How to Track Paid Ad Performance with HubSpot & Databox | Data Snacks | Reporting Tutorial

Hey everybody, this is Devin Littlefield over at MarketVantage, and I'm super excited to share a little bit today about how we're using Databox and bringing in HubSpot data to help better understand our advertising performance and using it to help consult with clients in particular about how campaign performance is going. Now I'll put out a Quick caveat before we get started. A lot of this data, you know, we're just looking at a small sample of test data here, and obviously it's been anonymized for our purposes today. But generally speaking, very excited to just showcase a glimpse of how we are kind of most critically looking through the lens of, you know, with Databox data, HubSpot empowering us to make better decisions for performance-based reporting. So here we are, we are inside of Databox. Now obviously I've done a little bit of work here to connect in Google Ads, LinkedIn Ads, and then different, the two different HubSpot connectors that we're using today, which is the HubSpot Marketing Connector and the HubSpot CRM Connector. Both of those are needed to be able to better understand how performance is going overall, and I'll dive into why here in just a second. But at a glance, you know, there are a couple key metrics that we really try to center around for all of our advertising performance reporting. And really it's beyond what we would see inside of the ad platforms. So we look at things like what are the total number of, MQLs that are being driven, SQLs, and then further down the line pipeline that we're driving through different paid activities. So one thing I want to make sure that I touch on here before we get going too far is that as we have kind of been diving in and we look at each of these different use cases, every HubSpot implementation is a little bit different. We see that the biggest and the most amount of value here comes from making sure that your data is set up properly inside of the account and having a, as generally as best as you can, right, long-term consistency of using this different data. So things like maybe custom objects that are getting used inside of HubSpot, if ideally the, the definitions have been clear on that and that you can continue using that for a long enough period of time, because Remember, you know, as DataBox is a great reporting tool, it's only as good as the data and the cleanliness of the data coming into the platform. So we want to make sure that we take that into account here in terms of building out these types of custom reports. But back to the topic at hand, so for building and coming, kind of coming to finalization of decisions, one of the things we really strive to look at here are some of the key metrics related to things like cost per MQL, cost per SQL from these different ad platforms. And as you can see, we have a number of metrics that are already built out here to be able to help us just quick at a glance look at these things. We also do this through what we call a scorecard. The scorecard is what we are reviewing on a regular basis. And the idea is that we're at a glance getting a 12-week look back at what are the performance metrics that we're seeing from either paid search or paid social. And it can help us make decisions around how we'd move forward. So one example of this is certainly just beyond just the number of conversions that are getting reported inside of each platform, we wanna look at how many new MQLs are we bringing into the door, and then separately what the cost per MQL is associated with those different channels. At a glance, this will help give us a clear indication of how things are going. So we can see, you know, from this test data over the last 12 weeks, 53% improvement in our paid social efforts, which is awesome. That is 100% LinkedIn and LinkedIn ads. So nearly, you know, nearly 50, over 50% increase in the volume, which is awesome. Paid search is obviously down quite a bit, and there's certainly good indications and easy understandings of why that actually is. And certainly happy, you know, with this client, that's a good opportunity for us to now start engaging in conversation around why that might be. What are things that we could be doing to help help continue working or bringing those costs, you know, bringing the lead new MQL volume up and cost per MQL down. In a similar fashion, looking at the cost per MQL for both of those different platforms, we can obviously see trends that we either like or we don't like. And so I think as far as a report goes, this gives us a really clear kind of indication, okay, paid search is clearly an area that needs some monitoring and, you know, some optimization opportunities. MQA, you know, and from a paid social with LinkedIn, there's clearly some very good things that are happening and we're seeing really positive trends over the last 12 weeks that will indicate, okay, we like what we're doing, let's keep doubling down on that platform as a whole. What I also love about DataBox is its ability to be able to bring in a lot of like custom objects inside of HubSpot. And this is where I think we find as a differentiator there's limitations around native HubSpot reporting that make it a little bit tricky to actually get to these kind of specific answers or things that we'd like to be able to see. This particular test client that we're looking at here has what's called an NBM or a New Booked Meeting. That is effectively a sales meeting. But it's more than just using native meeting functions. If you're familiar with the HubSpot you know, meeting functionality. This is a custom object that they've created and gets attached to different contact records. The reason why they've done that is because you may end up having several different meetings with a contact before an opportunity gets created in their sales pipeline. So we needed to be able to look at and bring in the new booked meetings by source and then start filtering that down so we could better understand at a glance you know, what individual sources or campaigns are actually driving towards MBMs. So I will also point out this particular test client. This is why I brought up the point at the beginning about, you know, having clean, consistent data. They had changed the parameters of MBMs and they were using native meeting functions prior. So tracking became a little bit of a shift over the course of 2025 in particular. So it's really important to make sure that there's that consistency here amongst your, amongst your tracking inside of HubSpot in particular. So now we can see, okay, over the last several weeks, here's where the volume of MBMs have been driven from. And then certainly we could even go further and break this down by particular source. Right now this is just grouped together as paid as an individual CRM source. But we can go even further down in the reporting if we chose to. The other thing, of course, is then we're looking at deal volume. Now, I will also point out historically and what we know, and even something that we could look at, is that during this timeframe of the last 12 weeks, April into June, there's— that's a pretty typical seasonal sales cycle. And so then doing a quick comparison to look at that look at that trend and confirm that yes, it's part of a seasonal trend is certainly very helpful. So I think overall, you know, when we look at DataBox and its ability to be able to do custom, you know, do customizations here, particularly around our custom metrics or calculated metrics, we get a really big benefit that we certainly wouldn't be able to have inside of HubSpot. And I will just showcase really quick, here's a few that we've made inside of this test account just so we can get a sense of it. Where, you know, if we wanted to understand the cost per MQL as an example, we can quickly create that metric by tallying up the total cost in Google Ads divided by our MQL volume. And then we can get to, you know, what is that cost per MQL specifically out of Google Ads. Certainly we can do the same thing for LinkedIn Ads. We can then also do some other fun stuff like then our total ad spend, as you can imagine, just adding up you know, total Google Ads and LinkedIn Ads spend. And then separately, tallying up the new leads from paid sources, whether paid search and social, so then we get to our total within this timeframe, so on and so forth. So as you can imagine, I mean, the number of potential reporting use cases that you could create around this are immense. And certainly, even further, one report that's not showcasing here, as this is just a test account, is then what are the MQLs per campaign that you're actually reporting on, or even further, you know, number of MQLs, SQLs, or opportunities being generated. So I think this is where it's really important. And one thing that we identified pretty early on with, you know, this actual customer that we're working with that I'm using for the purpose of test data today is that having that clear understanding and mapping out what are all the different fields or most important metrics to the customer, and then making sure that we can have DataBox reporting that supports that. And it works out incredibly well, especially when we look at this scorecard, because to, you know, to this customer that we're talking about here, for them, we really need to map out and make sure that we understand the MQLs from different paid channels as well as the NVMs by paid channel. These are all metrics that are getting tracked regularly, things that are incredibly important to that client. And what we've found is that it's really helped us when we evaluate our ad performance against these metrics, we get a very clear understanding of, you know, success metrics and we can kind of optimize towards these particular metrics. So in short, I guess to kind of cap this all off, where we see again the most amount of value is being able to go a little bit further and Databox helps us be able to do that beyond just looking at conversion reporting or, you know, click-through rate reporting or lead, you know, conversion rate reporting in platform. We now have the tools and the capabilities to be able to kind of blend a lot of this data together and give us a succinct view of what is it actually doing, what are these different advertising channels, as an example, doing for the business, like driving, you know, additional pipeline. So anyways, that's pretty much it in terms of this overview here. Hopefully this has been helpful. And yeah, thanks again so much for having me here. I'm really honored. And certainly if there are any questions, don't hesitate to reach out to me. All my information is being provided.