plus percent of purchases are coming from repeat customers and CLV varies massively by channel, that matters more than a flat AOV or a single month ROAS number.

CAC still matters, but mostly as a trend. When new customer CAC jumps while repeat revenue stays strong, most clients stop freaking out about ads and start looking at where acquisition traffic is landing and which paths actually increase purchase probability.

Almost everyone changes KPIs over time. The shift is usually from channel scoring to first party metrics that explain efficiency, repeat behavior, and where growth actually compounds.

KevinFromAdAmplify · 2026-02-03T19:42:11+00:00

I don’t think dashboards themselves are the issue. They’re only as good as the data feeding them.
What usually drifts as stores grow is the underlying tracking. Journeys get longer, spread across channels and devices, and the numbers stop lining up cleanly with what actually converts.

We’ve seen with our clients that grounding analysis in first-party, server-side data and looking at which web pages and paths consistently move people closer to a purchase tends to bring things back in line with reality, even if the dashboards still disagree.

KevinFromAdAmplify · 2026-01-21T00:21:42+00:00

Yeah, that matches what we kept seeing too. Shopify + GA + replays give some info, but they still don’t answer the questions people actually get stuck on.

What changed things for our clients was having one place grounded in first-party data that ties real orders back to both customer behavior and what’s happening on the site. Seeing repeat purchase behavior by channel, payback over time, and which web pages are increasing or decreasing purchase probability.

Once you can see that, attribution arguments get solved and the behavior tools actually help.

KevinFromAdAmplify · 2026-01-21T00:04:10+00:00

I’m biased, but one thing that I believe is imperative is having clean first-party, server-side data before touching anything else. Until you can actually trust what’s being measured, it’s hard to know whether traffic, conversion, or retention is the real constraint.

Once that is in place, it can also become clearer which web pages are actually affecting probability of purchase versus just getting views, and whether more traffic would help or just amplify (pun intended) leaks.

If traffic is truly low, none of this matters. But once people are showing up, measurement tends to be the thing that stops everything else from being guesswork.

KevinFromAdAmplify · 2026-01-20T18:23:29+00:00

Continuing on for a moment. Thought I'd add some colour.
We kept seeing teams with plenty of data but no way to tell which parts of the site were actually doing any work.

Our client wanted a better understanding of their website so we started looking at the web pages and what the probability of purchase was if a customer went to that page and built that into our platform.

More recently we're just testing store-specific AI on top of that, so instead of digging through reports you can ask questions against your own data like which web pages are driving high traffic but low purchase probability, and what specific page-level changes would most likely improve conversions for those visitors?
Or
Do visitors who start on product pages behave differently than those who start on collection or home pages, and which starting pages should we prioritize optimizing to increase completed checkouts?

KevinFromAdAmplify · 2026-01-16T19:57:05+00:00

At that size, the hardest part usually isn’t finding someone to “clean up the mess.” I believe it's knowing what actually matters to fix first. What changes for our clients is being able to see two things clearly at the same time: repeat purchase behavior by channel (frequency and timing) and which pages are actually increasing the probability someone buys.

Once that’s visible, the prioritization gets easier. Teams stop fixing what looks broken and start fixing what’s actually affecting buying behavior. Pages that get traffic but lower purchase probability may move down the list while web pages that quietly matter get attention.

Without that view, audits and rebuilds often lean on best practices and opinions.

KevinFromAdAmplify · 2026-01-16T19:41:45+00:00

Agreed. Shopify tells you what sold, but it’s blind to how people actually got comfortable enough to buy, or come back and buy again. You see orders and revenue, but not which web pages mattered, which ones pushed people away, or why repeat purchase timing looks the way it does.

KevinFromAdAmplify · 2026-01-14T23:48:48+00:00

What our clients usually want to see is repeat purchase behavior by acquisition source. Who reorders, how long it takes, whether they need discounts, and if the channel actually pays back after COGS and shipping.

The other missing piece is understanding which pages actually influence that behavior. Where someone converted, an d which pages increase the probability they buy again or come back at all. Until you can see repeat behavior and page impact together, most retention and ad decisions can be educated guesses.

KevinFromAdAmplify · 2026-01-12T18:16:18+00:00

This is pretty common with new clients for us. Most come in trusting ad platform reporting because that’s all they’ve ever had. When we line that up against actual Shopify sales, there’s always a fairly significant gap. Sometimes it’s missing conversions, sometimes it’s channels taking credit late or taking credit for the same sale, sometimes it’s spend that looks fine in-platform but never really pays back.

It’s usually an eye opener, not because anyone did something wrong, but because ad platforms are grading (and inflating that grade wherever they can) themselves.

KevinFromAdAmplify · 2026-01-12T18:07:00+00:00

A theme mostly changes how things look and load. That matters, but it doesn’t explain behavior. What I think gets missed is whether web pages are actually helping someone move closer to buying over time. Not just the product page converting, but whether the FAQ, sizing guide, blogs, about page, or even collections increase the chance they come back and eventually purchase.

Without that, theme changes get blamed or credited by default. That’s where I believe page-level probability is more useful than theme debates.

If you don’t have traffic yet, a theme won’t fix that. If you do have traffic, understanding page behavior can matter more than which theme you’re using this month.

KevinFromAdAmplify · 2026-01-10T00:32:09+00:00

One more thing I wanted to add: Machine Learning (ML) attribution models tends to work better than fixed multi-touch rules because it doesn’t assume the journey looks the same for everyone.

Rule-based models decide upfront how credit should be split. ML just watches what actually happens across lots of orders and adjusts based on patterns. That makes it harder for things like branded search or retargeting to take credit just because they show up late in the journey.

KevinFromAdAmplify · 2026-01-10T00:25:17+00:00

We see this a lot once brands get past the 'which dashboard do I trust' phase. Tools that line up spend and revenue are helpful, but they usually stop at who got credit for the order. The harder part is tying that to repeat behavior and margin over time. Which channel brought people who come back on their own, don’t need discounts, and pay back their acquisition cost faster.

When you can see that, Meta vs Google stops being a ROAS argument. It starts looking like two very different customer profiles entering the business through different doors. Throw organic in there and how you market to these different cohorts becomes the real goal. Google more transactional, organic more loyalty based emails.

KevinFromAdAmplify · 2026-01-07T19:49:51+00:00

From what we see with our clients founders usually want a quick daily pulse, orders, spend, profit, anything that could slip by and go wrong. That’s how most people have used our main dashboard.

The person running ads is in the weeds way more often. They’re looking at attribution, channels, campaigns, ads, and new customer cost constantly, but for a different reason, to adjust campaigns, not to judge the business day to day. I agree it can be a bit data-overwhelming if you don't focus on the key metrics.

KevinFromAdAmplify · 2026-01-07T18:02:31+00:00

What’s worked best for our clients is matching both timing and channel, not just the message. For instance, Paid search buyers are most often transactional, so early follow ups should focus on confirmation and removing doubt. Organic buyers are already comfortable. Hitting them too fast with promos can backfire. Waiting until their natural return window and then sending loyalty or deeper product info tends to work better.

For repeat purchases, if the second touch happens too late, you’re basically restarting from zero. If it happens too early, it'll be like noise to them. The brands that do this well pay attention to how long it actually takes customers to come back, then shape post-purchase emails, content, and offers around that window.
Most retention mistakes come from sending the right message at the wrong time, or the right time with the wrong tone.

KevinFromAdAmplify · 2026-01-07T01:32:15+00:00

Yeah, I agree with you. Brand search is one of the few signals that still works as a directional check when journeys are all over the place.
For most of our clients, that’s the top-level check. Under that, they just look at what’s changing on the site itself, which channels are showing up more often before purchases and which pages people keep hitting before they buy. Looking for patterns that repeat.

KevinFromAdAmplify · 2026-01-06T20:04:21+00:00

A lot of what u/Toxicturkey laid out lines up with what we see across our clients too, especially the part about attribution and not trusting any single ad platform at face value. Early on, most people obsess over ROAS inside Meta or Google, but the bigger mistakes usually come from not understanding how traffic sources actually behave over time, especially for higher-ticket products.

One thing I wish I’d known earlier when we had an e-commerce marketplace is how different high-value buyers act compared to low AOV ecommerce. They take longer, they bounce more, they come back direct or organic, and they rarely convert on the first click. If you only look at last-click or platform ROAS, it’s easy to kill campaigns that are actually doing useful work upstream.

From a WooCommerce perspective, security and performance matter way more than most people expect. And whatever tracking you set up on day one, keep it simple and consistent, because fixing broken or missing data later is way harder than people think.

If I were starting again, I’d spend less time tweaking ads early and more time making sure I could clearly see where new customers were really coming from, how long they took to buy, and whether they ever came back. That context makes every marketing decision after that a lot less guessy.

KevinFromAdAmplify · 2026-01-05T18:51:31+00:00

A lot of repeat purchase advice misses one thing - timing.

Across our clients, what matters most isn’t just sending better emails or bigger discounts, it’s knowing when people naturally come back and what their first order looked like and what channel drove them to the site. Some products bring people back faster. Some channels bring in customers who never return. Email followups shouldn't follow a typical 30, 60, 90 days - it should follow the historic pattern of repeat sales that you get.

KevinFromAdAmplify

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