How to build smarter e-commerce marketing dashboards and avoid data overwhelm

Key takeaways:

1. More data ≠ better decisions

Marketers today use more than double the data compared to 2020, but 56% say they don’t have time to analyze it. The result? Overloaded dashboards and underinformed decisions.

2. Structure your dashboard by funnel stage

A full-funnel view—organized into attract, engage, convert, and retain stages—helps you tell a coherent performance story and identify where things break down.

3. Focus on actionable metrics

Every metric should answer: What are we trying to learn? What action do we take if this number changes? If you can’t answer these, you likely don’t need it.

4. Incorporate offline and inventory data

Blind spots like stock-outs or product issues aren’t visible in ad platforms or web analytics. Integrate CRM, warehouse, and review data to tell the full story.

5. Use custom data import for smarter reporting

Brands like Manscaped align product availability with campaign performance using Supermetrics' custom data import—so they don’t advertise products that are out of stock.

Related resources:

• Custom data import: ⁠https://supermetrics.com/blog/custom-data-import ⁠
• Essential ecommerce metrics for 2025 success: ⁠https://supermetrics.com/blog/ecommerce-metrics ⁠
• What is ecommerce reporting, and why does it matter?:⁠https://supermetrics.com/blog/ecommerce-reporting⁠
• The 2025 marketing data report: ⁠https://discover.supermetrics.com/marketing-data-report-2025/⁠

Transcript

Anna (00:41)
⁓ Welcome back to the Marketing Intelligence Show. Today we are going to cover a wonderful topic of e-commerce marketing analytics as a part of our discussion during this episode. I am joined by Tea our senior product marketing manager, who has learned a lot from the customers, the teams she's been working with.

Tea is an expert on all things marketing, data, e-commerce metrics, connectors, so really the best person to have on the show. Welcome Téa.

Tea (01:17)
Thank you Anna and thank you for having me in the show.

Anna (01:20)
Perfect. And this episode is going to be really, really exciting. I'm also really excited about this one because we are going to talk about the tools, about the metrics, about different kinds of approaches and how the teams can structure their dashboard to get the best e-commerce marketing report they can have. So my first question to Tea is going to be, so Tea.

A lot of teams have the tools, the data, they might already have some foundations in place, but why do you think their reports are still not working for them? Why is their analytics not working? Can we maybe analyze this on a bit of a deeper level and give some tips for the teams who looking to get started with their e-commerce marketing?

Tea (02:13)
Yes, for sure. And I think that's a common problem with marketers having way too much data, having reports that don't really work or serve them in the best possible way. But if we look at the e-commerce space specifically, because e-commerce is quite complex. If we think about the user making a purchase, it has a multiple touch points before ending to that ⁓ purchase or transaction and putting that thing in the cart and going to a checkout.

They might be seeing ads in different social media platforms. They might be browsing the site. They might come back from Google. And since this customer journey is so complex, the report should be a reflection of that, but it really is. So the reports, what marketers are trying to do here, they are trying to cram everything under one report, whether that's a performance dashboard, whether that's some sort of...

ROAS chart, web traffic graph, and it just doesn't tell the whole truth of the customer journey. And what we ended up with is fragmented insights from bits and pieces from there, but we are missing the whole picture.

Anna (03:21)
Absolutely. And I think this is exactly spot on. E-commerce marketing reporting is very, very complex because it consists of all those different elements. The marketing teams really need to grasp in order to create a really, really good report. And in order to make it perhaps a little bit easier, I wanted to talk about the metrics first a little bit. So generally when we're talking about the topic of marketing analytics, there are really four buckets.

metrics fall into. And these buckets are related to the stages of the marketing funnel. So there is the attract, engage, convert, and retain. So four stages. And during the attract stage, we're really talking about maybe perhaps a higher level of those marketing metrics. So those are your impressions, clicks, those campaign clicks, and they happen

when the user interacts with a particular ad, the next stage is going to be engage where the user lands on a particular website, on a particular page of a web store and there they perhaps are browsing, they're maybe are reading a blog article about a particular product. So here we would want to track metrics like average time on site and maybe perhaps engage

The next stage is convert. This is where an actual purchase usually takes place. Here we're talking about the metrics like conversion rate, number of conversions, maybe if you've set different goals, different goal types within your Google Analytics instance, you can track those different goals and your progression towards those goals, those different conversion types.

Lastly, you really want to keep your customers so you don't want those one-time purchases. So there is a retain stage where you want to track the number of repeat purchases. You want to track your customer lifetime value. You want to track the average order value. So now you have more data about the purchases themselves and who are actually buying those items. Do you have anything to add there? Maybe about the marketing funnel or

about the metrics themselves.

Tea (05:52)
Yeah,

I think that's a really good list of metrics and things to follow, and I think you're spot on there. And I think it's also important to know how these metrics relate to each other. So if we think about e-com specifically, you may have or you may see a high average order value, for example, but at the same time, it's not good if you also have high return rates.

whether you start to get a lot of purchases, but at the same time, you also see an increase in returns. And in more generally speaking, if you have a good CTR in your campaign, for example, but you see a plummet in your post-click conversions, then those are the things that you should be kind of seeing, because you cannot see that if you're only looking at a one item or one thing.

What you really need is all the pieces of the pie. So you want that kind of full funnel dashboard that shows you the entire journey.

Anna (07:00)
I definitely do agree. You really, really need to collect ⁓ enough information so that you could create a report that basically reflects the whole purchasing process and that can give you valuable insights. Now let's talk a little bit about the common pitfalls because we've just discussed there is a myriad of metrics and usually, more or less, do not often know what to do with all those metrics. So...

The one mistake I see a lot, everybody's trying to absolutely everything and that usually creates a report with a lot of data. However, it's difficult to draw conclusions from this data. Tea do you have any kind of maybe some tips what could bring marketers this kind of clarity?

Tea (07:54)
Yeah, absolutely. I think when you talk about like too much data, I think we have all been victim of that. think we all have a dashboard that's way too crowded and you look at it and you're not even sure what you're looking at. And actually we published a really good marketing data report probably two months ago. And according to that data, marketers are using 230 % more data than they were in 2020.

To me, that makes sense, but still the number is mind blowing. At the same time, according to the report, 56 % of the marketers say they don't have time to analyze the data thoroughly. So if you think about it, you have more data than before, but less time to actually analyze it. So what are you ended up with? You make decisions that are not packed with data. And if you think about having way too much data in your reports or spreadsheet or

whatever is your go-to destination with data, you ended up hoarding data. And that will lead to not only bad dashboard that end up crashing, it's also loaded data storage or data spreadsheet, whatever is your data destination, et cetera. Instead, think when you start collecting data, what you want to do is you want to start asking yourself or asking your team,

What are we trying to learn through these numbers? What's the simplest way to those numbers? And what action should we take if the numbers change? And if you cannot answer those simple questions, then you probably shouldn't be following that metric at all.

Anna (09:37)
Yeah, absolutely. I really, really liked your answer because it also emphasizes action. So not just simply putting the data into the spreadsheet or just being a data hoarder, but really trying to think, okay, what's my next step? So if I see my number of conversions plummeting down, what is that concrete action I should take so that I could improve my performance holistically across the board?

Tea (10:03)
Definitely. And I think if I add to that, it's time to do some spring cleaning to your dashboards. ⁓

Anna (10:10)
Yes. And on the topic of dashboards. So now that we've talked about the data volume and the fact that your dashboards have to be really, really actionable, you should learn how to structure them in order for all the data to make sense and how we prefer to do it at Supermetrics. When you're looking at the dashboard, start from the top line. So start from something general and move towards something more specific.

So for example, if I have my blank ⁓ Looker Studio report, I would start with the scorecards at the top that will give me a high level overview of how my key metrics are pacing. Then I would move to, for example, channel breakdown or movie product breakdown to show which channel has contributed to my sales numbers or how my campaigns are performing across different channels.

And after I have basically painted those larger strokes, I would move towards more specific numbers. And again, it's very important to have the right context here, whatever Theo was talking about. So really trying to think if we're, for example, measuring performance of a particular campaign, then, okay, how should I optimize this campaign so that it could improve over time? And...

Then as we progress towards the bottom part of the report, I would maybe include, like I mentioned, maybe some campaign data or maybe even some SKU data so that you know how your particular products, how specific variants of those products are selling. And it's very important to keep in mind that you should also have a correct benchmark because just saying something has increased or decreased by 15 %

doesn't often help. You really need to understand what you're comparing the number against and obviously you should look at a correct timeline. So is it your weekly report? Is it your monthly report? What are the expectations here?

Tea (12:28)
Definitely.

Yeah, I totally agree with everything you said. ⁓ Maybe one layer, additional layer to add to that is what we've seen is a lot of e-commerce marketers don't take into account that sort of external data that's tucked away in legacy CRM systems or some sort of third party platforms. For example, you may see a, all of a sudden you may see a dip in conversions.

that's linked to stock out, that's locked into a warehouse system, not visible in your Shopify reports, but you see a dip and you wonder why is that. Or you maybe start seeing a spike in your return rate and you wonder why is that? But you don't take into account the sizing issue flagged in customer reviews. So there is actually a really good customer case with Benefit Cosmetics, who is a customer of us, and they use Supermetrics to analyze customer reviews.

and understand product sentiment across regions, whether it's positive, whether it's negative, and optimize their messaging and optimizing their campaign performance accordingly. And the last thing I really liked what you were saying that the numbers itself don't tell the entire story. You need some sort of story behind the numbers, actually. ⁓ A dashboard is only for tracking. When you start to explain the performance to your stakeholders,

you do need some sort of storyline to follow. So if you say that your conversions have increased all of a sudden or they've plummeted over the weekend, ⁓ you may start to wonder why is that? And you may want to start looking at your website traffic or landing page conversion rates and you start to kind of understand where is the conversion plummeted or spike coming from.

Anna (14:17)
Absolutely, absolutely. I really, really like your answer. Also just wanted to highlight here, maybe once again for our listeners, that it's very important to draw those lines between the metrics like you've mentioned. So our conversions are plummeting by 15%. What is the whole story we are telling with those numbers? For example, now you mentioned the conversions are plummeting by 15%. Should we look at the website traffic? Should we look at the website page optimization? Maybe it's difficult.

for someone to add something particular to a shopping cart or then proceed with the checkout. So again you can take a look at it from the perspective of a whole marketing funnel and don't forget to again draw those parallels between the metrics so that you could optimize the whole flow, not just a particular.

Tea (15:12)
Definitely.

Anna (15:14)
anything

to maybe end with, any final thoughts, anything e-commerce marketers should be on lookout for.

Tea (15:24)
Yes,

I would want to emphasize on the blind spots. What I just said is that there are several cases where marketers, we have data a lot. We have data everywhere. If you think about the different touch points that we interact with our users, with our customers, with our leads, with our prospects, the touch points are almost endless and especially in the e-commerce field. As said, it's very complex.

But the way you want that data in is, well, it's a bit tricky because you can have these sort of different hacks or you can manually upload data from, let's say, the legacy CRM system, for example, or you have your event sales in some spreadsheet or something and you want to incorporate that to your marketing performance dashboard to actually see, well, how did we do in the event? How did we do in the trade show? How much sales we actually did?

compared to how much we paid for the spot in the trade show. So how you incorporate that data to your marketing reports is really important. So you're not left with those blind spots. And what we've seen is super beneficial for, especially for e-commerce brands to make sure that the reports include your product feed data from your product inventory so that you don't end up advertising stuff that is already out of stock.

or low in stock. For example, one of our customers, Manscape, ⁓ they build a dashboard that pulls together their product performance and marketing performance data so their teams could align on which SKOs perform better and understand what to promote when and why. But yeah, ⁓ but like I said, there's plenty of hacks how to incorporate this data to the reports, whether it's manually

some sort of building a hack or uploading, copy pasting. But what we've seen work really well for our customers is this new feature that we just introduced called custom data import. It basically lets you upload structured data to Supermetrics and then Supermetrics standardize that data to fit to your online marketing data or that kind of data that you normally analyze through Supermetrics, for example.

And if we look at the Manscape case again, it really is one of the strongest example of how custom data import works in action. It helps align marketing campaigns with inventory. And to me, that is just, that's just golden. Cause let's say that you're running a promotion for a product, but it's low on low in stock or almost already sold out. If you're not syncing that data.

product feed or warehouse data to your performance dashboard, you could be wasting ad spend, you could be frustrating your potential customers, and they may end up with a bad experience and leave a bad review. For God's sake, you don't want to end up with a situation like that. So with custom data import, e-commerce teams are pulling in real-time inventory data, warehouse data, to Supermetrics and then reporting all of that together, building that kind of comprehensive reports that we...

said in the beginning that would be the kind of destination to be in.

Anna (18:50)
Absolutely. And I really, really love how you mentioned the importance of this offline data as a data engineer that's music to my ears. And yeah, just wanted to emphasize that it's indeed very, very important to include every piece of data because just pulling data from your Google Analytics or just marketing campaign data or only Shopify data doesn't tell the whole story in many instances. So do do take advantage of

of our custom data import feature, and it has proven to be very useful for a number of brands already. So I highly recommend you check it out, especially if you have the data that's now stored in some other destination that you want to bring into your report. And other than that, I just wanted to say, Tea, thank you so much for joining us today. It was amazing to have a conversation with you, and I've learned a lot.

Just to recap what Tea and I have discussed today, it's important to understand which place, which stage of the funnel different metrics belong to. It's important to create a report that answers the question, what is the next action I should perform based on the data. So it's important to be action oriented. It's important to structure the report correctly. And it's important to take advantage of

all your data, including offline data that you can bring into Supermetrics with our custom data import feature. So thank you everybody for joining us on this episode of the Marketing Intelligence Show.

Tea (20:34)
Thank you.