The SECRET Google Ads Script That Unlocked Millions for My eCommerce Clients

Here at growmads, we have lots of e commerce clients, so we manage millions and millions of dollars in e commerce spend per month with our e commerce clients. And many of them have lots of SKUs in their shopping feeds. Now, if you've ever seen a video of mine or you've gone through the performance max course that I've built or have on YouTube for free, you'll know I love working and segmenting based off product performance. That's easy when you have, you know, 10 SK SKUs, 20 SKUs, but once you get into the thousands, tens of thousands, hundreds of thousands, sometimes in the millions, it's nearly impossible to categorize products by performance at a level that a human should even be doing or analyzing. So there's a script. I have no affiliation with the guys that created this script. I stumbled upon it. I love this script. I just wanted to share it with the followers we have on the YouTube channel, our school community, and anyone who stumbles upon this video. This script has brought great clarity to some of our e commerce clients that have lots of SKUs in their shopping feed. And it helps then categorize and label products based on performance, which then helps us as we segment our campaigns inside of the account. So let's go ahead, dive in. I can't wait to show you the script. Okay, so the script is called Flow Boost labelizer. I have again no affiliation. I will have the link to this page I'm looking at on their website that explains everything and has the script there. It was created by a guy named Flores. I hope I pronounced his name right. I have no affiliation with these people. I've never talked to them. I'm in the link that I'll have in the description. It's not an affiliate link. So I stumbled upon this. I just really, really like this script. There's a free version of paid version. I did not use the free version. I used the paid version. I wanted to support them. So I'm not really sure how the free version is compared to the paid version. So what I am sharing with you today in this video will be from the paid version data. So the reason I like this script is it will label products and group them based off of how they perform. And it does it. Since it is a script, it does it in an automatic way and which is very useful when you're working with tens of thousands of SKUs and sometimes even greater. So they on this page they break everything down Again, I'll have this in the description, come in and read. But they took Performance max into consideration. Since with performance max there's lots of differences. Sometimes when it pushes products on the display network. So there will be variations in like conversion rates there and click through rate. And so if a product wasn't being pushed on display and it does, it can really fluctuate those metrics. And they took that into consideration when creating their groupings. So I really liked how they created sort of the formula for their groupings. Now their groupings consist of an over index condition. And again, the breakdown of the formulas are all here. I'm not going to go through all of that over index. Just think of as high val, high value overperforming products based off of your baseline performance goal. Index condition would be baseline performance goal. These are right within that range. And near index is close to your baseline performance goal. Under index would be low performers. These are money suckers. These are losers. These are products that you really should be analyzing. Can we make them work? Why aren't they working? Should we even be advertising them? Then you have the no index condition. No index is not enough data on these products to really even group in one of these over index, index, near index or under index conditions. And so no indexes need more data on them. Now let me show you inside of the sheet from the script running in a real account and I'll show you why I love this. Okay, so I'm inside the Google sheet. Again, the script pushes all of this here you'll see I've got tons of products in here. And then you'll it's custom label zero that I'm using that is automatically labeling them based off of their performance. Now when I go to this overview, this is the beautiful data of the groupings and it really tells the story. So here it has over 70,000 products that are in this client's feed and it's got them grouped out. Now based off of the last 30 days worth of data. Now you have a configuration. Again, this is a paid version, so I don't know if the free version has this, but here's, you know, you can configure the amount of days that you want to run this. In this specific case, I just ran it for 30 days. And here in the last 30 days, you know, we've spent about $14,000 for this client and we've generated a total of 115,000 in revenue. However, when we look at the groupings, there's some, some work to be done. There's some more data analysis and this has really influenced how we look at this client's account and how we're starting to segment our campaigns. Most of them are performance max campaigns for this client. So let's take a look over indexed. Again these are over high performing products. We have eight of them that fall into this category. Within the last 30 days we spent $470, generated 7,000 in revenue, super high roas, obviously good profit. I have a profit metric here too for this client index. These are, these are products again they're like right within our sort of baseline performance goal. We spent about 3,700. We did, sorry, that's clicks. We did 750 in cost on these 44 products, generated 12,000 revenue. Very, very high ROAS, high profit of about 5,000 there. Then near index. This is a good grouping of products. We have about 700 products here or 696. We spent 2700, generated a whopping 90,000 from those 696 products. Very high roas, very high profit. In fact most my revenue and most my profit came from this near index group. Again this is an E Commerce account that has tons of products, lots of product categories, but they still have core product categories and products that generate most of the revenue. Now when we were before we started using the script we were you know, probably too focused on certain categories. This really opened our eye up. So we went through and we, we analyzed the products that fall especially within the over index index and near index range. And how can we start then grouping these inside of their campaigns that make sense to, to increase and maximize our ad impressions and sales from those products then no index. That's where almost most of our products fall into. And this makes sense to me. So you know we spent most of our money here, 8,500 and we only did about 2,800 in revenue from that. So that's when you have large, large product feeds. There are going to be a lot of products in there that are getting onesies and twosies on clicks. They may never be big movers for you and what you end up with is, you know Google's going to give them those clicks. So we got 33,000 clicks on those. But a lot of these products just aren't high volume big sellers. So what are we doing with that information? Well there's 70,000 products to be reviewing but we're going through that data and we're looking at what product categories fall within this no index range. What products fall in this index no index range. And we're treating these differently than near index, index and over index because this doesn't mean that there's not products here that aren't going to do well. And that's what's beautiful about running a script. If any product that is inside of this noindex group becomes a winner or based off the, you know, the formulas is a near index, index or over index, then it will automatically move to those labels. And if we have it segmented correctly in our campaign structures, that will automatically be done because it it's a script that's doing it. There's no human that needs to be doing any of that. So our job though is to analyze these products and product categories that fall within here and how do we structure our campaigns and then our bids for these no index products to give them the best chance of being able to actually work or to give us the chance to find those losers that may not even work. And it might not make sense to advertise all 70,000 products then that fall within that range. Based off the data that this has given us and the clarity that it's given us, it's really helped us formulate better structures for their account. Again, large, large amount of SKUs here, then we have under index, under index there was only 82 products that fell within that. These are just losers. So we spent 1400. We only did 3k. So really lousy roas there. Somehow we eked out a little profit. So here we went through and actually analyzed these 82 products that were under index. Now listen, they were under index within the last 30 days. Does that mean these products are bad forever? Well, that's when we go in and we start looking at what products are these. Is there a pattern with what product categories they might fall into? Are these like a seasonal product? Should we have those labeled in the feed then differently? Like if they are seasonal, what patterns can we find here? We don't, it doesn't necessarily we want to mean kill these 82 products. We want to now do a further data analysis of out of these 82. Should we be still running some of them, testing them? Why aren't they working? And then sure, there are actual products here that we found within this 82 where we have made the decision stop advertising these products. They've no history in the data that proves that these are a winner. And so in our opinion, based off of that data, these are losers. Obviously we check with the client, hey, are these products doing well for you ever? They can pull sales history for those. And when you have, you know, close to 80,000 SKUs, you're going to have a grouping of products that don't really sell ever, or if they do, you might get a few sales a year. Not worth our time. Not worth spending any advertising dollars trying to push those. There you go. We got a glimpse into the Flow Boost Labelizer script. My personal favorite script right now. URL to their website on the page that I had up earlier explaining the script and the grouping conditions as well as the free script. Again, they have a paid version as well, which is what I use and showed you in this video. So if you like it, hopefully you support these guys and and buy the paid version. If not, no worries, I'll see you on the next video. It.